Analysing Six Types of Protein-protein Interfaces
Overview
Molecular Biology
Authors
Affiliations
Non-covalent residue side-chain interactions occur in many different types of proteins and facilitate many biological functions. Are these differences manifested in the sequence compositions and/or the residue-residue contact preferences of the interfaces? Previous studies analysed small data sets and gave contradictory answers. Here, we introduced a new data-mining method that yielded the largest high-resolution data set of interactions analysed. We introduced an information theory-based analysis method. On the basis of sequence features, we were able to differentiate six types of protein interfaces, each corresponding to a different functional or structural association between residues. Particularly, we found significant differences in amino acid composition and residue-residue preferences between interactions of residues within the same structural domain and between different domains, between permanent and transient interfaces, and between interactions associating homo-oligomers and hetero-oligomers. The differences between the six types were so substantial that, using amino acid composition alone, we could predict statistically to which of the six types of interfaces a pool of 1000 residues belongs at 63-100% accuracy. All interfaces differed significantly from the background of all residues in SWISS-PROT, from the group of surface residues, and from internal residues that were not involved in non-trivial interactions. Overall, our results suggest that the interface type could be predicted from sequence and that interface-type specific mean-field potentials may be adequate for certain applications.
Assessing the functional impact of protein binding site definition.
Nandigrami P, Fiser A Protein Sci. 2024; 33(6):e5026.
PMID: 38757384 PMC: 11099757. DOI: 10.1002/pro.5026.
Statistical analysis of sequential motifs at biologically relevant protein-protein interfaces.
Frank Y, Unger R, Senderowitz H Comput Struct Biotechnol J. 2024; 23:1244-1259.
PMID: 38550974 PMC: 10973581. DOI: 10.1016/j.csbj.2024.03.004.
Madsen A, Mejias-Gomez O, Pedersen L, Morth J, Kristensen P, Jenkins T Comput Struct Biotechnol J. 2024; 23:199-211.
PMID: 38161735 PMC: 10755492. DOI: 10.1016/j.csbj.2023.11.056.
Mutation Edgotype Drives Fitness Effect in Human.
Ghadie M, Xia Y Front Bioinform. 2022; 1:690769.
PMID: 36303776 PMC: 9581054. DOI: 10.3389/fbinf.2021.690769.
Deep learning frameworks for protein-protein interaction prediction.
Hu X, Feng C, Ling T, Chen M Comput Struct Biotechnol J. 2022; 20:3223-3233.
PMID: 35832624 PMC: 9249595. DOI: 10.1016/j.csbj.2022.06.025.