期刊
JOURNAL OF CHEMICAL PHYSICS
卷 154, 期 16, 页码 -出版社
AIP Publishing
DOI: 10.1063/5.0041022
关键词
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资金
- National Science Foundation [CHE-1738990, CHE-1900374, PHY-1427654]
- Welch Foundation [C-1570]
- German MATH+ Excellence Cluster [AA1-6, EF1-2]
- Deutsche Forschungsgemeinschaft [SFB 1114/C03, SFB/TRR 186/A12, SFB 1078/C7]
- European Commission [ERC CoG 772230]
- Einstein Foundation Berlin
- Big-Data Private-Cloud Research Cyberinfrastructure MRI-award (NSF) [CNS-1338099]
The use of coarse-grained models is popular in studying complex biomolecular systems, allowing exploration of long time and length scales by reducing degrees of freedom. Incorporating multi-body interactions in the effective CG model's energy function has been shown to improve accuracy. A neural network based approach is proposed in this work for constructing CG models with multi-body expansions, with slow convergence observed requiring up to five-body interactions for accurate modeling.
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.
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