Prediction of protein-carbohydrate complex binding affinity using structural features

Siva Shanmugam, NR and Jino Blessy, J and Veluraja, K and Michael Gromiha, M (2021) Prediction of protein-carbohydrate complex binding affinity using structural features. In: Briefings in Bioinformatics, 22 (4).

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Abstract

Protein-carbohydrate interactions play a major role in several cellular and biological processes. Elucidating the factors influencing the binding affinity of protein-carbohydrate complexes and predicting their free energy of binding provide deep insights for understanding the recognition mechanism. In this work, we have collected the experimental binding affinity data for a set of 389 protein-carbohydrate complexes and derived several structure-based features such as contact potentials, interaction energy, number of binding residues and contacts between different types of atoms. Our analysis on the relationship between binding affinity and structural features revealed that the important factors depend on the type of the complex based on number of carbohydrate and protein chains. Specifically, binding site residues, accessible surface area, interactions between various atoms and energy contributions are important to understand the binding affinity. Further, we have developed multiple regression equations for predicting the binding affinity of protein-carbohydrate complexes belonging to six categories of protein-carbohydrate complexes. Our method showed an average correlation and mean absolute error of 0.731 and 1.149 kcal/mol, respectively, between experimental and predicted binding affinities on a jackknife test. We have developed a web server PCA-Pred, Protein-Carbohydrate Affinity Predictor, for predicting the binding affinity of protein-carbohydrate complexes. The web server is freely accessible at https://web.iitm.ac.in/bioinfo2/pcapred/. The web server is implemented using HTML and Python and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera. © 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved.

Item Type:	Journal Article
Publication:	Briefings in Bioinformatics
Publisher:	Oxford University Press
Additional Information:	The copyright for this article belongs to Oxford University Press.
Keywords:	carbohydrate; protein; protein binding, chemistry; computer language; molecular model; protein structure, Carbohydrates; Models, Molecular; Programming Languages; Protein Binding; Protein Structural Elements; Proteins
Department/Centre:	Division of Biological Sciences > Molecular Biophysics Unit
Date Deposited:	08 Mar 2023 09:17
Last Modified:	08 Mar 2023 09:17
URI:	https://eprints.iisc.ac.in/id/eprint/80822

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