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Fu Y, He P, Zhou Y, Huang S, Liang L, Liu S. Exploring the systematic effect of
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‐substituted PxxP motifs on peptoid affinity to ARHGEF5/TIM SH3 domain and its relationship with ARHGEF5/TIM activation. Proteins 2019; 87:979-991. [PMID: 31197859 DOI: 10.1002/prot.25760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 06/04/2019] [Accepted: 06/07/2019] [Indexed: 11/09/2022]
Affiliation(s)
- Yong Fu
- Department of Endocrine and Breast SurgeryThe First Affiliated Hospital of Chongqing Medical University Chongqing China
- Department of Cardiothoracic SurgeryDianjiang People's Hospital of Chongqing Chongqing China
| | - Ping He
- Department of Cardiac SurgerySouthwest Hospital, Third Army Medical University Chongqing China
| | - Yu Zhou
- Department of Cardiothoracic SurgeryDianjiang People's Hospital of Chongqing Chongqing China
| | - Shengyuan Huang
- Department of Cardiothoracic SurgeryDianjiang People's Hospital of Chongqing Chongqing China
| | - Lin Liang
- Department of Cardiothoracic SurgeryDianjiang People's Hospital of Chongqing Chongqing China
| | - Shengchun Liu
- Department of Endocrine and Breast SurgeryThe First Affiliated Hospital of Chongqing Medical University Chongqing China
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Li Z, Miao Q, Yan F, Meng Y, Zhou P. Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design. Curr Drug Metab 2019; 20:170-176. [DOI: 10.2174/1389200219666181012151944] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 11/07/2017] [Accepted: 08/20/2018] [Indexed: 01/03/2023]
Abstract
Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.
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Affiliation(s)
- Zhongyan Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Qingqing Miao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Fugang Yan
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Yang Meng
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Peng Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
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To Determine Biologically Important Mutations in Oxytocin. Int J Pept Res Ther 2014. [DOI: 10.1007/s10989-014-9412-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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He P, Wu W, Wang HD, Liao KL, Zhang W, Lv FL, Yang K. Why ligand cross-reactivity is high within peptide recognition domain families? A case study on human c-Src SH3 domain. J Theor Biol 2014; 340:30-7. [DOI: 10.1016/j.jtbi.2013.08.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2013] [Revised: 07/26/2013] [Accepted: 08/21/2013] [Indexed: 10/26/2022]
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Structure-based characterization of the binding of peptide to the human endophilin-1 Src homology 3 domain using position-dependent noncovalent potential analysis. J Mol Model 2011; 18:2153-61. [DOI: 10.1007/s00894-011-1197-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2011] [Accepted: 07/20/2011] [Indexed: 02/05/2023]
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