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Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins

Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application...

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Autores principales: Ward, Katrisa M., Pickett, Brandon D., Ebbert, Mark T. W., Kauwe, John S. K., Miller, Justin B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407263/
https://www.ncbi.nlm.nih.gov/pubmed/36011253
http://dx.doi.org/10.3390/genes13081346
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author Ward, Katrisa M.
Pickett, Brandon D.
Ebbert, Mark T. W.
Kauwe, John S. K.
Miller, Justin B.
author_facet Ward, Katrisa M.
Pickett, Brandon D.
Ebbert, Mark T. W.
Kauwe, John S. K.
Miller, Justin B.
author_sort Ward, Katrisa M.
collection PubMed
description Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10(−108)) and 1.94 times higher in bacteria (p = 1.22 × 10(−35)). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.
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spelling pubmed-94072632022-08-26 Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins Ward, Katrisa M. Pickett, Brandon D. Ebbert, Mark T. W. Kauwe, John S. K. Miller, Justin B. Genes (Basel) Article Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10(−108)) and 1.94 times higher in bacteria (p = 1.22 × 10(−35)). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups. MDPI 2022-07-27 /pmc/articles/PMC9407263/ /pubmed/36011253 http://dx.doi.org/10.3390/genes13081346 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ward, Katrisa M.
Pickett, Brandon D.
Ebbert, Mark T. W.
Kauwe, John S. K.
Miller, Justin B.
Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins
title Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins
title_full Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins
title_fullStr Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins
title_full_unstemmed Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins
title_short Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins
title_sort web-based protein interactions calculator identifies likely proteome coevolution with alzheimer’s disease-associated proteins
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9407263/
https://www.ncbi.nlm.nih.gov/pubmed/36011253
http://dx.doi.org/10.3390/genes13081346
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