ArticleNature methods2020
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 346 papers.
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Who cites it
346 citing papers in PubMed.
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- MED-PPIS: Multi-order Moments External Graph Attention Network with Dual-Axis Attention for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Combating antimicrobial resistance through multimodal data via artificial intelligence.One health (Amsterdam, Netherlands) · 2026Review
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- DyProL: Dynamic Ensemble Representation Learning for Protein-Nucleic Acid Binding Site Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Surf2Spot: a surface-informed geometry-aware model for predicting partner-independent binder and nanobody design hotspots.Briefings in bioinformatics · 2026Article
- DSC-bsite: a dynamic-static collaborative multimodal graph learning method for protein-small molecule binding site prediction.Molecular diversity · 2026Article
- Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Proteome-wide identification of the druggable CRBN interactome.Nature biotechnology · 2026Article
- Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.Journal of chemical information and modeling · 2026Article
- Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder.bioRxiv : the preprint server for biology · 2026Article
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.Journal of chemical information and modeling · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
286 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.