숙명여자대학교 비교과통합관리시스템

Invited Seminar

Department of Mechanical Systems Engineering

The multi-dimensional generalized Langevin equation for conformational motion of proteins

날짜  2019년 12월 6일 금요일

시각 16시 – 17시

장소 프라임 201호

신청  SNOWAY 비교과프로그램(마일리지 2000점 부여)

대상  공개강의 (타과생 환영합니다^^)

연사  이희선 박사님

약력  2018. 11 – present  Data Scientist at Autogrid systems (Redwood City, CA)

2012. 09 – 2018. 09 Stanford University, CA Ph.D. in Mechanical Engineering

2010. 09 – 2013. 01 Stnford University M.S. in Mechanical Engineering

2006. 03 – 2010. 06 Seoul National University, B.S. in Mechanical & Aerospace Engineering, Electrical Engineering (Minor)

Using the generalized Langevin equation (GLE) is a promising approach to build coarse-grained (CG) models of molecular systems since the GLE model often leads to more accurate thermodynamic and kinetic predictions than Brownian dynamics or Langevin models by including a more sophisticated friction with memory. The GLE approach has been used for CG coordinates such as the center of mass of a group of atoms with pairwise decomposition and for a single CG coordinate. We present a GLE approach when CG coordinates are multiple generalized coordinates, defined, in general, as nonlinear functions of microscopic atomic coordinates. The CG model for multiple generalized coordinates is described by the multidimensional GLE from the Mori-Zwanzig formalism, which includes an exact memory matrix. We first present a method to compute the memory matrix in a multidimensional GLE using trajectories of a full system. Then, in order to reduce the computational cost of computing the multidimensional friction with memory, we introduce a method that maps the GLE to an extended Markovian system. In addition, we study the effect of using a non-constant mass matrix in the CG model. In particular, we include mass-dependent terms in the mean force. We used the proposed CG model to describe the conformational motion of a solvated alanine dipeptide system, with two dihedral angles as the CG coordinates. We showed that the CG model can accurately reproduce two important kinetic quantities: the velocity autocorrelation function and the distribution of first passage times.

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