Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
arXiv:2208.11626 · doi:10.1371/journal.pcbi.1011010
Abstract
Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species. We show that these two signals can be combined to improve the performance of the inference of interaction partners among paralogs. For this, we first align the sequence-similarity graphs of the two families through simulated annealing, yielding a robust partial pairing. We next use this partial pairing to seed a coevolution-based iterative pairing algorithm. This combined method improves performance over either separate method. The improvement obtained is striking in the difficult cases where the average number of paralogs per species is large or where the total number of sequences is modest.
arXiv comment19 pages
References in corpus (11)
- Direct-coupling analysis of residue co-evolution captures native contacts across many protein families
- Identification of direct residue contacts in protein-protein interaction by message passing
- Inverse Statistical Physics of Protein Sequences: A Key Issues Review
- Inferring interaction partners from protein sequences
- Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
- Inter-residue, inter-protein and inter-family coevolution: bridging the scales
- Inferring interaction partners from protein sequences using mutual information
- Phylogenetic correlations can suffice to infer protein partners from sequences
- Aligning graphs and finding substructures by a cavity approach
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences
- Statistical physics of interacting proteins: impact of dataset size and quality assessed in synthetic sequences