AutoCoEv-A High-Throughput In Silico Pipeline for Predicting Inter-Protein Coevolution
Protein-protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and understanding connections between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Coevo...
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Published in | International journal of molecular sciences Vol. 23; no. 6; p. 3351 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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20.03.2022
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Abstract | Protein-protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and understanding connections between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, an evolutionary analysis of large arrays of proteins in silico is a highly time-consuming effort that has limited the usage of this method for protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly, open source, computational pipeline for the search of coevolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting coevolution, AutoCoEv achieves a seamless automation and parallelization of the workflow. Importantly, we provide a patch to the CAPS2 source code to strengthen its statistical output, allowing for multiple comparison corrections and an enhanced analysis of the results. We apply the pipeline to inspect coevolution among 324 proteins identified to be located at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, can be used to predict functional interactions from large datasets in a time- and cost-efficient manner. |
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AbstractList | Protein–protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and understanding connections between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Coevolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, an evolutionary analysis of large arrays of proteins in silico is a highly time-consuming effort that has limited the usage of this method for protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly, open source, computational pipeline for the search of coevolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting coevolution, AutoCoEv achieves a seamless automation and parallelization of the workflow. Importantly, we provide a patch to the CAPS2 source code to strengthen its statistical output, allowing for multiple comparison corrections and an enhanced analysis of the results. We apply the pipeline to inspect coevolution among 324 proteins identified to be located at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, can be used to predict functional interactions from large datasets in a time- and cost-efficient manner. |
Author | Šuštar, Vid Petrov, Petar B Awoniyi, Luqman O Mattila, Pieta K Balci, M Özge |
AuthorAffiliation | 2 Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland 1 MediCity Research Laboratories, Institute of Biomedicine, University of Turku, 20014 Turku, Finland; luqman.awoniyi@utu.fi (L.O.A.); vidsustar@gmail.com (V.Š.); meryemozge.balci@utu.fi (M.Ö.B.) |
AuthorAffiliation_xml | – name: 1 MediCity Research Laboratories, Institute of Biomedicine, University of Turku, 20014 Turku, Finland; luqman.awoniyi@utu.fi (L.O.A.); vidsustar@gmail.com (V.Š.); meryemozge.balci@utu.fi (M.Ö.B.) – name: 2 Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland |
Author_xml | – sequence: 1 givenname: Petar B surname: Petrov fullname: Petrov, Petar B organization: Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland – sequence: 2 givenname: Luqman O surname: Awoniyi fullname: Awoniyi, Luqman O organization: Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland – sequence: 3 givenname: Vid surname: Šuštar fullname: Šuštar, Vid organization: MediCity Research Laboratories, Institute of Biomedicine, University of Turku, 20014 Turku, Finland – sequence: 4 givenname: M Özge surname: Balci fullname: Balci, M Özge organization: Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland – sequence: 5 givenname: Pieta K surname: Mattila fullname: Mattila, Pieta K organization: Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland |
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Snippet | Protein-protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and... Protein–protein interactions govern cellular processes via complex regulatory networks, which are still far from being understood. Thus, identifying and... |
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SubjectTerms | Amino acids Automation Coevolution Computer applications correlation Evolution, Molecular Functionals interaction Lipid rafts Lipids Lymphocytes Lymphocytes B Phylogenetics pipeline Pipelines protein Protein interaction Proteins Proteins - genetics Proteins - metabolism Software Source code Trees Workflow |
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Title | AutoCoEv-A High-Throughput In Silico Pipeline for Predicting Inter-Protein Coevolution |
URI | https://www.ncbi.nlm.nih.gov/pubmed/35328772 https://www.proquest.com/docview/2642499792/abstract/ https://search.proquest.com/docview/2644003469 https://pubmed.ncbi.nlm.nih.gov/PMC8952222 https://doaj.org/article/90d2c20ca7b5456480acbe042edd6ff0 |
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