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Related Experiment Video

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Prediction of protein-carbohydrate complex binding affinity using structural features.

N R Siva Shanmugam¹, J Jino Blessy¹, K Veluraja²

¹Indian Institute of Technology Madras.

Briefings in Bioinformatics

|December 14, 2020

Summary

This summary is machine-generated.

Researchers developed PCA-Pred, a web server predicting protein-carbohydrate binding affinity. It uses structural features to analyze interactions, aiding in understanding biological recognition mechanisms.

Keywords:

binding free energy contact potentials protein–carbohydrate complex structure-based features

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Last Updated: Nov 26, 2025

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Area of Science:

Biochemistry and Molecular Biology
Computational Biology

Background:

Protein-carbohydrate interactions are crucial for cellular processes.
Understanding binding affinity is key to deciphering molecular recognition.

Purpose of the Study:

To identify key structural factors influencing protein-carbohydrate binding affinity.
To develop a predictive model for binding free energy in these complexes.

Main Methods:

Collected experimental binding affinity data for 389 protein-carbohydrate complexes.
Derived structure-based features including contact potentials and interaction energies.
Developed multiple regression equations for affinity prediction across six complex categories.

Main Results:

Identified binding site residues, accessible surface area, and atomic interactions as critical factors.
Achieved an average correlation of 0.731 and mean absolute error of 1.149 kcal/mol in jackknife testing.
Developed the PCA-Pred web server for predicting protein-carbohydrate binding affinity.

Conclusions:

Structural features significantly impact protein-carbohydrate binding affinity.
The developed regression models and PCA-Pred web server offer a reliable method for predicting binding affinities.
This work provides valuable insights into protein-carbohydrate recognition mechanisms.