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PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity. Sci Rep 2020; 10:1278. [PMID: 31992738 PMCID: PMC6987227 DOI: 10.1038/s41598-020-57778-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 01/06/2020] [Indexed: 11/17/2022] Open
Abstract
The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.
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102
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Improved protein structure prediction using predicted interresidue orientations. Proc Natl Acad Sci U S A 2020; 117:1496-1503. [PMID: 31896580 DOI: 10.1073/pnas.1914677117] [Citation(s) in RCA: 817] [Impact Index Per Article: 204.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.
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103
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Pandurangan AP, Blundell TL. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning. Protein Sci 2020; 29:247-257. [PMID: 31693276 PMCID: PMC6933854 DOI: 10.1002/pro.3774] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/31/2019] [Accepted: 10/31/2019] [Indexed: 02/02/2023]
Abstract
Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.
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Affiliation(s)
- Arun Prasad Pandurangan
- Department of BiochemistryUniversity of CambridgeCambridgeUK
- MRC Laboratory of Molecular BiologyCambridgeUK
| | - Tom L. Blundell
- Department of BiochemistryUniversity of CambridgeCambridgeUK
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104
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Qiu L, Zou X. Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions. COMMUNICATIONS IN INFORMATION AND SYSTEMS 2020; 20:1-22. [PMID: 33867869 DOI: 10.4310/cis.2020.v20.n1.a1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.
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Affiliation(s)
- Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri 65211
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri 65211.,Department of Physics & Astronomy, University of Missouri, Columbia, Missouri 65211.,Department of Biochemistry, University of Missouri, Columbia, Missouri 65211.,Informatics Institute, University of Missouri, Columbia, Missouri 65211
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105
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Abstract
Modeling the tertiary structure of protein-protein interaction complex has been well studied over many years, especially in the case where the structures of both binding partners are roughly the same before and after binding. However, the assembly of complexes with less-ordered partners is a much harder problem, and modeling even small amounts of flexibility can pose a challenge. In an extreme case, where one of the binding partners is intrinsically disordered before binding, we have previously shown that by initially disregarding the coupling between windows of these intrinsically disordered proteins (IDPs), we can reliably assemble complexes involving IDPs up to at least 69 residues long. Here, we detail the use of the IDP-LZerD package and protocol.
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106
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Geng C, Jung Y, Renaud N, Honavar V, Bonvin AMJJ, Xue LC. iScore: a novel graph kernel-based function for scoring protein-protein docking models. Bioinformatics 2020; 36:112-121. [PMID: 31199455 PMCID: PMC6956772 DOI: 10.1093/bioinformatics/btz496] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 05/08/2019] [Accepted: 06/11/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. RESULTS Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. AVAILABILITY AND IMPLEMENTATION The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Yong Jung
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16823, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Nicolas Renaud
- Netherlands eScience Center, Amsterdam 1098 XG, The Netherlands
| | - Vasant Honavar
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16823, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Center for Big Data Analytics and Discovery Informatics, Pennsylvania State University, University Park, PA 16823, USA
- Institute for Cyberscience, University Park, PA 16802, USA
- Clinical and Translational Sciences Institute, University Park, PA 16802, USA
- College of Information Sciences & Technology, Pennsylvania State University, University Park, PA 16802, USA
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Utrecht 3584 CH, The Netherlands
| | - Li C Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science – Chemistry, Utrecht University, Utrecht 3584 CH, The Netherlands
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107
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Renaud N, Jung Y, Honavar V, Geng C, Bonvin AM, Xue LC. iScore: An MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines. SOFTWAREX 2020; 11:100462. [PMID: 35419466 PMCID: PMC9005067 DOI: 10.1016/j.softx.2020.100462] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Computational docking is a promising tool to model three-dimensional (3D) structures of protein-protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein-protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.
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Affiliation(s)
- Nicolas Renaud
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Yong Jung
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
| | - Vasant Honavar
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- College of Information Science & Technology, Pennsylvania State University, University Park, PA 16802, USA
| | - Cunliang Geng
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
- Bijvoet Centre for Biomolecular Research Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Alexandre M.J.J. Bonvin
- Bijvoet Centre for Biomolecular Research Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Li C. Xue
- Bijvoet Centre for Biomolecular Research Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
- Center for Molecular and Biomolecular Informatics, Radboudumc, Nijmegen, The Netherlands
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108
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Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes. Biomolecules 2019; 10:biom10010067. [PMID: 31906171 PMCID: PMC7023245 DOI: 10.3390/biom10010067] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Revised: 12/19/2019] [Accepted: 12/20/2019] [Indexed: 11/16/2022] Open
Abstract
Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab experiments to get a better understanding of protein mutations is costly and time consuming, and because of huge number of possible mutations the need of computational methods that could accurately predict effects of amino acid mutations is of greatest importance. In this research, we present a robust methodology to predict the energy changes of a proteins upon mutations. The proposed prediction scheme is based on two step algorithm that is a Holdout Random Sampler followed by a neural network model for regression. The Holdout Random Sampler is utilized to analysis the energy change, the corresponding uncertainty, and to obtain a set of admissible energy changes, expressed as a cumulative distribution function. These values are further utilized to train a simple neural network model that can predict the energy changes. Results were blindly tested (validated) against experimental energy changes, giving Pearson correlation coefficients of 0.66 for Single Point Mutations and 0.77 for Multiple Point Mutations. These results confirm the successfulness of our method, since it outperforms majority of previous studies in this field.
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109
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Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling. PLoS Comput Biol 2019; 15:e1007219. [PMID: 31846452 PMCID: PMC6938380 DOI: 10.1371/journal.pcbi.1007219] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/31/2019] [Accepted: 11/13/2019] [Indexed: 01/02/2023] Open
Abstract
The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program’s predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER’s objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance. Proteins are fundamental biological molecules that carry out countless activities in living beings. Since the function of proteins is dictated by their three-dimensional atomic structures, acquiring structural details of proteins provides deep insights into their function. Currently, the most frequently used computational approach for protein structure prediction is template-based modeling. In this approach, a target protein is modeled using the experimentally-derived structural information of a template protein assumed to have a similar structure to the target. MODELLER is the most frequently used program for template-based 3D model building. Despite its success, its predictions are not always accurate enough to be useful in Biomedical Research. Here, we show that it is possible to greatly increase the performance of MODELLER by modifying two aspects of its algorithm. First, we demonstrate that providing the program with accurate estimations of local target-template structural divergence greatly increases the quality of its predictions. Additionally, we show that modifying MODELLER’s scoring function with statistical potential energetic terms also helps to improve modeling quality. This work will be useful in future research, since it reports practical strategies to improve the performance of this core tool in Structural Bioinformatics.
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110
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Park T, Woo H, Baek M, Yang J, Seok C. Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45. Proteins 2019; 88:1009-1017. [PMID: 31774573 DOI: 10.1002/prot.25859] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 11/11/2019] [Accepted: 11/23/2019] [Indexed: 12/12/2022]
Abstract
We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.
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Affiliation(s)
- Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Jinsol Yang
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
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111
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On the possible origin of protein homochirality, structure, and biochemical function. Proc Natl Acad Sci U S A 2019; 116:26571-26579. [PMID: 31822617 DOI: 10.1073/pnas.1908241116] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Living systems have chiral molecules, e.g., native proteins that almost entirely contain L-amino acids. How protein homochirality emerged from a background of equal numbers of L and D amino acids is among many questions about life's origin. The origin of homochirality and its implications are explored in computer simulations examining the stability and structural and functional properties of an artificial library of compact proteins containing 1:1 (termed demi-chiral), 3:1, and 1:3 ratios of D:L and purely L or D amino acids generated without functional selection. Demi-chiral proteins have shorter secondary structures and fewer internal hydrogen bonds and are less stable than homochiral proteins. Selection for hydrogen bonding yields a preponderance of L or D amino acids. Demi-chiral proteins have native global folds, including similarity to early ribosomal proteins, similar small molecule ligand binding pocket geometries, and many constellations of L-chiral amino acids with a 1.0-Å RMSD to native enzyme active sites. For a representative subset containing 550 active site geometries matching 457 (2) 4-digit (3-digit) enzyme classification (E.C.) numbers, native active site amino acids were generated at random for 472 of 550 cases. This increases to 548 of 550 cases when similar residues are allowed. The most frequently generated sequences correspond to ancient enzymatic functions, e.g., glycolysis, replication, and nucleotide biosynthesis. Surprisingly, even without selection, demi-chiral proteins possess the requisite marginal biochemical function and structure of modern proteins, but were thermodynamically less stable. If demi-chiral proteins were present, they could engage in early metabolism, which created the feedback loop for transcription and cell formation.
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112
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Lensink MF, Brysbaert G, Nadzirin N, Velankar S, Chaleil RAG, Gerguri T, Bates PA, Laine E, Carbone A, Grudinin S, Kong R, Liu RR, Xu XM, Shi H, Chang S, Eisenstein M, Karczynska A, Czaplewski C, Lubecka E, Lipska A, Krupa P, Mozolewska M, Golon Ł, Samsonov S, Liwo A, Crivelli S, Pagès G, Karasikov M, Kadukova M, Yan Y, Huang SY, Rosell M, Rodríguez-Lumbreras LA, Romero-Durana M, Díaz-Bueno L, Fernandez-Recio J, Christoffer C, Terashi G, Shin WH, Aderinwale T, Subraman SRMV, Kihara D, Kozakov D, Vajda S, Porter K, Padhorny D, Desta I, Beglov D, Ignatov M, Kotelnikov S, Moal IH, Ritchie DW, de Beauchêne IC, Maigret B, Devignes MD, Echartea MER, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Cao Y, Shen Y, Baek M, Park T, Woo H, Seok C, Braitbard M, Bitton L, Scheidman-Duhovny D, Dapkūnas J, Olechnovič K, Venclovas Č, Kundrotas PJ, Belkin S, Chakravarty D, Badal VD, Vakser IA, Vreven T, Vangaveti S, Borrman T, Weng Z, Guest JD, Gowthaman R, Pierce BG, Xu X, Duan R, Qiu L, Hou J, Merideth BR, Ma Z, Cheng J, Zou X, Koukos PI, Roel-Touris J, Ambrosetti F, Geng C, Schaarschmidt J, Trellet ME, Melquiond ASJ, Xue L, Jiménez-García B, van Noort CW, Honorato RV, Bonvin AMJJ, Wodak SJ. Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment. Proteins 2019; 87:1200-1221. [PMID: 31612567 PMCID: PMC7274794 DOI: 10.1002/prot.25838] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 12/28/2022]
Abstract
We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.
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Affiliation(s)
- Marc F. Lensink
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Guillaume Brysbaert
- University of Lille, CNRS UMR8576 UGSF, Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France
| | - Nurul Nadzirin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Tereza Gerguri
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
| | - Elodie Laine
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
| | - Alessandra Carbone
- CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Sorbonne Université, Paris, France
- Institut Universitaire de France (IUF), Paris, France
| | - Sergei Grudinin
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ran-Ran Liu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Xi-Ming Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, China
| | - Miriam Eisenstein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
| | | | | | - Emilia Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland
| | | | - Paweł Krupa
- Polish Academy of Sciences, Institute of Physics, Warsaw, Poland
| | | | - Łukasz Golon
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
| | | | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
| | | | - Guillaume Pagès
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
| | | | - Maria Kadukova
- Université Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, Grenoble, France
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Mireia Rosell
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | - Luis A. Rodríguez-Lumbreras
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
| | | | | | - Juan Fernandez-Recio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Instituto de Ciencias de la Vid y del Vino (ICVV-CSIC), Logroño, Spain
- Instituto de Biología Molecular de Barcelona (IBMB-CSIC), Barcelona, Spain
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
- Department of Chemistry, Boston University, Boston, Massachusetts
| | - Kathryn Porter
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Israel Desta
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Sergey Kotelnikov
- Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Iain H. Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | | | | | | | | | - Didier Barradas-Bautista
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhen Cao
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Luigi Cavallo
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Romina Oliva
- Department of Sciences and Technologies, University of Naples “Parthenope”, Napoli, Italy
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Minkyung Baek
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Taeyong Park
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Hyeonuk Woo
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Merav Braitbard
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lirane Bitton
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Scheidman-Duhovny
- Department of Biological Chemistry, Institute of Live Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Petras J. Kundrotas
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Saveliy Belkin
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Devlina Chakravarty
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Varsha D. Badal
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Ilya A. Vakser
- Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Thom Vreven
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Sweta Vangaveti
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Tyler Borrman
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Zhiping Weng
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts
| | - Johnathan D. Guest
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Brian G. Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
| | - Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Rui Duan
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Liming Qiu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
| | - Jie Hou
- Department of Computer Science, University of Missouri, Columbia, Missouri
| | - Benjamin Ryan Merideth
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Zhiwei Ma
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
| | - Jianlin Cheng
- Department of Computer Science, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, Missouri
- Informatics Institute, University of Missouri, Columbia, Missouri
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri
- Department of Biochemistry, University of Missouri, Columbia, Missouri
| | - Panagiotis I. Koukos
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Francesco Ambrosetti
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Cunliang Geng
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jörg Schaarschmidt
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mikael E. Trellet
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Adrien S. J. Melquiond
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Li Xue
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Brian Jiménez-García
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Charlotte W. van Noort
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Rodrigo V. Honorato
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Computational Structural Biology Group, Department of Chemistry, Faculty of Science, Utrecht University, Utrecht, The Netherlands
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113
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Naderi M, Lemoine JM, Govindaraj RG, Kana OZ, Feinstein WP, Brylinski M. Binding site matching in rational drug design: algorithms and applications. Brief Bioinform 2019; 20:2167-2184. [PMID: 30169563 PMCID: PMC6954434 DOI: 10.1093/bib/bby078] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 07/18/2018] [Accepted: 07/29/2018] [Indexed: 01/06/2023] Open
Abstract
Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.
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Affiliation(s)
- Misagh Naderi
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jeffrey Mitchell Lemoine
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | | | - Omar Zade Kana
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Wei Pan Feinstein
- High-Performance Computing, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
- Center for Computation & Technology, Louisiana State University, Baton Rouge, LA 70803, USA
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114
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Christoffer C, Terashi G, Shin WH, Aderinwale T, Maddhuri Venkata Subramaniya SR, Peterson L, Verburgt J, Kihara D. Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46. Proteins 2019; 88:948-961. [PMID: 31697428 DOI: 10.1002/prot.25850] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 10/07/2019] [Accepted: 11/03/2019] [Indexed: 01/17/2023]
Abstract
We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.
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Affiliation(s)
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana.,Department of Chemistry Education, Sunchon National University, Suncheon, Jeollanam-do, Republic of Korea
| | - Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | | | - Lenna Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Jacob Verburgt
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, Indiana.,Department of Biological Sciences, Purdue University, West Lafayette, Indiana.,Purdue University Center for Cancer Research, Purdue University, West Lafayette, Indiana.,Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio
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115
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Mirzaie M. Identification of native protein structures captured by principal interactions. BMC Bioinformatics 2019; 20:604. [PMID: 31752663 PMCID: PMC6873546 DOI: 10.1186/s12859-019-3186-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 11/01/2019] [Indexed: 11/20/2022] Open
Abstract
Background Evaluation of protein structure is based on trustworthy potential function. The total potential of a protein structure is approximated as the summation of all pair-wise interaction potentials. Knowledge-based potentials (KBP) are one type of potential functions derived by known experimentally determined protein structures. Although several KBP functions with different methods have been introduced, the key interactions that capture the total potential have not studied yet. Results In this study, we seek the interaction types that preserve as much of the total potential as possible. We employ a procedure based on the principal component analysis (PCA) to extract the significant and key interactions in native protein structures. We call these interactions as principal interactions and show that the results of the model that considers only these interactions are very close to the full interaction model that considers all interactions in protein fold recognition. In fact, the principal interactions maintain the discriminative power of the full interaction model. This method was evaluated on 3 KBPs with different contact definitions and thresholds of distance and revealed that their corresponding principal interactions are very similar and have a lot in common. Additionally, the principal interactions consisted of 20 % of the full interactions on average, and they are between residues, which are considered important in protein folding. Conclusions This work shows that all interaction types are not equally important in discrimination of native structure. The results of the reduced model based on principal interactions that were very close to the full interaction model suggest that a new strategy is needed to capture the role of remaining interactions (non-principal interactions) to improve the power of knowledge-based potential functions.
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Affiliation(s)
- Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O.Box: 14115-134, Tehran, Iran.
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116
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Ambrosetti F, Jiménez-García B, Roel-Touris J, Bonvin AMJJ. Modeling Antibody-Antigen Complexes by Information-Driven Docking. Structure 2019; 28:119-129.e2. [PMID: 31727476 DOI: 10.1016/j.str.2019.10.011] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 07/03/2019] [Accepted: 10/18/2019] [Indexed: 10/25/2022]
Abstract
Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen.
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Affiliation(s)
- Francesco Ambrosetti
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00184 Rome, Italy; Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Brian Jiménez-García
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Jorge Roel-Touris
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Alexandre M J J Bonvin
- Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.
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117
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Structural bases that underline Trypanosoma cruzi calreticulin proinfective, antiangiogenic and antitumor properties. Immunobiology 2019; 225:151863. [PMID: 31732192 DOI: 10.1016/j.imbio.2019.10.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 10/29/2019] [Indexed: 12/24/2022]
Abstract
Microbes have developed mechanisms to resist the host immune defenses and some elicit antitumor immune responses. About 6 million people are infected with Trypanosoma cruzi, the protozoan agent of Chagas' disease, the sixth neglected tropical disease worldwide. Eighty years ago, G. Roskin and N. Klyuyeva proposed that T. cruzi infection mediates an anti-cancer activity. This observation has been reproduced by several other laboratories, but no molecular basis has been proposed. We have shown that the highly pleiotropic chaperone calreticulin (TcCalr, formerly known as TcCRT), translocates from the parasite ER to the exterior, where it mediates infection. Similar to its human counterpart HuCALR (formerly known as HuCRT), TcCalr inhibits C1 in its capacity to initiate the classical pathway of complement activation. We have also proposed that TcCalr inhibits angiogenesis and it is a likely mediator of antitumor effects. We have generated several in silico structural TcCalr models to delimit a peptide (VC-TcCalr) at the TcCalr N-domain. Chemically synthesized VC-TcCalr did bind to C1q and was anti-angiogenic in Gallus gallus chorioallantoic membrane assays. These properties were associated with structural features, as determined in silico. VC-TcCalr, a strong dipole, interacts with charged proteins such as collagen-like tails and scavenger receptors. Comparatively, HuCALR has less polarity and spatial stability, probably due to at least substitutions of Gln for Gly, Arg for Lys, Arg for Asp and Ser for Arg that hinder protein-protein interactions. These differences can explain, at least in part, how TcCalr inhibits the complement activation pathway and has higher efficiency as an antiangiogenic and antitumor agent than HuCALR.
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118
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Natarajan A, Thangarajan R, Kesavan S. Repurposing Drugs by In Silico Methods to Target BCR Kinase Domain in Chronic Myeloid Leukemia. Asian Pac J Cancer Prev 2019; 20:3399-3406. [PMID: 31759365 PMCID: PMC7063026 DOI: 10.31557/apjcp.2019.20.11.3399] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Targeted therapy in the form of highly selective tyrosine kinase inhibitors (TKIs) has transformed the treatment of chronic myeloid leukemia (CML). However, mutations in the kinase domain contribute to drug resistance against TKIs which compromises the treatment response. Our aim is to explore regions outside the BCR-ABL oncoprotein to identify potential therapeutic targets to curb drug resistance by targeting growth factor receptor-bound protein-2 (Grb-2) which binds to BCR-ABL at the phosphorylated tyrosine (Y177) thereby activating the Ras and PI3K/AKT signaling pathway. METHODS We have used in silico methods to repurpose drugs for identifying their potential to inhibit the binding of Grb-2 with Y177 by occupying the active binding site of the BCR domain. RESULTS Differentially expressed genes from GEO dataset were found to be associated with hematopoietic cell lineage, NK cell-mediated cytotoxicity, NF-κB and chemokine signaling, cytokine-cytokine receptor interaction, histidine metabolism and transcriptional misregulation in cancer. The fold recognition method of SPARKS-X tool was used to model the BCR domain (Z-score = 8.21). Connectivity Map generated a drug list based on the gene expression profile, which were docked with BCR. Schrodinger XP glide docking identified Diphosphopyridine nucleotide, Hesperidin, Butirosin, Ovoflavin, and Nor-dihydroguaiaretic acid to show strong interaction in close proximity to the active binding pocket containing Y177 of the target protein and was further validated using iGEMDOCK and Parallelized Open Babel and AutoDock suite Pipeline (POAP). CONCLUSION Our study not only extends our current knowledge about repurposing drugs for newer indications but also provides a route towards combinatorial therapy with standard drugs used for CML treatment. However, the efficacy of these repurposed drugs needs to be further investigated using in vitro and in vivo studies.<br />.
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Affiliation(s)
- Aparna Natarajan
- Department of Molecular Oncology, Cancer Institute (WIA), Adyar, Chennai, India
| | | | - Sabitha Kesavan
- Department of Molecular Oncology, Cancer Institute (WIA), Adyar, Chennai, India
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119
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Shruti SR, Rajasekaran R. Identification of therapeutic peptide scaffold from tritrpticin family for urinary tract infections using in silico techniques. J Biomol Struct Dyn 2019; 38:4407-4417. [DOI: 10.1080/07391102.2019.1680437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- S. R. Shruti
- Department of Biotechnology, School of Biosciences and Technology, VIT (Deemed to Be University), Vellore, India
| | - R. Rajasekaran
- Department of Biotechnology, School of Biosciences and Technology, VIT (Deemed to Be University), Vellore, India
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120
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Zhang T, Hu G, Yang Y, Wang J, Zhou Y. All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State. J Comput Biol 2019; 27:856-867. [PMID: 31638408 DOI: 10.1089/cmb.2019.0251] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Noncoding RNAs are increasingly found to play a wide variety of roles in living organisms. Yet, their functional mechanisms are poorly understood because their structures are difficult to determine experimentally. As a result, developing more effective computational techniques to predict RNA structures becomes increasingly an urgent task. One key challenge in RNA structure prediction is the lack of an accurate free energy function to guide RNA folding and discriminate native and near-native structures from decoy conformations. In this study, we developed an all-atom distance-dependent knowledge-based energy function for RNA that is based on a reference state (distance-scaled finite ideal-gas reference state, DFIRE) proven successful for protein structure discrimination. Using four separate benchmarks including RNA puzzles, we found that this DFIRE-based RNA statistical energy function is able to discriminate native and near-native structures against decoys with performance comparable with or better than several existing scoring functions compared. The energy function is expected to be useful for improving the detection of RNA near-native structures.
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Affiliation(s)
- Tongchuan Zhang
- Institute for Glycomics, School of Informatics and Communication Technology, Griffith University, Southport, Australia
| | - Guodong Hu
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
| | - Yuedong Yang
- School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China
| | - Jihua Wang
- Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
| | - Yaoqi Zhou
- Institute for Glycomics, School of Informatics and Communication Technology, Griffith University, Southport, Australia.,Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
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121
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Akhter N, Chennupati G, Kabir KL, Djidjev H, Shehu A. Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection. Biomolecules 2019; 9:E607. [PMID: 31615116 PMCID: PMC6843838 DOI: 10.3390/biom9100607] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 10/03/2019] [Accepted: 10/04/2019] [Indexed: 11/17/2022] Open
Abstract
The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, several challenges stand in the wayof leveraging energy landscapes for relating structure and structural dynamics to function. Energylandscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins inthem do not always correspond to stable structural states but are instead the result of inherentinaccuracies in semi-empirical molecular energy functions. Due to these challenges, energeticsis typically ignored in computational approaches addressing long-standing central questions incomputational biology, such as protein decoy selection. In the latter, the goal is to determine over apossibly large number of computationally-generated three-dimensional structures of a protein thosestructures that are biologically-active/native. In recent work, we have recast our attention on theprotein energy landscape and its role in helping us to advance decoy selection. Here, we summarizesome of our successes so far in this direction via unsupervised learning. More importantly, we furtheradvance the argument that the energy landscape holds valuable information to aid and advance thestate of protein decoy selection via novel machine learning methodologies that leverage supervisedlearning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitativeevaluation of how leveraging protein energy landscapes advances an important problem in proteinmodeling. However, the ideas and concepts presented here are generally useful to make discoveriesin studies aiming to relate molecular structure and structural dynamics to function.
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Affiliation(s)
- Nasrin Akhter
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
| | - Gopinath Chennupati
- Information Sciences (CCS-3) Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Kazi Lutful Kabir
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
| | - Hristo Djidjev
- Information Sciences (CCS-3) Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
- Center for Adaptive Human-Machine Partnership, George Mason University, Fairfax, VA 22030, USA.
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA.
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA.
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122
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Hu Z, Yu C, Furutsuki M, Andreoletti G, Ly M, Hoskins R, Adhikari AN, Brenner SE. VIPdb, a genetic Variant Impact Predictor Database. Hum Mutat 2019; 40:1202-1214. [PMID: 31283070 PMCID: PMC7288905 DOI: 10.1002/humu.23858] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 06/27/2019] [Indexed: 12/30/2022]
Abstract
Genome sequencing identifies vast number of genetic variants. Predicting these variants' molecular and clinical effects is one of the preeminent challenges in human genetics. Accurate prediction of the impact of genetic variants improves our understanding of how genetic information is conveyed to molecular and cellular functions, and is an essential step towards precision medicine. Over one hundred tools/resources have been developed specifically for this purpose. We summarize these tools as well as their characteristics, in the genetic Variant Impact Predictor Database (VIPdb). This database will help researchers and clinicians explore appropriate tools, and inform the development of improved methods. VIPdb can be browsed and downloaded at https://genomeinterpretation.org/vipdb.
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Affiliation(s)
- Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Changhua Yu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Department of Bioengineering, University of California, Berkeley, California 94720, USA
| | - Mabel Furutsuki
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Gaia Andreoletti
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Melissa Ly
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Division of Data Sciences, University of California, Berkeley, California 94720, USA
| | - Roger Hoskins
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Aashish N. Adhikari
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Steven E. Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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123
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DLIGAND2: an improved knowledge-based energy function for protein-ligand interactions using the distance-scaled, finite, ideal-gas reference state. J Cheminform 2019; 11:52. [PMID: 31392430 PMCID: PMC6686496 DOI: 10.1186/s13321-019-0373-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/27/2019] [Indexed: 12/14/2022] Open
Abstract
Performance of structure-based molecular docking largely depends on the accuracy of scoring functions. One important type of scoring functions are knowledge-based potentials derived from known three-dimensional structures of proteins and/or protein–ligand complex structures. This study seeks to improve a knowledge-based protein–ligand potential based on a distance-scale finite ideal-gas reference (DFIRE) state (DLIGAND) by expanding the representation of protein atoms from 13 mol2 atom types to 167 residue-specific atom types, and employing a recently updated dataset containing 12,450 monomer protein chains for training. We found that the updated version DLIGAND2 has a consistent improvement over DLIGAND in predicting binding affinities for either native complex structures or docking-generated poses. More importantly, DLIGAND2 has a 52% increase over DLIGAND in enrichment factors in top 1% predictions based on the DUD-E decoy set, and consistently improves over Autodock Vina and other statistical energy functions in all three benchmark tests. We further found that DLIGAND2 outperforms empirical and machine-learning methods compared for virtual screening on new targets that are not homologous to the DUD-E training set. Given the best performance as a parameter-free statistical potential and among the best in all performance measures, DLIGAND2 should be useful for re-assessing the poses generated by docking software, or acting as one term in other scoring functions. The program is available at https://github.com/sysu-yanglab/DLIGAND2.![]()
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124
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Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
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Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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125
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Fang J. A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation. Brief Bioinform 2019; 21:1285-1292. [PMID: 31273374 DOI: 10.1093/bib/bbz071] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 05/14/2019] [Accepted: 05/16/2019] [Indexed: 01/02/2023] Open
Abstract
A number of machine learning (ML)-based algorithms have been proposed for predicting mutation-induced stability changes in proteins. In this critical review, we used hypothetical reverse mutations to evaluate the performance of five representative algorithms and found all of them suffer from the problem of overfitting. This approach is based on the fact that if a wild-type protein is more stable than a mutant protein, then the same mutant is less stable than the wild-type protein. We analyzed the underlying issues and suggest that the main causes of the overfitting problem include that the numbers of training cases were too small, and the features used in the models were not sufficiently informative for the task. We make recommendations on how to avoid overfitting in this important research area and improve the reliability and robustness of ML-based algorithms in general.
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Affiliation(s)
- Jianwen Fang
- Computational & Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20850, USA
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126
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Wang D, Geng L, Zhao YJ, Yang Y, Huang Y, Zhang Y, Shen HB. Artificial intelligence-based multi-objective optimization protocol for protein structure refinement. Bioinformatics 2019; 36:437-448. [PMID: 31274151 PMCID: PMC7999140 DOI: 10.1093/bioinformatics/btz544] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 06/06/2019] [Accepted: 07/04/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Protein structure refinement is an important step of protein structure prediction. Existing approaches have generally used a single scoring function combined with Monte Carlo method or Molecular Dynamics algorithm. The one-dimension optimization of a single energy function may take the structure too far away without a constraint. The basic motivation of our study is to reduce the bias problem caused by minimizing only a single energy function due to the very diversity of different protein structures. RESULTS We report a new Artificial Intelligence-based protein structure Refinement method called AIR. Its fundamental idea is to use multiple energy functions as multi-objectives in an effort to correct the potential inaccuracy from a single function. A multi-objective particle swarm optimization algorithm-based structure refinement is designed, where each structure is considered as a particle in the protocol. With the refinement iterations, the particles move around. The quality of particles in each iteration is evaluated by three energy functions, and the non-dominated particles are put into a set called Pareto set. After enough iteration times, particles from the Pareto set are screened and part of the top solutions are outputted as the final refined structures. The multi-objective energy function optimization strategy designed in the AIR protocol provides a different constraint view of the structure, by extending the one-dimension optimization to a new three-dimension space optimization driven by the multi-objective particle swarm optimization engine. Experimental results on CASP11, CASP12 refinement targets and blind tests in CASP 13 turn to be promising. AVAILABILITY AND IMPLEMENTATION The AIR is available online at: www.csbio.sjtu.edu.cn/bioinf/AIR/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Yu-Jun Zhao
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Yang Yang
- Department of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Huang
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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127
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Montanucci L, Capriotti E, Frank Y, Ben-Tal N, Fariselli P. DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC Bioinformatics 2019; 20:335. [PMID: 31266447 PMCID: PMC6606456 DOI: 10.1186/s12859-019-2923-1] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (∆∆G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted ∆∆G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., ∆∆G(A → B) = −∆∆G(B → A), where A and B are amino acids. Results Here we introduce simple anti-symmetric features, based on evolutionary information, which are combined to define an untrained method, DDGun (DDG untrained). DDGun is a simple approach based on evolutionary information that predicts the ∆∆G for single and multiple variations from sequence and structure information (DDGun3D). Our method achieves remarkable performance without any training on the experimental datasets, reaching Pearson correlation coefficients between predicted and measured ∆∆G values of ~ 0.5 and ~ 0.4 for single and multiple site variations, respectively. Surprisingly, DDGun performances are comparable with those of state of the art methods. DDGun also naturally predicts multiple site variations, thereby defining a benchmark method for both single site and multiple site predictors. DDGun is anti-symmetric by construction predicting the value of the ∆∆G of a reciprocal variation as almost equal (depending on the sequence profile) to -∆∆G of the direct variation. This is a valuable property that is missing in the majority of the methods. Conclusions Evolutionary information alone combined in an untrained method can achieve remarkably high performances in the prediction of ∆∆G upon protein mutation. Non-trained approaches like DDGun represent a valid benchmark both for scoring the predictive power of the individual features and for assessing the learning capability of supervised methods. Electronic supplementary material The online version of this article (10.1186/s12859-019-2923-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ludovica Montanucci
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Viale dell'Università 16, 35020, Legnaro, Italy
| | - Emidio Capriotti
- BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via Selmi 3, 40126, Bologna, Italy.
| | - Yotam Frank
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, 69978, Tel Aviv, Israel
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, 69978, Tel Aviv, Israel
| | - Piero Fariselli
- Department of Comparative Biomedicine and Food Science (BCA), University of Padova, Viale dell'Università 16, 35020, Legnaro, Italy. .,Now at the Department of Medical Sciences, University of Torino, via Santena 19, 10126, Torino, Italy.
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128
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Maghrabi AHA, McGuffin LJ. ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models. Nucleic Acids Res 2019; 45:W416-W421. [PMID: 28460136 PMCID: PMC5570241 DOI: 10.1093/nar/gkx332] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 04/21/2017] [Indexed: 11/24/2022] Open
Abstract
Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/.
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Affiliation(s)
- Ali H A Maghrabi
- School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK
| | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AS, UK
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129
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Tan YL, Feng CJ, Jin L, Shi YZ, Zhang W, Tan ZJ. What is the best reference state for building statistical potentials in RNA 3D structure evaluation? RNA (NEW YORK, N.Y.) 2019; 25:793-812. [PMID: 30996105 PMCID: PMC6573789 DOI: 10.1261/rna.069872.118] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 04/06/2019] [Indexed: 05/14/2023]
Abstract
Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walk-chain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.
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Affiliation(s)
- Ya-Lan Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Chen-Jie Feng
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Lei Jin
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematics and Computer Science, Wuhan Textile University, Wuhan 430073, China
| | - Wenbing Zhang
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Zhi-Jie Tan
- Center for Theoretical Physics and Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China
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130
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Olson MA, Legler PM, Zabetakis D, Turner KB, Anderson GP, Goldman ER. Sequence Tolerance of a Single-Domain Antibody with a High Thermal Stability: Comparison of Computational and Experimental Fitness Profiles. ACS OMEGA 2019; 4:10444-10454. [PMID: 31460140 PMCID: PMC6648363 DOI: 10.1021/acsomega.9b00730] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 05/09/2019] [Indexed: 06/10/2023]
Abstract
The sequence fitness of a llama single-domain antibody with an unusually high thermal stability is explored by a combined computational and experimental study. Starting with the X-ray crystallographic structure, RosettaBackrub simulations were applied to model sequence-structure tolerance profiles and identify key substitution sites. From the model calculations, an experimental site-directed mutagenesis was used to produce a panel of mutants, and their melting temperatures were determined by thermal denaturation. The results reveal a sequence fitness of an excess stability of approximately 12 °C, a value taken from a decrease in the melting temperature of an electrostatic charge-reversal substitution in the CRD3 without a deleterious effect on the binding affinity to the antigen. The tolerance for the disruption of antigen recognition without loss in the thermal stability was demonstrated by the introduction of a proline in place of a tyrosine in the CDR2, producing a mutant that eliminated binding. To further assist the sequence design and the selection of engineered single-domain antibodies, an assessment of different computational strategies is provided of their accuracy in the detection of substitution "hot spots" in the sequence tolerance landscape.
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Affiliation(s)
- Mark A. Olson
- Systems
and Structural Biology Division, USAMRIID, Frederick, Maryland 21702, United States
| | - Patricia M. Legler
- Center
for Bio/Molecular Science and Engineering, Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, District of Columbia 20375, United States
| | - Daniel Zabetakis
- Center
for Bio/Molecular Science and Engineering, Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, District of Columbia 20375, United States
| | - Kendrick B. Turner
- Center
for Bio/Molecular Science and Engineering, Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, District of Columbia 20375, United States
| | - George P. Anderson
- Center
for Bio/Molecular Science and Engineering, Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, District of Columbia 20375, United States
| | - Ellen R. Goldman
- Center
for Bio/Molecular Science and Engineering, Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, District of Columbia 20375, United States
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131
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Yu Z, Yao Y, Deng H, Yi M. ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment. BMC Bioinformatics 2019; 20:299. [PMID: 31159742 PMCID: PMC6547486 DOI: 10.1186/s12859-019-2898-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/13/2019] [Indexed: 01/05/2023] Open
Abstract
Background The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials. Results We developed an atomic ANgle- and DIStance-dependent (ANDIS) statistical potential for protein structure quality assessment with distance cutoff being a tunable parameter. When distance cutoff is ≤9.0 Å, “effective atomic interaction” is employed to enhance the ability of native recognition. For a distance cutoff of ≥10 Å, the distance-dependent atom-pair potential with random-walk reference state is combined to strengthen the ability of decoy discrimination. Benchmark tests on 632 structural decoy sets from diverse sources demonstrate that ANDIS outperforms other state-of-the-art potentials in both native recognition and decoy discrimination. Conclusions Distance cutoff is a crucial parameter for distance-dependent statistical potentials. A lower distance cutoff is better for native recognition, while a higher one is favorable for decoy discrimination. The ANDIS potential is freely available as a standalone application at http://qbp.hzau.edu.cn/ANDIS/. Electronic supplementary material The online version of this article (10.1186/s12859-019-2898-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhongwang Yu
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuangen Yao
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Haiyou Deng
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. .,Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Ming Yi
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. .,Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070, China.
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132
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Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
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133
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Wang X, Huang SY. Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction. J Chem Inf Model 2019; 59:3080-3090. [DOI: 10.1021/acs.jcim.9b00057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Xinxiang Wang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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134
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Xu G, Ma T, Wang Q, Ma J. OPUS-SSF: A side-chain-inclusive scoring function for ranking protein structural models. Protein Sci 2019; 28:1157-1162. [PMID: 30919509 DOI: 10.1002/pro.3608] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 03/21/2019] [Accepted: 03/27/2019] [Indexed: 12/21/2022]
Abstract
We introduce a side-chain-inclusive scoring function, named OPUS-SSF, for ranking protein structural models. The method builds a scoring function based on the native distributions of the coordinate components of certain anchoring points in a local molecular system for peptide segments of 5, 7, 9, and 11 residues in length. Differing from our previous OPUS-CSF [Xu et al., Protein Sci. 2018; 27: 286-292], which exclusively uses main chain information, OPUS-SSF employs anchoring points on side chains so that the effect of side chains is taken into account. The performance of OPUS-SSF was tested on 15 decoy sets containing totally 603 proteins, and 571 of them had their native structures recognized from their decoys. Similar to OPUS-CSF, OPUS-SSF does not employ the Boltzmann formula in constructing scoring functions. The results indicate that OPUS-SSF has achieved a significant improvement on decoy recognition and it should be a very useful tool for protein structural prediction and modeling.
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Affiliation(s)
- Gang Xu
- School of Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China
| | - Tianqi Ma
- Applied Physics Program, Rice University, Houston, Texas 77005.,Department of Bioengineering, Rice University, Houston, Texas 77005
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030
| | - Jianpeng Ma
- School of Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China.,Applied Physics Program, Rice University, Houston, Texas 77005.,Department of Bioengineering, Rice University, Houston, Texas 77005.,Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030
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135
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Cao H, Wang J, He L, Qi Y, Zhang JZ. DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks. J Chem Inf Model 2019; 59:1508-1514. [DOI: 10.1021/acs.jcim.8b00697] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Huali Cao
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Jingxue Wang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Liping He
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Yifei Qi
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center
for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - John Z. Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center
for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Department of Chemistry, New York University, New York, New York 10003, United States
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136
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Su Z, Wu Y. Computational studies of protein-protein dissociation by statistical potential and coarse-grained simulations: a case study on interactions between colicin E9 endonuclease and immunity proteins. Phys Chem Chem Phys 2019; 21:2463-2471. [PMID: 30652698 DOI: 10.1039/c8cp05644g] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Proteins carry out their diverse functions in cells by forming interactions with each other. The dynamics of these interactions are quantified by the measurement of association and dissociation rate constants. Relative to the efforts made to model the association of biomolecules, little has been studied to understand the principles of protein complex dissociation. Using the interaction between colicin E9 endonucleases and immunity proteins as a test system, here we develop a coarse-grained simulation method to explore the dissociation mechanisms of protein complexes. The interactions between proteins in the complex are described by the knowledge-based potential that was constructed by the statistics from available protein complexes in the structural database. Our study provides the supportive evidences to the dual recognition mechanism for the specificity of binding between E9 DNase and immunity proteins, in which the conserved residues of helix III of Im2 and Im9 proteins act as the anchor for binding, while the sequence variations in helix II make positive or negative contributions to specificity. Beyond that, we further suggest that this binding specificity is rooted in the process of complex dissociation instead of association. While we increased the flexibility of protein complexes, we further found that they are less prone to dissociation, suggesting that conformational fluctuations of protein complexes play important functional roles in regulating their binding and dissociation. Our studies therefore bring new insights to the molecule mechanisms of protein-protein interactions, while the method can serve as a new addition to a suite of existing computational tools for the simulations of protein complexes.
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Affiliation(s)
- Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
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137
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In silico prediction of prolactin molecules as a tool for equine genomics reproduction. Mol Divers 2019; 23:1019-1028. [PMID: 30740642 DOI: 10.1007/s11030-018-09914-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 12/31/2018] [Indexed: 10/27/2022]
Abstract
The prolactin hormone is involved in several biological functions, although its main role resides on reproduction. As it interferes on fertility changes, studies focused on human health have established a linkage of this hormone to fertility losses. Regarding animal research, there is still a lack of information about the structure of prolactin. In case of horse breeding, prolactin has a particular influence; once there is an individualization of these animals and equines are known for presenting several reproductive disorders. As there is no molecular structure available for the prolactin hormone and receptor, we performed several bioinformatics analyses through prediction and refinement softwares, as well as manual modifications. Aiming to elucidate the first computational structure of both molecules and analyse structural and functional aspects related to these proteins, here we provide the first known equine model for prolactin and prolactin receptor, which obtained high global quality scores in diverse software's for quality assessment. QMEAN overall score obtained for ePrl was (- 4.09) and QMEANbrane for ePrlr was (- 8.45), which proves the structures' reliability. This study will implement another tool in equine genomics in order to give light to interactions of these molecules, structural and functional alterations and therefore help diagnosing fertility problems, contributing in the selection of a high genetic herd.
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138
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Pražnikar J, Tomić M, Turk D. Validation and quality assessment of macromolecular structures using complex network analysis. Sci Rep 2019; 9:1678. [PMID: 30737447 PMCID: PMC6368557 DOI: 10.1038/s41598-019-38658-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 01/07/2019] [Indexed: 02/06/2023] Open
Abstract
Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure.
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Affiliation(s)
- Jure Pražnikar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia.
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia.
| | - Miloš Tomić
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia
| | - Dušan Turk
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia
- Center of excellence for Integrated Approaches in Chemistry and Biology of Proteins, Jamova 39, Ljubljana, Slovenia
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139
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Pei J, Zheng Z, Merz KM. Random Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection. J Chem Inf Model 2019; 59:1919-1929. [DOI: 10.1021/acs.jcim.8b00734] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jun Pei
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zheng Zheng
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
- Institute for Cyber Enabled Research, Michigan State University, 567 Wilson Road, East Lansing, Michigan 48824, United States
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140
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Zhan J, Jia H, Semchenko EA, Bian Y, Zhou AM, Li Z, Yang Y, Wang J, Sarkar S, Totsika M, Blanchard H, Jen FEC, Ye Q, Haselhorst T, Jennings MP, Seib KL, Zhou Y. Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria: a new strategy to generate antimicrobial peptides. FASEB J 2019; 33:2095-2104. [PMID: 30260702 PMCID: PMC6338635 DOI: 10.1096/fj.201700613rr] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 08/27/2018] [Indexed: 11/11/2022]
Abstract
Bacterial infection is one of the leading causes of death in young, elderly, and immune-compromised patients. The rapid spread of multi-drug-resistant (MDR) bacteria is a global health emergency and there is a lack of new drugs to control MDR pathogens. We describe a heretofore-unexplored discovery pathway for novel antibiotics that is based on self-targeting, structure-disrupting peptides. We show that a helical peptide, KFF- EcH3, derived from the Escherichia coli methionine aminopeptidase can disrupt secondary and tertiary structure of this essential enzyme, thereby killing the bacterium (including MDR strains). Significantly, no detectable resistance developed against this peptide. Based on a computational analysis, our study predicted that peptide KFF- EcH3 has the strongest interaction with the structural core of the methionine aminopeptidase. We further used our approach to identify peptide KFF- NgH1 to target the same enzyme from Neisseria gonorrhoeae. This peptide inhibited bacterial growth and was able to treat a gonococcal infection in a human cervical epithelial cell model. These findings present an exciting new paradigm in antibiotic discovery using self-derived peptides that can be developed to target the structures of any essential bacterial proteins.-Zhan, J., Jia, H., Semchenko, E. A., Bian, Y., Zhou, A. M., Li, Z., Yang, Y., Wang, J., Sarkar, S., Totsika, M., Blanchard, H., Jen, F. E.-C., Ye, Q., Haselhorst, T., Jennings, M. P., Seib, K. L., Zhou, Y. Self-derived structure-disrupting peptides targeting methionine aminopeptidase in pathogenic bacteria: a new strategy to generate antimicrobial peptides.
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Affiliation(s)
- Jian Zhan
- Institute for Glycomics, Griffith University, Queensland, Australia
| | - Husen Jia
- Institute for Glycomics, Griffith University, Queensland, Australia
| | | | - Yunqiang Bian
- Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
| | - Amy M. Zhou
- Queensland Academies–Health Sciences, Southport, Queensland, Australia
| | - Zhixiu Li
- Indiana University School of Informatics, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana, USA
| | - Yuedong Yang
- Institute for Glycomics, Griffith University, Queensland, Australia
| | - Jihua Wang
- Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
| | - Sohinee Sarkar
- Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Makrina Totsika
- Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Helen Blanchard
- Institute for Glycomics, Griffith University, Queensland, Australia
| | - Freda E.-C. Jen
- Institute for Glycomics, Griffith University, Queensland, Australia
| | - Qizhuang Ye
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- School of Medicine, Shenzhen University, Shenzhen, China
| | | | | | - Kate L. Seib
- Institute for Glycomics, Griffith University, Queensland, Australia
| | - Yaoqi Zhou
- Institute for Glycomics, Griffith University, Queensland, Australia
- Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou, China
- Indiana University School of Informatics, Indiana University–Purdue University Indianapolis, Indianapolis, Indiana, USA
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141
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Litfin T, Yang Y, Zhou Y. SPOT-Peptide: Template-Based Prediction of Peptide-Binding Proteins and Peptide-Binding Sites. J Chem Inf Model 2019; 59:924-930. [DOI: 10.1021/acs.jcim.8b00777] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Thomas Litfin
- School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
| | - Yuedong Yang
- School of Data and Computer Science, Sun-Yat Sen University, Guangzhou, Guangdong 510006, China
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Southport, QLD 4222, Australia
- Institute for Glycomics, Griffith University, Southport, QLD 4222, Australia
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142
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Geng C, Xue LC, Roel‐Touris J, Bonvin AMJJ. Finding the ΔΔ
G
spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1410] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Cunliang Geng
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Li C. Xue
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Jorge Roel‐Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry Utrecht University Utrecht The Netherlands
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143
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López-Blanco JR, Chacón P. KORP: knowledge-based 6D potential for fast protein and loop modeling. Bioinformatics 2019; 35:3013-3019. [DOI: 10.1093/bioinformatics/btz026] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 01/03/2019] [Accepted: 01/08/2019] [Indexed: 12/18/2022] Open
Abstract
Abstract
Motivation
Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation.
Results
We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function.
Availability and implementation
http://chaconlab.org/modeling/korp.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- José Ramón López-Blanco
- Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
| | - Pablo Chacón
- Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C, Madrid, Spain
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144
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Jiang F, Wu HN, Kang W, Wu YD. Developments and Applications of Coil-Library-Based Residue-Specific Force Fields for Molecular Dynamics Simulations of Peptides and Proteins. J Chem Theory Comput 2019; 15:2761-2773. [DOI: 10.1021/acs.jctc.8b00794] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fan Jiang
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Hao-Nan Wu
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Wei Kang
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yun-Dong Wu
- Laboratory of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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145
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Blaszczyk M, Gront D, Kmiecik S, Kurcinski M, Kolinski M, Ciemny MP, Ziolkowska K, Panek M, Kolinski A. Protein Structure Prediction Using Coarse-Grained Models. SPRINGER SERIES ON BIO- AND NEUROSYSTEMS 2019. [DOI: 10.1007/978-3-319-95843-9_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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146
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147
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Song S, Ji J, Chen X, Gao S, Tang Z, Todo Y. Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.07.042] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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148
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The state-of-the-art strategies of protein engineering for enzyme stabilization. Biotechnol Adv 2018; 37:530-537. [PMID: 31138425 DOI: 10.1016/j.biotechadv.2018.10.011] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 10/12/2018] [Accepted: 10/25/2018] [Indexed: 12/11/2022]
Abstract
Enzymes generated by natural recruitment and protein engineering have greatly contribute in various sets of applications. However, their insufficient stability is a bottleneck that limit the rapid development of biocatalysis. Novel approaches based on precise and global structural dissection, advanced gene manipulation, and combination with the multidisciplinary techniques open a new horizon to generate stable enzymes efficiently. Here, we comprehensively introduced emerging advances of protein engineering strategies for enzyme stabilization. Then, we highlighted practical cases to show importance of enzyme stabilization in pharmaceutical and industrial applications. Combining computational enzyme design with molecular evolution will hold considerable promise in this field.
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149
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Montanucci L, Martelli PL, Ben-Tal N, Fariselli P. A natural upper bound to the accuracy of predicting protein stability changes upon mutations. Bioinformatics 2018; 35:1513-1517. [DOI: 10.1093/bioinformatics/bty880] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 09/07/2018] [Accepted: 10/16/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ludovica Montanucci
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Piero Fariselli
- Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro, Italy
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150
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Ganesan P, Ramalingam R. Investigation of structural stability and functionality of homodimeric gramicidin towards peptide‐based drug: a molecular simulation approach. J Cell Biochem 2018; 120:4903-4911. [DOI: 10.1002/jcb.27765] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Accepted: 09/06/2018] [Indexed: 12/17/2022]
Affiliation(s)
- Pavithrra Ganesan
- Bioinformatics Lab, Department of Biotechnology, School of Biosciences and Technology Vellore Institute of Technology (VIT) Vellore India
| | - Rajasekaran Ramalingam
- Bioinformatics Lab, Department of Biotechnology, School of Biosciences and Technology Vellore Institute of Technology (VIT) Vellore India
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