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QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs
The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annota...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403031/ https://www.ncbi.nlm.nih.gov/pubmed/33631427 http://dx.doi.org/10.1016/j.gpb.2021.02.001 |
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author | Smaili, Fatima Zohra Tian, Shuye Roy, Ambrish Alazmi, Meshari Arold, Stefan T. Mukherjee, Srayanta Hefty, P. Scott Chen, Wei Gao, Xin |
author_facet | Smaili, Fatima Zohra Tian, Shuye Roy, Ambrish Alazmi, Meshari Arold, Stefan T. Mukherjee, Srayanta Hefty, P. Scott Chen, Wei Gao, Xin |
author_sort | Smaili, Fatima Zohra |
collection | PubMed |
description | The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated. |
format | Online Article Text |
id | pubmed-9403031 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-94030312022-08-26 QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs Smaili, Fatima Zohra Tian, Shuye Roy, Ambrish Alazmi, Meshari Arold, Stefan T. Mukherjee, Srayanta Hefty, P. Scott Chen, Wei Gao, Xin Genomics Proteomics Bioinformatics Method The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated. Elsevier 2021-12 2021-02-23 /pmc/articles/PMC9403031/ /pubmed/33631427 http://dx.doi.org/10.1016/j.gpb.2021.02.001 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Method Smaili, Fatima Zohra Tian, Shuye Roy, Ambrish Alazmi, Meshari Arold, Stefan T. Mukherjee, Srayanta Hefty, P. Scott Chen, Wei Gao, Xin QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs |
title | QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs |
title_full | QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs |
title_fullStr | QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs |
title_full_unstemmed | QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs |
title_short | QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs |
title_sort | qaust: protein function prediction using structure similarity, protein interaction, and functional motifs |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9403031/ https://www.ncbi.nlm.nih.gov/pubmed/33631427 http://dx.doi.org/10.1016/j.gpb.2021.02.001 |
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