Current Computational Methods for Protein-peptide Complex Structure Prediction.

Curr Med Chem

School of Computer Science and Technology, Aba Teachers University, Aba, Sichuan 623002, China.

Published: July 2024

AI Article Synopsis

  • Peptide-mediated protein-protein interactions are crucial for various biological processes, and peptide-based drugs are gaining attention for their high specificity and low toxicity.
  • The article reviews recent protein-peptide docking methods, categorizing them into template-based, template-free, and hybrid approaches, while also discussing benchmarking sets and evaluation metrics for assessing performance.
  • Additionally, it covers the roles of molecular dynamics simulations and deep learning techniques in predicting protein-peptide complexes.

Article 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.

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Source
http://dx.doi.org/10.2174/0109298673263447230920151524DOI Listing

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