Enhancing the prediction of protein pairings between interacting families using orthology information

Izarzugaza, José M. G.; Juan, David; Pons, Carles; Pazos, Florencio; Valencia, Alfonso

doi:10.1186/1471-2105-9-35

Cited by 20 publications

(41 citation statements)

References 21 publications

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“…This makes sense, as corrected distances increase exponentially, and thus differences tend to be magnified as distances grow large and it becomes more difficult to satisfy MMM's match criteria. ClustalW-type distances are thus better suited for coevolutionary analysis as had previously been observed Izarzugaza et al 2008). However, ClustalW distances can present a very high probability of false-positive identification when sequences are saturated with substitutions and the alignment becomes inaccurate.…”

Section: Quality Of Distance Matrices For Coevolutionary Analysismentioning

confidence: 86%

The human protein coevolution network

Tillier

Charlebois²

2009

Genome Res.

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Coevolution maintains interactions between phenotypic traits through the process of reciprocal natural selection. Detecting molecular coevolution can expose functional interactions between molecules in the cell, generating insights into biological processes, pathways, and the networks of interactions important for cellular function. Prediction of interaction partners from different protein families exploits the property that interacting proteins can follow similar patterns and relative rates of evolution. Current methods for detecting coevolution based on the similarity of phylogenetic trees or evolutionary distance matrices have, however, been limited by requiring coevolution over the entire evolutionary history considered and are inaccurate in the presence of paralogous copies. We present a novel method for determining coevolving protein partners by finding the largest common submatrix in a given pair of distance matrices, with the size of the largest common submatrix measuring the strength of coevolution. This approach permits us to consider matrices of different size and scale, to find lineage-specific coevolution, and to predict multiple interaction partners. We used MatrixMatchMaker to predict protein-protein interactions in the human genome. We show that proteins that are known to interact physically are more strongly coevolving than proteins that simply belong to the same biochemical pathway. The human coevolution network is highly connected, suggesting many more protein-protein interactions than are currently known from high-throughput and other experimental evidence. These most strongly coevolving proteins suggest interactions that have been maintained over long periods of evolutionary time, and that are thus likely to be of fundamental importance to cellular function.

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Section: Quality Of Distance Matrices For Coevolutionary Analysismentioning

confidence: 86%

The human protein coevolution network

Tillier

Charlebois²

2009

Genome Res.

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“…Eukaryotic complexes present a paradoxical scenario: Large families of eukaryotic proteins are the result of duplicationbased expansions, but these duplications make uncertain which paralogues of one family interact with which ones of the other. In the future, improvements aimed to disentangle the network of paralogous interactions will be fundamental to deal with eukaryotic interactions (44)(45)(46)(47). Our approach, based on preferential conservation, tackles this problem for proteins with prokaryotic homologs by looking at very divergent, well-populated, and easy-to-couple pairs of interacting prokaryotic proteins.…”

Section: Discussionmentioning

confidence: 99%

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Rodriguez-Rivas

Marsili

Juan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequencebased coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequencebased accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.coevolution | protein-protein interaction | protein complex | homology modeling | contact prediction C ells function as a remarkably synchronized orchestra of finely tuned molecular interactions, and establishing this molecular network has become a major goal of molecular biology. Important methodological and technical advances have led to the identification of a large number of novel protein-protein interactions and to major contributions to our understanding of the functioning of cells and organisms (1, 2). In contrast, and despite relevant advances in EM (3) and crystallography (4), the molecular details of a large number of interactions remain unknown.When experimental structural data are absent or incomplete, template-based homology modeling of protein complexes represents the most reliable option (5, 6). Similarly to modeling of tertiary structure for single-chain proteins, homology modeling of protein-protein interactions follows a conservation-based approach, in which the quaternary structure of one or more experimentally solved complexes with enough sequence similarity to a target complex (the templates) is projected onto the target. Templatebased techniques have provided models for a large number of protein complexes with structurally solved homologous complexes (7-10). Unfortunately, proteins involved in homologous protein dimers tend to systema...

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“…That is, if two proteins interact in a given organism, then their orthologs in another organism (“interologs” (127)) are likely to interact as well (36, 78). The relative location/order of orthologous proteins from two genomes has also been used as the basis of interaction prediction.…”

Section: Building Interactomesmentioning

confidence: 99%

Structural Bioinformatics of the Interactome

Petrey

2014

Annu. Rev. Biophys.

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The last decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information, and to analyze this information so as to infer both the function of individual molecules and how they interact to modulate the behavior of biological systems. Here we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance the basic understanding of biological systems and their disregulation, and how they are being applied in drug development.

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Enhancing the prediction of protein pairings between interacting families using orthology information

Cited by 20 publications

References 21 publications

The human protein coevolution network

The human protein coevolution network

Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

Structural Bioinformatics of the Interactome

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