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Nipah virus is a pathogen considered highly infectious, and its lethality can cause between 40 to 70% of deaths in those infected. At present, no effective treatment is available which results in an imperative need to explore new approaches to the search for drugs. Through virtual screening techniques, docking and molecular dynamics, 183 ligands were evaluated against the Nipah virus glycoprotein (NiV-G), involved throughout the process of virus entry to the host cell, resulting in a good target for blocking the infection. Of the 183 drugs computationally screened, three of them (MMV020537, MMV688888 and MMV019838) were found to be potential inhibitors of NiV-G. Their calculated dissociation constants were 0.03 nM, 2.18 nM and 31.61 nM, respectively. Molecular dynamics studies confirm their stability binding modes in the active site of the protein. These potential inhibitors can be used later as leads for the development of new drugs that allow effective treatment of the disease.
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... Interestingly, the present study findings are highly corroborated with the earlier reports where similar types of molecular docking interactions have been found between NiV-G protein and three studied compounds viz. MMV020537, MMV019838 and MMV688888 . More precisely, the earlier study has demonstrated that a few amino acid residues such as Arg236, Cys240, His281, Pro441, Trp504, Tyr508, Ala532 and Lys560 of NiV-G protein implicated potential inter-molecular interactions with the three ligands mediated through H-bond and hydrophobic interactions . ...
... MMV020537, MMV019838 and MMV688888 . More precisely, the earlier study has demonstrated that a few amino acid residues such as Arg236, Cys240, His281, Pro441, Trp504, Tyr508, Ala532 and Lys560 of NiV-G protein implicated potential inter-molecular interactions with the three ligands mediated through H-bond and hydrophobic interactions . Such observations undoubtedly favor the stability of the interaction of the NiV-G protein-ligand complex at the molecular level. ...
... Undoubtedly, RMSD settles at lower values for all compounds when bound with NiV-G protein suggested that the convergence of the structure towards an equilibrium state. Likewise, similarities found in docking-based analyses reported by Georcki Ropón-Palacios et al.  and the performance of MD simulations also revealed very interesting RMSD profiles. Although, the above mentioned study has restricted the simulation time span up to only 40 ns, however, RMSD profile for compound MMV020537 implicated stable RMSD values as alike to the RMSD profiles of all compounds (G1 -G5) analyzed for 100 ns MD simulation run, in the present study. ...
Nipah virus (NiV) infections are highly contagious and can cause severe febrile encephalitis. An outbreak of NiV infection has reported high mortality rates in Southeast Asian countries including Bangladesh, East Timor, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, Philippines, Thailand and India. Considering the high risk for an epidemic outbreak, the World Health Organization (WHO) declared NiV as an emerging priority pathogen. However, there are no effective therapeutics or any FDA approved drugs available for the treatment of this infection. Among the known nine proteins of NiV, glycoprotein plays an important role in initiating the entry of viruses and attaching to the host cell receptors. Herein, three antiviral databases consisting of 79,892 chemical entities have been computationally screened against NiV glycoprotein (NiV-G). Particularly, multi-step molecular docking followed by extensive molecular binding interactions analyses, binding free energy estimation, in silico pharmacokinetics, synthetic accessibility and toxicity profile evaluations has been carried out for initial identification of potential NiV-G inhibitors. Further, molecular dynamics (MD) simulation has been performed to understand the dynamic properties of NiV-G protein-bound with proposed five inhibitors (G1-G5) and their interactions behavior, and any conformational changes in NiV-G protein during simulations. Moreover, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) based binding free energies (∆G) has been calculated from all MD simulated trajectories to understand the energy contribution of each proposed compound in maintaining and stabilizing the complex binding interactions with NiV-G protein. Proposed compounds showed high negative ∆G values ranging from −166.246 to −226.652 kJ/mol indicating a strong affinity towards the NiV-G protein.
... The structure of NiV with glycoprotein (G) attachment has 602 amino acid residues where the glycoprotein functions as a receptor-binding protein providing attachment to host cell receptors. This protein plays a vital role in facilitating the fusion of cell membrane with the virus through F protein by interacting with Ephirin-B2 receptors present on host cell and thereby making it an effective target for inhibition . The World Health Organization (WHO) has set NiV as a priority disease on the WHO R&D Blueprint . ...
Favipiravir is found to show excellent in-vitro inhibition activity against Nipah Virus. To
explore the structure-property relationship of Favipiravir, in silico designing of a series of piperazine substituted Favipiravir derivatives are attempted and computational screening has been done to evaluate to its bimolecular interactions with Nipah Virus. The geometrical features of all the molecules have been addressed from Density Functional Theory calculations. Chemical reactivity descriptor analysis was carried out to understand various reactivity parameters. The drug-likeness properties were estimated by a detailed ADMETstudy. The binding ability and the mode of binding of these derivatives into the Nipah Virus are obtained from molecular docking studies. Our calculations show greater binding ability for the designed inhibitors compared to that of the experimentally reported molecule. Overall, the present work proves to offers new insights and guidelines for synthetic chemists to develop new drugs using piperazine substituted Favipiravir in the treatment of Nipah Virus.
... These compounds were filtered, under the criterion of selecting those that do not present any violation of Lipinski's Rule of Five . Next, each chemical compound was prepared for virtual screening, for this reason, they were transformed from SMILE files to SDF, PDB and PDBQT, using OpenBabel v.2.4.0 , which polar hydrogens and a protonation at pH 7.4 were added, following the methodology described in Ref. . Subsequently, to achieve the optimization of the geometry of the structures of these compounds, these were minimized using the force field MMFF94 . ...
The world is currently facing a pandemic caused by the new 2019 coronavirus disease (COVID-19), caused by SARS-CoV-2. Among the fundamental processes of this virus are viral transcription and replication. They allow the synthesis
of genetic material and the consequent multiplication of the virus to infect other cells or organisms. These are performed by a multi-subunit machinery of various nonstructural proteins (nsp); among which the RNA-dependent RNA
polymerase (RdRp or nsp12) is the most important, and, at the same time, conserved among coronaviruses. The structure of this protein (PDB ID: 6M71) was used as a target in the application of computational strategies for drug
search, like virtual screening and molecular docking. The region considered for virtual screening has three important amino acids for protein catalysis: T680 (located in Motif A), N691 and D623 (located in Motif B), where a grid box was located. In turn, applying the concept of drug repositioning is
considered as a quick response in the treatment of sudden outbreaks of diseases. Here, we used the Pathogen Box, a database of chemical compounds analyzed for the treatment against malaria, which were filtered under the criteria of selecting those that do not present any violation of Lipinski's
Rule of Five. At the same time, the Remdesivir, Beclabuvir and Sofosbuvir drug, previously used in in silico and clinical studies for inhibition of nsp12, were used as positive controls. The results showed a Top10 potential target inhibitors, with binding energy higher than those of the positive controls, of which TCMDC-134153 and TCMDC-135052, both with -7.53 kcal/mol, present interactions with the three important residues of the nsp12 catalytic site. These proposed ligands would be used for subsequent validation by molecular dynamics, where they can be
considered as drugs for the development of effective treatments against this new pandemic.
... These compounds were filtered, under the criterion of selecting those that do not present any violation of Lipinski's Rule of Five . Next, each chemical compound was prepared for virtual screening, for this reason, they were transformed from SMILE files to SDF, PDB and PDBQT, using OpenBabel v.2.4.0 , which polar hydrogens and a protonation at pH 7.4 were added, following the methodology described in Ref. . Subsequently, to achieve the optimization of the geometry of the structures of these compounds, these were minimized using the force field MMFF94 . ...
The world is currently facing a pandemic caused by the new 2019 coronavirus disease (COVID-19), caused by SARS-CoV-2. Among the fundamental processes of this virus are viral transcription and replication. They allow the synthesis of genetic material and the consequent multiplication of the virus to infect other cells or organisms. These are performed by a multi-subunit machinery of various nonstructural proteins (nsp); among which the RNA-dependent RNA polymerase (RdRp or nsp12) is the most important, and, at the same time, conserved among coronaviruses. The structure of this protein (PDB ID: 6M71) was used as a target in the application of computational strategies for drug search, like virtual screening and molecular docking. The region considered for virtual screening has three important amino acids for protein catalysis: T680 (located in Motif A), N691 and D623 (located in Motif B), where a grid box was located. In turn, applying the concept of drug repositioning is considered as a quick response in the treatment of sudden outbreaks of diseases. Here, we used the Pathogen Box, a database of chemical compounds analyzed for the treatment against malaria, which were filtered under the criteria of selecting those that do not present 1 any violation of Lipinski's Rule of Five. At the same time, the Remdesivir, Beclabuvir and Sofosbuvir drug, previously used in in silico and clinical studies for inhibition of nsp12, were used as positive controls. The results showed a Top10 potential target inhibitors, with binding energy (∆G) higher than those of the positive controls, of which TCMDC-134153 and TCMDC-135052, both with ∆G = −7.53 kcal/mol, present interactions with the three important residues of the nsp12 catalytic site. These proposed ligands would be used for subsequent validation by molecular dynamics, where they can be considered as drugs for the development of effective treatments against this new pandemic.
... Lipinsky's rule of 5 , selecting only those molecules that do not show any violation of the rule. Subsequently, the compounds were converted to SDF, PDB and PDBQT formats, in this order consecutively, using OpenBabel v2.4.1 software , adding polar hydrogens for pH 7.4, following the methodology described in Ref. . In addition, the three-dimensional structures of the compounds were minimized using the MMFF96 force field, implemented in the OpenBabel software, in order to optimize their geometry. ...
SARS-CoV-2, the causative agent of the disease known as Covid-19, has so far reported around 3,435,000 cases of human infections, including more than 239,000 deaths in 187 countries, with no effective treatment currently available. For this reason, it is necessary to explore new approaches for the development of a drug capable of inhibiting the entry of the virus into the host cell. Therefore, this work includes the exploration of potential inhibitory compounds for the Spike protein of SARS-CoV-2 (PDB ID: 6VSB), which were obtained from The Patogen Box. Later, they were filtered through virtual screening and molecular docking techniques, thus obtaining a top of 1000 compounds, which were used against a binding site located in the Receptor Binding Domain (RBD) and a cryptic site located in the N-Terminal Domain (NTD), resulting in good pharmaceutical targets for the blocking the infection. From the top 1000, the best compound (TCMDC-124223) was selected for the binding site. It interacts with specific residues that intervene in the recognition and subsequent entry into the host cell, resulting in a more favorable binding free energy in comparison to the control compounds (Hesperidine and Emodine). In the same way, the compound TCMDC-133766 was selected for the cryptic site. These identified compounds are potential inhibitors that can be used for the development of new drugs that allow effective treatment for the disease.
Protein-ligand docking is an essential process that has accelerated drug discovery. How to accurately and effectively optimize the predominant position and orientation of ligands in the binding pocket of a target protein is a major challenge. This paper proposed a novel ligand binding pose search method called FWAVina based on the fireworks algorithm, which combined the fireworks algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon local search method adopted in AutoDock Vina to address the pose search problem in docking. The FWA was used as a global optimizer to rapidly search promising poses, and the Broyden-Fletcher-Goldfarb-Shannon method was incorporated into FWAVina to perform an exact local search. FWAVina was developed and tested on the PDBbind and DUD-E datasets. The docking performance of FWAVina was compared with the original Vina program. The results showed that FWAVina achieves a remarkable execution time reduction of more than 50% than Vina without compromising the prediction accuracies in the docking and virtual screening experiments. In addition, the increase in the number of ligand rotatable bonds has almost no effect on the efficiency of FWAVina. The higher accuracy, faster convergence and improved stability make the FWAVina method a better choice of docking tool for computer-aided drug design. The source code is available at https://github.com/eddyblue/FWAVina/.
Chitin can be widely found in the fungal cell wall, nematode eggshells, and the exoskeleton of arthropods; however, it is completely absent from higher plants and mammals. The process of chitin degradation is essential for both growth and maturation of insects. Thus, inhibiting chitin degradation is a promising strategy for the control and management of pests. The chitinolytic β-N-acetyl-D-hexosaminidase OfHex1 of Ostrinia furnacalis (one of the most destructive pests) has been suggested as a potential target for the design of eco-friendly pesticides. This study presents the sequential virtual screening of the ZINC library with 8 million compounds, targeting OfHex1. After confirmation via enzyme inhibition experiments, compound 5 exhibited potential inhibitory activity against OfHex1 with a Ki of 28.9 ± 0.5 μM and significant selectivity (IC50 > 100 μM against HsHexB and hOGA). Molecular dynamics simulations combined with binding free energy and free energy decomposition calculations were conducted to investigate the molecular basis underlying the potency of these inhibitors toward OfHex1. The present work provides useful information for the future rational design of novel and potent OfHex1 inhibitors
Communicated by Ramaswamy H. Sarma
Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliable protein-ligand x-ray structures and binding affinity data has required the use of constructed datasets for the training and evaluation of CNN molecular recognition models. Here, we outline various sources of bias in one such widely-used dataset, the Directory of Useful Decoys: Enhanced (DUD-E). We have constructed and performed tests to investigate whether CNN models developed using DUD-E are properly learning the underlying physics of molecular recognition, as intended, or are instead learning biases inherent in the dataset itself. We find that superior enrichment efficiency in CNN models can be attributed to the analogue and decoy bias hidden in the DUD-E dataset rather than successful generalization of the pattern of protein-ligand interactions. Comparing additional deep learning models trained on PDBbind datasets, we found that their enrichment performances using DUD-E are not superior to the performance of the docking program AutoDock Vina. Together, these results suggest that biases that could be present in constructed datasets should be thoroughly evaluated before applying them to machine learning based methodology development.
The number of entries in the Protein Data Bank (PDB) has doubled in the last decade, and it has increased tenfold in the last twenty years. The availability of an ever-growing number of structures is having a huge impact on the Structure-Based Drug Discovery (SBDD), allowing investigation of new targets and giving the possibility to have multiple structures of the same macromolecule in a complex with different ligands. Such a large resource often implies the choice of the most suitable complex for molecular docking calculation, and this task is complicated by the plethora of possible posing and scoring function algorithms available, which may influence the quality of the outcomes. Here, we report a large benchmark performed on the PDBbind database containing more than four thousand entries and seventeen popular docking protocols. We found that, even in protein families wherein docking protocols generally showed acceptable results, certain ligand-protein complexes are poorly reproduced in the self-docking procedure. Such a trend in certain protein families is more pronounced, and this underlines the importance in identification of a suitable protein–ligand conformation coupled to a well-performing docking protocol.
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, rcsb.org), the US data center for the global PDB archive, serves thousands of Data Depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without usage restrictions to more than 1 million rcsb.org Users worldwide and 600 000 pdb101.rcsb.org education-focused Users around the globe. PDB Data Depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy and 3D electron microscopy. PDB Data Consumers include researchers, educators and students studying Fundamental Biology, Biomedicine, Biotechnology and Energy. Recent reorganization of RCSB PDB activities into four integrated, interdependent services is described in detail, together with tools and resources added over the past 2 years to RCSB PDB web portals in support of a 'Structural View of Biology.'
Nipah virus (NiV), a newly emergent zoonotic paramyxovirus, has caused several outbreaks in humans and associated with severe encephalitic diseases. Till these days, neither vaccines nor drugs with optimal appeasement against the virus are available. The attachment glycoprotein (NiV-G) on the surface of the virus is an important virulent factor and a promising antiviral target. To identify novel inhibitors of NiV-G using computer aided virtual screening of NCI diversity set 2 and 20,000 commercially available drug-like compounds in the ZINC database. Structure based molecular docking studies using the crystal structure of the NiV-G were performed to virtually screen for novel inhibitors of NiV-G and 4 potential compounds with potential ability to inhibit the NiV-G by competing with Ephrin binding site and prevent NiV encephalitis by blocking the Ephrin recognition zone at the peripheral site were found.
Nipah and Hendra viruses are recently emerged bat-borne paramyxoviruses (genus Henipavirus) causing severe encephalitis and respiratory disease in humans with fatality rates ranging from 40-75%. Despite the severe pathogenicity of these viruses and their pandemic potential, no therapeutics or vaccines are currently approved for use in humans. Favipiravir (T-705) is a purine analogue antiviral approved for use in Japan against emerging influenza strains; and several phase 2 and 3 clinical trials are ongoing in the United States and Europe. Favipiravir has demonstrated efficacy against a broad spectrum of RNA viruses, including members of the Paramyxoviridae, Filoviridae, Arenaviridae families, and the Bunyavirales order. We now demonstrate that favipiravir has potent antiviral activity against henipaviruses. In vitro, favipiravir inhibited Nipah and Hendra virus replication and transcription at micromolar concentrations. In the Syrian hamster model, either twice daily oral or once daily subcutaneous administration of favipiravir for 14 days fully protected animals challenged with a lethal dose of Nipah virus. This first successful treatment of henipavirus infection in vivo with a small molecule drug suggests that favipiravir should be further evaluated as an antiviral treatment option for henipavirus infections.
Nipah virus is an emerging, highly pathogenic, zoonotic virus of the Paramyxoviridae family. Human transmission occurs by close contact with infected animals, the consumption of contaminated food, or, occasionally, via other infected individuals. Currently, we lack therapeutic or prophylactic treatments for Nipah virus. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. This aim led us to perform the present work, in which we identified 101 human-Nipah virus protein-protein interactions (PPIs), most of which (88) are novel. This data set provides a comprehensive view of the host complexes that are manipulated by viral proteins. Host targets include the PRP19 complex and the microRNA (miRNA) processing machinery. Furthermore, we explored the biologic consequences of the interaction with the PRP19 complex and found that the Nipah virus W protein is capable of altering p53 control and gene expression. We anticipate that these data will help in guiding the development of novel interventional strategies to counter this emerging viral threat.
Nipah virus is a recently discovered virus that infects a wide range of mammals, including humans. Since its discovery there have been yearly outbreaks, and in some of them the mortality rate has reached 100% of the confirmed cases. However, the study of Nipah virus has been largely neglected, and currently we lack treatments for this infection. To develop these agents we must now improve our understanding of the host-virus interactions that underpin a productive infection. In the present work, we identified 101 human-Nipah virus protein-protein interactions using an affinity purification approach coupled with mass spectrometry. Additionally, we explored the cellular consequences of some of these interactions. Globally, this data set offers a comprehensive and detailed view of the host machinery's contribution to the Nipah virus's life cycle. Furthermore, our data present a large number of putative drug targets that could be exploited for the treatment of this infection.
CTX-M-15 are the most prevalent types of β-lactamases that hydrolyze almost all antibiotics of β-lactam group lead to multiple-antibiotic resistance in bacteria. Three β-lactam inhibitors are available for use in combination with different antibiotics of cephalosporine group against the CTX-M-15 producing strains. Therefore, strategies to identify novel anti β-lactamase agents with specific mechanisms of action are theneed of an hour. In this study, we screened three novel non-β-lactaminhibitors against CTX-M-15 by multi-step virtual screening approach. The potential for virtually screened drugs was estimated through in vitro cell assays.Hence, we proposed a study to understand the binding mode of CTX-M-15 with inhibitors by usingbioinformatic and experimental approach. We calculated the dissociation constants (Kd), association constant (Ka), stoichiometry (n) and binding energies (ΔG)of compoundswith the respective targets.Molecular dynamic simulation carried out for 25 ns, revealed that these complexes were found stable throughout the simulation with relative RMSD in acceptable range. Moreover, microbiological and kinetic studies, further confirmed high efficacies of these inhibitors by reducing the minimum inhibitory concentration (MIC) and catalysis of antibiotics by β-lactamases in the presence of inhibitors. Therefore, we conclude that these potential inhibitors may be used as lead molecule for future drug candidates against β-lactamases producing bacteria.
Context: Alzheimer’s disease (AD) is the most common form of dementia affecting the aged population and neuroinflammation is one of the most observed AD pathologies. NF-κB is the central regulator of inflammation and inhibitor κB kinase (IKK) is the converging point in NF-κB activation. Celastrol is a natural triterpene used as a treatment for inflammatory conditions.
Objective: This study determines the neuroprotective and inhibitory effect of celastrol on amyloid beta1-42 (Aβ1-42) induced cytotoxicity and IKKβ activity, respectively.
Materials and methods: Retinoic acid differentiated IMR-32 cells were treated with celastrol (1 μM) before treatment with Aβ1-42 (IC30 10 μM) for 24 h. The cytotoxicity and IKK phosphorylation were measured by MTT and western blotting analysis, respectively. We screened 36 celastrol analogues for the IKKβ inhibition by molecular docking and evaluated their drug like properties to delineate the neuroprotective effects.
Results: Celastrol (1 μM) inhibited Aβ1-42 (10 μM) induced IκBα phosphorylation and protected IMR-32 cells from cell death. Celastrol and 25 analogues showed strong binding affinity with IKKβ as evidenced by strong hydrogen-bonding interactions with critical active site residues. All the 25 analogues displayed strong anti-inflammatory properties but only 11 analogues showed drug-likeness. Collectively, molecule 15 has highest binding affinity, CNS activity and more drug likeness than parent compound celastrol.
Discussion and conclusion: The decreased expression of pIκBα in celastrol pretreated cells affirms the functional representation of inhibited IKKβ activity in these cells. The neuroprotective potentials of celastrol and its analogues may be related to IKK inhibition.
In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatics tools on the basis of their synergy.
Hydrogen (H)-bonds potentiate diverse cellular functions by facilitating molecular interactions. The mechanism and the extent to which H-bonds regulate molecular interactions are a largely unresolved problem in biology because the H-bonding process continuously competes with bulk water. This interference may significantly alter our understanding of molecular function, for example, in the elucidation of the origin of enzymatic catalytic power. We advance this concept by showing that H-bonds regulate molecular interactions via a hitherto unappreciated donor-acceptor pairing mechanism that minimizes competition with water. On the basis of theoretical and experimental correlations between H-bond pairings and their effects on ligand binding affinity, we demonstrate that H-bonds enhance receptor-ligand interactions when both the donor and acceptor have either significantly stronger or significantly weaker H-bonding capabilities than the hydrogen and oxygen atoms in water. By contrast, mixed strong-weak H-bond pairings decrease ligand binding affinity due to interference with bulk water, offering mechanistic insight into why indiscriminate strengthening of receptor-ligand H-bonds correlates poorly with experimental binding affinity. Further support for the H-bond pairing principle is provided by the discovery and optimization of lead compounds targeting dietary melamine and Clostridium difficile toxins, which are not realized by traditional drug design methods. Synergistic H-bond pairings have therefore evolved in the natural design of high-affinity binding and provide a new conceptual framework to evaluate the H-bonding process in biological systems. Our findings may also guide wider applications of competing H-bond pairings in lead compound design and in determining the origin of enzymatic catalytic power.
The viruses that infect humans cause a huge global disease burden and produce immense challenge towards healthcare system. Glycoproteins are one of the major components of human pathogenic viruses. They have been demonstrated to have important role(s) in infection and immunity. Concomitantly high titres of antibodies against these antigenic viral glycoproteins have paved the way for development of novel diagnostics. Availability of appropriate biomarkers is necessary for advance diagnosis of infectious diseases especially in case of outbreaks. As human mobilization has increased manifold nowadays, dissemination of infectious agents became quicker that paves the need of rapid diagnostic system. In case of viral infection it is an emergency as virus spreads and mutates very fast. This review encircles the vast arena of viral glycoproteins, their importance in health and disease and their diagnostic applications.
Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed.
Computational docking is the core process of computer-aided drug design (CADD); it aims at predicting the best orientation and conformation of a small molecule (drug ligand) when bound to a target large receptor molecule (protein) in order to form a stable complex molecule. The docking quality is typically measured by a scoring function: a mathematical predictive model that produces a score representing the binding free energy and hence the stability of the resulting complex molecule. An effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening (HTS) process. Therefore, the key to CADD is the design of an efficient highly accurate scoring function. Many traditional techniques have been proposed, however, the performance was generally poor. Only in the last few years the application of the machine learning (ML) technology has been applied in the design of scoring functions; and the results have been very promising.
Identification of Nipah virus (NiV) T-cell-specific antigen is urgently needed for appropriate diagnostic and vaccination. In the present study, prediction and modeling of T-cell epitopes of Nipah virus antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class I alleles were done. Immunoinformatic tool ProPred1 was used to predict the promiscuous MHC class I epitopes of viral antigenic proteins. The molecular modelings of the epitopes were done by PEPstr server. And alleles structure were predicted by MODELLER 9.10. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. Epitopes VPATNSPEL, NPTAVPFTL and LLFVFGPNL of Nucleocapsid, V protein and Fusion protein have considerable binding energy and score with HLA-B7, HLA-B*2705 and HLA-A2MHC class I allele, respectively. These three predicted peptides are highly potential to induce T-cell-mediated immune response and are expected to be useful in designing epitope-based vaccines against Nipah virus after further testing by wet laboratory studies.
The Research Collaboratory for Structural Bioinformatics (RCSB) Molecule of the Month series provides a curated introduction to the 3-D biomolecular structures available in the Protein Data Bank archive and the tools that are available at the RCSB website for accessing and exploring them. A variety of educational materials, such as articles, videos, posters, hands-on activities, lesson plans, and curricula, build on this series for use in a variety of educational settings as a general introduction to key topics, such as enzyme action, protein synthesis, and viruses. The series and associated educational materials are freely available at www.rcsb.org.
KPC-2 β-lactamase demonstrates a wide substrate spectrum that includes carbapenamases, oxyimino-cephalosporins, and cephamycins. In addition, strains harboring KPC-type β-lactamases are often identified as resistant to standard β-lactamase inhibitors. Thus, KPC-2 carbapenems present a significant clinical challenge, as the mechanistic bases for KPC-2-associated phenotypes remain mysterious. Inhibiting the function of these resistance enzymes could control the hydrolysis of antibiotics. In the present study, we have reported two novel (non-β-lactatam) compounds that inhibit the activity of the KPC-2 enzyme. These compounds were identified by structure-based virtual screening using computational docking programs and molecular dynamics simulations with the solved crystal structure. Two compounds (ZINC01807204 and ZINC02318494) were selected on the basis of fitness scores from docking program and 5 ns molecular dynamics simulations. These commercially available compounds have been procured and their biological activity was experimentally evaluated on the E. coli strain carrying recombinant KPC-2. These new compounds in combination with ceftazidime and cefoxitin exhibited the Minimum Inhibitory Concentration (MIC) values of 2 and 8 μg/ml respectively, which were found to be lower as compared to known β-lactamase inhibitors. Moreover, these compounds were also found to have comparable MICs values being 64 μg/ml in combination with ceftriaxone. This study explored novel inhibitors against KPC-2, a class A β-lactamase, which may be putative drug candidates against KPC-2 producing bacterial infection.
A central part of the rational drug development process is the prediction of the complex structure of a small ligand with a protein, the so-called protein-ligand docking problem, used in virtual screening of large databases and lead optimization. In the work presented here, we introduce a new docking algorithm called PLANTS (Protein-Ligand ANT System) which is based on ant colony optimization (ACO) (1). An articial ant colony is employed to nd a minimum energy conformation of the ligand in the protein's binding site. We present the eectiv eness of PLANTS for several parameter settings as well as a direct comparison to a state of the art program called GOLD (2), which is based on a genetic algorithm (GA). Last but not least, results for a virtual screening on the protein target factor Xa are presented.
A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats.
We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion.
Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.
The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.
Nipah virus (NiV) and Hendra virus are the type species of the highly pathogenic paramyxovirus genus Henipavirus, which can cause severe respiratory disease and fatal encephalitis infections in humans, with case fatality rates approaching 75%. NiV contains two envelope glycoproteins, the receptor-binding G glycoprotein (NiV-G) that facilitates attachment to host cells and the fusion (F) glycoprotein that mediates membrane merger. The henipavirus G glycoproteins lack both hemagglutinating and neuraminidase activities and, instead, engage the highly conserved ephrin-B2 and ephrin-B3 cell surface proteins as their entry receptors. Here, we report the crystal structures of the NiV-G both in its receptor-unbound state and in complex with ephrin-B3, providing, to our knowledge, the first view of a paramyxovirus attachment complex in which a cellular protein is used as the virus receptor. Complex formation generates an extensive protein–protein interface around a protruding ephrin loop, which is inserted in the central cavity of the NiV-G β-propeller. Analysis of the structural data reveals the molecular basis for the highly specific interactions of the henipavirus G glycoproteins with only two members (ephrin-B2 and ephrin-B3) of the very large ephrin family and suggests how they mediate in a unique fashion both cell attachment and the initiation of membrane fusion during the virus infection processes. The structures further suggest that the NiV-G/ephrin interactions can be effectively targeted to disrupt viral entry and provide the foundation for structure-based antiviral drug design.
• viral attachment
In structure-based drug design, scoring functions are often employed to evaluate protein-ligand interactions. A variety of scoring functions have been developed so far, and thus some objective benchmarks are desired for assessing their strength and weakness. The comparative assessment of scoring functions (CASF) benchmark developed by us provides an answer to this demand. CASF is designed as a "scoring benchmark", where the scoring process is decoupled from the docking process to depict the performance of scoring function more precisely. Here, we describe the latest update of this benchmark, i.e. CASF-2016. Each scoring function is still evaluated by four metrics, including "scoring power", "ranking power", "docking power", and "screening power". Nevertheless, the evaluation methods have been improved considerably in several aspects. A new test set is compiled, which consists of 285 protein−ligand complexes with high-quality crystal structures and reliable binding constants. A panel of 25 scoring functions are tested on CASF-2016 as demonstration. Our results reveal that the performance of current scoring functions is more promising in terms of docking power than scoring, ranking, and screening power. Scoring power is somewhat correlated with ranking power, so are docking power and screening power. The results obtained on CASF-2016 may provide valuable guidance for the end users to make smart choices among available scoring functions. Moreover, CASF is created as an open-access benchmark so that other researchers can utilize it to test a wider range of scoring functions. The complete CASF-2016 benchmark will be released on the PDBbind-CN web server (http://www.pdbbind-cn.org/casf.asp/) once this article is published.
Persistent outbreaks of Nipah virus (NiV) with severe case fatality throw a major challenge on researchers to develop a drug or vaccine to combat the disease. With little knowledge on its molecular mechanisms, we utilized the proteome data of NiV to evaluate the potency of three major proteins (phosphoprotein, polymerase and nucleocapsid protein) in the RdRp complex to count as a possible candidate for epitope based vaccine design. Profound computational analysis were employed on the above proteins individually to explore the T cell immune properties like antigenicity, immunogenicity, binding to MHC classI and classII alleles, conservancy, toxicity and population coverage. Based on these predictions the peptide ‘ELRSELIGY’ of phosphoprotein and ‘YPLLWSFAM’ of nulceocapsid protein were identified as the best predicted T cell epitopes and molecular docking with HLA-C*12:03 molecule was effectuated followed by validation with molecular dynamics simulation. The B cell epitope predictions suggests that the sequence positions 421-471 in phosphoprotein, 606-640 in polymerase and 496-517 in nucleocapsid protein are the best predicted regions for B cell immune response. However, further experimental circumstance is required to test and validate the efficacy of the subunit peptide for potential candidacy against NiV.
In this work we made use of fragment-based drug design (FBDD) and de novo design to obtain more powerful acetylcholinesterase (AChE) inhibitors. AChE is associated with Alzheimer's disease (AD). It was found that the cholinergic pathways in the cerebral cortex are compromised in AD and the accompanying cholinergic deficiency contributes to the cognitive deterioration of AD patients. In the FBDD approach, fragments are docked into the active site of the protein. As fragments are molecular groups with a low number of atoms, it is possible to study their interaction with localized amino acids. Once the interactions are measured, the fragments are organized by affinity and then linked together to form new molecules with a high degree of interaction with the active site. In the other approach, we used the de novo design technique starting from reference drugs used in the AD treatment. These drugs were broken into fragments (seeds). In the growing strategy, fragments were added to each seed, growing new molecules. In the linking strategy, two or more separated seeds were linked with different fragments. Both strategies combined produced a library of more than 2 million compounds. This library was filtered using absorption, distribution, metabolism and excretion properties. The resulting library with around 6 thousand compounds was filtered again. In this case, structures with Tanimoto coefficients > 0.85 were discarded. The final library with 1, 500 compounds was submitted to docking studies. As a result, 10 compounds with better interaction energy than the reference drugs were obtained.
Scoring functions are a group of computational methods widely applied in structure-based drug design for fast evaluation of protein–ligand interactions. To date, a whole spectrum of scoring functions have been developed based on different assumptions or algorithms. Therefore, it is important to both the end users and the developers of scoring functions that their performance be objectively assessed. We have developed the comparative assessment of scoring functions (CASF) benchmark as an open-access solution for scoring function evaluation. The latest CASF-2013 benchmark enables evaluation of the so-called 'scoring power', 'ranking power', 'docking power', and 'screening power' of a given scoring function with a high-quality test set of 195 complexes formed between diverse protein molecules and their small-molecule ligands. Evaluation results of the standard scoring functions implemented in several mainstream software programs (including Schrödinger, MOE, Discovery Studio, SYBYL, and GOLD) are provided as reference. This benchmark has become popular among the scoring function community since its first release. In this protocol, we provide detailed descriptions of the data files included in the CASF-2013 package and step-by-step instructions on how to conduct the performance tests with the ready-to-use computer scripts included in the package. This protocol is expected to lower the technical hurdles in front of new and existing users of the CASF-2013 benchmark. On a standard desktop workstation, it takes roughly half an hour to complete the whole evaluation procedure for one scoring function, once the required inputs, i.e., the results computed on the test set, are ready to use.
This volume collects the contributions! to the NATO Advanced Study Institute (ASI) held in Aussois (France) by March 25 - April 5, 1991. This NATO ASI was intended to present and illustrate recent advances in computer simulation techniques applied to the study of materials science problems. Introductory lectures have been devoted to classical simulations with special reference to recent technical improvements, in view of their application to complex systems (glasses, molecular systems . . . ). Several other lectures and seminars focused on the methods of elaboration of interatomic potentials and to a critical presentation of quantum simulation techniques. On the other hand, seminars and poster sessions offered the opportunity to discuss the results of a great variety of simulation studies dealing with materials and complex systems. We hope that these proceedings will be of some help for those interested in simulations of material properties. The scientific committee advises have been of crucial importance in determining the conference program. The directors of the ASI express their gratitude to the colleagues who have participated to the committee: Y. Adda, A. Bellemans, G. BIeris, J. Castaing, C. R. A. Catlow, G. Ciccotti, J. Friedel, M. Gillan, J. P. Hansen, M. L. Klein, G. Martin, S. Nose, L. Rull-Fernandez, J. Valleau, J. Villain. The main financial support has been provided by the NATO Scientific Affairs Division and the Commission of European Communities (plan Science).
This volume collects the contributions! to the NATO Advanced Study Institute (ASI)
held in Aussois (France) by March 25 - April 5, 1991. This NATO ASI was intended to
present and illustrate recent advances in computer simulation techniques applied to
the study of materials science problems. Introductory lectures have been devoted to
classical simulations with special reference to recent technical improvements, in view
of their application to complex systems (glasses, molecular systems . . . ). Several other
lectures and seminars focused on the methods of elaboration of interatomic potentials
and to a critical presentation of quantum simulation techniques. On the other hand,
seminars and poster sessions offered the opportunity to discuss the results of a great
variety of simulation studies dealing with materials and complex systems. We hope
that these proceedings will be of some help for those interested in simulations of
material properties. The scientific committee advises have been of crucial importance
in determining the conference program. The directors of the ASI express their gratitude
to the colleagues who have participated to the committee: Y. Adda, A. Bellemans, G.
BIeris, J. Castaing, C. R. A. Catlow, G. Ciccotti, J. Friedel, M. Gillan, J. P. Hansen, M. L. Klein,
G. Martin, S. Nose, L. Rull-Fernandez, J. Valleau, J. Villain. The main financial support has
been provided by the NATO Scientific Affairs Division and the Commission of European
Communities (plan Science).
Molecular docking is a kind of bioinformatic modelling which involves the interaction of two or more molecules
to give the stable adduct. Depending upon binding properties of ligand and target, it predicts the three-dimensional
structure of any complex. Molecular docking generates different possible adduct structures that are ranked and
grouped together using scoring function in the software. Docking simulations predict optimized docked conformer
based upon total energy of the system. In spite of all potential approaches, ligand chemistry (tautomerism and
ionization), receptor flexibility (single conformation of rigid receptor) and scoring function (differentiate true binding
mode) still remained the challenge. Many important aspects of molecular docking in terms of its approaches, types,
applications and challenges are briefly discussed in this article.
Protein kinase C (PKC) is an intracellular effector of the inositol phosphate-mediated signal transduction pathway. Evidence is emerging that certain general anaesthetics can influence the activity of PKC by interacting with the regulatory domain of the enzyme, and targeting PKC kinase domain is considered as a strategy to modulate the anaesthetic effects. Here, an integrated method was used to perform virtual screening against a large library of natural compounds for the discovery of new and potent PKC modulators. A number of hits were identified and their inhibitory activity against PKC kinase domain was measured by using a standard kinase assay protocol. Three and five compounds were determined to have high and moderate activities with IC50 values at nanomolar and micromolar levels, respectively. These compounds can be considered as promising lead molecular entities to develop efficacious anaesthetic modulators. Structural examination revealed a variety of nonbonded interactions such as hydrogen bonds, cation-π contacts, and hydrophobic forces across the complex interface of PKC with the identified compounds. This study helps to establish an integrative approach to rational kinase inhibitor discovery by efficiently exploiting various existing natural products.
Molecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug-target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug-target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand-target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization. We conclude by calling for more prospective studies to attest to these methods' utility in discovering novel drug candidates.
A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individual's phenotype are reverse transcribed into its genotype and become . heritable traits sic . We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein)ligand test systems having known three-dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein)ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure-derived molecular properties was performed. The final model had a residual standard y1 y1 .
Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson > 0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Albeit possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches.
Abstract Nipah virus is a biosafety level 4 (BSL-4) pathogen that causes severe respiratory illness and encephalitis in humans. To identify novel small molecules that target Nipah virus replication as potential therapeutics, Southern Research Institute and Galveston National Laboratory jointly developed an automated high-throughput screening platform that is capable of testing 10,000 compounds per day within BSL-4 biocontainment. Using this platform, we screened a 10,080-compound library using a cell-based, high-throughput screen for compounds that inhibited the virus-induced cytopathic effect. From this pilot effort, 23 compounds were identified with EC50 values ranging from 3.9 to 20.0 μM and selectivities >10. Three sulfonamide compounds with EC50 values <12 μM were further characterized for their point of intervention in the viral replication cycle and for broad antiviral efficacy. Development of HTS capability under BSL-4 containment changes the paradigm for drug discovery for highly pathogenic agents because this platform can be readily modified to identify prophylactic and postexposure therapeutic candidates against other BSL-4 pathogens, particularly Ebola, Marburg, and Lassa viruses.
AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.
Molecular Dynamics (MD) techniques are uniquely suited for simulating sonoluminescing bubbles, thanks to the bubbles' small size. Unlike hydrodynamic methods, MD does not assume local thermodynamic equilibrium, neither does it require knowledge of equation of state and transport properties at high pressures and temperatures. Full-scale MD simulations of experimentally observable bubbles, however, are still too expensive computationally. A symmetry reduction technique that makes use of the bubble's spherical symmetry is proposed. This technique is shown to be capable of manifold reduction of the machine time required to simulate a bubble collapse, while the few artifacts introduced by it are carefully analyzed. The model developed is then applied to a variety of experimentally observed bubbles, in particular to a class of "extreme" bubbles with collapse ratios of around 25:1. It is shown that different noble gases exhibit vastly different behaviors under such conditions, largely explained by the difference in the speed of sound at a given temperature. Heavier gases generate strong shock waves and reach much higher temperatures than lighter gases. However if a small amount of lighter gas is added to the heavier gas, the two gases will segregate, often completely, during the final stage of the collapse, resulting in the lighter gas being trapped in the center of the bubble and heating up to temperatures by several orders of magnitude exceeding those attained with the lighter gas alone. While the simulations presented in this work constitute an approach to a well defined mathematical problem they have been carried out with goal of gaining insight into a real phenomenon: light emission from a rapidly collapsing bubble of gas. In this process---sonoluminescence---acoustic energy density concentrates by at least 12 orders of magnitude to generate picosecond flashes of ultraviolet light. The simulations in this dissertation are aimed at explaining and predicting the experimental parameters which could lead to even greater levels of energy focusing in these bubbly systems.
A third-order algorithm for stochastic dynamics (SD) simulations is proposed, identical to the powerful molecular dynamics leap-frog algorithm in the limit of infinitely small friction coefficient γ. It belongs to the class of SD algorithms, in which the integration time step Δt is not limited by the condition Δt ≤ γ−1, but only by the properties of the systematic force. It is shown how constraints, such as bond length or bond angle constraints, can be incorporated in the computational scheme. It is argued that the third-order Verlet-type SD algorithm proposed earlier may be simplified without loosing its third-order accuracy. The leap-frog SD algorithm is proven to be equivalent to the verlet-type SD algorithm. Both these SD algorithms are slightly more economical on computer storage than the Beeman-type SD algorithm.
The two-dimensional representation of molecules is a popular communication medium in chemistry and the associated scientific fields. Computational methods for drawing small molecules with and without manual investigation are well-established and widely spread in terms of numerous software tools. Concerning the planar depiction of molecular complexes, there is considerably less choice. We developed the software PoseView, which automatically generates two-dimensional diagrams of macromolecular complexes, showing the ligand, the interactions, and the interacting residues. All depicted molecules are drawn on an atomic level as structure diagrams; thus, the output plots are clearly structured and easily readable for the scientist. We tested the performance of PoseView in a large-scale application on nearly all druglike complexes of the PDB (approximately 200000 complexes); for more than 92% of the complexes considered for drawing, a layout could be computed. In the following, we will present the results of this application study.Keywords: molecular visualization; PDB; protein−ligand complexes; structure diagram; two dimensions
The Born model of ionic solvation prescribes the solvation free energy of ions in a dielectric continuum and is widely used. Despite the fact that the solvent molecules around the ions are highly structured giving rise to molecular phenomena such as dielectric saturation and electrostriction, the Born model yields accurate results compared to experimental data if a proper radius for the ion, the effective Born radius, Reff, is chosen. On the basis of molecular dynamics simulations of ions of varying charge and size in TIP3P water, Reff is identified as the mean of the ionic radius (Rion) and the first peak position in the ion−water pair distribution function (Rgmax). This relationship was verified using experimentally available Rgmax for ions of varying size (0.4−3.0 Å) and charge (−3e to +4e). The new interpretation of Reff implies that the correct expression for the solvation free energy is a combination of two Born free energies, which incorporate the effects of dielectric saturation and electrostriction implicitly. The new solvation free energy formula was used to derive unknown Rgmax for a number of ions whose free energy data are available as well as new expressions for the solvation entropies and enthalpies. An empirical relationship between Rgmax and Rion is established. Potential applications of the findings of this work to free energy simulations, solvation in different solvents, solvation of molecular systems, and biomolecules are discussed.
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.
Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and
following behavior of some ant species . Artificial ants in ACO essentially are randomized construction procedures that
generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components.
Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers
and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique
for tackling a wide variety of computationally hard problems.
In this article a recently proposed method called the particle mesh Ewald (PME) method for computing the long ranged Coulomb interactions in for example molecular dynamics simulations is studied. The PME method has a complexity O(N log N), where N is the total number of charges. This complexity should in particular be compared with the complexity O(N-3/2) for the well known Ewald method and O(N) for the rather new (but already famous) fast multipole method (FMM). However, these complexities say nothing about which method is fastest at some finite N. The purpose of this article is thus to study the PME method and compare its efficiency with the Ewald method and the fast multipole method. To enable this, a theoretical estimate for the accuracy of the PME method as function of its truncation parameters is derived. It is shown that this estimate is very precise by comparing it with results obtained from molecular dynamics simulations of a molten NaCl. Based on this estimate and very careful time experiments, the overall necessary time overhead for the PME method as function of N and a required accuracy is predicted. By a direct comparison with a similar prediction for the Ewald method and by studying existing Ewald-FMM comparisons, it is found that the PME method is significantly faster than both the Ewald method and the fast multipole method in the important decades N similar or equal to 10(4)-10(5). (C) 1995 American Institute of Physics.
A new Lagrangian formulation is introduced. It can be used to make molecular dynamics (MD) calculations on systems under the most general, externally applied, conditions of stress. In this formulation the MD cell shape and size can change according to dynamical equations given by this Lagrangian. This new MD technique is well suited to the study of structural transformations in solids under external stress and at finite temperature. As an example of the use of this technique we show how a single crystal of Ni behaves under uniform uniaxial compressive and tensile loads. This work confirms some of the results of static (i.e., zero temperature) calculations reported in the literature. We also show that some results regarding the stress‐strain relation obtained by static calculations are invalid at finite temperature. We find that, under compressive loading, our model of Ni shows a bifurcation in its stress‐strain relation; this bifurcation provides a link in configuration space between cubic and hexagonal close packing. It is suggested that such a transformation could perhaps be observed experimentally under extreme conditions of shock.
Amongst the usual components of free energy of interaction between molecules, particular hydrogen bonds may be crucial for the specificity of interaction. Without information on the receptor structure, it is, however, difficult to single out the role of hydrogen bonding from that of other components. This problem will be illustrated by examples of various drugs and corroborated by energy analysis of a simple computational model of receptor-drug interaction. The model permits conjecture about a possible receptor triggering mechanism involving proton transfer.
A parallel message-passing implementation of a molecular dynamics (MD) program that is useful for bio(macro)molecules in aqueous environment is described. The software has been developed for a custom-designed 32-processor ring GROMACS (GROningen MAchine for Chemical Simulation) with communication to and from left and right neighbours, but can run on any parallel system onto which a a ring of processors can be mapped and which supports PVM-like block send and receive calls. The GROMACS software consists of a preprocessor, a parallel MD and energy minimization program that can use an arbitrary number of processors (including one), an optional monitor, and several analysis tools. The programs are written in ANSI C and available by ftp (information: [email protected]
/* */). The functionality is based on the GROMOS (GROningen MOlecular Simulation) package (van Gunsteren and Berendsen, 1987; BIOMOS B.V., Nijenborgh 4, 9747 AG Groningen). Conversion programs between GROMOS and GROMACS formats are included. The MD program can handle rectangular periodic boundary conditions with temperature and pressure scaling. The interactions that can be handled without modification are variable non-bonded pair interactions with Coulomb and Lennard-Jones or Buckingham potentials, using a twin-range cut-off based on charge groups, and fixed bonded interactions of either harmonic or constraint type for bonds and bond angles and either periodic or cosine power series interactions for dihedral angles. Special forces can be added to groups of particles (for non-equilibrium dynamics or for position restraining) or between particles (for distance restraints). The parallelism is based on particle decomposition. Interprocessor communication is largely limited to position and force distribution over the ring once per time step.