Exploring the computational methods for protein-ligand binding site prediction

Computational and Structural Biotechnology Journal

Le Zhang¹  Yang Cao²  Jingtian Zhao³ 

[1] Corresponding authors at: No. 24 South Section 1, Yihuan Road, Chengdu, Sichuan 610065, China.;Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China;College of Computer Science, Sichuan University, Chengdu 610065, China;

关键词: Protein;    Ligand binding site;    Machine learning;    Deep learning;    Protein–ligand binding;   

DOI  :  

来源: DOAJ

【 摘 要 】

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein–ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein–ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

【 授权许可】

Unknown