Predicting protein function by genomic context: quantitative evaluation and qualitative inferences.

By Martijn A. Huynen, Berend Snel, Warren C. Lathe, Peer Bork. Genome Research, 2000

505 citations recorded by the source corpus.

Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes.

Subfields: Ecology, Microbiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/755b79d7125ecae163c64ace64eb314e36e0e409

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Citations in the corpus

Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners (2007, background): “Analysis of gene order conservation within three bacterial and archaeal genomes found that 63%–75% of co-regulated genes interact physically [7,11].”
Similarity of phylogenetic trees as indicator of protein-protein interaction. (2001, methodology): “The accuracy and coverage of these techniques were recently compared (Huynen et al., 2000) (see Discussion).”
The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest (2022, background): “These so-called ‘genomic context’ channels (reviewed in (35,36)) are based on detecting evolutionary constraints arising from functional gene-gene partnerships, and are best applicable in prokaryotic genomes.”
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, background): “These evolutionary events are known to be retained non-randomly with respect to the functional roles of genes, and thus allow the inference of functional associations between genes even for otherwise rarely studied organisms (genomic context techniques are reviewed in (44,45)).”
Identification of protein secretion systems in bacterial genomes (2015, background): “This information can facilitate the identification of genes encoding secretion systems in genome data 22,23 .”
Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes (2016, background): “PTHR22624 ATG4A 137 9 52 27 137 10 1356 10 ATG8 protease PTHR11227 WIPI1, WIPI2 199 14 135 65 199 12 2174 12 PI(3,5)P2 regulatory complex”
Functional organization of the yeast proteome by systematic analysis of protein complexes (2002, background): “), computational in silico methods (protein fusions, gene neighbouring, structural predictions”
MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems (2014, background): “This facilitates the assignment of certain components to a system [6–9].”
A novel method for accurate operon predictions in all sequenced prokaryotes (2005, background): “Previous workers discarded closely related genomes (10,17) or reduced sensitivity when they were present (7), as these genomes show conserved pairs of unrelated genes because of insufficient evolutionary time to shuffle them apart.”
STRING v9.1: protein-protein interaction networks, with increased coverage and integration (2012, background): “Together with direct, physical interactions, such indirect interactions constitute the larger superset of ‘functional protein–protein associations’ or ‘functional protein linkages’ (4,5).”
Predicting protein-protein interactions from primary structure (2001, background): “, 1997); using genomic context to infer functional protein interactions between the gene products (Huynen et al., 2000); or inference from genome sequences, given observed homologies in other organisms, where interacting proteins have fused into a single protein chain (Marcotte et al.”
Prolinks: a database of protein functional linkages derived from coevolution (2004, background): “This example illustrates one possible use of predicted networks, which is to the assign a function to uncharacterized genes [24-26].”
STRING: a database of predicted functional associations between proteins (2003, methodology): “Remarkably, gene-order conservation remains clearly the most power-full method of the three (21).”
Comparative Genomics in Eukaryotes (2005, methodology): “Unfortunately, gene fusions do not occur very often and therefore they are quantitatively not so powerful as a tool to identify functional links (Huynen et al., 2000).”