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CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function.

Thanh Binh Nguyen1,2,3, Douglas E V Pires1,2,4, David B Ascher1,2,3,5

  • 1Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.

Briefings in Bioinformatics
|December 9, 2021
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Summary
This summary is machine-generated.

A new machine learning tool, cutoff scanning matrix (CSM)-carbohydrate, accurately predicts protein-carbohydrate binding affinity. This computational method aids in understanding molecular interactions and assessing docking poses for biological research.

Keywords:
binding free energygraph-based signaturesprotein–carbohydrate complexstructure-based features

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Area of Science:

  • Structural biology
  • Computational chemistry
  • Biophysics

Background:

  • Protein-carbohydrate interactions are vital for cellular functions.
  • Characterizing these interactions experimentally is complex.
  • Accurate modeling requires robust computational tools.

Purpose of the Study:

  • To develop and validate a machine learning tool, CSM-carbohydrate, for predicting protein-carbohydrate binding affinity.
  • To improve the assessment of docking poses and mutation effects on binding.
  • To provide a freely accessible resource for researchers.

Main Methods:

  • Utilized a dataset of 370 protein-carbohydrate complexes with experimental data.
  • Employed machine learning algorithms with graph-based structural signatures.
  • Trained and validated the CSM-carbohydrate tool using cross-validation and independent test sets.

Main Results:

  • Achieved high prediction accuracy with Pearson's correlations of 0.72 (cross-validation) and 0.67 (independent test).
  • Demonstrated robust performance across various carbohydrate types (mono-, di-, oligosaccharides).
  • CSM-carbohydrate significantly outperformed existing computational approaches.

Conclusions:

  • CSM-carbohydrate is a reliable and generalizable tool for analyzing protein-carbohydrate interactions.
  • The tool enhances the understanding of binding affinity and docking pose evaluation.
  • Freely available web interface and API facilitate broader scientific application.