Estimating Boltzmann Averages for Protein Structural Quantities Using Sequential Monte Carlo Article Swipe

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YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.5705/ss.202022.0340

Sequential Monte Carlo (SMC) methods are widely used to draw samples from intractable target distributions.Weight degeneracy can hinder the use of SMC when the target distribution is highly constrained.As a motivating application, we consider the problem of sampling protein structures from the Boltzmann distribution.This paper proposes a general SMC method that propagates multiple descendants for each particle, followed by resampling to maintain the desired number of particles.A simulation study demonstrates the efficacy of the method for tackling the protein sampling problem, compared to existing SMC methods.As a real data example, we estimate the number of atomic contacts for a key segment of the SARS-CoV-2 viral spike protein.

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Monte Carlo method Statistical physics Boltzmann constant Monte Carlo molecular modeling Mathematics Applied mathematics Computer science Markov chain Monte Carlo Statistics Physics Thermodynamics

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Type: article
Language: en
Landing Page: http://doi.org/10.5705/ss.202022.0340
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References: 64
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DOI: https://doi.org/10.5705/ss.202022.0340

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Title: Estimating Boltzmann Averages for Protein Structural Quantities Using Sequential Monte Carlo

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-11-07

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Authors: Zhaoran Hou, Samuel W. K. Wong

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Landing page: https://doi.org/10.5705/ss.202022.0340

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Concepts: Monte Carlo method, Statistical physics, Boltzmann constant, Monte Carlo molecular modeling, Mathematics, Applied mathematics, Computer science, Markov chain Monte Carlo, Statistics, Physics, Thermodynamics

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