Algorithm-supported, mass and sequence diversity-oriented random peptide library design.

Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of librar...

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Detalhes bibliográficos
Autores: Kalafatovic, Daniela, Mausa, Goran, Todorovski, Toni, Giralt Lledó, Ernest
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Recursos:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/165344
Acesso em linha:https://hdl.handle.net/2445/165344
Access Level:acceso abierto
Palavra-chave:Pèptids
Algorismes genètics
Optimització combinatòria
Peptides
Genetic algorithms
Combinatorial optimization
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spelling Algorithm-supported, mass and sequence diversity-oriented random peptide library design.Kalafatovic, DanielaMausa, GoranTodorovski, ToniGiralt Lledó, ErnestPèptidsAlgorismes genèticsOptimització combinatòriaPeptidesGenetic algorithmsCombinatorial optimizationRandom peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.BioMed Central2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/165344Articles publicats en revistes (Química Inorgànica i Orgànica)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1186/s13321-019-0347-6Journal of Cheminformatics, 2019, vol. 11, p. 25https://doi.org/10.1186/s13321-019-0347-6info:eu-repo/grantAgreement/EC/FP7/600404cc-by (c) Kalafatovic, Daniela et al., 2019http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1653442026-05-27T06:46:51Z
dc.title.none.fl_str_mv Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
title Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
spellingShingle Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
Kalafatovic, Daniela
Pèptids
Algorismes genètics
Optimització combinatòria
Peptides
Genetic algorithms
Combinatorial optimization
title_short Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
title_full Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
title_fullStr Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
title_full_unstemmed Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
title_sort Algorithm-supported, mass and sequence diversity-oriented random peptide library design.
dc.creator.none.fl_str_mv Kalafatovic, Daniela
Mausa, Goran
Todorovski, Toni
Giralt Lledó, Ernest
author Kalafatovic, Daniela
author_facet Kalafatovic, Daniela
Mausa, Goran
Todorovski, Toni
Giralt Lledó, Ernest
author_role author
author2 Mausa, Goran
Todorovski, Toni
Giralt Lledó, Ernest
author2_role author
author
author
dc.subject.none.fl_str_mv Pèptids
Algorismes genètics
Optimització combinatòria
Peptides
Genetic algorithms
Combinatorial optimization
topic Pèptids
Algorismes genètics
Optimització combinatòria
Peptides
Genetic algorithms
Combinatorial optimization
description Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/165344
url https://hdl.handle.net/2445/165344
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1186/s13321-019-0347-6
Journal of Cheminformatics, 2019, vol. 11, p. 25
https://doi.org/10.1186/s13321-019-0347-6
info:eu-repo/grantAgreement/EC/FP7/600404
dc.rights.none.fl_str_mv cc-by (c) Kalafatovic, Daniela et al., 2019
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Kalafatovic, Daniela et al., 2019
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv Articles publicats en revistes (Química Inorgànica i Orgànica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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