  Molecular dynamics (MD) simulation is widely used to study protein
conformations and dynamics. However, conventional simulation suffers from being
trapped in some local energy minima that are hard to escape. Thus, most
computational time is spent sampling in the already visited regions. This leads
to an inefficient sampling process and further hinders the exploration of
protein movements in affordable simulation time. The advancement of deep
learning provides new opportunities for protein sampling. Variational
autoencoders are a class of deep learning models to learn a low-dimensional
representation (referred to as the latent space) that can capture the key
features of the input data. Based on this characteristic, we proposed a new
adaptive sampling method, latent space assisted adaptive sampling for protein
trajectories (LAST), to accelerate the exploration of protein conformational
space. This method comprises cycles of (i) variational autoencoders training,
(ii) seed structure selection on the latent space and (iii) conformational
sampling through additional MD simulations. The proposed approach is validated
through the sampling of four structures of two protein systems: two metastable
states of E. Coli adenosine kinase (ADK) and two native states of Vivid (VVD).
In all four conformations, seed structures were shown to lie on the boundary of
conformation distributions. Moreover, large conformational changes were
observed in a shorter simulation time when compared with conventional MD (cMD)
simulations in both systems. In metastable ADK simulations, LAST explored two
transition paths toward two stable states while cMD became trapped in an energy
basin. In VVD light state simulations, LAST was three times faster than cMD
simulation with a similar conformational space.
