biostudies-literatureNguyen TBNational Health and Medical Research Council of AustraliaMedical Research CouncilVictorian Government’s Operational Infrastructure Support ProgramWellcome Trustbbab512https://www.ebi.ac.uk/biostudies/studies/S-EPMC8769910biostudies-literatureUnknown23(1)Protein-carbohydrate interactions are crucial for many cellular processes but can be challenging to biologically characterise. To improve our understanding and ability to model these molecular interactions, we used a carefully curated set of 370 protein-carbohydrate complexes with experimental structural and biophysical data in order to train and validate a new tool, cutoff scanning matrix (CSM)-carbohydrate, using machine learning algorithms to accurately predict their binding affinity and rank docking poses as a scoring function. Information on both protein and carbohydrate complementarity, in terms of shape and chemistry, was captured using graph-based structural signatures. Across both training and independent test sets, we achieved comparable Pearson's correlations of 0.72 under crossBriefings in bioinformaticsCSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function.PMC8769910093167/Z/10/ZGNT1174405MR/M026302/1Nguyen TBAscher DBPires DEVfalseCSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function.Protein-carbohydrate interactions are crucial for many cellular processes but can be challenging to biologically characterise. To improve our understanding and ability to model these molecular interactions, we used a carefully curated set of 370 protein-carbohydrate complexes with experimental structural and biophysical data in order to train and validate a new tool, cutoff scanning matrix (CSM)-carbohydrate, using machine learning algorithms to accurately predict their binding affinity and rank docking poses as a scoring function. Information on both protein and carbohydrate complementarity, in terms of shape and chemistry, was captured using graph-based structural signatures. Across both training and independent test sets, we achieved comparable Pearson's correlations of 0.72 under cross2022-01-01T00:00:00Z2022 Jan2026-07-09T12:15:39.13Z2025-05-29T21:24:07.464ZS-EPMC87699103488223210.1093/bib/bbab512