ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution.

ABSTRACT: Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomeras

SUBMITTER: Kurkcuoglu Z 

PROVIDER: S-EPMC5088496 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature