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High-throughput molecular dynamics: The powerful new tool for drug discovery
... It explains the behavious of small chemical entities in the active sites of selected protein/receptor, and helpful in determining the activity of selected checmials [46,47]. Many advance tools has been discovered for easy docking proceudres such as Autodock Vina [44], molecular operating environement (MOE) [48], Gold [49], Glide [50], LigandFit [51], and FlexX [52] and for molecular dynamic simulation, most important tools are Amber [53], Gromacs [54], and Desmond [55]. There are many studied supported with molecular docking and molecular dynamic simulation studies for COVID-19 disease and reproposed new drugs based on the available antiviral drugs dataset [56,57,58]. ...
... Homoharringtonine (17) Dolastatin (18) Halichondrin (19) Plicamycin (20) Arvoside (21) Glycyrrhizic acid (22) (continued on next page) I. Rani et al. Acetoside (47) Curcumin (48) Darunavir (49) Lisinopril (50) Norquinadoline A (51) Deoxynortryptoquivaline (52) Thalimonine (53) Sophaline (54) Tomatidine (55) Cryptospirolepine (56) (continued on next page) (57) Cryptoquindoline (58) Lopinavir (59) Stychnopentamine (60) Usambarensine (61) Remdesivir (62) Sofosbuvir (63) Cryptomisrine (64) Biscryptolepine (65) Vanillin (66) Crambescidin 786 (67) Crambescidin 826 (68) (continued on next page) I. Rani et al. Monolaurin (69) Sepiapterin (70) Tetrodotoxin (71) Caulerpin (72) Simeprevir (73) Hydroxychloroquine (74) Chloroquine (75) Amprenavir (74) Lutein (75) Ellagic acid (76) (continued on next page) I. Rani et al. (77) Galanthamine (78) Nomilin (79) Deacetylnomilin (80) Ichangin (81) Amyrin (82) 24-dimethylene cycloartenol (83) Isoiguesterin (84) (continued on next page) phytomolecular research could have a direct and long-term impact on the security of the health protection regime for humans and animals due to the growing interest of the public and governments around the world in improving the immunity of humans and animals to lethal viruses, such as SARS-CoV-2. ...
a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.
... It explains the behavious of small chemical entities in the active sites of selected protein/receptor, and helpful in determining the activity of selected checmials [46,47]. Many advance tools has been discovered for easy docking proceudres such as Autodock Vina [44], molecular operating environement (MOE) [48], Gold [49], Glide [50], LigandFit [51], and FlexX [52] and for molecular dynamic simulation, most important tools are Amber [53], Gromacs [54], and Desmond [55]. There are many studied supported with molecular docking and molecular dynamic simulation studies for COVID-19 disease and reproposed new drugs based on the available antiviral drugs dataset [56,57,58]. ...
... Homoharringtonine (17) Dolastatin (18) Halichondrin (19) Plicamycin (20) Arvoside (21) Glycyrrhizic acid (22) (continued on next page) I. Rani et al. Acetoside (47) Curcumin (48) Darunavir (49) Lisinopril (50) Norquinadoline A (51) Deoxynortryptoquivaline (52) Thalimonine (53) Sophaline (54) Tomatidine (55) Cryptospirolepine (56) (continued on next page) (57) Cryptoquindoline (58) Lopinavir (59) Stychnopentamine (60) Usambarensine (61) Remdesivir (62) Sofosbuvir (63) Cryptomisrine (64) Biscryptolepine (65) Vanillin (66) Crambescidin 786 (67) Crambescidin 826 (68) (continued on next page) I. Rani et al. Monolaurin (69) Sepiapterin (70) Tetrodotoxin (71) Caulerpin (72) Simeprevir (73) Hydroxychloroquine (74) Chloroquine (75) Amprenavir (74) Lutein (75) Ellagic acid (76) (continued on next page) I. Rani et al. (77) Galanthamine (78) Nomilin (79) Deacetylnomilin (80) Ichangin (81) Amyrin (82) 24-dimethylene cycloartenol (83) Isoiguesterin (84) (continued on next page) phytomolecular research could have a direct and long-term impact on the security of the health protection regime for humans and animals due to the growing interest of the public and governments around the world in improving the immunity of humans and animals to lethal viruses, such as SARS-CoV-2. ...
In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.
... Despite the low algorithmic complexity of MD in comparison to quantum chemistry methods, the computational cost is such that high performance computing (HPC) systems have been required to perform simulations of sufficient length to approach biologically relevant timescales [2]. The size and specialisation of the parallel HPC systems required has made MD sampling of even small biologicallyinteresting systems very costly in terms of Euro per simulated time. ...
... Early work on benzamidine-trypsin established the foundations for what later would become known as high throughput molecular dynamics (HTMD), a new paradigm where ligand-receptor binding poses, affinities and kinetics can be reproduced within affordable time by producing multiple parallel MD simulations [2]. However, late in 2013 it became evident that to fully take advantage of this unbiased high-throughput approach, smarter and automatic sampling schemes should be designed to reduce the computational cost. ...
Bio-molecular dynamics (MD) simulations based on graphical processing units (GPUs) were first released to the public in the early 2009 with the code ACEMD. Almost 8 years after, applications now encompass a broad range of molecular studies, while throughput improvements have opened the way to millisecond sampling timescales. Based on an extrapolation of the amount of sampling in published literature, the second timescale will be reached by the year 2022, and therefore we predict that molecular dynamics is going to become one of the main tools in drug discovery in both academia and industry. Here, we review successful applications in the drug discovery domain developed over these recent years of GPU-based MD. We also retrospectively analyse limitations that have been overcome over the years and give a perspective on challenges that remain to be addressed.
... Rare events generally can be observed with some specific approaches, frequently via the use of large ensembles of simulations or biased simulations. [9][10][11] Since the non-equilibrium nature of ion bombardment makes a biased approach relatively difficult to perform rigorously, we conclude that using an ensemble of simulations would be a much more tractable solution. ...
In many modern applications, it is important to understand mechanisms of non-equilibrium chemistry and physics that are driven by low energy ion bombardment of solid surfaces. However, the study of these processes has been challenging as it demands a relatively unique balance between chemical fidelity and computational cost. To this end, we have proposed and constructed a new, high-throughput simulation pipeline based on density functional tight binding simulations. Additionally, we have extended the parameter set pbc-0-3 with the addition of Ar, thereby enabling the simulation of Ar bombardment. This pipeline was then applied to study the structural and compositional evolution of silicon nitride (SiN) under Ar bombardment. We identified a possible rate limiting step of bombardment-driven sputtering of SiN and suggested underlying mechanisms of Si and N removal. Damage from the bombardment, including generation of surface defects and Ar implantation, are further discussed. These findings and the newly developed simulation framework will serve as a useful foundation for further research in processes driven by ion bombardment.
... Biochemical approaches show function and interactions of biologically relevant molecules only on larger scales, though more detailed insights even at the atomistic (or molecular) level are indispensible. The lack of suitable experimental methods to gain atomistic details for describing the dynamic evolution of the structure can be circumvented by theoretical simulations [9][10][11][12][13]. ...
EPI-X4, an endogenous peptide inhibitor, has exhibited potential as a blocker of CXCR4—a G protein-coupled receptor. This unique inhibitor demonstrates the ability to impede HIV-1 infection and halt CXCR4-dependent processes such as tumor cell migration and invagination. Despite its promising effects, a comprehensive understanding of the interaction between EPI-X4 and CXCR4 under natural conditions remains elusive due to experimental limitations. To bridge this knowledge gap, a simulation approach was undertaken. Approximately 150,000 secondary structures of EPI-X4 were subjected to simulations to identify thermodynamically stable candidates. This simulation process harnessed a self-developed reactive force field operating within the ReaxFF framework. The application of the Two-Phase Thermodynamic methodology to ReaxFF facilitated the derivation of crucial thermodynamic attributes of the EPI-X4 conformers. To deepen insights, an ab initio density functional theory calculation method was employed to assess the electrostatic potentials of the most relevant (i.e., stable) EPI-X4 structures. This analytical endeavor aimed to enhance comprehension of the inhibitor’s structural characteristics. As a result of these investigations, predictions were made regarding how EPI-X4 interacts with CXCR4. Two pivotal requirements emerged. Firstly, the spatial conformation of EPI-X4 must align effectively with the CXCR4 receptor protein. Secondly, the functional groups present on the surface of the inhibitor’s structure must complement the corresponding features of CXCR4 to induce attraction between the two entities. These predictive outcomes were based on a meticulous analysis of the conformers, conducted in a gaseous environment. Ultimately, this rigorous exploration yielded a suitable EPI-X4 structure that fulfills the spatial and functional prerequisites for interacting with CXCR4, thus potentially shedding light on new avenues for therapeutic development.
... Molecular dynamics simulations provide a stable interaction between the behavior of proteins and other small molecules in detail so the interaction stability can be appropriately described [43]. With the accuracy and accessibility of simulation, followed by experimental structural data, it can be used well and help practical work that requires a lot of time and effort [44]. ...
Objective: This research was conducted to find potential candidate compounds from one hundred thirty-seven Indonesian marine natural products capable of preventing SARS-CoV-2 with a computational approach. Methods: The physicochemical properties and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile of compounds were predicted using ADMETLab. The candidate compounds were filtered using AutodockVina. Molecular docking was carried out using AutoDockTools on the SARS-CoV-2 3-Chymotrypsin-like protease (3CLpro) and Papain-like protease (PLpro) that is essential for the SARS-CoV-2 life cycle. Also, AMBER22 was used to perform molecular dynamics simulations in this study. Results: Based on molecular docking results, Pre-Neo-Kaluamine has good activity against 3CLpro with a bond energy value of-10.35 kcal/mol. Cortistatin F showed excellent binding activity on PLpro, with energy value results of-10.62 kcal/mol. Acanthomanzamine C has dual targeting activity and interacts well with protein 3CLpro and PLpro with binding energy values ranging from 10 kcal/mol to 14 kcal/mol. Conclusion: The molecular docking results were corroborated by molecular dynamics simulation results and showed good stability of the candidate ligands, and we found that there were three potential compounds as protease inhibitors of SARS-CoV-2 including Pre-Neo-Kaluamine for 3CLpro, Cortistatin F for PLpro, and Acanthomanzamine C which had dual targeting activity against both proteases.
... In the tool HTMD, more detailed parameters such as relaxation or equilibrium time scales, folding/unfolding pathways, standard free energy, protein conformation, and secondary structure changes can be screened (47). Other high throughput MDS methods and algorithms assess the mechanism or kinetics of protein-ligand association and modulation by amino acid substitutions (48). ...
With the advent of next-generation whole genome sequencing, many variants of uncertain significance (VUS) have been identified in individuals suffering from inheritable hypertrophic cardiomyopathy (HCM). Unfortunately, this classification of a genetic variant results in ambiguity in interpretation, risk stratification, and clinical practice. Here, we aim to review some basic science methods to gain a more accurate characterization of VUS in HCM. Currently, many genomic data-based computational methods have been developed and validated against each other to provide a robust set of resources for researchers. With the continual improvement in computing speed and accuracy, in silico molecular dynamic simulations can also be applied in mutational studies and provide valuable mechanistic insights. In addition, high throughput in vitro screening can provide more biologically meaningful insights into the structural and functional effects of VUS. Lastly, multi-level mathematical modeling can predict how the mutations could cause clinically significant organ-level dysfunction. We discuss emerging technologies that will aid in better VUS characterization and offer a possible basic science workflow for exploring the pathogenicity of VUS in HCM. Although the focus of this mini review was on HCM, these basic science methods can be applied to research in dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic cardiomyopathy (ACM), or other genetic cardiomyopathies.
... Therefore, MD simulation provides a novel tool for this kind of research. MD simulation is based on Newtonian mechanics, and a large number of theoretical calculations could be used to describe the interaction between molecules to obtain the mechanism of action at the atomic scale [31,32]. Furthermore, the conformations produced in MD simulation can be displayed in three dimensions by visualization software, which makes the outputs of MD more intuitive and easier to understand [33]. ...
It has been confirmed that skeletal muscle cells have the capability to receive foreign plasmid DNA (pDNA) and express functional proteins. This provides a promisingly applicable strategy for safe, convenient, and economical gene therapy. However, intramuscular pDNA delivery efficiency was not high enough for most therapeutic purposes. Some non-viral biomaterials, especially several amphiphilic triblock copolymers, have been shown to significantly improve intramuscular gene delivery efficiency, but the detailed process and mechanism are still not well understood. In this study, the molecular dynamics simulation method was applied to investigate the structure and energy changes of the material molecules, the cell membrane, and the DNA molecules at the atomic and molecular levels. From the results, the interaction process and mechanism of the material molecules with the cell membrane were revealed, and more importantly, the simulation results almost completely matched the previous experimental results. This study may help us design and optimize better intramuscular gene delivery materials for clinical applications.
... Classical molecular dynamics (MD) simulations allow the implementation of SBDD strategies that fully account for the structural flexibility of the overall drug−target model system [38]. ...
We elaborate new models for ACE and ACE2 receptors with an excellent prediction power compared to previous models. We propose promising workflows for working with huge compound collections, thereby enabling us to discover optimized protocols for virtual screening management. The efficacy of elaborated roadmaps is demonstrated through the cost-effective molecular docking of 1.4 billion compounds. Savings of up to 10-fold in CPU time are demonstrated. These developments allowed us to evaluate ACE2/ACE selectivity in silico, which is a crucial checkpoint for developing chemical probes for ACE2.
... One solution to the latter problem is usage of a workflow management system such as Galaxy [2], which provides a selection of tools for molecular dynamics simulation and analysis [3]. MD simulations are rarely performed singly; in recent years, the concept of high-throughput molecular dynamics (HTMD) has come to the fore [4,5]. Galaxy lends itself well to this kind of study, as we will demonstrate in this paper, thanks to features allowing construction of complex workflows, which can then be executed on multiple inputs in parallel. ...
This paper is a tutorial developed for the data analysis platform Galaxy. The purpose of Galaxy is to make high-throughput computational data analysis, such as molecular dynamics, a structured, reproducible and transparent process. In this tutorial we focus on 3 questions: How are protein-ligand systems parameterized for molecular dynamics simulation? What kind of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? After finishing you will have learned about force-fields and MD parameterization, how to conduct MD simulation and analysis for a protein-ligand system, and understand how different molecular interactions contribute to the binding affinity of ligands to the Hsp90 protein.
... In this perspective article, we propose that the true limiting fac-tor for molecular dynamics is rather the high hardware and electrical power costs "which "constrain not only the length of runs but also the number that can be performed concurrently. (Harvey and Fabritiis, 2012) Drug repositioning by structure-based virtual screening: This audit condenses the essential standards and most recent advancements of structure-based virtual screening and features the solid cooperative energies of PC innovation in medicate repositioning (Okimoto et al., 2009)A Review: The advancement of lead particles will help us to pick just powerful wires to treat particular maladies. Subsequently, the PC technique in the objective acknowledgment and forecast of new medications has been vital (Alonso et al., 2006) The application of molecular dynamics simulation in lead screening virtual screening is both effective and practical. ...
In our work we are working with HIV genomes and AIDS proteins. In our method we are performing sequence alignment using Needle man wunch algorithm of development and application the approach to calculate free energy and entropy by using non-physical, alchemical path through a thermodynamic series while the drug will interact with the protein. Here we are working in the principle based on protein-protein, protein-peptide, protein-ligand, virtual screening and compulsory free energy estimation for HIV genomics and AIDS proteins.for sequence comparison we are using Needleman Wunsch algorithm .
... In addition, MD can be used to assess the stability of a suggested molecular docking ligand-receptor complex (Durrant and McCammon 2011). If an MD-generated ligand conformation deviates from the respective docking solution by more than a specified RMSD value, the anticipated ligand-receptor complex may be regarded unstable (Harvey and De Fabritiis 2012). Newton's motion equations, as described in classical mechanics, are used in molecular dynamics to specify the position and speed of each atom in the system being studied (Nichols et al. 2011). ...
Phytocompounds are gaining popularity, due to lesser toxicity, greater bioavailability and high chemodiversity. Phytocompounds are evolving as new leads for the development of a novel drug. Phytocompounds exhibit a wide range of biological properties, and are being used as antioxidants, immunomodulatory, antimicrobial, cardiovascular, and anticancer drugs. However, their identification is still relatively limited. The major hurdle in the discovery of an efficient phytocompound is a complete dependence on time-consuming in vitro and in vivo screening systems. Alternatively, the computational drug discovery approach of using an online database and bioinformatics tools would be a cost-effective and time-saving option. Recent advancements in computational and structural biology have attributed the three-dimensional (3D) structure to many natural drug-like compounds and disease-related target macromolecules, and have been stored into renowned databases like Protein Data Bank (RCSB-PDB), DrugBank, and ZINC. The drug discovery filed is swiftly advancing with bioinformatics applications, and the availability of digitalized molecular data is paving new avenues in Computer-aided drug discovery (CADD) area. Virtual library screening (VLS), a widely recognized technique due to it being cost-effective, time-saving, and less laborious, evaluates drug candidates using computational tools, such as AutoDock Vina, GOLD, and Glide. This chapter provides comprehensive information about the available biological databases and bioinformatics tools that are useful for the analysis of plant-derived bioactive compounds and their molecular interactions in diseases. Applications of advanced bioinformatics tools and methods for designing, optimization, and high-throughput screening of phytocompounds are detailed. In addition, it emphasizes the advantages, limitations, challenges, and future perspectives of the computational approaches in analyzing phytocompound interactions.
... One solution to the latter problem is usage of a workflow management system such as Galaxy (2), which provides a selection of tools for molecular dynamics simulation and analysis (3). MD simulations are rarely performed singly; in recent years, the concept of high-throughput molecular dynamics (HTMD) has come to the fore (4,5). Galaxy lends itself well to this kind of study, as we will demonstrate in this paper, thanks to features allowing construction of complex workflows, which can then be executed on multiple inputs in parallel. ...
This paper is a tutorial developed for the data analysis platform Galaxy. The purpose of Galaxy is to make high-throughput computational data analysis, such as molecular dynamics, a structured, reproducible and transparent process. In this tutorial we focus on 3 questions: How are protein-ligand systems parameterized for molecular dynamics simulation? What kind of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? After finishing you will have learned about force-fields and MD parameterization, how to conduct MD simulation and analysis for a protein-ligand system, and understand how different molecular interactions contribute to the binding affinity of ligands to the Hsp90 protein.
... In the drug industry, the use of MD simulations helps drug designers to visualise for the first time many important biochemical phenomena that are not possible in laboratory experiments (Shaw et al., 2008). MD simulation is now a key technology for in silico drug discovery (Harvey and De Fabritiis, 2012). As is reported, drug discovery and development takes roughly 10-15 years (Macalino et al., 2015) with costs ranging from US$800 million (DiMasi et al., 2003, Song et al., 2009 to US$1.8 billion (Paul et al., 2010). ...
Given their biodegradable, biocompatible, renewable and non-toxic nature as well as favourable rheological properties, many industries have now opted to use biopolymers in their products and operations. Biopolymers, such as xanthan gum, provide good suspension and stability to solutions and exhibit excellent resistance against high shear and high temperature environments. To further expand the application of these biopolymers, there is a need to understand their rheological properties and the mechanism of biopolymer-water interactions. To date, there is still no consensus on the measurement of the solid-like behaviour or yield stress that is an important physical property to suspending particles in viscoelastic fluids. An understanding of the mechanism of hydrogen bonding (HB), which plays a crucial role in the biopolymer-water interactions, is also lacking in the existing literature.
In this thesis, both experimental and simulation approaches were employed to conduct the research. From the various rheological measurement methods conducted, it was found that the small amplitude oscillatory shear measurement provides a reliable yield stress value that is well matched with the steady shear and creep test results. Following these experimental studies, molecular dynamics (MD) simulations that can probe atomic interactions at a fine temporal and spatial resolution were run, given that the unique rheological properties of biopolymers arise from their atomic interactions. Here, the MD simulation studies were presented in a systematic progression that first begins with the study of water-water, biopolymer-water and finally carbon nanotube (CNT)-biopolymer-water interactions. From the simulation results, it was found that HB is pivotal in determining the thermodynamic properties of water. In both water and biopolymers, the number of HB is found to decrease with increasing temperature and this helps in explaining the changes in macroscopic properties (e.g. density and dynamic viscosity) when the temperature varies. Due to the presence of extensive hydroxyl groups, HB is found to be important in the conformational properties of biopolymers such as maintaining their helical conformation to suspend CNTs and adopting a more extended conformation that increases the van der Waals interaction between biopolymers and CNTs. The current research sheds some light on the rheology and dynamic properties of biopolymers.
... Molecular Dynamics (MD) simulations are increasingly employed to study biological molecules of biomedical interest, in particular in the drug discovery field, where they are being more and more used. [101][102][103] MD simulations have been combined with a wide variety of different approximations to study mobility related effects, such as the impact of protein motions on catalytic activity 104 and binding of ligands. [105][106][107][108] The central ideas in MD simulations are that biological activity is the result of time dependent interactions between molecules, that macroscopic observables (as observed in laboratory experiments) are related to microscopic behavior on the atomic level, and that the microscopic behavior of a molecule can be calculated by MD simulations. ...
Despite years of intensive research and development, cancer remains one of the leading causes of death worldwide. Chemotherapy is the most commonly used treatment for cancer, as surgery and radiation therapy are often not effective in treating cancer at every location where it spreads. However,drug resistance of cancer cells to chemotherapeutic agents and/or reduction in effectiveness of a drug is the leading cause of failure of chemotherapy. Drugs are developed to bind efficiently to a given therapeutic target, called the primary target. Unfortunately, drug treatments can suffer from bindingto a secondary target that perturbs drug activity and/or impacts its metabolism. The main aim of the PhD project was to develop an integrative chemoinformatics approach to optimize drug design by studying not only the primary target but also putative secondary effects at the atomic level in order to compute more accurate binding modes and to derive better affinity estimates.The presented drug design project aims for an improved inhibitor of the serine/threonine kinase mutant BRAFV600E with simultaneous loss of binding to the secondary target PXR. Focus is on the study of both, protein kinase BRAF and nuclear receptor PXR, which is involved in regulation of xenobiotic metabolism. A machine learning tool is first developed on the well studied nuclear receptor ERα due to large amounts of experimental data, and subsequently similarly generated for BRAFV600E. Despite its recognized importance in drug metabolism, we are still lacking sufficient structural information and affinity measurements to develop machine learning models for PXR. So, an alternative approach that relies on molecular dynamics combined with the Molecular Mechanics Poisson-Boltzmann Surface Area method is employed in order to obtain a precise estimation of ligand affinities. Finally, diverse computational tools are applied to design new derivatives of the initial drug, which is too rapidly metabolized in many patients resulting in resistance and cancer relapse. The properties of the new compounds prevent activation of metabolizing enzymes that are degrading the original drug. This is expected to provide a new drug-candidate with much better pharmacokintics properties and enhanced efficacy.This thesis comprises a complete drug design pipeline and presents an integrated strategy that includes modeling, in silico design and synthesis, virtual screening, affinity predictions, in vitro tests and X-ray crystallography. The main focus is on the computational part that comprises complementary approaches from the drug’s and from the proteins’ point of view.
... This provides an additionally precise approximation of the thermodynamics and kinetics in relation to drug-target recognizing and binding, as improved algorithms and hardware constructions increase their application. Classical MD simulations nowadays permit implementation of structure-based drug design approaches which fully explains structural flexibility of the overall drugtarget model arrangement (Durrant and McCammon 2011;Harvey and De Fabritiis 2012) Certainly, now it is publicly acknowledged that two main drug-binding models (induced-fit and conformational selection) have outdated Emil Fischer's rigid lock-and-key binding paradigm (Boehr et al. 2009;Changeux and Edelstein 2011;Vogt and Di Cera 2012). Researchers have lately illustrated the supremacy of these approaches for investigating protein-ligand binding and determining the associated free energy and kinetics. ...
The main aim of in silico drug design approaches is to take the best chemical substances to wet laboratory investigation through the reduction of cost and last stage attrition. In silico drug design approaches can utilize natural products and their semi-synthetic derivatives as starting material for discovery/design of small molecule drugs. The application of computers and computational approaches help in all areas of drug discovery and create the core of structure-based drug design.
... While, molecular dynamics (MD)-based computational methods are used to overcome the limitation of using a static-structure in addressing flexibility of binding sites [28] and accounting for other interactions that are stabilized by explicit water molecules. Examples of these MD methods include relaxed complex scheme(RCS) [29][30][31], probe-based molecular dynamics (pMD) [32][33][34][35][36][37], accelerated [38] MD simulations, high throughput MD simulations [39,40], Wrap 'n' Shake (WnS) [41] and MDpocket [42]. In RCS, MD simulations are used to generate an ensemble of target conformations that are afterward used in blind dockings to identify binding sites on the surface of each conformer [43]. ...
Molecular dynamics (MD) based computational co-solvent mapping methods involve the generation of an ensemble of MD-sampled target protein conformations and using selected small molecule fragments to identify and characterize binding sites on the surface of a target protein. This approach incorporates atomic-level solvation effects and protein mobility. It has shown great promise in the identification of conventional competitive and allosteric binding sites. It is also currently emerging as a useful tool in early stages of drug discovery. This review summarizes efforts as well as discusses some methodological advances and challenges in binding site identification process through these co-solvent mapping methods.
... Interestingly, MD simulation assisted in the discovery and development of antiviral drugs (198)(199)(200). For the first time, a combination of MD refinements of post-docking complexes and ensemble-based molecular docking has helped to reveal a unique symmetrical binding mode of daclatasvir with hepatitis C virus (HCV) NS5A protein. ...
Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11500 deaths according to a recent 2018 WHO report. With mortality rates up to 90 %, it is nowadays one of the most deadly infectious diseases. However, no FDA approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination makes Ebola virus a category A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from R&D to clinical trials in the development of small‐molecule antiviral drugs, small interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.
... But nevertheless, these codes were designed and work in programming environments in CPUs. On the other hand, with the advent of graphics processing units (GPUs), the field of High-throughput MD (HTMD) has become a fundamental tool for pharmaceutical research [37], accelerate the innovation in materials research [38], in revolutionizing the genome-wide [39], to explore large effects of structural changes ensemble of proteins [40], for drug discovery [41], just to mention a few applications. However, these areas have been developed using huge and expensive CPUS clusters. ...
Molecular dynamics simulation is currently the theoretical technique eligible to simulate a wide range of systems from soft condensed matter to biological systems. However, of the excellent results that the technique has arrogated, this approach remains computationally expensive, but with the emergence of the new supercomputing technologies bases on graphics processing units graphical processing units-based systems GPUs, the perspective has changed. The GPUs allow performing large and complex simulations at a significantly reduced time. In this work, we present recent innovations in the acceleration of molecular dynamics in GPUs to simulate non-Hamiltonian systems. In particular, we show the performance of measure-preserving geometric integrator in the canonical ensemble, that is, at constant temperature. We provide a validation and performance evaluation of the code by calculating the thermodynamic properties of a Lennard-Jones fluid. Our results are in excellent agreement with reported data reported from literature, which were calculated with CPUs. The scope and limitations for performing simulations of high-throughput MD under rigorous statistical thermodynamics in the canonical ensemble are discussed and analyzed.
... To date, most Markov state models were targeted to the folding of small proteins [156,157] (and, recently, also conformational changes in disordered proteins [158] and in RNA oligonucleotides [159]). However, a growing field of application is the binding of small ligands to proteins, a problem of great interest for drug discovery [120,[160][161][162][163][164]. Still, many important biomolecular processes have time scales beyond the second and remain hard to tackle with Markov state models based on standard MD. ...
This review discusses successful strategies and key open problems in the kinetic and thermodynamic characterization of complex biomolecular systems by computer simulations. The main focus is on established techniques and emerging trends in the fields of enhanced sampling and of kinetic models, as applied to biological problems ranging from protein folding and conformational dynamics to protein–protein and protein– ligand interaction. We address especially the following ques- tions: How to choose a computational approach suited to a particular problem? What are the strengths and limitations of alternative approaches? What is the current accuracy of thermodynamic and kinetic predictions? What are today’s open challenges and promising development directions? Towards the aim of accurately reproducing and interpreting experimental results, we briefly discuss hybrid approaches that combine together theoretical and experimental information.
... For this reason, together with few preliminary applications in the field of molecular docking (Korb et al., 2011;Khar et al., 2013), GPUs have been mainly exploited for MD simulations, which can be parallelized at the level of atoms. In fact, nowadays, simulations of hundreds of nanoseconds are easily performed, and reaching the microsecond timescale is an affordable issue on a GPU-equipped workstation (Harvey and De Fabritiis, 2012). In addition, cloud computing has been emerging nowadays, not just through the use of webservers intended to make molecular modeling accessible to a community of non-developers users, but also with the provision of computation power scalable and on-demand (Ebejer et al., 2013). ...
Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.
... Molecular docking predicts the conformation of a ligand within a binding pocket driven by binding energetics and affinity [164]. It can be applied to generate a series of docking samples when no crystallographic structure of the target is available [165]. In addition, MD can also be used to estimate the stability of a ligand-receptor complex obtained by molecular docking [155,166]. ...
Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host–pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
... Drug discovery deals with the energetics, affinity and selectivity of binding/unbinding events between naturally occurring biological receptors and synthetic inhibitors [148]. These events, however, span microseconds to milliseconds, while ab initio molecular dynamics can effectively sample a few femtoseconds at best [149]. G. de Fabritiis et al. published a paper [150] on the quantitative reconstruction of the binding process between β-trypsin and benzamidine using a Markov state model (MSM) [151]. ...
Chemical synthesis has its roots in the empirical approach of alchemy. Nonetheless, the birth of the scientific method, the technical and technological advances (exploiting revolutionary discoveries in physics) and the improved management and sharing of growing databases greatly contributed to the evolution of chemistry from an esoteric ground into a mature scientific discipline during these last 400 years. Furthermore, thanks to the evolution of computational resources, platforms and media in the last 40 years, theoretical chemistry has added to the puzzle the final missing tile in the process of “rationalizing” chemistry. The use of mathematical models of chemical properties, behaviors and reactivities is nowadays ubiquitous in literature. Theoretical chemistry has been successful in the difficult task of complementing and explaining synthetic results and providing rigorous insights when these are otherwise unattainable by experiment. The first part of this review walks the reader through a concise historical overview on the evolution of the “model” in chemistry. Salient milestones have been highlighted and briefly discussed. The second part focuses more on the general description of recent state-of-the-art computational techniques currently used worldwide by chemists to produce synergistic models between theory and experiment. Each section is complemented by key-examples taken from the literature that illustrate the application of the technique discussed therein.
... Indeed such simulations have been performed on several model systems [10,11,12] to study the complete association process in atomic detail either using hundreds of simulations each in the range of microseconds [13,14] or ultra-long simulations reaching the millisecond regime [15,16]. It is possible to predict the binding process of ligands to their target molecules as well as kinetics and energy barriers [15]. ...
A molecular dynamics replica exchange based method has been developed that allows rapid identification of putative ligand binding sites on the surface of biomolecules. The approach employs a set of ambiguity restraints in replica simulations between receptor and ligand that allow close contacts in the reference replica but promotes transient dissociation in higher replicas. This avoids long-lived trapping of the ligand or partner proteins at nonspecific, sticky, sites on the receptor molecule and results in accelerated exploration of the possible binding regions. In contrast to common docking methods that require knowledge of the binding site, exclude solvent and often keep parts of receptor and ligand rigid the approach allows for full flexibility of binding partners. Application to peptide-protein, protein-protein and a drug-receptor system indicate rapid sampling of near-native binding regions even in case of starting far away from the native binding site outperforming continuous MD simulations. An application on a DNA minor groove binding ligand in complex with DNA demonstrates that it can also be used in explicit solvent simulations.
Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor-ligand complexes include the...
Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.
Adenosine receptors have been studied for many years now, their structures have been resolved and many of the determinants for their interaction patterns have been highlighted. Nevertheless, they still represent extremely fascinating but challenging biological targets, and much effort has been put by both academic and industrial drug discovery groups into finding novel and proficient molecular candidates for the regulations of these purinergic species. Computational methods have been vastly implemented for the research of adenosine receptors modulators, allowing to speed up and optimize the development of potent and selective compounds. In this paper, we describe the main computer-based methods that have been used and that are still exploited in this field, reporting also many of the cases in which their application brought to light novel and promising molecular species with great therapeutic potential. While the research on adenosine receptors is still very open, the evolution and amelioration of computational approaches is expected to fill the gaps that we still miss about these beautiful targets, in a way that we can better communicate with them, finally providing valid and efficacious therapeutic solutions through their regulation.KeywordsAdenosineAdenosine receptorsCADDComputational chemistryDrug designDrug discoveryGPCRsMD simulationsMolecular dockingMolecular dynamicsMolecular modelingPurine
Objectives:
Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors.
Methods:
The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB: 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations.
Results:
The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins.
Conclusion:
Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.
Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.
A set of 220 inhibitors belonging to different structure classes and having HIV-1 integrase activity were collected along with their experimental pIC50 values. Geometries of all the inhibitors were fully optimized using B3LYP/6-31 + G(d) level of theory. These ligands were docked against 4 different HIV-1 integrase receptors (PDB IDs: 4LH5, 5KRS, 3ZSQ and 3ZSV). 30 docked poses were generated for all 220 inhibitors and ligand interaction of the first docked pose and the docked pose with the highest score were analysed. Residue GLU170 of 4LH5 receptor shows the highest number of interactions followed by ALA169, GLN168, HIS171 and ASP167 residues. Hydrogen bonding and stacking are mainly responsible for the interactions of these inhibitors with the receptor. We performed Molecular Dynamics (MD) simulation to observe the root-mean-square deviation (RMSD), for measure the average change of displacement between the atoms for a particular frame with respect to a reference and The Root Mean Square Fluctuation (RMSF) for characterization of local changes along the protein chain of the docked complexes. Analogue based models were generated to predict the pIC50 values for integrase inhibitors using various types of descriptors such as constitutional, geometrical, topological, quantum chemical and docking based descriptors. The best models were selected on the basis of statistical parameters and were validated by training and test set division. A few new inhibitors were designed on the basis of structure activity relationship and their pIC50 values were predicted using the generated models. All the designed new inhibitors a very high potential and may be used as potent inhibitors of HIV integrase. These models may be useful for further design and development of new and potent HIV integrase inhibitors.
Communicated by Ramaswamy H. Sarma
Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex datasets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD datasets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.
The properties of High Entropy Alloys (HEAs) strongly depend on the composition and content of elements. However, it was difficult to obtain the optimized element composition through the traditional "trial and error" method. The non-equiatomic HEAs have a large range for composition exploration by changing the content of elements, but the current research methods are difficult to analyze comprehensively. In this work, the prediction model with high accuracy is established by mixture design, the first principles calculation and machine learning. The model is used to predict the elastic properties and Poisson's ratio of non-equiatomic Mo–Nb–Ta–Ti–V HEAs, and the prediction results agree well with experimental data. The optimal element composition range of elastic properties and Poisson's ratio could be obtained. The influence of elements on the elastic properties and Poisson's ratio is analyzed through the calculation of features' importance. The results show that the content of Ti has the greatest contribution to the elastic properties and Poisson's ratio of the alloy. This model can not only obtain a large amount of data quickly and accurately but also help us to establish the relationship between element content and mechanical properties of non-equiatomic Mo–Nb–Ta–Ti–V RHEAs and provide theoretical guidance for experiments.
Researchers widely apply enzyme inhibition to chemicals such as pesticides, nerve gases, and anti-Alzheimer's drugs. However, application of enzyme inhibition to odorant sensors is less common because the corresponding reaction mechanisms have not yet been clarified in detail. In this study, we propose a new strategy for highly selective detection of odorant molecules by using an inhibitor-specific enzyme. As an example, we analyzed the selective interactions between acetylcholinesterase (AChE) and limonene─the major odorant of citrus and an AChE inhibitor─using molecular dynamics simulations. In these simulations, limonene was found to be captured at specific binding sites of AChE by modifying the binding site of acetylcholine (ACh), which induced inhibition of the catalytic activity of AChE toward ACh hydrolysis. We confirmed the simulation results by experiments using an ion-sensitive field-effect transistor, and the degree of inhibition of ACh hydrolysis depended on the limonene concentration. Accordingly, we quantitatively detected limonene at a detection limit of 5.7 μM. We furthermore distinguished the response signals to limonene from those to other odorants, such as pinene and perillic acid. Researchers will use our proposed odorant detection method for other odorant-enzyme combinations and applications of miniaturized odorant-sensing systems based on rapid testing.
Deep learning has disrupted nearly every field of research, including those of direct importance to drug discovery, such as medicinal chemistry and pharmacology. This revolution has largely been attributed to the unprecedented advances in highly parallelizable graphics processing units (GPUs) and the development of GPU-enabled algorithms. In this Review, we present a comprehensive overview of historical trends and recent advances in GPU algorithms and discuss their immediate impact on the discovery of new drugs and drug targets. We also cover the state-of-the-art of deep learning architectures that have found practical applications in both early drug discovery and consequent hit-to-lead optimization stages, including the acceleration of molecular docking, the evaluation of off-target effects and the prediction of pharmacological properties. We conclude by discussing the impacts of GPU acceleration and deep learning models on the global democratization of the field of drug discovery that may lead to efficient exploration of the ever-expanding chemical universe to accelerate the discovery of novel medicines. GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.
Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.
Adaptive sampling molecular dynamics based on Markov State Models use short parallel MD simulations to accelerate simulations, and are proven to identify hidden conformers. The accuracy of the predictions provided by it depends on the features extracted from the simulated data that is used to construct it. The identification of the most important features in the trajectories of the simulated system has a considerable effect on the results.
Methods
In this study, we use a combination of Laplacian scoring and genetic algorithms to obtain an optimized feature subset for the construction of the MSM. The approach is validated on simulations of three protein folding complexes, and two protein ligand binding complexes.
Results
Our experiments show that this approach produces better results when the number of samples is significantly lesser than the number of features extracted. We also observed that this method mitigates over fitting that occurs due to high dimensionality of large biosystems with shorter simulation times.
In medium/high entropy alloys, their mechanical properties are strongly dependent on the chemical-elemental composition. Thus, searching for optimum elemental composition remains a critical issue to maximize the mechanical performance. However, this issue solved by traditional optimization process via “trial and error” or experiences of domain experts is extremely difficult. Here we propose an approach based on high-throughput simulation combined machine learning to obtain medium entropy alloys with high strength and low cost. This method not only obtains a large amount of data quickly and accurately, but also helps us to determine the relationship between the composition and mechanical properties. The results reveal a vital importance of high-throughput simulation combined machine learning to find best mechanical properties in a wide range of elemental compositions for development of alloys with expected performance.
Bacterial infection and its resistance have become a major human health concern worldwide, and the number of resistant bacteria is increasing daily. Hence, antibacterial agents should possess the capability of treating resistant infections and have novel mode of action. Conventional drug development approaches are time-consuming and involve huge investments, and they frequently result in failure at the clinical trial phase due side effects. Modern computational approaches are an alternative to conventional modes and are the most effective, greatly improving on the former drug target identification and optimization of the lead compound. In this chapter, we focus on the advent of a few such computational approaches and the classical methods they aid in the identification of potential lead molecules. These techniques may be used as a resource to complement drug discovery programs for novel antibiotic discovery.
The δ‐opioid receptor (δ‐OR) couples to Gi/Go proteins to modulate a variety of responses in the nervous system. Τhe Regulator of G protein Signaling 4 (RGS4) was previously shown to directly interact within the C‐terminal region of δ‐OR using its N‐terminal domain to negatively modulate opioid receptor signaling. Herein, using molecular dynamics simulations and in vitro pull down experiments we delimit this interaction to twelve helix 8 residues of δ‐ΟR and to the first seventeen N‐terminal residues (NT) of RGS4. Monitoring the complex arrangement and stabilization between RGS4 and δ‐OR by molecular dynamics simulations combined with mutagenesis studies we defined that two critical interactions are formed: one between Phe329 of helix8 of δ‐ΟR and Pro9 of the NT of RGS4 and the other a salt bridge between Glu323 of δ‐ΟR and Lys17 of RGS4. Our observations allow drafting for the first time a structural model of a ternary complex including the δ‐opioid receptor, a G protein and a RGS protein. Furthermore, the high degree of conservation among opioid receptors of the RGS4‐binding region, points to a conserved interaction mode between opioid receptors and this important regulatory protein. This article is protected by copyright. All rights reserved.
High-throughput simulations can be a powerful tool in the discovery of new materials and behaviors. As part of a special issue on Rising Stars in Computational Materials Science, this article uses the work of the author to show how high-throughput simulations have had an impact in grain boundary structure-property relationships and other complex microstructural phenomena. The work demonstrates how new tools designed to analyze large datasets produced by high-throughput simulations are enabling comprehensive grain boundary structure-property relationships to be obtained. High-throughput simulations are also used to demonstrate the impact descriptive and inferential statistics has had in extracting key aspects of deformation in metallic glasses. Finally, several different examples are used to show a balanced approach between simulations designed to survey and simulations designed for detailed analysis. Together, the two approaches provide a comprehensive picture of the variety of behaviors that exist, while ensuring that the physics underlying the behaviors are thoroughly understood.
Accurately predicting three dimensional protein structures from sequences would present us with targets for drugs via molecular dynamics that would treat cancer, viral infections and neurological diseases. These treatments would have a far reaching impact to our economy, quality of life and society. The goal of this research was to build a data mining framework to predict cysteine connectivity in proteins from the sequence and oxidation state of cysteines. Accurately predicting the cysteine bonding configuration improves the TM-Score, a quantitative measurement of protein structure prediction accuracy. We provided state of the art Qp and Qc on the PDBCYS and IVD-54 Datasets. Furthermore, we have produced a Local Similarity Matrix that compares favorably to the default PSSMs generated from PSI-Blast in a statistically significant way. Our Qp for SP39, PDBCYS and IVD-54 were 90.6, 80.6 and 68.5 respectively.
Ion channels are implicated in many essential physiological events such as electrical signal propagation and cellular communication. The advent of K+ and Na+ ion channel structure determination has facilitated numerous investigations of molecular determinants of their behavior. At the same time, rapid development of computer hardware and molecular simulation methodologies has made computational studies of large biological molecules in all‐atom representation tractable. The concurrent evolution of experimental structural biology with biomolecular computer modeling has yielded mechanistic details of fundamental processes unavailable through experiments alone, such as ion conduction and ion channel gating. The following is a short survey of the atomistic computational investigations of K+ and Na+ ion channels, focusing on KcsA and several voltage‐gated channels from the KV and NaV families, that have garnered many successes and engendered several long‐standing controversies regarding the nature of their structure‐function relationship. We review the latest advancements and challenges facing the field of molecular modeling and simulation regarding the structural and energetic determinants of ion channel function and their agreement with experimental observations. This article is protected by copyright. All rights reserved
Advances in the structural biology of G-protein Coupled Receptors have resulted in a significant step forward in our understanding of how this important class of drug targets function at the molecular level. However, it has also become apparent that they are very dynamic molecules, and moreover, that the underlying dynamics is crucial in shaping the response to different ligands. Molecular dynamics simulations can provide unique insight into the dynamic properties of GPCRs in a way that is complementary to many experimental approaches. In this chapter, we describe progress in three distinct areas that are particularly difficult to study with other techniques: atomic level investigation of the conformational changes that occur when moving between the various states that GPCRs can exist in, the pathways that ligands adopt during binding/unbinding events and finally, the influence of lipids on the conformational dynamics of GPCRs.
The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS) allows to perform molecular simulations in an open boundary framework. It allows to change on the fly the resolution of specific subset of molecules (usually the solvent), which are free to diffuse between the atomistic region and the coarse-grained reservoir. So far, the method has been successfully applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a promising approach for rigorous calculations of ligand binding free energies in low-resolution protein models. Towards this goal, here we apply for the first time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its ability to reproduce structural and dynamic properties of both the proteins and the solvent, as obtained from atomistic simulations.
The function of complex biomolecular machines relies heavily on their conformational changes. Investigating these functional conformational changes is therefore essential for understanding the corresponding biological processes and promoting bioengineering applications and rational drug design. Constructing Markov State Models (MSMs) based on large‐scale molecular dynamics simulations has emerged as a powerful approach to model functional conformational changes of the biomolecular system with sufficient resolution in both time and space. However, the rapid development of theory and algorithms for constructing MSMs has made it difficult for nonexperts to understand and apply the MSM framework, necessitating a comprehensive guidance toward its theory and practical usage. In this study, we introduce the MSM theory of conformational dynamics based on the projection operator scheme. We further propose a general protocol of constructing MSM to investigate functional conformational changes, which integrates the state‐of‐the‐art techniques for building and optimizing initial pathways, performing adaptive sampling and constructing MSMs. We anticipate this protocol to be widely applied and useful in guiding nonexperts to study the functional conformational changes of large biomolecular systems via the MSM framework. We also discuss the current limitations of MSMs and some alternative methods to alleviate them. WIREs Comput Mol Sci 2018, 8:e1343. doi: 10.1002/wcms.1343
This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics
Theoretical and Physical Chemistry > Statistical Mechanics
We introduce a novel method, Pathway Search guided by Internal Motions (PSIM), that efficiently finds molecular dissociation pathways of a ligand-receptor system with guidance from principal component (PC) modes obtained from molecular dynamics (MD) simulations. Modeling ligand-receptor dissociation pathways can provide insights into molecular recognition and has practical applications, including understanding kinetic mechanisms and barriers to binding/unbinding as well as design of drugs with desired kinetic properties. PSIM uses PC modes in multi-level internal coordinates to identify natural molecular motions that guide the search for conformational switches and unbinding pathways. The new multi-level internal coordinates overcome problems with Cartesian and classical internal coordinates that fail to smoothly present dihedral rotation or generate non-physical distortions. We used HIV-1 protease, which has large-scale flap motions, as an example protein to demonstrate use of the multi-level internal coordinates. We provide examples of algorithms and implementation of PSIM with alanine dipeptide and chemical host-guest systems, 2-naphthylethanol-β-cyclodextrin and tetramethylammonium-cryptophane complexes. Tetramethylammonium-cryptophane has slow binding/unbinding kinetics. Its residence time, the length to dissociate tetramethylammonium from the host, is ~14 s from experiments, and PSIM revealed 4 dissociation pathways in approximately 150 CPU hr. We also searched the releasing pathways for the product glyceraldehyde-3-phosphate from tryptophan synthase, and one complete dissociation pathway was constructed after running multiple search iterations in approximately 300 CPU hr. With guidance by internal PC modes from MD simulations, the PSIM method has advantages over simulation-based methods to search for dissociation pathways of molecular systems with slow non-covalent kinetic behavior.
Protein folding involves physical timescales—microseconds to seconds—that are too long to be studied directly by straightforward molecular dynamics simulation, where the fundamental timestep is constrained to femtoseconds. Here we show how the long-time statistical dynamics of a simple solvated biomolecular system can be well described by a discrete-state Markov chain model constructed from trajectories that are an order of magnitude shorter than the longest relaxation times of the system. This suggests that such models, appropriately constructed from short molecular dynamics simulations, may have utility in the study of long-time conformational dynamics.
Metadynamics is a powerful algorithm that can be used both for reconstructing the free energy and for accelerating rare events in systems described by complex Hamiltonians, at the classical or at the quantum level. In the algorithm the normal evolution of the system is biased by a history-dependent potential constructed as a sum of Gaussians centered along the trajectory followed by a suitably chosen set of collective variables. The sum of Gaussians is exploited for reconstructing iteratively an estimator of the free energy and forcing the system to escape from local minima. This review is intended to provide a comprehensive description of the algorithm, with a focus on the practical aspects that need to be addressed when one attempts to apply metadynamics to a new system: (i) the choice of the appropriate set of collective variables; (ii) the optimal choice of the metadynamics parameters and (iii) how to control the error and ensure convergence of the algorithm.
Accurate simulation of biophysical processes requires vast computing resources. Folding@home is a distributed computing system first released in 2000 to provide such resources needed to simulate protein folding and other biomolecular phenomena. Now operating in the range of 5 PetaFLOPS sustained, it provides more computing power than can typically be gathered and operated locally due to cost, physical space, and electrical/cooling load. This paper describes the architecture and operation of Folding@home, along with some lessons learned over the lifetime of the project.
The understanding of protein-ligand binding is of critical importance for biomedical research, yet the process itself has been very difficult to study because of its intrinsically dynamic character. Here, we have been able to quantitatively reconstruct the complete binding process of the enzyme-inhibitor complex trypsin-benzamidine by performing 495 molecular dynamics simulations of free ligand binding of 100 ns each, 187 of which produced binding events with an rmsd less than 2 Å compared to the crystal structure. The binding paths obtained are able to capture the kinetic pathway of the inhibitor diffusing from solvent (S0) to the bound (S4) state passing through two metastable intermediate states S2 and S3. Rather than directly entering the binding pocket the inhibitor appears to roll on the surface of the protein in its transition between S3 and the final binding pocket, whereas the transition between S2 and the bound pose requires rediffusion to S3. An estimation of the standard free energy of binding gives ΔG° = -5.2 ± 0.4 kcal/mol (cf. the experimental value -6.2 kcal/mol), and a two-states kinetic model k(on) = (1.5 ± 0.2) × 10(8) M(-1) s(-1) and k(off) = (9.5 ± 3.3) × 10(4) s(-1) for unbound to bound transitions. The ability to reconstruct by simple diffusion the binding pathway of an enzyme-inhibitor binding process demonstrates the predictive power of unconventional high-throughput molecular simulations. Moreover, the methodology is directly applicable to other molecular systems and thus of general interest in biomedical and pharmaceutical research.
Molecular recognition is determined by the structure and dynamics of both a protein and its ligand, but it is difficult to directly assess the role of each of these players. In this study, we use Markov State Models (MSMs) built from atomistic simulations to elucidate the mechanism by which the Lysine-, Arginine-, Ornithine-binding (LAO) protein binds to its ligand. We show that our model can predict the bound state, binding free energy, and association rate with reasonable accuracy and then use the model to dissect the binding mechanism. In the past, this binding event has often been assumed to occur via an induced fit mechanism because the protein's binding site is completely closed in the bound state, making it impossible for the ligand to enter the binding site after the protein has adopted the closed conformation. More complex mechanisms have also been hypothesized, but these have remained controversial. Here, we are able to directly observe roles for both the conformational selection and induced fit mechanisms in LAO binding. First, the LAO protein tends to form a partially closed encounter complex via conformational selection (that is, the apo protein can sample this state), though the induced fit mechanism can also play a role here. Then, interactions with the ligand can induce a transition to the bound state. Based on these results, we propose that MSMs built from atomistic simulations may be a powerful way of dissecting ligand-binding mechanisms and may eventually facilitate a deeper understanding of allostery as well as the prediction of new protein-ligand interactions, an important step in drug discovery.
Markov state models of molecular kinetics (MSMs), in which the long-time statistical dynamics of a molecule is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. This approach has many appealing characteristics compared to straightforward molecular dynamics simulation and analysis, including the potential to mitigate the sampling problem by extracting long-time kinetic information from short trajectories and the ability to straightforwardly calculate expectation values and statistical uncertainties of various stationary and dynamical molecular observables. In this paper, we summarize the current state of the art in generation and validation of MSMs and give some important new results. We describe an upper bound for the approximation error made by modeling molecular dynamics with a MSM and we show that this error can be made arbitrarily small with surprisingly little effort. In contrast to previous practice, it becomes clear that the best MSM is not obtained by the most metastable discretization, but the MSM can be much improved if non-metastable states are introduced near the transition states. Moreover, we show that it is not necessary to resolve all slow processes by the state space partitioning, but individual dynamical processes of interest can be resolved separately. We also present an efficient estimator for reversible transition matrices and a robust test to validate that a MSM reproduces the kinetics of the molecular dynamics data.
Multidrug resistance is a serious problem in current chemotherapy. The efflux system largely responsible for resistance in Escherichia coli contains the drug transporter, AcrB. The structures of AcrB were solved in 2002 as the symmetric homo-trimer, and then in 2006 as the asymmetric homo-trimer. The latter suggested a functionally rotating mechanism. Here, by molecular simulations of the AcrB porter domain, we uncovered allosteric coupling and the drug export mechanism in the AcrB trimer. Allosteric coupling stabilized the asymmetric structure with one drug molecule bound, which validated the modelling. Drug dissociation caused a conformational change and stabilized the symmetric structure, providing a unified view of the structures reported in 2002 and 2006. A dynamic study suggested that, among the three potential driving processes, only protonation of the drug-bound protomer can drive the functional rotation and simultaneously export the drug.
Recent advances in hardware and software have enabled increasingly long molecular dynamics (MD) simulations of biomolecules, exposing certain limitations in the accuracy of the force fields used for such simulations and spurring efforts to refine these force fields. Recent modifications to the Amber and CHARMM protein force fields, for example, have improved the backbone torsion potentials, remedying deficiencies in earlier versions. Here, we further advance simulation accuracy by improving the amino acid side-chain torsion potentials of the Amber ff99SB force field. First, we used simulations of model alpha-helical systems to identify the four residue types whose rotamer distribution differed the most from expectations based on Protein Data Bank statistics. Second, we optimized the side-chain torsion potentials of these residues to match new, high-level quantum-mechanical calculations. Finally, we used microsecond-timescale MD simulations in explicit solvent to validate the resulting force field against a large set of experimental NMR measurements that directly probe side-chain conformations. The new force field, which we have termed Amber ff99SB-ILDN, exhibits considerably better agreement with the NMR data.
Characterizing the equilibrium ensemble of folding pathways, including their relative probability, is one of the major challenges in protein folding theory today. Although this information is in principle accessible via all-atom molecular dynamics simulations, it is difficult to compute in practice because protein folding is a rare event and the affordable simulation length is typically not sufficient to observe an appreciable number of folding events, unless very simplified protein models are used. Here we present an approach that allows for the reconstruction of the full ensemble of folding pathways from simulations that are much shorter than the folding time. This approach can be applied to all-atom protein simulations in explicit solvent. It does not use a predefined reaction coordinate but is based on partitioning the state space into small conformational states and constructing a Markov model between them. A theory is presented that allows for the extraction of the full ensemble of transition pathways from the unfolded to the folded configurations. The approach is applied to the folding of a PinWW domain in explicit solvent where the folding time is two orders of magnitude larger than the length of individual simulations. The results are in good agreement with kinetic experimental data and give detailed insights about the nature of the folding process which is shown to be surprisingly complex and parallel. The analysis reveals the existence of misfolded trap states outside the network of efficient folding intermediates that significantly reduce the folding speed.
In this article, we analyze the folding dynamics of an all-atom model of a polyphenylacetylene (pPA) 12-mer in explicit solvent for four common organic and aqueous solvents: acetonitrile, chloroform, methanol, and water. The solvent quality has a dramatic effect on the time scales in which pPA 12-mers fold. Acetonitrile was found to manifest ideal folding conditions as suggested by optimal folding times on the order of approximately 100-200 ns, depending on temperature. In contrast, chloroform and water were observed to hinder the folding of the pPA 12-mer due to extreme solvation conditions relative to acetonitrile; chloroform denatures the oligomer, whereas water promotes aggregation and traps. The pPA 12-mer in a pure methanol solution folded in approximately 400 ns at 300 K, compared relative to the experimental 12-mer folding time of approximately 160 ns measured in a 1:1 v/v THF/methanol solution. Requisite in drawing the aforementioned conclusions, analysis techniques based on Markov state models are applied to multiple short independent trajectories to extrapolate the long-time scale dynamics of the 12-mer in each respective solvent. We review the theory of Markov chains and derive a method to impose detailed balance on a transition-probability matrix computed from simulation data.
Molecular dynamics (MD) is a powerful tool for investigating detailed atomic-scale behavior on time scales of a nanosecond or less. For slower, infrequent-event processes, transition state theory (TST) can be employed, provided the the nature of the transition states are known -- i.e., if the relevant saddle points can be found. However, in many cases, the reactive events that will occur are not known in advance, or the transition states are very complicated. I will briefly discuss a new method for treating this type of case for solid-state systems. A bias potential, constructed from the gradient and Hessian, raises the energy of the system without affecting the TST dividing surfaces. Performing MD on the biased potential leads to accelerated transitions from state to state. In this ``hyper-MD" approach, time is no longer an independent variable; the elapsed time is estimated as the simulation proceeds, converging on the correct time in the long-time limit. Hyper-MD simulations of metallic surface diffusion on the microsecond time scale will be presented.
The high arithmetic performance and intrinsic parallelism of recent graphical processing units (GPUs) can offer a technological edge for molecular dynamics simulations. ACEMD is a production-class biomolecular dynamics (MD) engine supporting CHARMM and AMBER force fields. Designed specifically for GPUs it is able to achieve supercomputing scale performance of 40 ns/day for all-atom protein systems with over 23 000 atoms. We provide a validation and performance evaluation of the code and run a microsecond-long trajectory for an all-atom molecular system in explicit TIP3P water on a single workstation computer equipped with just 3 GPUs. We believe that microsecond time scale molecular dynamics on cost-effective hardware will have important methodological and scientific implications.
Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems. Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest numbers of standard cluster nodes.
I derive a general method for accelerating the molecular-dynamics (MD) simulation of infrequent events in solids. A bias potential (Delta V-b) raises the energy in regions other than the transition states b between potential basins. Transitions occur at an accelerated rate and the elapsed time becomes a statistical property of the system. Delta V-b can be constructed without knowing the location of the transition states and implementation requires only first derivatives. I examine the diffusion mechanisms of a 10-atom Ag cluster on the Ag(111) surface using a 220 mu s hyper-MD simulation.
Biomolecular simulation is a core application on supercomputers, but it is exceptionally difficult to achieve the strong scaling necessary to reach biologically relevant timescales. Here, we present a new paradigm for parallel adaptive molecular dynamics and a publicly available implementation: Copernicus. This framework combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring. Copernicus enables execution as single parallel jobs with automatic resource allocation. Even for a small protein such as villin (9,864 atoms), Copernicus exhibits near-linear strong scaling from 1 to 5,376 AMD cores. Starting from extended chains we observe structures 0.6 Å from the native state within 30h, and achieve sufficient sampling to predict the native state without a priori knowledge after 80--90h. To match Copernicus' efficiency, a classical simulation would have to exceed 50 microseconds per day, currently infeasible even with custom hardware designed for simulations.
The prediction of protein–ligand binding free energies is an important goal of computational biochemistry, yet accuracy, reproducibility, and cost remain a problem. Nevertheless, these are essential requirements for computational methods to become standard binding prediction tools in discovery pipelines. Here, we present the results of an extensive search for an optimal method based on an ensemble of umbrella sampling all-atom molecular simulations tested on the phosphorylated tetrapeptide, pYEEI, binding to the SH2 domain, resulting in an accurate and converged binding free energy of −9.0 ± 0.5 kcal/mol (compared to an experimental value of −8.0 ± 0.1 kcal/mol). We find that a minimum of 300 ns of sampling is required for every prediction, a target easily achievable using new generation accelerated MD codes. Convergence is obtained by using an ensemble of simulations per window, each starting from different initial conformations, and by optimizing window-width, orthogonal restraints, reaction coordinate harmonic potentials, and window-sample time. The use of uncorrelated initial conformations in neighboring windows is important for correctly sampling conformational transitions from the unbound to bound states that affect significantly the precision of the calculations. This methodology thus provides a general recipe for reproducible and practical computations of binding free energies for a class of semirigid protein–ligand systems, within the limit of the accuracy of the force field used.
Molecular dynamics simulations hold the promise of providing an atomic-level description of protein folding that cannot easily be obtained from experiments. Here, we examine the extent to which the molecular mechanics force field used in such simulations might influence the observed folding pathways. To that end, we performed equilibrium simulations of a fast-folding variant of the villin headpiece using four different force fields. In each simulation, we observed a large number of transitions between the unfolded and folded states, and in all four cases, both the rate of folding and the structure of the native state were in good agreement with experiments. We found, however, that the folding mechanism and the properties of the unfolded state depend substantially on the choice of force field. We thus conclude that although it is important to match a single, experimentally determined structure and folding rate, this does not ensure that a given simulation will provide a unique and correct description of the full free-energy surface and the mechanism of folding.
Protein-ligand interactions are essential for nearly all biological processes, and yet the biophysical mechanism that enables potential binding partners to associate before specific binding occurs remains poorly understood. Fundamental questions include which factors influence the formation of protein-ligand encounter complexes, and whether designated association pathways exist. To address these questions, we developed a computational approach to systematically analyze the complete ensemble of association pathways. Here, we use this approach to study the binding of a phosphate ion to the Escherichia coli phosphate-binding protein. Various mutants of the protein are considered, and their effects on binding free-energy profiles, association rates, and association pathway distributions are quantified. The results reveal the existence of two anion attractors, i.e., regions that initially attract negatively charged particles and allow them to be efficiently screened for phosphate, which is subsequently specifically bound. Point mutations that affect the charge on these attractors modulate their attraction strength and speed up association to a factor of 10 of the diffusion limit, and thus change the association pathways of the phosphate ligand. It is demonstrated that a phosphate that prebinds to such an attractor neutralizes its attraction effect to the environment, making the simultaneous association of a second phosphate ion unlikely. This study suggests ways in which structural properties can be used to tune molecular association kinetics so as to optimize the efficiency of binding, and highlights the importance of kinetic properties.
The mouse major urinary protein (MUP) has proved to be an intriguing test bed for detailed studies on protein-ligand recognition. NMR, calorimetric, and modeling investigations have revealed that the thermodynamics of ligand binding involve a complex interplay between competing enthalpic and entropic terms. We performed six independent, 1.2 micros molecular-dynamics simulations on MUP--three replicates on the apo-protein, and three on the complex with the pheromone isobutylmethoxypyrazine. Our findings provide the most comprehensive picture to date of the structure and dynamics of MUP, and how they are modulated by ligand binding. The mechanical pathways by which amino acid side chains can transmit information regarding ligand binding to surface loops and either increase or decrease their flexibility (entropy-entropy compensation) are identified. Dewetting of the highly hydrophobic binding cavity is confirmed, and the results reveal an aspect of ligand binding that was not observed in earlier, shorter simulations: bound ligand retains extensive rotational freedom. Both of these features have significant implications for interpretations of the entropic component of binding. More generally, these simulations test the ability of current molecular simulation methods to produce a reliable and reproducible picture of protein dynamics on the microsecond timescale.
Computational chemistry--in particular, virtual screening--can provide valuable contributions in hit- and lead-compound discovery. Numerous software tools have been developed for this purpose. However, despite the applicability of virtual screening technology being well established, it seems that there are relatively few examples of drug discovery projects in which virtual screening has been the key contributor. Has virtual screening reached its peak? If not, what aspects are limiting its potential at present, and how can significant progress be made in the future?
Although molecular dynamics simulation methods are useful in the modeling of macromolecular systems, they remain computationally expensive, with production work requiring costly high-performance computing (HPC) resources. We review recent innovations in accelerating molecular dynamics on graphics processing units (GPUs), and we describe GPUGRID, a volunteer computing project that uses the GPU resources of nondedicated desktop and workstation computers. In particular, we demonstrate the capability of simulating thousands of all-atom molecular trajectories generated at an average of 20 ns/day each (for systems of approximately 30 000-80 000 atoms). In conjunction with a potential of mean force (PMF) protocol for computing binding free energies, we demonstrate the use of GPUGRID in the computation of accurate binding affinities of the Src SH2 domain/pYEEI ligand complex by reconstructing the PMF over 373 umbrella sampling windows of 55 ns each (20.5 mus of total data). We obtain a standard free energy of binding of -8.7 +/- 0.4 kcal/mol within 0.7 kcal/mol from experimental results. This infrastructure will provide the basis for a robust system for high-throughput accurate binding affinity prediction.
To date, the slowest-folding proteins folded ab initio by all-atom molecular dynamics simulations have had folding times in the range of nanoseconds to microseconds. We report simulations of several folding trajectories of NTL9(1-39), a protein which has a folding time of approximately 1.5 ms. Distributed molecular dynamics simulations in implicit solvent on GPU processors were used to generate ensembles of trajectories out to approximately 40 micros for several temperatures and starting states. At a temperature less than the melting point of the force field, we observe a small number of productive folding events, consistent with predictions from a model of parallel uncoupled two-state simulations. The posterior distribution of the folding rate predicted from the data agrees well with the experimental folding rate (approximately 640/s). Markov State Models (MSMs) built from the data show a gap in the implied time scales indicative of two-state folding and heterogeneous pathways connecting diffuse mesoscopic substates. Structural analysis of the 14 out of 2000 macrostates transited by the top 10 folding pathways reveals that native-like pairing between strands 1 and 2 only occurs for macrostates with p(fold) > 0.5, suggesting beta(12) hairpin formation may be rate-limiting. We believe that using simulation data such as these to seed adaptive resampling simulations will be a promising new method for achieving statistically converged descriptions of folding landscapes at longer time scales than ever before.
Part of understanding a molecule's conformational dynamics is mapping out the dominant metastable, or long lived, states that it occupies. Once identified, the rates for transitioning between these states may then be determined in order to create a complete model of the system's conformational dynamics. Here we describe the use of the MSMBuilder package (now available at http://simtk.org/home/msmbuilder/) to build Markov State Models (MSMs) to identify the metastable states from Generalized Ensemble (GE) simulations, as well as other simulation datasets. Besides building MSMs, the code also includes tools for model evaluation and visualization.
Binding estimation after refinement (BEAR) is a novel automated computational procedure suitable for correcting and overcoming limitations of docking procedures such as poor scoring function and the generation of unreasonable ligand conformations. BEAR makes use of molecular dynamics simulation followed by MM-PBSA and MM-GBSA binding free energy estimates as tools to refine and rescore the structures obtained from docking virtual screenings. As binding estimation after refinement relies on molecular dynamics, the entire procedure can be tailored to the needs of the end-user in terms of computational time and the desired accuracy of the results. In a validation test, binding estimation after refinement and rescoring resulted in a significant enrichment of known ligands among top scoring compounds compared with the original docking results. Binding estimation after refinement has direct and straightforward application in virtual screening for correcting both false-positive and false-negative hits, and should facilitate more reliable selection of biologically active molecules from compound databases.
The recent introduction of cost-effective accelerator processors (APs), such as the IBM Cell processor and Nvidia's graphics processing units (GPUs), represents an important technological innovation which promises to unleash the full potential of atomistic molecular modeling and simulation for the biotechnology industry. Present APs can deliver over an order of magnitude more floating-point operations per second (flops) than standard processors, broadly equivalent to a decade of Moore's law growth, and significantly reduce the cost of current atom-based molecular simulations. In conjunction with distributed and grid-computing solutions, accelerated molecular simulations may finally be used to extend current in silico protocols by the use of accurate thermodynamic calculations instead of approximate methods and simulate hundreds of protein-ligand complexes with full molecular specificity, a crucial requirement of in silico drug discovery workflows.
The dynamics of a folded globular protein (bovine pancreatic trypsin inhibitor) have been studied by solving the equations of motion for the atoms with an empirical potential energy function. The results provide the magnitude, correlations and decay of fluctuations about the average structure. These suggest that the protein interior is fluid-like in that the local atom motions have a diffusional character.
Many interesting dynamic properties of biological molecules cannot be simulated directly using molecular dynamics because of nanosecond time scale limitations. These systems are trapped in potential energy minima with high free energy barriers for large numbers of computational steps. The dynamic evolution of many molecular systems occurs through a series of rare events as the system moves from one potential energy basin to another. Therefore, we have proposed a robust bias potential function that can be used in an efficient accelerated molecular dynamics approach to simulate the transition of high energy barriers without any advance knowledge of the location of either the potential energy wells or saddle points. In this method, the potential energy landscape is altered by adding a bias potential to the true potential such that the escape rates from potential wells are enhanced, which accelerates and extends the time scale in molecular dynamics simulations. Our definition of the bias potential echoes the underlying shape of the potential energy landscape on the modified surface, thus allowing for the potential energy minima to be well defined, and hence properly sampled during the simulation. We have shown that our approach, which can be extended to biomolecules, samples the conformational space more efficiently than normal molecular dynamics simulations, and converges to the correct canonical distribution.
NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.
The future of integrated electronics is the future of electronics itself. Integrated circuits will lead to such wonders as home computers, automatic controls for automobiles, and personal portable communications equipment. But the biggest potential lies in the production of large systems. In telephone communications, integrated circuits in digital filters will separate channels on multiplex equipment. Integrated circuits will also switch telephone circuits and perform data processing. In addition, the improved reliability made possible by integrated circuits will allow the construction of larger processing units. Machines similar to those in existence today will be built at lower costs and with faster turnaround.
ACEMD: accelerated molecular dynamics simulations in the microseconds timescale
- Harvey
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Desmond Performance on a Cluster of Multicore Processors, DES-RES Technical Report Gromacs 4: algorithms for highly efficient load-balanced and scalable molecular simulation
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Scalable molecular dynamics with NAMD The impact of accelerator processors for high-throughput molecular modeling and simulation Anton, a special-purpose machine for molecular dynamics simulation Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
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Folding@Home: lessons from eight years of volunteer distributed computing. IPDPS'09 Copernicus: a new paradigm for parallel adaptive molecular dynamics
- A Beberg
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Anton, a special-purpose machine for molecular dynamics simulation
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The impact of accelerator processors for high-throughput molecular modeling and simulation
- Giupponi
Optimised potential of mean force calculations of standard binding free energy
- Buch
Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations
- Noé
Folding@Home: lessons from eight years of volunteer distributed computing. IPDPS’09
- Beberg
Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1-39)
- Voelz
Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules
- Hamelberg
How robust are protein folding simulations with respect to force field parameterization?
- Piana
Using generalized ensemble simulations and Markov state models to identify conformational states
- Bowman