ArticleBiophysical journal2018
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model.
Article in Biophysical journal, 2018. 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, 31 citations in OpenAlex.
- Simulation of cell-size systems at long timescales with flexible protein structures.bioRxiv : the preprint server for biology · 2026Article
- Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength.Computational and structural biotechnology journal · 2024Article
- A structural biology community assessment of AlphaFold2 applications.Nature structural & molecular biology · 2022Article
- PSP-GNM: Predicting Protein Stability Changes upon Point Mutations with a Gaussian Network Model.International journal of molecular sciences · 2022Article
- On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations.Antibodies (Basel, Switzerland) · 2022Article
- Article
- Dockground scoring benchmarks for protein docking.Proteins · 2022Article
- Improved prediction of protein-protein interactions using AlphaFold2.Nature communications · 2022Article
- rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation.Biophysical journal · 2022Article
- Size of the protein-protein energy funnel in crowded environment.Frontiers in molecular biosciences · 2022Article
- Application of docking methodologies to modeled proteins.Proteins · 2020Article
- Computational approaches to macromolecular interactions in the cell.Current opinion in structural biology · 2019Review
- Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function.Proteins · 2018Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.
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