Generating Protein Ensembles by Applying Forces to an Improved Elastic Network Model

Abstract

The lack of experimental structures showing sufficient conformational diversity introduces limitations for the study of protein mechanisms. Computed ensembles of protein conformations can aid in investigations of functional mechanisms. Application of our recent development of a protein packing based hinge prediction method (PACKMAN) and an improved elastic network model to study protein global motions (hd-ANM) together enable improved protein ensemble generations. Here, we apply random forces on the surface to generate the ensemble. Multiple independent Monte Carlo Markov Chain force applications and energy based sampling is implemented to obtain a protein ensemble. We observe a 70% PCA overlap between an ensemble generated by using our method with that of the corresponding protein crystal structure ensemble available in the PDB database. Moreover, results provide mechanistic insights into the conformational transition pathway of proteins. We aim to utilize native and mutant protein ensembles generated using our method to estimate the change in global motion of protein and the allosteric pathway changes induced by a point mutation.