Predicting protein-carbohydrate binding sites: a deep learning approach integrating protein language model embeddings and structural features.

ABSTRACT: Protein-carbohydrate interactions play an important role in many biological processes and functions, like inflammation, signal transduction, and cell adhesion. In our work, we will study non-covalent carbohydrate binding sites. In this paper, we aim to build a deep-learning model to predict non-covalent protein-carbohydrate binding sites. We were motivated by the fact that experimental approaches for predicting these sites are expensive. So, computational tools are necessary for identifying these interactions. We explored several sequence-based features as well as structural features. We also leveraged protein language model embeddings. We analyzed different architectures and selected the most suitable deep learning architecture for our finalized prediction model, DeepCPBSite. DeepCPBSite

SUBMITTER: Muhaiminul Islam Nafi M 

PROVIDER: S-EPMC12853128 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature