Derivation of a Markov state model of the dynamics of a protein-like chain immersed in an implicit solvent
Jeremy Schofield, Hanif Bayat

TL;DR
This paper derives a Markov state model from first principles to describe the dynamics of a protein-like chain in an implicit solvent, analyzing folding behavior and relaxation modes across different chain lengths and temperatures.
Contribution
It introduces a microscopic, first-principles derivation of a Markov state model for protein-like chains in implicit solvent, linking rate constants to escape rates and analyzing folding pathways.
Findings
Short chains exhibit single-exponential relaxation at low temperatures.
Longer chains show multi-exponential relaxation at intermediate temperatures.
Folding becomes rapid and single exponential at low temperatures for longer chains.
Abstract
A Markov state model of the dynamics of a protein-like chain immersed in an implicit hard sphere solvent is derived from first principles for a system of monomers that interact via discontinuous potentials designed to account for local structure and bonding in a coarse-grained sense. The model is based on the assumption that the implicit solvent interacts on a fast time scale with the monomers of the chain compared to the time scale for structural rearrangements of the chain and provides sufficient friction so that the motion of monomers is governed by the Smoluchowski equation. A microscopic theory for the dynamics of the system is developed that reduces to a Markovian model of the kinetics under well-defined conditions. Microscopic expressions for the rate constants that appear in the Markov state model are analyzed and expressed in terms of a temperature-dependent linear combination…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
