Thanks to constant technological and methodological advancements, physics-based molecular dynamics (MD) simulations have become increasingly powerful tools in modelling biological problems like protein folding routes, structure prediction aggregation, and small molecules docking. One major bottleneck, however, has been the vast conformational space that has to be explored in such simulations, which often makes the computational cost of MD prohibitively expensive. Modelling Employing Limited Data (MELD) is an accelerator of MD simulations hat uses external information to reduce the search space, often giving speed-ups of several orders of magnitude compared to straight MD. MELD's Bayesian framework allows us to integrate into MD different sources of information -- sparse, noisy, or ambiguous. Regions of the phase space that do not agree with the external data are penalized energetically, while other regions are left untouched. This ensures relative Boltzmann populations and meaningful free energy differences.<br/><br/>We show that MELD accelerated MD (MELD x MD) predicts well protein native structures as well as binding poses and affinities of drugs and peptides to proteins, and can help data-driven structure determination in many cases. Many of our tests are in blind competitions, such as CASP, CAPRI, and D3R.

MELD x MD: Using external information to speed-up molecular dynamics simulations of proteins

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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