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Srivastava M, Singh K, Kumar S, Hasan SM, Mujeeb S, Kushwaha SP, Husen A. In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review. Mini Rev Med Chem 2024; 24:1481-1495. [PMID: 38288816 DOI: 10.2174/0113895575287322240115115125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/27/2023] [Accepted: 01/03/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement. OBJECTIVES This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research. METHODS Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed. RESULT The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them. CONCLUSION In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.
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Affiliation(s)
- Manisha Srivastava
- Reseach scholar, Integral University, Kursi Road, Lucknow, Uttar Pradesh, India
| | - Kuldeep Singh
- Faculty of Pharmacy, Integral University, Kursi Road, Lucknow, Uttar Pradesh, India
| | - Sanjay Kumar
- Hygia Institute of Pharmacy, Lucknow, Uttar Pradesh, India
| | - Syed Misbahul Hasan
- Faculty of Pharmacy, Integral University, Kursi Road, Lucknow, Uttar Pradesh, India
| | - Samar Mujeeb
- Hygia Institute of Pharmacy, Lucknow, Uttar Pradesh, India
| | | | - Ali Husen
- Hygia Institute of Pharmacy, Lucknow, Uttar Pradesh, India
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2
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Li D, Cao J, Zhang J, Mu T, Wang R, Li H, Tang H, Chen L, Lin X, Peng X, Zhao K. The Effects and Regulatory Mechanism of Casein-Derived Peptide VLPVPQK in Alleviating Insulin Resistance of HepG2 Cells. Foods 2023; 12:2627. [PMID: 37444365 DOI: 10.3390/foods12132627] [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: 05/21/2023] [Revised: 06/30/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
The liver plays a key role in keeping the homeostasis of glucose and lipid metabolism. Insulin resistance of the liver induced by extra glucose and lipid ingestion contributes greatly to chronic metabolic disease, which is greatly threatening to human health. The small peptide, VLPVPQK, originating from casein hydrolysates of milk, shows various health-promoting functions. However, the effects of VLPVPQK on metabolic disorders of the liver are still not fully understood. Therefore, in the present study, the effects and regulatory mechanism of VLPVPQK on insulin-resistant HepG2 cells was further investigated. The results showed that VLPVPQK exerted strong scavenging capacities against various free radicals, including oxygen radicals, hydroxyl radicals, and cellular reactive oxygen species. In addition, supplementation of VLPVPQK (62.5, 125, and 250 μM) significantly reversed the high glucose and fat (30 mM glucose and 0.2 mM palmitic acid) induced decrement of glucose uptake in HepG2 cells without affecting cell viability. Furthermore, VLPVPQK intervention affected the transcriptomic profiling of the cells. The differentially expressed (DE) genes (FDR < 0.05, and absolute fold change (FC) > 1.5) between VLPVPQK and the model group were mostly enriched in the carbohydrate metabolism-related KEGG pathways. Interestingly, the expression of two core genes (HKDC1 and G6PC1) involved in the above pathways was dramatically elevated after VLPVPQK intervention, which played a key role in regulating glucose metabolism. Furthermore, supplementation of VLPVPQK reversed the high glucose and fat-induced depression of AKR1B10. Overall, VLPVPQK could alleviate the metabolic disorder of hepatocytes by elevating the glucose uptake and eliminating the ROS, while the HKDC1 and AKR1B10 genes might be the potential target genes and play important roles in the process.
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Affiliation(s)
- Dapeng Li
- College of Life Science, Yantai University, Yantai 264005, China
| | - Jianxin Cao
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an 710062, China
| | - Jin Zhang
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Tong Mu
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
- College of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an 710062, China
| | - Rubin Wang
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Huanhuan Li
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Honggang Tang
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Lihong Chen
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Xiuyu Lin
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Xinyan Peng
- College of Life Science, Yantai University, Yantai 264005, China
| | - Ke Zhao
- Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
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3
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C-terminal sequence deletion effect on antioxidative characteristics of VLPVPQK bioactive peptide from buffalo milk casein. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2019.108816] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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4
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Hayashi T, Matsuzaki Y, Yanagisawa K, Ohue M, Akiyama Y. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions. BMC Bioinformatics 2018; 19:62. [PMID: 29745830 PMCID: PMC5998897 DOI: 10.1186/s12859-018-2073-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Background Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. Results In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. Conclusion MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/. Electronic supplementary material The online version of this article (10.1186/s12859-018-2073-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Takanori Hayashi
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Yuri Matsuzaki
- Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Keisuke Yanagisawa
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan.,Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan. .,AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8560, Japan.
| | - Yutaka Akiyama
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Education Academy of Computational Life Sciences, Tokyo Institute of Technology, 2-12-1 W8-93 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan. .,Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, 4259 Nagatsutacho, Midori-ku, Yokohama, Kanagawa, 226-8501, Japan. .,AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki, 305-8560, Japan. .,Molecular Profiling Research Center for Drug Discovery (molprof), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
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Dos Santos Vasconcelos CR, de Lima Campos T, Rezende AM. Building protein-protein interaction networks for Leishmania species through protein structural information. BMC Bioinformatics 2018; 19:85. [PMID: 29510668 PMCID: PMC5840830 DOI: 10.1186/s12859-018-2105-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 03/01/2018] [Indexed: 12/21/2022] Open
Abstract
Background Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. Results The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. Conclusions The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported. Electronic supplementary material The online version of this article (10.1186/s12859-018-2105-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Crhisllane Rafaele Dos Santos Vasconcelos
- Microbiology Department of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil. .,Genetics Department of Universidade Federal de Pernambuco, Recife, PE, Brazil.
| | - Túlio de Lima Campos
- Microbiology Department of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil.,Bioinformatics Plataform of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil
| | - Antonio Mauro Rezende
- Microbiology Department of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil. .,Bioinformatics Plataform of Instituto Aggeu Magalhães - FIOCRUZ, Recife, PE, Brazil. .,Genetics Department of Universidade Federal de Pernambuco, Recife, PE, Brazil.
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6
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Peterson LX, Togawa Y, Esquivel-Rodriguez J, Terashi G, Christoffer C, Roy A, Shin WH, Kihara D. Modeling the assembly order of multimeric heteroprotein complexes. PLoS Comput Biol 2018; 14:e1005937. [PMID: 29329283 PMCID: PMC5785014 DOI: 10.1371/journal.pcbi.1005937] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 01/25/2018] [Accepted: 12/19/2017] [Indexed: 12/31/2022] Open
Abstract
Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.
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Affiliation(s)
- Lenna X. Peterson
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Yoichiro Togawa
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Juan Esquivel-Rodriguez
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Amitava Roy
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America
- Bioinformatics and Computational Biosciences Branch, Rocky Mountain Laboratories, NIAID, National Institutes of Health, Hamilton, Montana, United States of America
| | - Woong-Hee Shin
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America
- Department of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
- * E-mail:
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7
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Maheshwari S, Brylinski M. Across-proteome modeling of dimer structures for the bottom-up assembly of protein-protein interaction networks. BMC Bioinformatics 2017; 18:257. [PMID: 28499419 PMCID: PMC5427563 DOI: 10.1186/s12859-017-1675-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 05/03/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Deciphering complete networks of interactions between proteins is the key to comprehend cellular regulatory mechanisms. A significant effort has been devoted to expanding the coverage of the proteome-wide interaction space at molecular level. Although a growing body of research shows that protein docking can, in principle, be used to predict biologically relevant interactions, the accuracy of the across-proteome identification of interacting partners and the selection of near-native complex structures still need to be improved. RESULTS In this study, we developed a new method to discover and model protein interactions employing an exhaustive all-to-all docking strategy. This approach integrates molecular modeling, structural bioinformatics, machine learning, and functional annotation filters in order to provide interaction data for the bottom-up assembly of protein interaction networks. Encouragingly, the success rates for dimer modeling is 57.5 and 48.7% when experimental and computer-generated monomer structures are employed, respectively. Further, our protocol correctly identifies 81% of protein-protein interactions at the expense of only 19% false positive rate. As a proof of concept, 61,913 protein-protein interactions were confidently predicted and modeled for the proteome of E. coli. Finally, we validated our method against the human immune disease pathway. CONCLUSIONS Protein docking supported by evolutionary restraints and machine learning can be used to reliably identify and model biologically relevant protein assemblies at the proteome scale. Moreover, the accuracy of the identification of protein-protein interactions is improved by considering only those protein pairs co-localized in the same cellular compartment and involved in the same biological process. The modeling protocol described in this communication can be applied to detect protein-protein interactions in other organisms and pathways as well as to construct dimer structures and estimate the confidence of protein interactions experimentally identified with high-throughput techniques.
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Affiliation(s)
- Surabhi Maheshwari
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA. .,Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, USA.
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8
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You ZH, Zhou M, Luo X, Li S. Highly Efficient Framework for Predicting Interactions Between Proteins. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:731-743. [PMID: 28113829 DOI: 10.1109/tcyb.2016.2524994] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Protein-protein interactions (PPIs) play a central role in many biological processes. Although a large amount of human PPI data has been generated by high-throughput experimental techniques, they are very limited compared to the estimated 130 000 protein interactions in humans. Hence, automatic methods for human PPI-detection are highly desired. This work proposes a novel framework, i.e., Low-rank approximation-kernel Extreme Learning Machine (LELM), for detecting human PPI from a protein's primary sequences automatically. It has three main steps: 1) mapping each protein sequence into a matrix built on all kinds of adjacent amino acids; 2) applying the low-rank approximation model to the obtained matrix to solve its lowest rank representation, which reflects its true subspace structures; and 3) utilizing a powerful kernel extreme learning machine to predict the probability for PPI based on this lowest rank representation. Experimental results on a large-scale human PPI dataset demonstrate that the proposed LELM has significant advantages in accuracy and efficiency over the state-of-art approaches. Hence, this work establishes a new and effective way for the automatic detection of PPI.
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9
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Rigid-Docking Approaches to Explore Protein-Protein Interaction Space. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:33-55. [PMID: 27830312 DOI: 10.1007/10_2016_41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.
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10
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Matsuzaki Y, Ohue M, Uchikoga N, Akiyama Y. Protein-protein interaction network prediction by using rigid-body docking tools: application to bacterial chemotaxis. Protein Pept Lett 2015; 21:790-8. [PMID: 23855669 PMCID: PMC4440392 DOI: 10.2174/09298665113209990066] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2012] [Revised: 02/27/2013] [Accepted: 03/03/2013] [Indexed: 11/22/2022]
Abstract
Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many
parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational
method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures.
The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible
interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing
predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate
complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation
requires massive computational resources, recent advancements in the computational sciences have made such
large-scale calculations feasible. different rigid-body docking tools with different scoring models. We found that the predicted interactions were different
between the results from the two tools. When the positive predictions from both of the docking tools were combined, all
the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation.
Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications.
This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior.
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Affiliation(s)
| | | | | | - Yutaka Akiyama
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
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11
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Ohue M, Matsuzaki Y, Uchikoga N, Ishida T, Akiyama Y. MEGADOCK: an all-to-all protein-protein interaction prediction system using tertiary structure data. Protein Pept Lett 2015; 21:766-78. [PMID: 23855673 PMCID: PMC4443796 DOI: 10.2174/09298665113209990050] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2012] [Revised: 03/20/2013] [Accepted: 03/25/2013] [Indexed: 12/02/2022]
Abstract
The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure
and function and structure-based drug design. However, the development of an effective method to conduct exhaustive
PPI screening represents a computational challenge. We have been investigating a protein docking approach based on
shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking
software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK
reduces the calculation time required for docking by using several techniques such as a novel scoring function
called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive
PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while
maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset
to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231
was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening
problem with accuracy better than random. When our approach is combined with parallel high-performance computing
systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional
structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock.
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Affiliation(s)
| | | | | | | | - Yutaka Akiyama
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
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12
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You ZH, Yu JZ, Zhu L, Li S, Wen ZK. A MapReduce based parallel SVM for large-scale predicting protein–protein interactions. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.05.072] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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13
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Gao L, Jiang X, Fu S, Gong H. In silico identification of potential virulence genes in 1,3-propanediol producer Klebsiella pneumonia. J Biotechnol 2014; 189:9-14. [DOI: 10.1016/j.jbiotec.2014.08.027] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2014] [Revised: 08/19/2014] [Accepted: 08/20/2014] [Indexed: 11/24/2022]
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14
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Ohue M, Shimoda T, Suzuki S, Matsuzaki Y, Ishida T, Akiyama Y. MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers. ACTA ACUST UNITED AC 2014; 30:3281-3. [PMID: 25100686 PMCID: PMC4221127 DOI: 10.1093/bioinformatics/btu532] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of >97% strong scaling. Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock. Contact:akiyama@cs.titech.ac.jp Supplementary information:Supplementary data are available at Bioinformatics online
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Affiliation(s)
- Masahito Ohue
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Takehiro Shimoda
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Shuji Suzuki
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Yuri Matsuzaki
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Takashi Ishida
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - Yutaka Akiyama
- Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 W8-76, Ookayama, Meguro-ku, Tokyo 152-8550, Japan, Japan Society for the Promotion of Science (JSPS) and Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, 2-12-1 W8-93, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
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Zhang C, Tang B, Wang Q, Lai L. Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening. Proteins 2014; 82:2472-82. [PMID: 24854898 DOI: 10.1002/prot.24611] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Revised: 05/06/2014] [Accepted: 05/09/2014] [Indexed: 12/18/2022]
Abstract
Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets.
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Affiliation(s)
- Changsheng Zhang
- BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Peking-Tsinghua Center for Life Sciences at College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
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Ohue M, Matsuzaki Y, Shimoda T, Ishida T, Akiyama Y. Highly precise protein-protein interaction prediction based on consensus between template-based and de novo docking methods. BMC Proc 2013; 7:S6. [PMID: 24564962 PMCID: PMC4044902 DOI: 10.1186/1753-6561-7-s7-s6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Elucidation of protein-protein interaction (PPI) networks is important for understanding disease mechanisms and for drug discovery. Tertiary-structure-based in silico PPI prediction methods have been developed with two typical approaches: a method based on template matching with known protein structures and a method based on de novo protein docking. However, the template-based method has a narrow applicable range because of its use of template information, and the de novo docking based method does not have good prediction performance. In addition, both of these in silico prediction methods have insufficient precision, and require validation of the predicted PPIs by biological experiments, leading to considerable expenditure; therefore, PPI prediction methods with greater precision are needed. Results We have proposed a new structure-based PPI prediction method by combining template-based prediction and de novo docking prediction. When we applied the method to the human apoptosis signaling pathway, we obtained a precision value of 0.333, which is higher than that achieved using conventional methods (0.231 for PRISM, a template-based method, and 0.145 for MEGADOCK, a non-template-based method), while maintaining an F-measure value (0.285) comparable to that obtained using conventional methods (0.296 for PRISM, and 0.220 for MEGADOCK). Conclusions Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.
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Matsuzaki Y, Uchikoga N, Ohue M, Shimoda T, Sato T, Ishida T, Akiyama Y. MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments. SOURCE CODE FOR BIOLOGY AND MEDICINE 2013; 8:18. [PMID: 24004986 PMCID: PMC3847482 DOI: 10.1186/1751-0473-8-18] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2013] [Accepted: 08/07/2013] [Indexed: 11/10/2022]
Abstract
Background Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures. Results We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, “MEGADOCK”, by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis. Conclusions We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.
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Affiliation(s)
- Yuri Matsuzaki
- Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan.
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Pierri CL, Parisi G, Porcelli V. Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2010; 1804:1695-712. [PMID: 20433957 DOI: 10.1016/j.bbapap.2010.04.008] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2010] [Revised: 03/04/2010] [Accepted: 04/14/2010] [Indexed: 12/12/2022]
Abstract
The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.
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Affiliation(s)
- Ciro Leonardo Pierri
- Department of Pharmaco-Biology, Laboratory of Biochemistry and Molecular Biology, University of Bari, Va E. Orabona, 4 - 70125 Bari, Italy.
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