Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA.
School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA.
STAR Protoc. 2022 Feb 24;3(1):101194. doi: 10.1016/j.xpro.2022.101194. eCollection 2022 Mar 18.
Comparative analysis of protein structure or sequence alignments often ignores the protein dynamics and function. We offer a graphical user interface to a computing pipeline, complete with molecular visualization, enabling the biophysical simulation and statistical comparison of two-state functional protein dynamics (i.e., single unbound state vs. complex with a ligand, DNA, or protein). We utilize multi-agent machine learning classifiers to identify functionally conserved dynamic motions and compare them in genetic or drug-class variants. For complete details on the use and execution of this profile, please refer to Babbitt et al. (2020b, 2020a, 2018) and Rynkiewicz et al. (2021).
蛋白质结构或序列比对的比较分析通常忽略了蛋白质的动态和功能。我们提供了一个计算流程的图形用户界面,包括分子可视化,使我们能够对两种状态功能蛋白动力学(即单一未结合状态与配体、DNA 或蛋白质的复合物)进行生物物理模拟和统计比较。我们利用多代理机器学习分类器来识别功能保守的动态运动,并在遗传或药物类别变体中进行比较。有关此配置文件的使用和执行的详细信息,请参考 Babbitt 等人(2020b、2020a、2018)和 Rynkiewicz 等人(2021)。