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Chen YC, Chen YH, Wright JD, Lim C. PPI-Hotspot DB: Database of Protein-Protein Interaction Hot Spots. J Chem Inf Model 2022; 62:1052-1060. [PMID: 35147037 DOI: 10.1021/acs.jcim.2c00025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Single-point mutations of certain residues (so-called hot spots) impair/disrupt protein-protein interactions (PPIs), leading to pathogenesis and drug resistance. Conventionally, a PPI-hot spot is identified when its replacement decreased the binding free energy significantly, generally by ≥2 kcal/mol. The relatively few mutations with such a significant binding free energy drop limited the number of distinct PPI-hot spots. By defining PPI-hot spots based on mutations that have been manually curated in UniProtKB to significantly impair/disrupt PPIs in addition to binding free energy changes, we have greatly expanded the number of distinct PPI-hot spots by an order of magnitude. These experimentally determined PPI-hot spots along with available structures have been collected in a database called PPI-HotspotDB. We have applied the PPI-HotspotDB to create a nonredundant benchmark, PPI-Hotspot+PDBBM, for assessing methods to predict PPI-hot spots using the free structure as input. PPI-HotspotDB will benefit the design of mutagenesis experiments and development of PPI-hot spot prediction methods. The database and benchmark are freely available at https://ppihotspot.limlab.dnsalias.org.
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Affiliation(s)
- Yao Chi Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Yu-Hsien Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Jon D Wright
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Carmay Lim
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan.,Department of Chemistry, National Tsing Hua University, Hsinchu 300, Taiwan
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2
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A two-step ensemble learning for predicting protein hot spot residues from whole protein sequence. Amino Acids 2022; 54:765-776. [DOI: 10.1007/s00726-022-03129-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 01/17/2022] [Indexed: 11/26/2022]
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3
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James SA, Ong HS, Hari R, Khan AM. A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences. BMC Genomics 2021; 22:700. [PMID: 34583643 PMCID: PMC8477458 DOI: 10.1186/s12864-021-07657-4] [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: 03/14/2021] [Accepted: 04/28/2021] [Indexed: 11/10/2022] Open
Abstract
Background Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications. Results This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions. Conclusion Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07657-4.
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Affiliation(s)
- Stephen Among James
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia.,Department of Biochemistry, Faculty of Science, Kaduna State University, Kaduna, 800211, Nigeria
| | - Hui San Ong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia
| | - Ranjeev Hari
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia
| | - Asif M Khan
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Damansara Heights, Kuala Lumpur, 50490, Malaysia. .,Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Beykoz, Istanbul, 34820, Turkey.
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4
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Gheyouche E, Bagueneau M, Loirand G, Offmann B, Téletchéa S. Structural Design and Analysis of the RHOA-ARHGEF1 Binding Mode: Challenges and Applications for Protein-Protein Interface Prediction. Front Mol Biosci 2021; 8:643728. [PMID: 34109211 PMCID: PMC8181724 DOI: 10.3389/fmolb.2021.643728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/13/2021] [Indexed: 01/02/2023] Open
Abstract
The interaction between two proteins may involve local movements, such as small side-chains re-positioning or more global allosteric movements, such as domain rearrangement. We studied how one can build a precise and detailed protein-protein interface using existing protein-protein docking methods, and how it can be possible to enhance the initial structures using molecular dynamics simulations and data-driven human inspection. We present how this strategy was applied to the modeling of RHOA-ARHGEF1 interaction using similar complexes of RHOA bound to other members of the Rho guanine nucleotide exchange factor family for comparative assessment. In parallel, a more crude approach based on structural superimposition and molecular replacement was also assessed. Both models were then successfully refined using molecular dynamics simulations leading to protein structures where the major data from scientific literature could be recovered. We expect that the detailed strategy used in this work will prove useful for other protein-protein interface design. The RHOA-ARHGEF1 interface modeled here will be extremely useful for the design of inhibitors targeting this protein-protein interaction (PPI).
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Affiliation(s)
| | | | - Gervaise Loirand
- Université de Nantes, CHU Nantes, CNRS, Inserm, L'institut Du Thorax, Nantes, France
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5
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Wang W, Zhou Y, Cheng MT, Wang Y, Zheng CH, Xiong Y, Chen P, Ji Z, Wang B. Potential Pathogenic Genes Prioritization Based on Protein Domain Interaction Network Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1026-1034. [PMID: 32248121 DOI: 10.1109/tcbb.2020.2983894] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.
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6
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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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7
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Preto AJ, Moreira IS. SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features. Int J Mol Sci 2020; 21:ijms21197281. [PMID: 33019775 PMCID: PMC7582262 DOI: 10.3390/ijms21197281] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/26/2020] [Accepted: 09/30/2020] [Indexed: 01/02/2023] Open
Abstract
Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein–protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array of protein sequences in comparison to determined tree-dimensional structures indicates that a sequence-based HS predictor has the potential to be more useful for the scientific community. Herein, we present SPOTONE, a new ML predictor able to accurately classify protein HS via sequence-only features. This algorithm shows accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, on an independent testing set. The algorithm is deployed within a free-to-use webserver, only requiring the user to submit a FASTA file with one or more protein sequences.
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Affiliation(s)
- A. J. Preto
- CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal;
| | - Irina S. Moreira
- Department of Life Sciences, Center for Neuroscience and Cell Biology, Coimbra University, 3000-456 Coimbra, Portugal
- Correspondence:
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8
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Wang Y, Mei C, Zhou Y, Wang Y, Zheng C, Zhen X, Xiong Y, Chen P, Zhang J, Wang B. Semi-supervised prediction of protein interaction sites from unlabeled sample information. BMC Bioinformatics 2019; 20:699. [PMID: 31874616 PMCID: PMC6929468 DOI: 10.1186/s12859-019-3274-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Background The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of prediction accuracy and generalization ability of predictors in protein interaction sites prediction. Therefore, it is necessary to effectively improve prediction performance of protein interaction sites using large amounts of unlabeled data together with small amounts of labeled data and background knowledge today. Results In this work, three semi-supervised support vector machine–based methods are proposed to improve the performance in the protein interaction sites prediction, in which the information of unlabeled protein sites can be involved. Herein, five features related with the evolutionary conservation of amino acids are extracted from HSSP database and Consurf Sever, i.e., residue spatial sequence spectrum, residue sequence information entropy and relative entropy, residue sequence conserved weight and residual Base evolution rate, to represent the residues within the protein sequence. Then three predictors are built for identifying the interface residues from protein surface using three types of semi-supervised support vector machine algorithms. Conclusion The experimental results demonstrated that the semi-supervised approaches can effectively improve prediction performance of protein interaction sites when unlabeled information is involved into the predictors and one of them can achieve the best prediction performance, i.e., the accuracy of 70.7%, the sensitivity of 62.67% and the specificity of 78.72%, respectively. With comparison to the existing studies, the semi-supervised models show the improvement of the predication performance.
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Affiliation(s)
- Ye Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243002, Anhui, China
| | - Changqing Mei
- School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243002, Anhui, China
| | - Yuming Zhou
- School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243002, Anhui, China
| | - Yan Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243002, Anhui, China
| | - Chunhou Zheng
- Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Xiao Zhen
- School of Computer Science and Technology, Anhui University of Technology, Maanshan, 243002, Anhui, China
| | - Yan Xiong
- School of Computer Science and Technology, University of Science & Technology, Hefei, 230026, Anhui, China
| | - Peng Chen
- Institute of Health Sciences, Anhui University, Hefei, 230601, Anhui, China.
| | - Jun Zhang
- College of Electrical Engineering and Automation, Anhui University, Hefei, 230601, Anhui, China
| | - Bing Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, 243002, Anhui, China. .,Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei, 230601, Anhui, China.
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9
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Wang Y, Xiao Q, Chen P, Wang B. In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method. Int J Mol Sci 2019; 20:E4106. [PMID: 31443562 PMCID: PMC6747689 DOI: 10.3390/ijms20174106] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 08/20/2019] [Accepted: 08/20/2019] [Indexed: 11/17/2022] Open
Abstract
Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug-induced liver injury prediction.
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Affiliation(s)
- Yangyang Wang
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Qingxin Xiao
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Peng Chen
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China.
- School of Computer Science and Technology, Anhui University, Hefei 230601, China.
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China.
| | - Bing Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China.
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10
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Haymond A, Davis JB, Espina V. Proteomics for cancer drug design. Expert Rev Proteomics 2019; 16:647-664. [PMID: 31353977 PMCID: PMC6736641 DOI: 10.1080/14789450.2019.1650025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Accepted: 07/26/2019] [Indexed: 12/29/2022]
Abstract
Introduction: Signal transduction cascades drive cellular proliferation, apoptosis, immune, and survival pathways. Proteins have emerged as actionable drug targets because they are often dysregulated in cancer, due to underlying genetic mutations, or dysregulated signaling pathways. Cancer drug development relies on proteomic technologies to identify potential biomarkers, mechanisms-of-action, and to identify protein binding hot spots. Areas covered: Brief summaries of proteomic technologies for drug discovery include mass spectrometry, reverse phase protein arrays, chemoproteomics, and fragment based screening. Protein-protein interface mapping is presented as a promising method for peptide therapeutic development. The topic of biosimilar therapeutics is presented as an opportunity to apply proteomic technologies to this new class of cancer drug. Expert opinion: Proteomic technologies are indispensable for drug discovery. A suite of technologies including mass spectrometry, reverse phase protein arrays, and protein-protein interaction mapping provide complimentary information for drug development. These assays have matured into well controlled, robust technologies. Recent regulatory approval of biosimilar therapeutics provides another opportunity to decipher the molecular nuances of their unique mechanisms of action. The ability to identify previously hidden protein hot spots is expanding the gamut of potential drug targets. Proteomic profiling permits lead compound evaluation beyond the one drug, one target paradigm.
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Affiliation(s)
- Amanda Haymond
- Center for Applied Proteomics and Molecular Medicine, George Mason University , Manassas , VA , USA
| | - Justin B Davis
- Center for Applied Proteomics and Molecular Medicine, George Mason University , Manassas , VA , USA
| | - Virginia Espina
- Center for Applied Proteomics and Molecular Medicine, George Mason University , Manassas , VA , USA
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11
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Liu Q, Chen P, Wang B, Zhang J, Li J. dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions. BMC Bioinformatics 2018; 19:455. [PMID: 30482172 PMCID: PMC6260753 DOI: 10.1186/s12859-018-2493-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2018] [Accepted: 11/13/2018] [Indexed: 02/06/2023] Open
Abstract
Background Protein-protein interactions (PPIs) play important roles in biological functions. Studies of the effects of mutants on protein interactions can provide further understanding of PPIs. Currently, many databases collect experimental mutants to assess protein interactions, but most of these databases are old and have not been updated for several years. Results To address this issue, we manually curated a kinetic and thermodynamic database of mutant protein interactions (dbMPIKT) that is freely accessible at our website. This database contains 5291 mutants in protein interactions collected from previous databases and the literature published within the last three years. Furthermore, some data analysis, such as mutation number, mutation type, protein pair source and network map construction, can be performed online. Conclusion Our work can promote the study on PPIs, and novel information can be mined from the new database. Our database is available in http://DeepLearner.ahu.edu.cn/web/dbMPIKT/ for use by all, including both academics and non-academics. Electronic supplementary material The online version of this article (10.1186/s12859-018-2493-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Quanya Liu
- Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China
| | - Peng Chen
- Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
| | - Bing Wang
- School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, 243032, Anhui, China
| | - Jun Zhang
- School of Electronic Engineering & Automation, Anhui University, Hefei, 230601, Anhui, China
| | - Jinyan Li
- Advanced Analytics Institute and Centre for Health Technologies, University of Technology, Broadway, Sydney, NSW, 2007, Australia
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12
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Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment. Molecules 2018; 23:molecules23102535. [PMID: 30287797 PMCID: PMC6222875 DOI: 10.3390/molecules23102535] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 09/27/2018] [Accepted: 10/02/2018] [Indexed: 12/27/2022] Open
Abstract
Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein–protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein–protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.
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13
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Abstract
Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches.
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Zou Q, He W. Special Protein Molecules Computational Identification. Int J Mol Sci 2018; 19:ijms19020536. [PMID: 29439426 PMCID: PMC5855758 DOI: 10.3390/ijms19020536] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 02/02/2018] [Accepted: 02/10/2018] [Indexed: 01/29/2023] Open
Abstract
Computational identification of special protein molecules is a key issue in understanding protein function. It can guide molecular experiments and help to save costs. I assessed 18 papers published in the special issue of Int. J. Mol. Sci., and also discussed the related works. The computational methods employed in this special issue focused on machine learning, network analysis, and molecular docking. New methods and new topics were also proposed. There were in addition several wet experiments, with proven results showing promise. I hope our special issue will help in protein molecules identification researches.
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Affiliation(s)
- Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin 300354, China.
| | - Wenying He
- School of Computer Science and Technology, Tianjin University, Tianjin 300354, China.
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