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Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis
Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational method of high-throughput PPI network prediction based on all-to-all rigid-...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Bentham Science Publishers
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4440392/ https://www.ncbi.nlm.nih.gov/pubmed/23855669 http://dx.doi.org/10.2174/09298665113209990066 |
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author | Matsuzaki, Yuri Ohue, Masahito Uchikoga, Nobuyuki Akiyama, Yutaka |
author_facet | Matsuzaki, Yuri Ohue, Masahito Uchikoga, Nobuyuki Akiyama, Yutaka |
author_sort | Matsuzaki, Yuri |
collection | PubMed |
description | Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures. The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation requires massive computational resources, recent advancements in the computational sciences have made such large-scale calculations feasible. different rigid-body docking tools with different scoring models. We found that the predicted interactions were different between the results from the two tools. When the positive predictions from both of the docking tools were combined, all the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation. Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications. This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior. |
format | Online Article Text |
id | pubmed-4440392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Bentham Science Publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-44403922015-05-22 Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis Matsuzaki, Yuri Ohue, Masahito Uchikoga, Nobuyuki Akiyama, Yutaka Protein Pept Lett Article Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. However, many parts of the cell regulatory systems include unknown PPIs. To approach this problem, we have developed a computational method of high-throughput PPI network prediction based on all-to-all rigid-body docking of protein tertiary structures. The prediction system accepts a set of data comprising protein tertiary structures as input and generates a list of possible interacting pairs from all the combinations as output. A crucial advantage of this docking based method is in providing predictions of protein pairs that increases our understanding of biological pathways by analyzing the structures of candidate complex structures, which gives insight into novel interaction mechanisms. Although such exhaustive docking calculation requires massive computational resources, recent advancements in the computational sciences have made such large-scale calculations feasible. different rigid-body docking tools with different scoring models. We found that the predicted interactions were different between the results from the two tools. When the positive predictions from both of the docking tools were combined, all the core signaling interactions were correctly predicted with the exception of interactions activated by protein phosphorylation. Large-scale PPI prediction using tertiary structures is an effective approach that has a wide range of potential applications. This method is especially useful for identifying novel PPIs of new pathways that control cellular behavior. Bentham Science Publishers 2014-08 2014-08 /pmc/articles/PMC4440392/ /pubmed/23855669 http://dx.doi.org/10.2174/09298665113209990066 Text en © 2014 Bentham Science Publishers http://creativecommons.org/licenses/by-nc/3.0/ This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited. |
spellingShingle | Article Matsuzaki, Yuri Ohue, Masahito Uchikoga, Nobuyuki Akiyama, Yutaka Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis |
title | Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis |
title_full | Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis |
title_fullStr | Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis |
title_full_unstemmed | Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis |
title_short | Protein-protein Interaction Network Prediction by Using Rigid-Body Docking Tools: Application to Bacterial Chemotaxis |
title_sort | protein-protein interaction network prediction by using rigid-body docking tools: application to bacterial chemotaxis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4440392/ https://www.ncbi.nlm.nih.gov/pubmed/23855669 http://dx.doi.org/10.2174/09298665113209990066 |
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