CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces

Area of Science:

• Structural biology
• Computational biology
• Bioinformatics

Background:

• Protein 3D structure dictates biological function.
• Advances in protein structure prediction (e.g., AlphaFold2) enable new computational analyses.
• Existing tools often focus on single interaction types and require expert knowledge.

Purpose of the Study:

• To develop CSM-Potential2, a deep learning platform for analyzing protein binding interfaces.
• To integrate diverse interaction prediction capabilities into a user-friendly tool.
• To facilitate the study of biological interactions for both expert and non-expert users.

Main Methods:

• Deep learning platform development.
• Analysis of binding interfaces on protein structures.
• Prediction of protein-protein, protein-ligand, and protein-nucleic acid interactions.
• Ligand transplantation based on sequence and structure similarity.

Main Results:

• CSM-Potential2 predicts protein-protein interaction binding sites.
• It classifies biological ligands and predicts nucleic acid interactions at the residue level.
• The platform enables ligand transplantation using sequence and structure similarity.

Conclusions:

• CSM-Potential2 offers a versatile platform for studying biological interactions.
• It provides easy access to advanced computational methods.
• The tool supports researchers in understanding protein structure-function relationships.