CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space

Ardita Shkurti, Ioanna Danai Styliari, Vivek Balasubramanian, Iain Bethune, Conrado Pedebos, Shantenu Jha, Charles A. Laughton

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3 Scopus citations

Abstract

CoCo ("complementary coordinates") is a method for ensemble enrichment based on principal component analysis (PCA) that was developed originally for the investigation of NMR data. Here we investigate the potential of the CoCo method, in combination with molecular dynamics simulations (CoCo-MD), to be used more generally for the enhanced sampling of conformational space. Using the alanine penta-peptide as a model system, we find that an iterative workflow, interleaving short multiple-walker MD simulations with long-range jumps through conformational space informed by CoCo analysis, can increase the rate of sampling of conformational space up to 10 times for the same computational effort (total number of MD timesteps). Combined with the reservoir-REMD method, free energies can be readily calculated. An alternative, approximate but fast and practically useful, alternative approach to unbiasing CoCo-MD generated data is also described. Applied to cyclosporine A, we can achieve far greater conformational sampling than has been reported previously, using a fraction of the computational resource. Simulations of the maltose binding protein, begun from the "open" state, effectively sample the "closed" conformation associated with ligand binding. The PCA-based approach means that optimal collective variables to enhance sampling need not be defined in advance by the user but are identified automatically and are adaptive, responding to the characteristics of the developing ensemble. In addition, the approach does not require any adaptations to the associated MD code and is compatible with any conventional MD package.

Original languageEnglish (US)
Pages (from-to)2587-2596
Number of pages10
JournalJournal of Chemical Theory and Computation
Volume15
Issue number4
DOIs
StatePublished - Apr 9 2019

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Physical and Theoretical Chemistry

Cite this

Shkurti, A., Styliari, I. D., Balasubramanian, V., Bethune, I., Pedebos, C., Jha, S., & Laughton, C. A. (2019). CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space. Journal of Chemical Theory and Computation, 15(4), 2587-2596. https://doi.org/10.1021/acs.jctc.8b00657