Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE

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,
ΔGb, 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 ΔGb, 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.