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.

中文翻译：

---

ATOMDANCE：基于内核的去噪和编排分析，用于蛋白质动态比较

分子进化和结构生物学中的比较方法在很大程度上依赖于对 DNA 序列和蛋白质结构（这两种静态信息形式）的位点分析。然而，人们普遍认为蛋白质功能是由进化上保守的分子相互作用引起的纳米级非随机机器状运动的结果。在代表不同功能或突变状态的同源位点之间进行的分子动力学（MD）模拟比较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，MD模拟中不同（即非同源）位点的比较可用于识别指示蛋白质内逻辑门控的局部时间协调动态的功能变化。然而，比较 MD 分析受到周围溶剂中随机热噪声引起的大部分蛋白质运动的挑战。因此，适当去噪的 MD 比较可以高精度地揭示涉及这些类机器动力学的功能位点。在这里，我们介绍 ATOMDANCE，这是一套基于比较机器学习的用户界面去噪工具，旨在识别功能位点以及它们可以在 MD 模拟中创建的协调运动模式。 ATOMDANCE-maxDemon4.0 采用高斯核函数来计算学习的运动特征之间的位点最大平均差异，从而评估功能或进化状态之间非随机运动的去噪差异（例如，配体结合与未结合、野生型与突变体） 。 ATOMDANCE-maxDemon4.0 还采用最大平均差异来分析潜在的随机氨基酸替换，从而可以对蛋白质同系物中动态功能差异的中性与非中性进化进行位点测试。最后，ATOMDANCE-Choreograph2.0 采用方差和图形网络的混合模型分析来检测动态发生时间同步变化的区域。在这里，我们展示了 ATOMDANCE 的实用性，用于识别涉及 DNA、小分子药物和病毒宿主识别的功能性结合相互作用期间涉及动态响应的关键位点，以及了解致病性变构激活过程中发生的全局和局部位点协调的变化蛋白酶。