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Increasing the precision of orthology-based complex prediction through network alignment

Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in mode...

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Autores principales: Pache, Roland A., Aloy, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4045337/
https://www.ncbi.nlm.nih.gov/pubmed/24918034
http://dx.doi.org/10.7717/peerj.413
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author Pache, Roland A.
Aloy, Patrick
author_facet Pache, Roland A.
Aloy, Patrick
author_sort Pache, Roland A.
collection PubMed
description Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein–protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data. Here, we show that incorporating interaction information through network alignment significantly increases the precision of orthology-based complex prediction. Moreover, we performed a large-scale in silico screen for protein complexes in human, yeast and fly, through the alignment of hundreds of known complexes to whole organism interactomes. Systematic comparison of the resulting network alignments to all complexes currently known in those species revealed many conserved complexes, as well as several novel complex components. In addition to validating our predictions using orthogonal data, we were able to assign specific functional roles to the predicted complexes. In several cases, the incorporation of interaction data through network alignment allowed to distinguish real complex components from other orthologous proteins. Our analyses indicate that current knowledge of yeast protein complexes exceeds that in other organisms and that predicting complexes in fly based on human and yeast data is complementary rather than redundant. Lastly, assessing the conservation of protein complexes of the human pathogen Mycoplasma pneumoniae, we discovered that its complexes repertoire is different from that of eukaryotes, suggesting new points of therapeutic intervention, whereas targeting the pathogen’s Restriction enzyme complex might lead to adverse effects due to its similarity to ATP-dependent metalloproteases in the human host.
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spelling pubmed-40453372014-06-10 Increasing the precision of orthology-based complex prediction through network alignment Pache, Roland A. Aloy, Patrick PeerJ Bioinformatics Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein–protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data. Here, we show that incorporating interaction information through network alignment significantly increases the precision of orthology-based complex prediction. Moreover, we performed a large-scale in silico screen for protein complexes in human, yeast and fly, through the alignment of hundreds of known complexes to whole organism interactomes. Systematic comparison of the resulting network alignments to all complexes currently known in those species revealed many conserved complexes, as well as several novel complex components. In addition to validating our predictions using orthogonal data, we were able to assign specific functional roles to the predicted complexes. In several cases, the incorporation of interaction data through network alignment allowed to distinguish real complex components from other orthologous proteins. Our analyses indicate that current knowledge of yeast protein complexes exceeds that in other organisms and that predicting complexes in fly based on human and yeast data is complementary rather than redundant. Lastly, assessing the conservation of protein complexes of the human pathogen Mycoplasma pneumoniae, we discovered that its complexes repertoire is different from that of eukaryotes, suggesting new points of therapeutic intervention, whereas targeting the pathogen’s Restriction enzyme complex might lead to adverse effects due to its similarity to ATP-dependent metalloproteases in the human host. PeerJ Inc. 2014-05-29 /pmc/articles/PMC4045337/ /pubmed/24918034 http://dx.doi.org/10.7717/peerj.413 Text en © 2014 Pache and Aloy http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Pache, Roland A.
Aloy, Patrick
Increasing the precision of orthology-based complex prediction through network alignment
title Increasing the precision of orthology-based complex prediction through network alignment
title_full Increasing the precision of orthology-based complex prediction through network alignment
title_fullStr Increasing the precision of orthology-based complex prediction through network alignment
title_full_unstemmed Increasing the precision of orthology-based complex prediction through network alignment
title_short Increasing the precision of orthology-based complex prediction through network alignment
title_sort increasing the precision of orthology-based complex prediction through network alignment
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4045337/
https://www.ncbi.nlm.nih.gov/pubmed/24918034
http://dx.doi.org/10.7717/peerj.413
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