ArticleScience (New York, N.Y.)2025
Predicting protein-protein interactions in the human proteome.
Article in Science (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Avl9 defines a family of DENN domain GTPase-activating proteins.The Journal of cell biology · 2026Article
- Article
- PrePPI - Structure-based Prediction of Protein-protein Interactomes and Networks.Journal of molecular biology · 2026Article
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- Solute carrier membrane transporters: emerging targets in CNS disorders.Nature reviews. Drug discovery · 2026Review
- The predicted interactome of the human mitochondrial proteome.Nature communications · 2026Article
- Predicted protein-protein interactions between sugar beet root maggot trypsins and sugar beet Kunitz trypsin inhibitors using deep learning.Data in brief · 2026Article
- DirectContacts2: a wiring diagram of human physical protein interactions.Nature communications · 2026Article
- Structure of cytoplasmic RNA polymerase II.Nature communications · 2026Article
- A human lysosomal storage disorder toolkit for decoding proteome landscapes in cortical-like and dopaminergic-like induced neurons.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- An Atlas of Short Linear Motif-Mediated Human Protein-Protein Interactions.bioRxiv : the preprint server for biology · 2026Article
- A broadly conserved gram-positive lipoprotein regulates cell elongation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Linear-time prediction of proteome-scale microbial protein interactions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- The nuclear envelope protein TMEM209 is an integral component of the nuclear pore complex and interacts with Nup210.Journal of cell science · 2026Article
- Structural motif search across the protein universe with Folddisco.Nature biotechnology · 2026Article
- Structural insights into predicted protein-protein interactions and protein complexes in the human proteome.Scientific reports · 2026Article
- Sketching microprotein portraits.Protein science : a publication of the Protein Society · 2026Review
- Protein-Protein Interaction Stabilizers from MD Simulation-Derived Pharmacophores.Journal of chemical information and modeling · 2026Article
- LEF1 gene mutation impairs the intestinal barrier and causes diarrhea.Molecular and cellular pediatrics · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Protein-protein interactions (PPIs) are essential for biological function. Coevolutionary analysis and deep-learning (DL)-based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches have had limited success for the more complex human proteome. We overcame this challenge by enhancing the coevolutionary signals with sevenfold-deeper multiple sequence alignments harvested from 30 petabytes of unassembled genomic data and developing a new DL network trained on augmented datasets of domain-domain interactions from 200 million predicted protein structures. We systematically screened 200 million human protein pairs and predicted 17,849 interactions with an expected precision of 90%, of which 3631 interactions were not identified in previous experimental screens. Three-dimensional models of these predicted interactions provide numerous hypotheses about protein function and mechanisms of human diseases.
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Registered trials
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.