Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs.

By Haiyuan Yu, Nicholas M. Luscombe, Haoxin Lu, Xiaowei Zhu, Yu Xia, Jing-Dong J. Han, Nicolas Bertin, Sambath Chung, Marc Vidal, Mark Gerstein. 2004

583 citations recorded by the source corpus.

Proteins function mainly through interactions, especially with DNA and other proteins. While some large-scale interaction networks are now available for a number of model organisms, their experimental generation remains difficult. Consequently, interolog mapping--the transfer of interaction annotation from one organism to another using comparative genomics--is of significant value. Here we quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins. Using interaction information from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori, we find that protein-protein interactions can be transferred when a pair of proteins has a joint sequence identity >80% or a joint E-value <10(-70). (These "joint" quantities are the geometric means of the identities or E-values for the two pairs of interacting proteins.) We generalize our interolog analysis to protein-DNA binding, finding such interactions are conserved at specific thresholds between 30% and 60% sequence identity depending on the protein family. Furthermore, we introduce the concept of a "regulog"--a conserved regulatory relationship between proteins across different species. We map interologs and regulogs from yeast to a number of genomes with limited experimental annotation (e.g., Arabidopsis thaliana) and make these available through an online database at http://interolog.gersteinlab.org. Specifically, we are able to transfer approximately 90,000 potential protein-protein interactions to the worm. We test a number of these in two-hybrid experiments and are able to verify 45 overlaps, which we show to be statistically significant.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

SWISS-MODEL: homology modelling of protein structures and complexes (2018, neutral): “The approach can be scaled to entire genomes and applied to binary as well as to higher-order protein assemblies (17,18,22,23).”
Integrative approaches for finding modular structure in biological networks (2013, background): “…to identifying conservation at the network level, individual interactions have been observed to occur between orthologous gene pairs in two species, corresponding to conserved protein–protein interactions (known as interologues) 111 or conserved regulatory interactions (known as regulogues) 112 .”
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, methodology): “For the latter transfer, the ‘interolog’ concept is applied (42,43); STRING uses hierarchically arranged orthologous group relations as A typical association network in STRING.”
Protein–protein interaction sites are hot spots for disease‐associated nonsynonymous SNPs (2012, background): “Additional human protein-protein interactions are inferred from experimentally observed interaction between orthologous proteins in another species [Yu et al., 2004].”
FlyMine: an integrated database for Drosophila and Anopheles genomics (2007, background): “Such transfer of information has, in previous studies, been used to provide detailed potential interaction networks for a number of organisms, for example, human [59], S. cerevisiae to C. elegans [13] and between S. cerevisiae , D. melanogaster , C. elegans and Helicobacter pylori [60].”
The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (2016, background): “This so-called ‘interolog’ transfer (38,39) is based on the observation that orthologs of interacting proteins in one organism are often also interacting in another organism – this inference is the more confident the better the orthology relationships can be established.”
The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees (2013, unknown): “Next we searched in the XYZ-network for so-called interologs , 4-node subgraphs (with motif specification XZ00ZY) consisting of conserved protein-protein interactions between orthologous protein pairs in yeast and human [37].”
The Genomes of Oryza sativa: A History of Duplications (2005, background): “The reliability of this approach, especially for transfer of interaction data between organisms, has been demonstrated in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori analyses [87].”
CORUM: the comprehensive resource of mammalian protein complexes (2007, background): “This is due to the availability of high-throughput datasets as well as a manually curated dataset, which received a status as ‘gold standard’ (3,4).”
Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, background): “In particular, cross-species data integration raises the classical problems of identifying orthologous genes 126 and interologue protein–protein interaction gene pairs 127 and of how to transfer functional information accurately across species.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “hothermorecloselythantheaverage,andtendtobemorepromiscuous in their function. Many of these essential genes are housekeeping genes with high and less ﬂuctuating expression levels (Jeong et al., 2001; Yu et al., 2004c). Later more global network measures, such as bottlenecks or more globally central ”