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Li S, Wu S, Wang L, Li F, Jiang H, Bai F. Recent advances in predicting protein-protein interactions with the aid of artificial intelligence algorithms. Curr Opin Struct Biol 2022; 73:102344. [PMID: 35219216 DOI: 10.1016/j.sbi.2022.102344] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 01/02/2022] [Accepted: 01/17/2022] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions (PPIs) are essential in the regulation of biological functions and cell events, therefore understanding PPIs have become a key issue to understanding the molecular mechanism and investigating the design of drugs. Here we highlight the major developments in computational methods developed for predicting PPIs by using types of artificial intelligence algorithms. The first part introduces the source of experimental PPI data. The second part is devoted to the PPI prediction methods based on sequential information. The third part covers representative methods using structural information as the input feature. The last part is methods designed by combining different types of features. For each part, the state-of-the-art computational PPI prediction methods are reviewed in an inclusive view. Finally, we discuss the flaws existing in this area and future directions of next-generation algorithms.
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Affiliation(s)
- Shiwei Li
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Sanan Wu
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Lin Wang
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Fenglei Li
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China; School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hualiang Jiang
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Pudong, Shanghai, 201203, China
| | - Fang Bai
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China; School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
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2
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Gharouni M, Mosaddeghi H, Mehrzad J, Es-haghi A, Motavalizadehkakhky A. Detecting a novel motif of O6-methyl guanine DNA methyltransferase, a DNA repair enzyme, involved in interaction with proliferating cell nuclear antigen through a computer modeling approach. COMPUT THEOR CHEM 2021. [DOI: 10.1016/j.comptc.2021.113471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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3
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Wang P, Zhang G, Yu ZG, Huang G. A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites. Front Genet 2021; 12:752732. [PMID: 34764983 PMCID: PMC8576272 DOI: 10.3389/fgene.2021.752732] [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: 08/03/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022] Open
Abstract
Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.
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Affiliation(s)
- Pan Wang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Guiyang Zhang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
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4
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Wang B, Mei C, Wang Y, Zhou Y, Cheng MT, Zheng CH, Wang L, Zhang J, Chen P, Xiong Y. Imbalance Data Processing Strategy for Protein Interaction Sites Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:985-994. [PMID: 31751283 DOI: 10.1109/tcbb.2019.2953908] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the number of experimental determined protein interaction sites is far less than that of protein sites in protein-protein interaction or protein complexes. Therefore, the negative and positive samples are usually imbalanced, which is common but bring result bias on the prediction of protein interaction sites by computational approaches. In this work, we presented three imbalance data processing strategies to reconstruct the original dataset, and then extracted protein features from the evolutionary conservation of amino acids to build a predictor for identification of protein interaction sites. On a dataset with 10,430 surface residues but only 2,299 interface residues, the imbalance dataset processing strategies can obviously reduce the prediction bias, and therefore improve the prediction performance of protein interaction sites. The experimental results show that our prediction models can achieve a better prediction performance, such as a prediction accuracy of 0.758, or a high F-measure of 0.737, which demonstrated the effectiveness of our method.
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5
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Slater O, Miller B, Kontoyianni M. Decoding Protein-protein Interactions: An Overview. Curr Top Med Chem 2021; 20:855-882. [PMID: 32101126 DOI: 10.2174/1568026620666200226105312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 12/24/2022]
Abstract
Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
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Affiliation(s)
- Olivia Slater
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Bethany Miller
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
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6
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Deng A, Zhang H, Wang W, Zhang J, Fan D, Chen P, Wang B. Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm. Int J Mol Sci 2020; 21:E2274. [PMID: 32218345 PMCID: PMC7178137 DOI: 10.3390/ijms21072274] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/10/2020] [Accepted: 03/23/2020] [Indexed: 12/27/2022] Open
Abstract
The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.
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Affiliation(s)
- Aijun Deng
- Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology), Ministry of Education, Ma'anshan 243002, China
- School of Metallurgical Engineering, Anhui University of Technology, Ma'anshan 243032, China
- Department of Engineering, University of Leicester, Leicester LE1 7RH, UK
| | - Huan Zhang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China
| | - Wenyan Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China
| | - Jun Zhang
- Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230032, China
| | - Dingdong Fan
- School of Metallurgical Engineering, Anhui University of Technology, Ma'anshan 243032, China
| | - Peng Chen
- Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230032, China
| | - Bing Wang
- Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology), Ministry of Education, Ma'anshan 243002, China
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China
- Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei 230032, China
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7
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Xie Z, Deng X, Shu K. Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets. Int J Mol Sci 2020; 21:E467. [PMID: 31940793 PMCID: PMC7013409 DOI: 10.3390/ijms21020467] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/23/2019] [Accepted: 01/08/2020] [Indexed: 12/20/2022] Open
Abstract
Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.
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Affiliation(s)
- Zengyan Xie
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
| | | | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
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8
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Barreto CAV, Baptista SJ, Preto AJ, Matos-Filipe P, Mourão J, Melo R, Moreira I. Prediction and targeting of GPCR oligomer interfaces. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 169:105-149. [PMID: 31952684 DOI: 10.1016/bs.pmbts.2019.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.
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Affiliation(s)
- Carlos A V Barreto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Salete J Baptista
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - António José Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Rita Melo
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - Irina Moreira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Science and Technology Faculty, University of Coimbra, Coimbra, Portugal.
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9
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Galano-Frutos JJ, García-Cebollada H, Sancho J. Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when. Brief Bioinform 2019; 22:3-19. [PMID: 31813950 DOI: 10.1093/bib/bbz146] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/22/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.
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Affiliation(s)
- Juan J Galano-Frutos
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
| | | | - Javier Sancho
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
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10
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Carter R, Luchini A, Liotta L, Haymond A. Next Generation Techniques for Determination of Protein-Protein Interactions: Beyond the Crystal Structure. CURRENT PATHOBIOLOGY REPORTS 2019; 7:61-71. [PMID: 33094031 PMCID: PMC7577580 DOI: 10.1007/s40139-019-00198-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE OF REVIEW We discuss recent advancements in structural biology methods for investigating sites of protein-protein interactions. We will inform readers outside the field of structural biology about techniques beyond crystallography, and how these different technologies can be utilized for drug development. RECENT FINDINGS Advancements in cryo-electron microscopy (cryoEM) and micro-electron diffraction (microED) may change how we view atomic resolution structural biology, such that well-ordered macrocrystals of protein complexes are not required for interface identification. However, some drug discovery applications, such as lead peptide compound generation, may not require atomic resolution; mass spectrometry techniques can provide an expedited path to generation of lead compounds. New crosslinking compounds, more user-friendly data analysis, and novel protocols such as protein painting can advance drug discovery programs, even in the absence of atomic resolution structural data. Finally, artificial intelligence and machine learning methods, while never truly replacing experimental methods, may provide rational ways to stratify potential druggable regions identified with mass spectrometry into higher and lower priority candidates. SUMMARY Electron diffraction of nanocrystals combines the benefits of both x-ray diffraction and cryoEM, and may prove to be the next generation of atomic resolution protein-protein interface identification. However, in situations such as peptide drug discovery, mass spectrometry techniques supported by advancements in computational methods will likely prove sufficient to support drug discovery efforts. In addition, these methods can be significantly faster than any crystallographic or cryoEM methods for identification of interacting regions.
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Affiliation(s)
- Rachel Carter
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA
| | - Alessandra Luchini
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA
| | - Lance Liotta
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA
| | - Amanda Haymond
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA
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11
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Sun Z, Liu Q, Qu G, Feng Y, Reetz MT. Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability. Chem Rev 2019; 119:1626-1665. [PMID: 30698416 DOI: 10.1021/acs.chemrev.8b00290] [Citation(s) in RCA: 264] [Impact Index Per Article: 52.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Zhoutong Sun
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West Seventh Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
| | - Qian Liu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ge Qu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West Seventh Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Manfred T. Reetz
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West Seventh Avenue, Tianjin Airport Economic Area, Tianjin 300308, China
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
- Chemistry Department, Philipps-University, Hans-Meerwein-Strasse 4, 35032 Marburg, Germany
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12
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Aker M, Ohanona S, Fisher S, Katsman E, Dvorkin S, Kopelowitz E, Goldstein M, Barnett-Itzhaki Z, Amitay M. CDB—a database for protein heterodimeric complexes. Protein Eng Des Sel 2018; 31:361-365. [DOI: 10.1093/protein/gzy030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 10/29/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Malka Aker
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
| | - Shirly Ohanona
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
| | - Shira Fisher
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
| | - Efrat Katsman
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
| | - Shirit Dvorkin
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
| | - Efrat Kopelowitz
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
| | - Moshe Goldstein
- Department of Computer Science, Jerusalem College of Technology, 21 Havaad Haleumi St., Jerusalem, Israel
| | - Zohar Barnett-Itzhaki
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
- Public Health Services, Ministry of Health, 39 Yirmiyahu Street, Jerusalem, Israel
| | - Moshe Amitay
- Department of Bioinformatics, Jerusalem College of Technology, 21 Havaad Haleumi Street, Jerusalem, Israel
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Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
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