Characterizing conformational transitions in physical systems remains a
fundamental challenge in the computational sciences. Traditional sampling
methods like molecular dynamics (MD) or MCMC often struggle with the
high-dimensional nature of molecular systems and the high energy barriers of
transitions between stable states. While these transitions are rare events in
simulation timescales, they often represent the most biologically significant
processes – for example, the conformational change of an ion channel protein
from its closed to open state, which controls cellular ion flow and is crucial
for neural signaling. Such transitions in real systems may take milliseconds to
seconds but could require months or years of continuous simulation to observe
even once. We present a method that reformulates transition path generation as
a continuous optimization problem solved through physics-informed neural
networks (PINNs) inspired by string methods for minimum-energy path (MEP)
generation. By representing transition paths as implicit neural functions and
leveraging automatic differentiation with differentiable molecular dynamics
force fields, our method enables the efficient discovery of physically
realistic transition pathways without requiring expensive path sampling. Noi
demonstrate our method’s effectiveness on two proteins, including an explicitly
hydrated bovine pancreatic trypsin inhibitor (BPTI) system with over 8,300
atoms.

Questo articolo esplora i giri e le loro implicazioni.

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