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

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 isomerase (TIM), and the 70S ribosomal complex.
The generated ensembles of conformers determined at atomic resolution
show good agreement with experimental data (979 structures resolved
by X-ray and/or NMR) and encompass the subspaces covered in independent
MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally
efficient tool for characterizing the conformational space of large
systems at atomic detail, in addition to generating a representative
ensemble of conformers that can be advantageously used in simulating
substrate/ligand-binding events.