TY - JOUR

T1 - Evolutionary Switches Structural Transitions via Coarse-Grained Models

AU - Delfino, Francesco

AU - Porozov, Yuri

AU - Stepanov, Eugene

AU - Tamazian, Gaik

AU - Tozzini, Valentina

N1 - Francesco Delfino, Yuri Porozov, Eugene Stepanov, Gaik Tamazian, and Valentina Tozzini.Journal of Computational Biology.Feb 2020.189-199.http://doi.org/10.1089/cmb.2019.0338

PY - 2020/2/1

Y1 - 2020/2/1

N2 - Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

AB - Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

KW - evolutionary switch

KW - minimalist coarse-grained model

KW - molecular evolution

KW - prompt

KW - transition path

KW - PROTEIN-STRUCTURE

KW - SERVER

KW - DYNAMICS

KW - MINIMALIST MODELS

KW - MOTIONS

KW - INSIGHTS

UR - http://www.scopus.com/inward/record.url?scp=85079666653&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/2ecd39cd-33e6-35c0-8d6d-090534e4f94b/

U2 - 10.1089/cmb.2019.0338

DO - 10.1089/cmb.2019.0338

M3 - Article

AN - SCOPUS:85079666653

VL - 27

SP - 189

EP - 199

JO - Journal of Computational Biology

JF - Journal of Computational Biology

SN - 1066-5277

IS - 2

ER -