ArticleNature communications2022
Improved prediction of protein-protein interactions using AlphaFold2.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 558 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
558 citing papers in PubMed.
- Article
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- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- PUM3 is an essential suppressor of replication stress in homologous recombination deficient breast cancer.Nature communications · 2026Article
- Induction of transferrin receptor 1-mediated ferroptosis by ultrasound-triggered microbubble destruction sensitizes glioblastoma to radiotherapy.Molecular biomedicine · 2026Article
- Integrating Epistatic Interactions into Genomic Prediction of Growth and Fillet Fat Content in Common Carp.International journal of molecular sciences · 2026Article
- Structural basis of active state coupling of metabotropic glutamate receptor 8 to beta-arrestins.Nature communications · 2026Article
- A simple probabilistic AlphaFold interaction score.Protein science : a publication of the Protein Society · 2026Article
- ABAG-Rank: improving model selection of AlphaFold antibody-antigen complexes by learning to rank.Bioinformatics (Oxford, England) · 2026Article
- How phosphorylation of α/β-tubulin perturbs microtubule structure: A computational study.The Journal of biological chemistry · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- Ultrapotent neutralization of Bacillus anthracis toxin by a human antibody via blockade of PA oligomerization.PLoS pathogens · 2026Article
- Reconstructing the evolutionary origin of β-catenin recognition in the Wnt destruction complex.Molecular biology and evolution · 2026Article
- DirectContacts2: a wiring diagram of human physical protein interactions.Nature communications · 2026Article
- AlphaFold reveals how pathogenicmBio · 2026Article
- Network-based machine learning to identify biomarkers for systemic lupus erythematosus.BMC biology · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- When can AlphaFold predict the oligomeric states of proteins?Protein science : a publication of the Protein Society · 2026Article
498 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.
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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.