Sampling Rare Conformational Transitions with a Quantum Computer
Spontaneous structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, physics-based computer simulations like Molecular Dynamics (MD) enable us to investigate these thermally activated processes with an atomic level of resolution. H...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
27.01.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Spontaneous structural rearrangements play a central role in the organization
and function of complex biomolecular systems. In principle, physics-based
computer simulations like Molecular Dynamics (MD) enable us to investigate
these thermally activated processes with an atomic level of resolution.
However, rare conformational transitions are intrinsically hard to investigate
with MD, because an exponentially large fraction of computational resources
must be invested to simulate thermal fluctuations in metastable states. Path
sampling methods like Transition Path Sampling hold the great promise of
focusing the available computational power on sampling the rare stochastic
transition between metastable states. In these approaches, one of the
outstanding limitations is to generate paths that visit significantly different
regions of the conformational space at a low computational cost. To overcome
these problems we introduce a rigorous approach that integrates a machine
learning algorithm and MD simulations implemented on a classical computer with
adiabatic quantum computing. First, using functional integral methods, we
derive a rigorous low-resolution representation of the system's dynamics, based
on a small set of molecular configurations generated with machine learning.
Then, a quantum annealing machine is employed to explore the transition path
ensemble of this low-resolution theory, without introducing un-physical biasing
forces to steer the system's dynamics. Using the D-Wave quantum computer, we
validate our scheme by simulating a benchmark conformational transition in a
state-of-the-art atomistic description. We show that the quantum computing step
generates uncorrelated trajectories, thus facilitating the sampling of the
transition region in configuration space. Our results provide a new paradigm
for MD simulations to integrate machine learning and quantum computing. |
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DOI: | 10.48550/arxiv.2201.11781 |