VitAL: Viterbi algorithm for de novo peptide design.

<h4>Background</h4>Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems...

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Autores principales:	E Besray Unal, Attila Gursoy, Burak Erman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fd2c529eea6c4e20b0c42fc1021c6d42
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spelling	oai:doaj.org-article:fd2c529eea6c4e20b0c42fc1021c6d422021-12-02T20:21:13ZVitAL: Viterbi algorithm for de novo peptide design.1932-620310.1371/journal.pone.0010926https://doaj.org/article/fd2c529eea6c4e20b0c42fc1021c6d422010-06-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20532195/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.<h4>Methodology/principal findings</h4>A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.<h4>Conclusions/significance</h4>The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.E Besray UnalAttila GursoyBurak ErmanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 6, p e10926 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q E Besray Unal Attila Gursoy Burak Erman VitAL: Viterbi algorithm for de novo peptide design.
description	<h4>Background</h4>Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.<h4>Methodology/principal findings</h4>A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.<h4>Conclusions/significance</h4>The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.
format	article
author	E Besray Unal Attila Gursoy Burak Erman
author_facet	E Besray Unal Attila Gursoy Burak Erman
author_sort	E Besray Unal
title	VitAL: Viterbi algorithm for de novo peptide design.
title_short	VitAL: Viterbi algorithm for de novo peptide design.
title_full	VitAL: Viterbi algorithm for de novo peptide design.
title_fullStr	VitAL: Viterbi algorithm for de novo peptide design.
title_full_unstemmed	VitAL: Viterbi algorithm for de novo peptide design.
title_sort	vital: viterbi algorithm for de novo peptide design.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/fd2c529eea6c4e20b0c42fc1021c6d42
work_keys_str_mv	AT ebesrayunal vitalviterbialgorithmfordenovopeptidedesign AT attilagursoy vitalviterbialgorithmfordenovopeptidedesign AT burakerman vitalviterbialgorithmfordenovopeptidedesign
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VitAL: Viterbi algorithm for de novo peptide design.

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