ArticleProteins2005
Inferring ideal amino acid interaction forms from statistical protein contact potentials.
Article in Proteins, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Article
- The Molecular Docking of MAX Fungal Effectors with Plant HMA Domain-Binding Proteins.International journal of molecular sciences · 2023Article
- Characterizing interactions in E-cadherin assemblages.Biophysical journal · 2023Article
- Using Surface Hydrophobicity Together with Empirical Potentials to Identify Protein-Protein Binding Sites: Application to the Interactions of E-cadherins.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A random forest classifier for protein-protein docking models.Bioinformatics advances · 2022Article
- Estimating the Influence of Physicochemical and Biochemical Property Indexes on Selection for Amino Acids Usage in Eukaryotic Cells.Journal of molecular evolution · 2021Article
- Amyloid Fibril Design: Limiting Structural Polymorphism in Alzheimer's Aβ Protofilaments.The journal of physical chemistry. B · 2018Article
- Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model.Biophysical journal · 2018Article
- Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials.PloS one · 2018Article
- Knowledge-based entropies improve the identification of native protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Conformational sampling and structure prediction of multiple interacting loops in soluble and β-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method.Bioinformatics (Oxford, England) · 2015Article
- The scoring of poses in protein-protein docking: current capabilities and future directions.BMC bioinformatics · 2013Article
- Random coil to globular thermal response of a protein (H3.1) with three knowledge-based coarse-grained potentials.PloS one · 2012Article
- Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models.Journal of structural and functional genomics · 2011Article
- Multibody coarse-grained potentials for native structure recognition and quality assessment of protein models.Proteins · 2011Article
- The relationship between relative solvent accessibility and evolutionary rate in protein evolution.Genetics · 2011Article
- Article
- New statistical potential for quality assessment of protein models and a survey of energy functions.BMC bioinformatics · 2010Article
- Amino acid interaction preferences in proteins.Protein science : a publication of the Protein Society · 2010Article
- Potentials 'R' Us web-server for protein energy estimations with coarse-grained knowledge-based potentials.BMC bioinformatics · 2010Article
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6 authors.
Funding
Abstract
We have analyzed 29 different published matrices of protein pairwise contact potentials (CPs) between amino acids derived from different sets of proteins, either crystallographic structures taken from the Protein Data Bank (PDB) or computer-generated decoys. Each of the CPs is similar to 1 of the 2 matrices derived in the work of Miyazawa and Jernigan (Proteins 1999;34:49-68). The CP matrices of the first class can be approximated with a correlation of order 0.9 by the formula e(ij) = h(i) + h(j), 1 <or= i, j <or= 20, where the residue-type dependent factor h is highly correlated with the frequency of occurrence of a given amino acid type inside proteins. Electrostatic interactions for the potentials of this class are almost negligible. In the potentials belonging to this class, the major contribution to the potentials is the one-body transfer energy of the amino acid from water to the protein environment. Potentials belonging to the second class can be approximated with a correlation of 0.9 by the formula e(ij) = c(0) - h(i)h(j) + q(i)q(j), where c(0) is a constant, h is highly correlated with the Kyte-Doolittle hydrophobicity scale, and a new, less dominant, residue-type dependent factor q is correlated ( approximately 0.9) with amino acid isoelectric points pI. Including electrostatic interactions significantly improves the approximation for this class of potentials. While, the high correlation between potentials of the first class and the hydrophobic transfer energies is well known, the fact that this approximation can work well also for the second class of potentials is a new finding. We interpret potentials of this class as representing energies of contact of amino acid pairs within an average protein environment.
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