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Analysis of Glycan Recognition by Concanavalin A Using Absolute Binding Free Energy Calculations
Sondos Musleh1,2, Irfan Alibay3,4, Philip C Biggin4
1Division of Pharmacy and Optometry, The University of Manchester, Manchester M13 9PT, U.K.
Journal of Chemical Information and Modeling
|October 16, 2024
Summary
Absolute binding free energy calculations accurately predict carbohydrate-protein interactions for concanavalin A, matching experimental data. This method shows promise for designing therapeutics and diagnostics.
Area of Science:
- Biochemistry
- Computational Chemistry
- Structural Biology
Background:
- Carbohydrates mediate crucial biological recognition and signaling.
- Understanding carbohydrate-protein interactions is vital for biological and medical research.
- Concanavalin A is a well-studied lectin, making it an excellent model system.
Purpose of the Study:
- To evaluate the efficacy of absolute binding free energy (ABFE) calculations in predicting carbohydrate ligand affinities for concanavalin A.
- To compare ABFE predictions with experimental microcalorimetry data.
- To assess the ability of ABFE to deconvolute binding contributions of different carbohydrate moieties.
Main Methods:
- Absolute binding free energy (ABFE) calculations were performed.
- Ligand affinities were predicted for five related carbohydrate ligands binding to concanavalin A.
- Results were compared with experimental data from isothermal titration calorimetry (ITC).
Main Results:
- ABFE calculations quantitatively ranked ligand affinities, showing good agreement with microcalorimetry (mean signed error of -0.63 ± 0.04 kcal/mol).
- The method accurately reproduced diminished binding efficiencies for larger carbohydrate ligands, including a pentasaccharide and its components.
- ABFE correctly identified the high affinity of the mannose residue and the low contribution of β-GlcNAc arms in the pentasaccharide.
Conclusions:
- ABFE calculations offer a reliable quantitative tool for predicting carbohydrate-protein binding affinities.
- The method successfully deconvolutes the contributions of individual residues within complex carbohydrate ligands.
- This approach holds significant potential for the rational design of carbohydrate-based therapeutics, vaccines, and diagnostics.
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