Sharp Morris E, Vázquez Francisco X, Wagner Jacob W, Dannenhoffer-Lafage Thomas, Voth Gregory A
Department of Chemistry, James Franck Institute, and Institute for Biophysical Dynamics , The University of Chicago , Chicago , Illinois 60637 , United States.
Department of Chemistry , St. John's University , Queens , New York 11439 , United States.
J Chem Theory Comput. 2019 May 14;15(5):3306-3315. doi: 10.1021/acs.jctc.8b01133. Epub 2019 Apr 2.
Standard low resolution coarse-grained modeling techniques have difficulty capturing multiple configurations of protein systems. Here, we present a method for creating accurate coarse-grained (CG) models with multiple configurations using a linear combination of functions or "states". Individual CG models are created to capture the individual states, and the approximate coupling between the two states is determined from an all-atom potential of mean force. We show that the resulting multiconfiguration coarse-graining (MCCG) method accurately captures the transition state as well as the free energy between the two states. We have tested this method on the folding of dodecaalanine, as well as the amphipathic helix of endophilin.
标准的低分辨率粗粒度建模技术难以捕捉蛋白质系统的多种构象。在此，我们提出一种方法，通过函数或“状态”的线性组合来创建具有多种构象的精确粗粒度（CG）模型。创建单个CG模型以捕捉各个状态，并根据平均力的全原子势确定两个状态之间的近似耦合。我们表明，由此产生的多构象粗粒度（MCCG）方法能够准确捕捉过渡态以及两个状态之间的自由能。我们已将此方法应用于十二聚丙氨酸的折叠以及内吞素的两亲螺旋。
