ArticleProceedings of the National Academy of Sciences of the United States of America2010
Recovering physical potentials from a model protein databank.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Top-Down Machine Learning of Coarse-Grained Protein Force Fields.Journal of chemical theory and computation · 2023Article
- Bottom-up Coarse-Graining: Principles and Perspectives.Journal of chemical theory and computation · 2022Review
- AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function.Journal of chemical information and modeling · 2021Article
- A hybrid, bottom-up, structurally accurate, Go¯-like coarse-grained protein model.The Journal of chemical physics · 2019Article
- Enriched Conformational Sampling of DNA and Proteins with a Hybrid Hamiltonian Derived from the Protein Data Bank.International journal of molecular sciences · 2018Article
- Rescoring of docking poses under Occam's Razor: are there simpler solutions?Journal of computer-aided molecular design · 2018Article
- Article
- An Anisotropic Coarse-Grained Model for Proteins Based On Gay-Berne and Electric Multipole Potentials.Journal of chemical theory and computation · 2014Article
- A nonadaptive origin of a beneficial trait: in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins.Journal of molecular evolution · 2014Article
- Recent advances in transferable coarse-grained modeling of proteins.Advances in protein chemistry and structural biology · 2014Review
- Efficient Parameter Estimation of Generalizable Coarse-Grained Protein Force Fields Using Contrastive Divergence: A Maximum Likelihood Approach.Journal of chemical theory and computation · 2013Article
- Computational methods of studying the binding of toxins from venomous animals to biological ion channels: theory and applications.Physiological reviews · 2013Review
- Reference state for the generalized Yvon-Born-Green theory: application for coarse-grained model of hydrophobic hydration.The Journal of chemical physics · 2010Article
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2 authors.
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Abstract
Knowledge-based approaches frequently employ empirical relations to determine effective potentials for coarse-grained protein models directly from protein databank structures. Although these approaches have enjoyed considerable success and widespread popularity in computational protein science, their fundamental basis has been widely questioned. It is well established that conventional knowledge-based approaches do not correctly treat many-body correlations between amino acids. Moreover, the physical significance of potentials determined by using structural statistics from different proteins has remained obscure. In the present work, we address both of these concerns by introducing and demonstrating a theory for calculating transferable potentials directly from a databank of protein structures. This approach assumes that the databank structures correspond to representative configurations sampled from equilibrium solution ensembles for different proteins. Given this assumption, this physics-based theory exactly treats many-body structural correlations and directly determines the transferable potentials that provide a variationally optimized approximation to the free energy landscape for each protein. We illustrate this approach by first constructing a databank of protein structures using a model potential and then quantitatively recovering this potential from the structure databank. The proposed framework will clarify the assumptions and physical significance of knowledge-based potentials, allow for their systematic improvement, and provide new insight into many-body correlations and cooperativity in folded proteins.
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