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Convert your favorite protein modeling program into a mutation predictor: “MODICT”
BACKGROUND: Predict whether a mutation is deleterious based on the custom 3D model of a protein. RESULTS: We have developed modict, a mutation prediction tool which is based on per residue rmsd (root mean square deviation) values of superimposed 3D protein models. Our mathematical algorithm was test...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070100/ https://www.ncbi.nlm.nih.gov/pubmed/27760515 http://dx.doi.org/10.1186/s12859-016-1286-0 |
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author | Tanyalcin, Ibrahim Stouffs, Katrien Daneels, Dorien Al Assaf, Carla Lissens, Willy Jansen, Anna Gheldof, Alexander |
author_facet | Tanyalcin, Ibrahim Stouffs, Katrien Daneels, Dorien Al Assaf, Carla Lissens, Willy Jansen, Anna Gheldof, Alexander |
author_sort | Tanyalcin, Ibrahim |
collection | PubMed |
description | BACKGROUND: Predict whether a mutation is deleterious based on the custom 3D model of a protein. RESULTS: We have developed modict, a mutation prediction tool which is based on per residue rmsd (root mean square deviation) values of superimposed 3D protein models. Our mathematical algorithm was tested for 42 described mutations in multiple genes including renin (REN), beta-tubulin (TUBB2B), biotinidase (BTD), sphingomyelin phosphodiesterase-1 (SMPD1), phenylalanine hydroxylase (PAH) and medium chain Acyl-Coa dehydrogenase (ACADM). Moreover, modict scores corresponded to experimentally verified residual enzyme activities in mutated biotinidase, phenylalanine hydroxylase and medium chain Acyl-CoA dehydrogenase. Several commercially available prediction algorithms were tested and results were compared. The modict perl package and the manual can be downloaded from https://github.com/IbrahimTanyalcin/MODICT. CONCLUSIONS: We show here that modict is capable tool for mutation effect prediction at the protein level, using superimposed 3D protein models instead of sequence based algorithms used by polyphen and sift. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1286-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5070100 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50701002016-10-24 Convert your favorite protein modeling program into a mutation predictor: “MODICT” Tanyalcin, Ibrahim Stouffs, Katrien Daneels, Dorien Al Assaf, Carla Lissens, Willy Jansen, Anna Gheldof, Alexander BMC Bioinformatics Software BACKGROUND: Predict whether a mutation is deleterious based on the custom 3D model of a protein. RESULTS: We have developed modict, a mutation prediction tool which is based on per residue rmsd (root mean square deviation) values of superimposed 3D protein models. Our mathematical algorithm was tested for 42 described mutations in multiple genes including renin (REN), beta-tubulin (TUBB2B), biotinidase (BTD), sphingomyelin phosphodiesterase-1 (SMPD1), phenylalanine hydroxylase (PAH) and medium chain Acyl-Coa dehydrogenase (ACADM). Moreover, modict scores corresponded to experimentally verified residual enzyme activities in mutated biotinidase, phenylalanine hydroxylase and medium chain Acyl-CoA dehydrogenase. Several commercially available prediction algorithms were tested and results were compared. The modict perl package and the manual can be downloaded from https://github.com/IbrahimTanyalcin/MODICT. CONCLUSIONS: We show here that modict is capable tool for mutation effect prediction at the protein level, using superimposed 3D protein models instead of sequence based algorithms used by polyphen and sift. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1286-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-19 /pmc/articles/PMC5070100/ /pubmed/27760515 http://dx.doi.org/10.1186/s12859-016-1286-0 Text en © The Author(s) 2016 Open Access This 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 | Software Tanyalcin, Ibrahim Stouffs, Katrien Daneels, Dorien Al Assaf, Carla Lissens, Willy Jansen, Anna Gheldof, Alexander Convert your favorite protein modeling program into a mutation predictor: “MODICT” |
title | Convert your favorite protein modeling program into a mutation predictor: “MODICT” |
title_full | Convert your favorite protein modeling program into a mutation predictor: “MODICT” |
title_fullStr | Convert your favorite protein modeling program into a mutation predictor: “MODICT” |
title_full_unstemmed | Convert your favorite protein modeling program into a mutation predictor: “MODICT” |
title_short | Convert your favorite protein modeling program into a mutation predictor: “MODICT” |
title_sort | convert your favorite protein modeling program into a mutation predictor: “modict” |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070100/ https://www.ncbi.nlm.nih.gov/pubmed/27760515 http://dx.doi.org/10.1186/s12859-016-1286-0 |
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