Gene

Protein–protein interaction

Function (biology)

Biology

Biochemistry

Protein ligand

Molecule

Computer science

Computational biology

Molecular recognition

Genetics

Organic chemistry

Fingerprint (computing)

Politics

GRASP

Political science

Protein function

Artificial intelligence

Representation (politics)

Chemistry

Programming language

Biomolecule

Nature Methods

Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein's modes of interactions with other biomolecules. We hypothesize that proteins participating in similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF (molecular surface interaction fingerprinting), a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning