Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔT_m). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

中文翻译：

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DynaMut2：评估单点和多点错义突变时稳定性和灵活性的变化。

预测错义变异对蛋白质稳定性和动力学的影响，对于理解其在疾病中的作用以及蛋白质结构与功能之间的联系非常重要。已经提出了估算这些变化的方法，但是大多数方法仅考虑单点错义变体和蛋白质的静态状态，而结合动力学的方法则在计算上昂贵。在这里，我们介绍了DynaMut2，这是一个结合了正常模式分析（NMA）方法来捕获蛋白质运动的Web服务器，以及我们基于图形的签名来代表野生型环境，以研究单点和多点突变对蛋白质稳定性和动力学的影响。DynaMut2能够准确预测错义突变对蛋白质稳定性的影响，从而使Pearson的相关性高达0.72（RMSE：1。在10倍交叉验证和独立盲测中，单点突变为02 kcal / mol），多点错义突变为0.64（RMSE：1.80 kcal / mol）。对于单点突变，当预测吉布斯自由能（ΔΔ）的变化时，DynaMut2的性能可与其他方法媲美。G）和熔融温度（ΔT _m）。我们预计我们的工具将成为蛋白质灵活性分析研究和变体在疾病中作用研究的宝贵套件。DynaMut2可作为Web服务器和API免费获得，网址为http://biosig.unimelb.edu.au/dynamut2。