Sequence and Structure-based Prediction of Allosteric Sites

Juan Xie; Gaoxiang Pan; Luhua Lai

doi:10.1016/j.jmb.2025.169305

Sequence and Structure-based Prediction of Allosteric Sites

J Mol Biol. 2025 Jun 24:169305. doi: 10.1016/j.jmb.2025.169305. Online ahead of print.

Authors

Juan Xie¹, Gaoxiang Pan¹, Luhua Lai²

Affiliations

¹ BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
² BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Science, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing 100871, China; Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, Sichuan 610213, China. Electronic address: lhlai@pku.edu.cn.

PMID: 40571274
DOI: 10.1016/j.jmb.2025.169305

Abstract

Allosteric regulation in proteins is a critical aspect of cellular function, influencing various biological processes through conformational or dynamic changes induced by effector molecules. Allosteric drugs possess significant therapeutic value due to their unique advantages, such as high specificity and diverse regulatory types, yet their presence in clinical applications remains limited. Understanding the relationship between protein sequence, structure, and allosteric regulation can promote insights into allosteric mechanisms and facilitate allosteric drug design. In this review, we present an overview of marketed allosteric drugs, summarize recent computational methods for predicting allosteric sites based on protein sequences and structures, together with case studies of recent rational allosteric drug design. We also discuss challenges and future directions in computer-aided allosteric drug design, with emphasis on the potential of multi-modal data integration and interpretable deep learning models in improving allosteric site prediction and rational allosteric drug design.

Keywords: allosteric site prediction; hidden allosteric site; protein sequence; protein structure; rational allosteric drug design.

Publication types

Review