Multi-body effects in a coarse-grained protein force field

2021/04/28 by Jiang Wang, Nicholas Charron, Nicholas E. Charron +5 · 63 citations
Biochemistry, Genetics and Molecular Biology · Materials Science · Mathematics · #Algorithm #Artificial intelligence #Computer science #Construct (python library) #Convergence (economics) #Degrees of freedom (physics and chemistry) #Energy (signal processing) #Enzyme Structure and Function #Field (mathematics) #Force field (fiction) #Function (biology) #Geometry #Machine Learning in Materials Science #Mathematics #Physics #Point (geometry) #Protein Structure and Dynamics #Pure mathematics #Statistical physics

paper · pdf · doi:10.1063/5.0041022

published in The Journal of Chemical Physics 154(16), 164113 (American Institute of Physics)

openalex publication_date 2021/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

The use of coarse-grained (CG) models is a popular approach to study complex biomolecular systems. By reducing the number of degrees of freedom, a CG model can explore long time- and length-scales inaccessible to computational models at higher resolution. If a CG model is designed by formally integrating out some of the system's degrees of freedom, one expects multi-body interactions to emerge in the effective CG model's energy function. In practice, it has been shown that the inclusion of multi-body terms indeed improves the accuracy of a CG model. However, no general approach has been proposed to systematically construct a CG effective energy that includes arbitrary orders of multi-body terms. In this work, we propose a neural network based approach to address this point and construct a CG model as a multi-body expansion. By applying this approach to a small protein, we evaluate the relative importance of the different multi-body terms in the definition of an accurate model. We observe a slow convergence in the multi-body expansion, where up to five-body interactions are needed to reproduce the free energy of an atomistic model.

Citations

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• Operator Forces For Coarse-Grained Molecular Dynamics
• Gaussian Process Regression for Materials and Molecules. [europepmc]
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• Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining† † Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3sc06749a [europepmc]

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