Reliable Identification of Homodimers Using AlphaFold

Data for paper "Reliable Identification Of Homodimers using AlphaFold". Abstract: Protein-protein interactions are central for understanding biological processes. The ability to predict interaction partners is extremely valuable for avoiding costly, time-consuming experiments. It has been shown that AlphaFold has an unsurpassed ability to accurately evaluate interacting protein pairs. However, a protein can also form homomeric interactions, i.e. interact with itself. We found that AlphaFold yielded a significantly higher false-positive rate for identifying homodimers than for heterodimers. True Positive Rate (TPR) at 1% False Positive Rate (FPR) drops from 63% for heterodimers to 18% for homodimers. When we investigated the high-scoring false positives, i.e., non-homodimers with high AlphaFold scores when predicted as such, we found that their homologs were enriched for homomultimeric proteins. Using a simple logistic regression model that combines AlphaFold scores with structural and homology information, we increased the TPR (at 1% FPR) to 42 +/- 8% (5-fold cross-validation) from 19%. If we excluded the homology information, we achieved a TPR of 28 +/- 7%, which is still better than using AlphaFold metrics. alphafold-homodimers-zenodo.tar.gz: The tar-file includes all the msas used for the protein-protein interaction prediction with AlphaFold. It also includes all predicted structures, fasta-files as well as the input_jsons used for AlphaFold3 structure predictions. Software: For software related to the publication see: https://github.com/SarahND97/alphafold-homodimers