Zhu Ruidi, Wu Chengwei, Zha Jinyin, Lu Shaoyong, Zhang Jian
Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine Shanghai 200025 China
College of Pharmacy, Ningxia Medical University Yinchuan Ningxia Hui Autonomous Region 750004 China
RSC Chem Biol. 2025 Feb 14;6(4):539-554. doi: 10.1039/d4cb00282b. eCollection 2025 Apr 2.
Allosteric regulation is a fundamental mechanism in enzyme function, enabling dynamic modulation of activity through ligand binding at sites distal to the active site. Allosteric modulators have gained significant attention due to their unique advantages, including enhanced specificity, reduced off-target effects, and the potential for synergistic interaction with orthosteric agents. However, the inherent complexity of allosteric mechanisms has posed challenges to the systematic discovery and design of allosteric modulators. This review discusses recent advancements in computational methodologies for identifying and characterizing allosteric sites in enzymes, emphasizing techniques such as molecular dynamics (MD) simulations, enhanced sampling methods, normal mode analysis (NMA), evolutionary conservation analysis, and machine learning (ML) approaches. Advanced tools like PASSer, AlloReverse, and AlphaFold have further enhanced the understanding of allosteric mechanisms and facilitated the design of selective allosteric modulators. Case studies on enzymes such as Sirtuin 6 (SIRT6) and MAPK/ERK kinase (MEK) demonstrate the practical applications of these approaches in drug discovery. By integrating computational predictions with experimental validation, this review highlights the transformative potential of computational strategies in advancing allosteric drug discovery, offering innovative opportunities to regulate enzyme activity for therapeutic benefits.
别构调节是酶功能中的一种基本机制，它能够通过配体结合在活性位点远端的位点来动态调节酶的活性。别构调节剂因其独特优势而备受关注，这些优势包括增强的特异性、降低的脱靶效应以及与正构剂协同相互作用的潜力。然而，别构机制固有的复杂性给别构调节剂的系统发现和设计带来了挑战。本综述讨论了用于识别和表征酶中别构位点的计算方法的最新进展，重点介绍了诸如分子动力学（MD）模拟、增强采样方法、简正模式分析（NMA）、进化保守性分析和机器学习（ML）方法等技术。像PASSer、AlloReverse和AlphaFold这样的先进工具进一步加深了对别构机制的理解，并促进了选择性别构调节剂的设计。以沉默调节蛋白6（SIRT6）和丝裂原活化蛋白激酶/细胞外信号调节激酶激酶（MEK）等酶为例的案例研究证明了这些方法在药物发现中的实际应用。通过将计算预测与实验验证相结合，本综述强调了计算策略在推进别构药物发现方面的变革潜力，为调节酶活性以实现治疗益处提供了创新机会。
