Recapturing the Correlated Motions of Protein by using Coarse Grained Models

Abstract

Long range interaction and allosteric behavior is important for many biological process, such as: metabolic mechanisms, signal transduction pathways. Increasing evidence show that internal dynamics may play an important role in such behavior. Investigating the dynamical effects of proteins is a challenging problem by using all atom molecular dynamics, because these proteins are usually large. Here we use three well-defined coarse grained model: Go, Martini and Cafemol model to examine if these coarse grained model can explore the dynamics effects efficiently, also to explore the effects of mutation. Eglin C is used as a model system. We found that both Go and Cafemol model can recapture the dynamical characteristic of proteins very well, especially for the defined secondary structures, while Martini model explore the dynamics characteristic fairly well. For coil part, three coarse grained models have higher fluctuation and correlated motions than all-atom model. Mutation effects are also discussed.