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modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks
BACKGROUND: PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031328/ https://www.ncbi.nlm.nih.gov/pubmed/27655048 http://dx.doi.org/10.1186/s13062-016-0151-4 |
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author | Sain, Neetu Mohanty, Debasisa |
author_facet | Sain, Neetu Mohanty, Debasisa |
author_sort | Sain, Neetu |
collection | PubMed |
description | BACKGROUND: PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. RESULTS: We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. CONCLUSIONS: In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html. REVIEWERS: This article was reviewed by Michael Gromiha and Zoltán Gáspári. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-016-0151-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5031328 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50313282016-09-29 modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks Sain, Neetu Mohanty, Debasisa Biol Direct Research BACKGROUND: PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. RESULTS: We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. CONCLUSIONS: In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html. REVIEWERS: This article was reviewed by Michael Gromiha and Zoltán Gáspári. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-016-0151-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-21 /pmc/articles/PMC5031328/ /pubmed/27655048 http://dx.doi.org/10.1186/s13062-016-0151-4 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Sain, Neetu Mohanty, Debasisa modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks |
title | modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks |
title_full | modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks |
title_fullStr | modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks |
title_full_unstemmed | modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks |
title_short | modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks |
title_sort | modpdzpep: a web resource for structure based analysis of human pdz-mediated interaction networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5031328/ https://www.ncbi.nlm.nih.gov/pubmed/27655048 http://dx.doi.org/10.1186/s13062-016-0151-4 |
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