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Current Drug Metabolism

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ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

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Review Article

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Author(s): Zhongyan Li, Qingqing Miao, Fugang Yan, Yang Meng and Peng Zhou*

Volume 20, Issue 3, 2019

Page: [170 - 176] Pages: 7

DOI: 10.2174/1389200219666181012151944

Price: $65

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.

Keywords: Protein-peptide affinity, therapeutic peptide design, machine learning, statistical regression, druggable target, molecular recognition, computational peptidology.

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