A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids. Issue 1 (20th December 2022)

• Record Type: Journal Article
• Authors: Nagaraju, Mulpuri; Liu, Haiguang
• Is Part Of: Acta crystallographica. Volume 79:Issue 1(2023)
• Page Start: 31
• Page End: 39
• Publication Date: 2022-12-20
• Subjects: protein–protein interactions -- interface residues -- iNepre -- scoring functions -- neighborhood preference of amino acids; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S2059798322011858
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : A scoring function has been developed for the prediction of protein complex interfaces based on neighboring amino acids. Abstract : Proteins often assemble into functional complexes, the structures of which are more difficult to obtain than those of the individual protein molecules. Given the structures of the subunits, it is possible to predict plausible complex models via computational methods such as molecular docking. Assessing the quality of the predicted models is crucial to obtain correct complex structures. Here, an energy‐scoring function was developed based on the interfacial residues of structures in the Protein Data Bank. The statistically derived energy function ( Nepre ) imitates the neighborhood preferences of amino acids, including the types and relative positions of neighboring residues. Based on the preference statistics, a program iNepre was implemented and its performance was evaluated with several benchmarking decoy data sets. The results show that iNepre scores are powerful in model ranking to select the best protein complex structures.