Current Computational Methods for Protein-peptide Complex Structure Prediction


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Abstract

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.

About the authors

Chao Yang

Department of Chemistry, New York University

Author for correspondence.
Email: info@benthamscience.net

Xianjin Xu

Dalton Cardiovascular Research Center,, University of Missouri

Email: info@benthamscience.net

Changcheng Xiang

School of Computer Science and Technology, Aba Teachers University

Email: info@benthamscience.net

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