ArticleProtein science : a publication of the Protein Society2010
Amino acid interaction preferences in proteins.
Article in Protein science : a publication of the Protein Society, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Alveolin proteins in the Toxoplasma inner membrane complex form a highly interconnected structure that maintains parasite shape and replication.PLoS biology · 2024Article
- Hydrophobicity-Based Force Field In Enzymes.ACS omega · 2024Article
- MHC II immunogenicity shapes the neoepitope landscape in human tumors.Nature genetics · 2023Article
- ATLIGATOR: editing protein interactions with an atlas-based approach.Bioinformatics (Oxford, England) · 2022Article
- Structural motifs in protein cores and at protein-protein interfaces are different.Protein science : a publication of the Protein Society · 2021Article
- Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes.Computational and structural biotechnology journal · 2021Article
- Predicting clinical benefit of immunotherapy by antigenic or functional mutations affecting tumour immunogenicity.Nature communications · 2020Article
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- Identification and analysis of structurally critical fragments in HopS2.BMC bioinformatics · 2019Article
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- Protein docking with predicted constraints.Algorithms for molecular biology : AMB · 2015Article
- Circles within circles: crosstalk between protein Ser/Thr/Tyr-phosphorylation and Met oxidation.BMC bioinformatics · 2013Article
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- Prediction of functionally important residues in globular proteins from unusual central distances of amino acids.BMC structural biology · 2011Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Understanding the key factors that influence the interaction preferences of amino acids in the folding of proteins have remained a challenge. Here we present a knowledge-based approach for determining the effective interactions between amino acids based on amino acid type, their secondary structure, and the contact based environment that they find themselves in the native state structure as measured by their number of neighbors. We find that the optimal information is approximately encoded in a 60 x 60 matrix describing the 20 types of amino acids in three distinct secondary structures (helix, beta strand, and loop). We carry out a clustering scheme to understand the similarity between these interactions and to elucidate a nonredundant set. We demonstrate that the inferred energy parameters can be used for assessing the fit of a given sequence into a putative native state structure.
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