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PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues

BACKGROUND: In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of pept...

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Autores principales: Singh, Sandeep, Singh, Harinder, Tuknait, Abhishek, Chaudhary, Kumardeep, Singh, Balvinder, Kumaran, S., Raghava, Gajendra P. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687368/
https://www.ncbi.nlm.nih.gov/pubmed/26690490
http://dx.doi.org/10.1186/s13062-015-0103-4
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author Singh, Sandeep
Singh, Harinder
Tuknait, Abhishek
Chaudhary, Kumardeep
Singh, Balvinder
Kumaran, S.
Raghava, Gajendra P. S.
author_facet Singh, Sandeep
Singh, Harinder
Tuknait, Abhishek
Chaudhary, Kumardeep
Singh, Balvinder
Kumaran, S.
Raghava, Gajendra P. S.
author_sort Singh, Sandeep
collection PubMed
description BACKGROUND: In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues. RESULTS: PEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81–4.05 Å. CONCLUSIONS: In summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/. REVIEWERS: This article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-015-0103-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-46873682015-12-23 PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues Singh, Sandeep Singh, Harinder Tuknait, Abhishek Chaudhary, Kumardeep Singh, Balvinder Kumaran, S. Raghava, Gajendra P. S. Biol Direct Research BACKGROUND: In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues. RESULTS: PEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81–4.05 Å. CONCLUSIONS: In summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/. REVIEWERS: This article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13062-015-0103-4) contains supplementary material, which is available to authorized users. BioMed Central 2015-12-21 /pmc/articles/PMC4687368/ /pubmed/26690490 http://dx.doi.org/10.1186/s13062-015-0103-4 Text en © Singh et al. 2015 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
Singh, Sandeep
Singh, Harinder
Tuknait, Abhishek
Chaudhary, Kumardeep
Singh, Balvinder
Kumaran, S.
Raghava, Gajendra P. S.
PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues
title PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues
title_full PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues
title_fullStr PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues
title_full_unstemmed PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues
title_short PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues
title_sort pepstrmod: structure prediction of peptides containing natural, non-natural and modified residues
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687368/
https://www.ncbi.nlm.nih.gov/pubmed/26690490
http://dx.doi.org/10.1186/s13062-015-0103-4
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