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Curr Drug Metab. 2019;20(3):170-176. doi: 10.2174/1389200219666181012151944.

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

Li Z1, Miao Q1, Yan F1, Meng Y1, Zhou P1,2,3.

Author information

1
Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.
2
Center for Information in BioMedicine, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.
3
Key Laboratory for Neuroinformation of the Ministry of Education, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China.

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; computational peptidology; druggable target; machine learning; molecular recognition; statistical regression; therapeutic peptide design.

[Indexed for MEDLINE]

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