João Marcelo Lamim Ribeiro, and Pratyush Tiwary (2019).
Journal of chemical theory and computation, 15, 708-719.   (PubMed)

In this work, we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 2018, 149, 072301) for investigating ligand-protein unbinding mechanisms and calculating absolute binding free energies, Δ Gb, when plagued with difficult to sample rare events. In order to do so, we introduce a simple but powerful extension to RAVE that allows learning a reaction coordinate expressed as a piecewise function that is linear over all intervals. Such an approach allows us to retain the physical interpretation of a RAVE-derived reaction coordinate while making the method more applicable to a wider range of complex biophysical problems. As we will demonstrate, using as our test-case the slow dissociation of benzene from the L99A variant of lysozyme, the RAVE extension led to observing an unbinding event in 100% of the independent all-atom MD simulations, all within 3-50 ns for a process that takes on an average close to few hundred milliseconds, which reflects a 7 orders of magnitude acceleration relative to straightforward MD. Furthermore, we will show that without the use of time-dependent biasing, clear back-and-forth movement between metastable intermediates was achieved during the various simulations, demonstrating the caliber of the RAVE-derived piecewise reaction coordinate and bias potential, which together drive efficient and accurate sampling of the ligand-protein dissociation event. Last, we report the results for Δ Gb, which via very short MD simulations, can form a strict lower-bound that is ∼2-3 kcal/mol off from experiments. We believe that RAVE, together with its multidimensional extension that we introduce here, will be a useful tool for simulating the slow unbinding process of practical ligand-protein complexes in an automated manner with minimal use of human intuition.

This work describes an example of using Reweighted autoencoded variational Bayes for enhanced sampling in kinetic calculations.