Inter-paralog amino acid inversion events in large phylogenies of duplicated proteins

• Published in: PLoS computational biology 2022-04, Vol.18 (4), p.e1010016-e1010016
• Main Authors: Pascarelli, Stefano, Laurino, Paola
• Format: Article
• Language: English
• Subjects: Amino Acid Sequence; Amino acids; Amino Acids - genetics; Animals; Annotations; Biology and Life Sciences; Chromosome Inversion; Computer and Information Sciences; Datasets; Divergence; Electronic data processing; Epidermal growth factor; Evolution; Evolution, Molecular; Gene Duplication; Genomes; Growth factors; Heredity; Homology; Inversion; Inversions; Ligands; Methods; Next-generation sequencing; Phylogenetics; Phylogeny; Physiological aspects; Protein research; Proteins; Reproduction (copying); Research and Analysis Methods; Sequences
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1010016

Connecting protein sequence to function is becoming increasingly relevant since high-throughput sequencing studies accumulate large amounts of genomic data. In order to go beyond the existing database annotation, it is fundamental to understand the mechanisms underlying functional inheritance and divergence. If the homology relationship between proteins is known, can we determine whether the function diverged? In this work, we analyze different possibilities of protein sequence evolution after gene duplication and identify "inter-paralog inversions", i.e., sites where the relationship between the ancestry and the functional signal is decoupled. The amino acids in these sites are masked from being recognized by other prediction tools. Still, they play a role in functional divergence and could indicate a shift in protein function. We develop a method to specifically recognize inter-paralog amino acid inversions in a phylogeny and test it on real and simulated datasets. In a dataset built from the Epidermal Growth Factor Receptor (EGFR) sequences found in 88 fish species, we identify 19 amino acid sites that went through inversion after gene duplication, mostly located at the ligand-binding extracellular domain. Our work uncovers an outcome of protein duplications with direct implications in protein functional annotation and sequence evolution. The developed method is optimized to work with large protein datasets and can be readily included in a targeted protein analysis pipeline.