Pith. sign in

Rapid calculation of side chain packing and free energy with applications to protein molecular dynamics

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

To address the large gap between time scales that can be easily reached by molecular simulations and those required to understand protein dynamics, we propose a rapid self-consistent approximation of the side chain free energy at every integration step. In analogy with the adiabatic Born-Oppenheimer approximation for electronic structure, the protein backbone dynamics are simulated as preceding according to the dictates of the free energy of an instantaneously-equilibrated side chain potential. The side chain free energy is computed on the fly, allowing the protein backbone dynamics to traverse a greatly smoothed energetic landscape. This results in extremely rapid equilibration and sampling of the Boltzmann distribution. Because our method employs a reduced model involving single-bead side chains, we also provide a novel, maximum-likelihood method to parameterize the side chain model using input data from high resolution protein crystal structures. We demonstrate state-of-the-art accuracy for predicting $\chi_1$ rotamer states while consuming only milliseconds of CPU time. We also show that the resulting free energies of side chains is sufficiently accurate for de novo folding of some proteins.

fields

cs.LG 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

SETOL: A Semi-Empirical Theory of (Deep) Learning

cs.LG · 2025-07-23 · conditional · novelty 7.0

SETOL derives the HTSR layer quality metrics as integrated R-transforms of the layer spectral density, and proposes a determinant condition (ERG) as a marker of ideal learning.

citing papers explorer

Showing 1 of 1 citing paper.

  • SETOL: A Semi-Empirical Theory of (Deep) Learning cs.LG · 2025-07-23 · conditional · none · ref 59 · internal anchor

    SETOL derives the HTSR layer quality metrics as integrated R-transforms of the layer spectral density, and proposes a determinant condition (ERG) as a marker of ideal learning.