Dissanayake Upeksha C, Roy Arkanil, Maghsoud Yazdan, Polara Sarthi, Debnath Tanay, Cisneros G Andrés
Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas, USA.
Department of Physics, The University of Texas at Dallas, Richardson, Texas, USA.
Protein Sci. 2025 Apr;34(4):e70081. doi: 10.1002/pro.70081.
Enzymes are critical biological catalysts involved in maintaining the intricate balance of metabolic processes within living organisms. Mutations in enzymes can result in disruptions to their functionality that may lead to a range of diseases. This review focuses on computational studies that investigate the effects of disease-associated mutations in various enzymes. Through molecular dynamics simulations, multiscale calculations, and machine learning approaches, computational studies provide detailed insights into how mutations impact enzyme structure, dynamics, and catalytic activity. This review emphasizes the increasing impact of computational simulations in understanding molecular mechanisms behind enzyme (dis)function by highlighting the application of key computational methodologies to selected enzyme examples, aiding in the prediction of mutation effects and the development of therapeutic strategies.
酶是至关重要的生物催化剂，参与维持生物体新陈代谢过程的复杂平衡。酶的突变会导致其功能紊乱，进而可能引发一系列疾病。本综述聚焦于研究各种酶中与疾病相关突变影响的计算研究。通过分子动力学模拟、多尺度计算和机器学习方法，计算研究深入洞察了突变如何影响酶的结构、动力学和催化活性。本综述通过突出关键计算方法在选定酶实例中的应用，强调了计算模拟在理解酶（功能）失调背后分子机制方面日益增加的影响，有助于预测突变效应和制定治疗策略。
