ArticleNature communications2023
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.
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Who cites it
91 citing papers in PubMed.
- Sequence and structural determinants of efficacious de novo chimaeric antigen receptors.Nature biomedical engineering · 2026Article
- DSC-bsite: a dynamic-static collaborative multimodal graph learning method for protein-small molecule binding site prediction.Molecular diversity · 2026Article
- Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.Journal of chemical information and modeling · 2026Article
- Structure-conditioned self-supervised learning of residue interaction constraints in protein kinases for variant interpretation.Bioinformatics (Oxford, England) · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- MD-Transformer: Multimodal Integration of ProtBERT Embeddings and Physicochemical Descriptors for Protein-Protein Interface Residue Prediction.International journal of molecular sciences · 2026Article
- Geometric Deep Learning Reveals Ligandable and Cryptic RNA Binding Small Molecule Pockets (SMARTPocket).bioRxiv : the preprint server for biology · 2026Article
- A 2025 meeting report from the Venice lagoon: International graduate programRNA (New York, N.Y.) · 2026Article
- CsmR controls both, motility and cell shape, in Haloferax volcanii.PLoS genetics · 2026Article
- Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- VUStruct: A compute pipeline for high throughput and personalized structural biology.PLoS computational biology · 2026Article
- AlphaFold-driven discovery of oxysterol-binding protein-related protein-phosphoinositide 3-, 4-, and 5-phosphatase interactions using new generation confidence scores.Protein science : a publication of the Protein Society · 2026Article
- CryptoBank: A resource for the identification and prediction of cryptic sites in proteins.Science advances · 2026Article
- Targeting protein protein interactions and their modulators to enable new therapeutic strategies for human diseases.NPJ systems biology and applications · 2026Review
- DeepISO: deep learning-powered prediction of protein-protein interaction rewiring generated by alternative splicing.Genome biology · 2026Article
- Regulated transformation system (RTS): sddi-mediated programmable shut-off and mode switching of base editors.Nucleic acids research · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
31 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their binding interfaces remains a challenge. In this study, we present a geometric transformer that acts directly on atomic coordinates labeled only with element names. The resulting model-the Protein Structure Transformer, PeSTo-surpasses the current state of the art in predicting protein-protein interfaces and can also predict and differentiate between interfaces involving nucleic acids, lipids, ions, and small molecules with high confidence. Its low computational cost enables processing high volumes of structural data, such as molecular dynamics ensembles allowing for the discovery of interfaces that remain otherwise inconspicuous in static experimentally solved structures. Moreover, the growing foldome provided by de novo structural predictions can be easily analyzed, providing new opportunities to uncover unexplored biology.
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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.