Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York.
Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, New York.
Biophys J. 2024 Sep 3;123(17):2705-2715. doi: 10.1016/j.bpj.2024.03.024. Epub 2024 Mar 21.
Comparative methods in molecular evolution and structural biology rely heavily upon the site-wise analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from nanoscale nonrandom machine-like motions induced by evolutionarily conserved molecular interactions. Comparisons of molecular dynamics (MD) simulations conducted between homologous sites representative of different functional or mutational states can potentially identify local effects on binding interaction and protein evolution. In addition, comparisons of different (i.e., nonhomologous) sites within MD simulations could be employed to identify functional shifts in local time-coordinated dynamics indicative of logic gating within proteins. However, comparative MD analysis is challenged by the large fraction of protein motion caused by random thermal noise in the surrounding solvent. Therefore, properly denoised MD comparisons could reveal functional sites involving these machine-like dynamics with good accuracy. Here, we introduce ATOMDANCE, a user-interfaced suite of comparative machine learning-based denoising tools designed for identifying functional sites and the patterns of coordinated motion they can create within MD simulations. ATOMDANCE-maxDemon4.0 employs Gaussian kernel functions to compute site-wise maximum mean discrepancy between learned features of motion, thereby assessing denoised differences in the nonrandom motions between functional or evolutionary states (e.g., ligand bound versus unbound, wild-type versus mutant). ATOMDANCE-maxDemon4.0 also employs maximum mean discrepancy to analyze potential random amino acid replacements allowing for a site-wise test of neutral versus nonneutral evolution on the divergence of dynamic function in protein homologs. Finally, ATOMDANCE-Choreograph2.0 employs mixed-model analysis of variance and graph network to detect regions where time-synchronized shifts in dynamics occur. Here, we demonstrate ATOMDANCE's utility for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small-molecule drugs, and virus-host recognition, as well as understanding shifts in global and local site coordination occurring during allosteric activation of a pathogenic protease.
比较分子进化和结构生物学方法主要依赖于 DNA 序列和蛋白质结构的逐点分析，这两种信息都是静态形式。然而，人们普遍认为，蛋白质功能是由进化保守的分子相互作用诱导的纳米级非随机机器样运动产生的。比较代表不同功能或突变状态的同源位点的分子动力学（MD）模拟，可以潜在地识别对结合相互作用和蛋白质进化的局部影响。此外，在 MD 模拟中比较不同（即非同源）的位点，可以识别局部时间协调动力学中的功能转变，这些转变表明蛋白质中的逻辑门控。然而，比较 MD 分析受到周围溶剂中随机热噪声引起的蛋白质运动的很大一部分的挑战。因此，适当去噪的 MD 比较可以以较高的准确性揭示涉及这些机器样动力学的功能位点。在这里，我们介绍了 ATOMDANCE，这是一个用户界面套件，基于比较机器学习的去噪工具，用于识别功能位点和它们在 MD 模拟中产生的协调运动模式。ATOMDANCE-maxDemon4.0 采用高斯核函数来计算运动学习特征之间的逐点最大均值差异，从而评估功能或进化状态（例如，配体结合与未结合、野生型与突变型）之间非随机运动的去噪差异。ATOMDANCE-maxDemon4.0 还采用最大均值差异来分析潜在的随机氨基酸替换，从而可以对蛋白质同源物中动态功能分歧的中性与非中性进化进行逐点测试。最后，ATOMDANCE-Choreograph2.0 采用混合模型方差分析和图网络来检测动力学同步变化发生的区域。在这里，我们展示了 ATOMDANCE 用于识别参与 DNA、小分子药物和病毒-宿主识别的功能结合相互作用期间动态响应的关键位点的效用，以及理解在致病蛋白酶变构激活过程中全局和局部位点协调的转变。
