{"id":"doi:10.1093/bioinformatics/btac258","kind":"article","title":"Topsy-Turvy: integrating a global view into sequence-based PPI prediction","authors":["Singh R","Devkota K","Sledzieski S","Berger B","Cowen L"],"author_count":5,"year":2022,"published":"2022-06-01","venue":"Bioinformatics (Oxford, England)","cited_by":89,"doi":"10.1093/bioinformatics/btac258","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/bioinformatics/btac258","archive_url":"https://space-kitty.com/papers/doi:10.1093/bioinformatics/btac258","updated":null,"pmid":"35758793","pmcid":"PMC9235477","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC9235477/pdf","abstract":"Summary Computational methods to predict protein-protein interaction (PPI) typically segregate into sequence-based 'bottom-up' methods that infer properties from the characteristics of the individual protein sequences, or global 'top-down' methods that infer properties from the pattern of already known PPIs in the species of interest. However, a way to incorporate top-down insights into sequence-based bottom-up PPI prediction methods has been elusive. We thus introduce Topsy-Turvy, a method that newly synthesizes both views in a sequence-based, multi-scale, deep-learning model for PPI prediction. While Topsy-Turvy makes predictions using only sequence data, during the training phase it takes a transfer-learning approach by incorporating patterns from both global and molecular-level views of protein interaction. In a cross-species context, we show it achieves state-of-the-art performance, offering the ability to perform genome-scale, interpretable PPI prediction for non-model organisms with no existing experimental PPI data. In species with available experimental PPI data, we further present a Topsy-Turvy hybrid (TT-Hybrid) model which integrates Topsy-Turvy with a purely network-based model for link prediction that provides information about species-specific network rewiring. TT-Hybrid makes accurate predictions for both well- and sparsely-characterized proteins, outperforming both its constituent components as well as other state-of-the-art PPI prediction methods. Furthermore, running Topsy-Turvy and TT-Hybrid screens is feasible for whole genomes, and thus these methods scale to settings where other methods (e.g. AlphaFold-Multimer) might be infeasible. The generalizability, accuracy and genome-level scalability of Topsy-Turvy and TT-Hybrid unlocks a more comprehensive map of protein interaction and organization in both model and non-model organisms. Availability and implementation https://topsyturvy.csail.mit.edu. Supplementary information Supplementary data are available at Bioinformatics online.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Topsy-Turvy: integrating a global view into sequence-based PPI prediction\" by Singh R, Devkota K, Sledzieski S et al., https://doi.org/10.1093/bioinformatics/btac258, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"protein-design","name":"AI Protein Design & Structure Prediction","domain_slug":"biotech"}],"cited_in_dossiers":[]}