ArticleProteomics2023
Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.
Article in Proteomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Article
- QSProteome: a community-driven interactive platform for large-scale exploration and evaluation of predicted protein complex structures.Nucleic acids research · 2026Article
- Assessing scoring metrics for AlphaFold2 and AlphaFold3 protein complex predictions.Protein science : a publication of the Protein Society · 2025Article
- Generation of a high confidence set of domain-domain interface types to guide protein complex structure predictions by AlphaFold.Bioinformatics (Oxford, England) · 2024Article
- ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model.Bioinformatics advances · 2024Article
- Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.Proteins · 2023Article
Corrections and comments
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Authors and funding
46 authors.
Funding
Abstract
Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.
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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.