[Show abstract][Hide abstract] ABSTRACT: Wheat germ agglutinin (WGA) is a plant lectin, which specifically recognizes the sugars NeuNAc and GlcNAc. Mutated WGA with enhanced binding specificity can be used as biomarkers for cancer. In silico mutations are performed at the active site of WGA to enhance the binding specificity towards sialylglycans, and molecular dynamics simulations of 20 ns are carried out for wild type and mutated WGAs (WGA1, WGA2, and WGA3) in complex with sialylgalactose to examine the change in binding specificity. MD simulations reveal the change in binding specificity of wild type and mutated WGAs towards sialylgalactose and bound conformational flexibility of sialylgalactose. The mutated polar amino acid residues Asn114 (S114N), Lys118 (G118K), and Arg118 (G118R) make direct and water mediated hydrogen bonds and hydrophobic interactions with sialylgalactose. An analysis of possible hydrogen bonds, hydrophobic interactions, total pair wise interaction energy between active site residues and sialylgalactose and MM-PBSA free energy calculation reveals the plausible binding modes and the role of water in stabilizing different binding modes. An interesting observation is that the binding specificity of mutated WGAs (cyborg lectin) towards
sialylgalactose is found to be higher in double point mutation (WGA3). One of the substituted residues Arg118 plays a crucial role in sugar binding. Based on the interactions and energy calculations, it is concluded that the order of binding specificity of WGAs towards sialylgalactose is WGA3>WGA1>WGA2>WGA. On comparing with the wild type, double point mutated WGA (WGA3) exhibits increased specificity towards sialylgalactose, and thus, it can be effectively used in targeted drug delivery and as biological cell marker in cancer therapeutics.
Journal of Molecular Recognition 08/2014; · 3.01 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: A method has been developed for predicting the tertiary structures of RNA-RNA complex structures using secondary structure information and a fragment assembly algorithm. The linker base pair and secondary structure potential derived from the secondary structure information are particularly useful for prediction. Application of this method to several kinds of RNA-RNA complex structures, including kissing loops, hammerhead ribozymes, and other functional RNAs, produced promising results. Use of the secondary structure potential effectively restrained the conformational search space, leading to successful prediction of kissing loop structures, which mainly consist of common structural elements. The failure to predict more difficult targets had various causes but should be overcome through such measures as tuning the balance of the energy contributions from the Watson-Crick and non- Watson-Crick base pairs, by obtaining knowledge about a wider variety of RNA structures.
Journal of Chemical Information and Modeling 01/2014; · 4.30 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Protein-carbohydrate interactions play important roles in several biological processes in living organisms. Understanding the recognition mechanism of protein-carbohydrate complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-carbohydrate complexes. Our method identified 3.3% of residues as binding sites in protein-carbohydrate complexes whereas the binding site residues in protein-protein, protein-RNA and protein-DNA complexes are 10.8%, 7.6% and 8.7%, respectively. In all these complexes, binding site residues are accommodated in single-residue segments so that the neighboring residues are not involved in binding. Binding propensity analysis indicates the dominance of Trp to interact with carbohydrates through aromatic-aromatic interactions. Further, the preference of residue pairs and tripeptides interacting with carbohydrates has been delineated. The information gained in the present study will be beneficial for understanding the recognition mechanism of protein-carbohydrate complexes and for predicting the binding sites in carbohydrate binding proteins.
Protein and Peptide Letters 07/2013; · 1.74 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Olfactory Receptors (ORs) are members of the Class A rhodopsin like G-protein coupled receptors (GPCRs) which are the initial players in the signal transduction cascade, leading to the generation of nerve impulses transmitted to the brain and resulting in the detection of odorant molecules. Despite the accumulation of thousands of olfactory receptor sequences, no crystal structures of ORs are known tο date. However, the recent availability of crystallographic models of a few GPCRs allows us to generate homology models of ORs and analyze their amino acid patterns, as there is a huge diversity in OR sequences. In this study, we have generated three-dimensional models of 100 representative ORs from Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans and Sacharomyces cerevisiae which were selected on the basis of a composite classification scheme and phylogenetic analysis. The crystal structure of bovine rhodopsin was used as a template and it was found that the full-length models have more than 90% of their residues in allowed regions of the Ramachandran plot. The structures were further used for analysis of conserved residues in the transmembrane and extracellular loop regions in order to identify functionally important residues. Several ORs are known to be functional as dimers and hence dimer interfaces were predicted for OR models to analyse their oligomeric functional state.
Journal of Molecular Biochemistry. 10/2012; 1:161-170.
[Show abstract][Hide abstract] ABSTRACT: Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, the docking scores are not sufficiently precise to represent the protein-ligand binding affinity. Here, we developed an efficient computational method for calculating protein-ligand binding affinity, which is based on molecular mechanics generalized Born/surface area (MM-GBSA) calculations and Jarzynski identity. Jarzynski identity is an exact relation between free energy differences and the work done through non-equilibrium process, and MM-GBSA is a semimacroscopic approach to calculate the potential energy. To calculate the work distribution when a ligand is pulled out of its binding site, multiple protein-ligand conformations are randomly generated as an alternative to performing an explicit single-molecule pulling simulation. We assessed the new method, multiple random conformation/MM-GBSA (MRC-MMGBSA), by evaluating ligand-binding affinities (scores) for four target proteins, and comparing these scores with experimental data. The calculated scores were qualitatively in good agreement with the experimental binding affinities, and the optimal docking structure could be determined by ranking the scores of the multiple docking poses obtained by the molecular docking process. Furthermore, the scores showed a strong linear response to experimental binding free energies, so that the free energy difference of the ligand binding (ΔΔG) could be calculated by linear scaling of the scores. The error of calculated ΔΔG was within ≈ ± 1.5 kcal.mol(-1) of the experimental values. Particularly, in the case of flexible target proteins, the MRC-MMGBSA scores were more effective in ranking ligands than those generated by the MM-GBSA method using a single protein-ligand conformation. The results suggest that, owing to its lower computational costs and greater accuracy, the MRC-MMGBSA offers efficient means to rank the ligands, in the post-docking process, according to their binding affinities, and to compare these directly with the experimental values.
PLoS ONE 08/2012; 7(8):e42846. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Recognition of cell-surface sialyldisaccharides by influenza A hemagglutinin (HA) triggers the infection process of influenza. The changes in glycosidic torsional linkage and the receptor conformations may alter the binding specificity of HAs to the sialylglycans. In this study, 10-ns molecular dynamics simulations were carried out to examine the structural and dynamic behavior of the HAs bound with sialyldisaccharides Neu5Acα(2-3)Gal (N23G) and Neu5Acα(2-6)Gal (N26G). The analysis of the glycosidic torsional angles and the pair interaction energy between the receptor and the interacting residues of the binding site reveal that N23G has two binding modes for H1 and H5 and a single binding mode for H3 and H9. For N26G, H1 and H3 has two binding modes, and H5 and H9 has a single binding mode. The direct and water-mediated hydrogen bonding interactions between the receptors and HAs play dominant roles in the structural stabilization of the complexes. It is concluded from pair interaction energy and Molecular Mechanic-Poisson-Boltzmann Surface Area calculations that N26G is a better receptor for H1 when compared with N23G. N23G is a better receptor for H5 when compared with N26G. However, H3 and H9 can recognize N23G and N26G in equal binding specificity due to the marginal energy difference (≈2.5 kcal/mol). The order of binding specificity of N23G is H3 > H5 > H9 > H1 and N26G is H1 > H3 > H5 > H9, respectively. The proposed conformational models will be helpful in designing inhibitors for influenza virus.
Journal of Biological Chemistry 07/2012; 287(41):34547-57. · 4.60 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The infrared multiple photon dissociation (IRMPD) spectra of O-glycosylated peptides in the gas phase were studied in the IR scanning range of 5.7-9.5 μm. Fragmentation of protonated and sodiated O-glycopeptides was investigated using electrospray ionization (ESI) Fourier-transform ion cyclotron resonance (FTICR) mass spectrometry (MS) with a free electron laser (FEL). FEL is used in the IRMPD technique as a tunable IR light source. In the IRMPD spectroscopic analysis of the protonated O-glycopeptide, fragment ions of the b/y and B/Y types were observed in the range of 5.7-9.5 μm, corresponding to the cleavage of the backbone in the parent amino acid sequence and glycosyl bonds, whereas the spectra of the sodiated glycopeptide showed major peaks of photoproducts of the B/Y type in the range of 8.4-9.5 μm. The IRMPD spectra of the O-glycopeptides were compared with simulated IR spectra for the structures obtained from the molecular dynamics.
[Show abstract][Hide abstract] ABSTRACT: We developed a method, called RNA Assembler using Secondary Structure Information Effectively (RASSIE), for predicting RNA tertiary structures using known secondary structure information. We attempted a fragment assembly-based method that uses a secondary structure-based fragment library. For several typical target structures such as stem-loops, bulge-loops, and 2-way junctions, our method provided numerous good quality candidate structures in less computational time than previously proposed methods. By using a high-resolution potential energy function, we were able to select good predicted structures from candidate structures. This method of efficient conformational search and detailed structure evaluation using high-resolution potential is potentially useful for the tertiary structure prediction of RNA.
Journal of Chemical Information and Modeling 02/2012; 52(2):557-67. · 4.30 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Olfactory receptors are key components in signal transduction. Mutations in olfactory receptors alter the odor response, which is a fundamental response of organisms to their immediate environment. Understanding the relationship between odorant response and mutations in olfactory receptors is an important problem in bioinformatics and computational biology. In this work, we have systematically analyzed the relationship between various physical, chemical, energetic and conformational properties of amino acid residues, and the change of odor response/compound's potency/half maximal effective concentration (EC50) due to amino acid substitutions.
We observed that both the characteristics of odorant molecule (ligand) and amino acid properties are important for odor response and EC50. Additional information on neighboring and surrounding residues of the mutants enhanced the correlation between amino acid properties and EC50. Further, amino acid properties have been combined systematically using multiple regression techniques and we obtained a correlation of 0.90-0.98 with odor response/EC50 of goldfish, mouse and human olfactory receptors. In addition, we have utilized machine learning methods to discriminate the mutants, which enhance or reduce EC50 values upon mutation and we obtained an accuracy of 93% and 79% for self-consistency and jack-knife tests, respectively.
Our analysis provides deep insights for understanding the odor response of olfactory receptor mutants and the present method could be used for identifying the mutants with enhanced specificity.
[Show abstract][Hide abstract] ABSTRACT: The G protein Coupled Receptor (GPCR) superfamily is one of the most important pharmaceutical targets. Studies of GPCRs have long been performed under the assumption that GPCRs function as monomers. However, recent studies have revealed that many GPCRs function as homo- and/or hetero-dimers or higher-order oligomeric molecular complexes. As a result, information about GPCR oligomerization is rapidly accumulating, although the molecular mechanisms of oligomerization are not fully understood. A comprehensive collection of information about oligomerization would accelerate investigations of the molecular mechanisms of GPCRs' oligomerization and involvement in signaling. Hence, we have developed a database, G protein coupled Receptor Interaction Partners DataBase (GRIPDB), which provides information about GPCR oligomerization. The entries in the database are divided into two sections: (I) Experiment Information section and (II) Prediction Information section. The Experiment Information section contains (I-i) experimentally indentified GPCR oligomers and their annotations, and (I-ii) experimentally suggested interfaces for the oligomerization. Since the number of experimentally suggested interfaces is limited, the entries in the Prediction Information section have been introduced to provide information about the oligomerization interfaces predicted by our computational method. The experimentally suggested or computationally predicted interfaces are displayed by 3D graphics, using GPCRs with available coordinates. The information in the GRIPDB, especially that about the interfaces, is useful to investigate the molecular mechanisms of signal transduction via GPCR oligomerization. The GRIPDB is available on the web at the following URL: http://grip.cbrc.jp/GDB/index.html .
Journal of Receptor and Signal Transduction Research 03/2011; 31(3):199-205. · 1.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Protein-DNA recognition plays an essential role in the regulation of gene expression. Understanding the recognition mechanism of protein-DNA complexes is a challenging task in molecular and computational biology. In this work, a scoring function based approach has been developed for identifying the binding sites and delineating the important residues for binding in protein-DNA complexes. This approach considers both the repulsive interactions and the effect of distance between atoms in protein and DNA. The results showed that positively charged, polar, and aromatic residues are important for binding. These residues influence the formation of electrostatic, hydrogen bonding, and stacking interactions. Our observation has been verified with experimental binding specificity of protein-DNA complexes and found to be in good agreement with experiments. The comparison of protein-RNA and protein-DNA complexes reveals that the contribution of phosphate atoms in DNA is twice as large as in protein-RNA complexes. Furthermore, we observed that the positively charged, polar, and aromatic residues serve as hotspot residues in protein-RNA complexes, whereas other residues also altered the binding specificity in protein-DNA complexes. Based on the results obtained in the present study and related reports, a plausible mechanism has been proposed for the recognition of protein-DNA complexes.
Journal of Chemical Information and Modeling 02/2011; 51(3):721-9. · 4.30 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Protein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology.
We have developed an energy based approach for identifying the binding site residues in protein-protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching.
We observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix.
The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.
[Show abstract][Hide abstract] ABSTRACT: Olfactory receptors are key components in signal transduction. The sequence and structural analysis of olfactory receptors provides deep insights to understand their function. In this work, we have systematically analyzed the relationship between various physical, chemical, energetic and conformational properties of amino acid residues, and the change of half maximal effective concentration (EC50) due to amino acid substitutions. We observed that the odorant molecule (lignad) as well as amino acid properties are important for EC50. The inclusion of neighboring residues information of the mutants enhanced the correlation. Further, amino acid properties have been combined systematically and we obtained a correlation of 0.90-0.98 with functional data for different (goldfish, mouse and human) olfactory receptors.
Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou,China, August 11-14. 2011, Revised Selected Papers; 01/2011
[Show abstract][Hide abstract] ABSTRACT: Protein-RNA interactions perform diverse functions within the cell. Understanding the recognition mechanism of protein-RNA complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-RNA complexes. The new approach considers the repulsive interactions as well as the effect of distance between the atoms in protein and RNA in terms of interaction energy, which are not considered in traditional distance based methods to identify the binding sites. We found that the positively charged, polar and aromatic residues are important for binding. These residues influence to form electrostatic, hydrogen bonding and stacking interactions. Our observation has been verified with the experimental binding specificity of protein-RNA complexes and found good agreement with experiments. Further, the propensities of residues/nucleotides in the binding sites of proteins/RNA and their atomic contributions have been derived. Based on these results we have proposed a novel mechanism for the recognition of protein-RNA complexes: the charged and polar residues in proteins initiate recognition with RNA by making electrostatic and hydrogen bonding interactions between them; the aromatic side chains tend to form aromatic-aromatic interactions and the hydrophobic residues aid to stabilize the complex.
Current Protein and Peptide Science 11/2010; 11(7):629-38. · 2.33 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Understanding the recognition mechanism of protein complexes is a challenging task in bioinformatics and computational biology.
We have developed a novel energy based approach for identifying the binding site residues in protein–protein, protein-RNA
and protein-DNA complexes. In protein-protein complexes, the residues and residue-pairs with charged and aromatic side chains
are important for binding. These residues influence to form cation–π, electrostatic and aromatic interactions. In protein-RNA complexes, the positively charged, polar and aromatic residues are
important for binding. These residues influence to form electrostatic, hydrogen bonding and stacking interactions. The positive
charged and polar residues are preferred to bind with DNA in protein-DNA complexes. These results provide an overall view
of binding in protein complexes. Our observations have been verified with the experimental binding specificity of protein-protein
and protein-nucleic acid complexes and found good agreement with experiments.
Advanced Intelligent Computing Theories and Applications, 6th International Conference on Intelligent Computing, ICIC 2010, Changsha, China, August 18-21, 2010. Proceedings; 01/2010
[Show abstract][Hide abstract] ABSTRACT: We have developed an energy based approach for identifying the binding site residues in protein-protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as neighboring residues in the vicinity of binding sites and conformational switching. We observed specific preferences of dipeptides and tripeptides for binding, which is unique to protein-protein complexes. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix. The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.
2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, Hong Kong, China, 18 - 21 December 2010, Proceedings; 01/2010
[Show abstract][Hide abstract] ABSTRACT: The binding sites in protein-protein complexes have been identified with different methods including atomic contacts, reduction in solvent accessibility and interaction energy between the interacting partners. In our earlier work, we have developed an energy-based criteria for identifying the binding sites in protein-protein complexes, which showed that the interacting residues are different from that obtained with distance-based methods. In this work, we analyzed the binding site residues based on sequence and structural properties, such as, neighboring residues, secondary structure, solvent accessibility, conservation of residues, medium and long-range contacts and surrounding hydrophobicity. Our results showed that the neighboring residues of binding sites in proteins and ligands are different from each other although the interacting pairs of residues have a common behavior. The analysis on surrounding hydrophobicity reveals that the binding residues are less hydrophobic than non-binding sites, which suggests that the hydrophobic core are important for folding and stability whereas the surface seeking residues play a critical role in binding. This tendency has been verified with the number of contacts in binding sites. In addition, the binding site residues are highly conserved compared with non-binding residues. We suggest that the incorporation of sequence and structure-based features may improve the prediction accuracy of binding sites in protein-protein complexes.
International journal of biological macromolecules 12/2009; 46(2):187-92. · 2.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: G-Protein Coupled Receptors (GPCRs) are one of the most important pharmaceutical targets. Recent studies have revealed that many GPCRs form homo- and/or hetero-oligomers. The molecular mechanisms of oligomerization are not fully understood yet, due to the lack of structural data for GPCR complexes. Therefore, accurate interface prediction would accelerate investigations of the molecular mechanisms of oligomerization and signaling via GPCRs. However, interface prediction for GPCR oligomerization is difficult, because the various GPCR subtypes often use different structural regions as their interfaces, even when the subtypes belong to the same subfamily. Previously, we developed a method to predict the interfaces for GPCR oligomerization, which overcomes the difficulty described above. We have now launched a web service, named G-protein coupled Receptors Interaction Partners (GRIP) ( http://grip.cbrc.jp/GRIP/index.html ), to predict the interfaces for GPCR oligomerization. As far as we know, it is the only service to predict the interfaces for GPCR oligomerization.
Journal of Receptor and Signal Transduction Research 11/2009; 29(6):312-7. · 1.63 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.
[Show abstract][Hide abstract] ABSTRACT: To elucidate the partners in protein-protein interactions (PPIs), we previously proposed an affinity prediction method called affinity evaluation and prediction (AEP), which is based on the shape complementarity characteristics between proteins. The structures of the protein complexes obtained in our shape complementarity evaluation were selected by a newly developed clustering method called grouping. Our previous experiments showed that AEP gave accuracies that differed with the data composition and scale. In this study, we set a data scale (84 x 84 = 7056 protein pairs) including 84 biologically relevant complexes and then designed 225 parameter sets based on four key parameters related to the grouping and the calculation of affinity scores. As a result of receiver operating characteristic analysis, we obtained 27.4% sensitivity (= recall), 91.0% specificity, 3.5% precision, 90.2% accuracy, 6.3% F-measure(max), and an area under the curve of 0.585. Chiefly by optimization of the grouping, AEP was able to provide prediction accuracy for a maximum F-measure that statistically distinguished 23 target complexes among 84 protein pairs. Moreover, the active sites of these complexes were successfully predicted with high accuracy (i.e., 2.37 angstroms in 1CGI and 2.38 angstroms in 1PPE) of interface RMSD. To assess the improvement in accuracy we compared the results of AEP of different data sets and of tentative methods using ZDOCK 3.0.1 or ZRANK scores.
Journal of Chemical Information and Modeling 04/2009; 49(3):693-703. · 4.07 Impact Factor