Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

ABSTRACT: Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved o

SUBMITTER: Xie ZR 

PROVIDER: S-EPMC5394550 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature