Reliable Identification of Homodimers Using AlphaFold

Data for paper "Reliable Identification Of Homodimers using AlphaFold". Abstract: Protein-protein interactions are central to understanding biological processes. Predicting interaction partners is extremely valuable for avoiding costly, time-consuming experiments.AlphaFold has shown an unsurpassed ability to predict protein-protein interactions accurately. However, a protein can also form homomeric interactions, i.e. interact with itself. When identifying homodimers, AlphaFold yielded a significantly higher false-positive rate than when identifying heterodimers. True Positive Rate (TPR) at 1% False Positive Rate (FPR) drops from 81% for heterodimers to 22% for homodimers. When we investigated the high-scoring false positives, i.e., non-homodimers with high AlphaFold scores that were predicted as such, we found that their homologs were enriched for homomultimeric proteins, indicating that stoichiometry is not always conserved among homologs. Using a simple logistic regression model that combines AlphaFold scores with structural and homology information, we increased the TPR (at 1% FPR) from 27% to 36%. Without homology information, no improvement was seen. The significantly higher number of high-scoring false predictions for homodimers indicates that homodimer prediction still has significant room for improvement. alphafold-homodimers-zenodo.tar.gz: The tar-file includes all the msas,structures,metrics and notebooks for the figures and tables in the manuscript. New for version 3 is the addition of the results of AlphaFold2.0, AlphaFold3 and qsproteome for version 2 of the dataset. Software: For software related to the publication see: https://github.com/SarahND97/alphafold-homodimers