Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling
Digital
- Autores:
-
Pinzón-Reyes, Efraín Hernando
Sierra-Bueno, Daniel Alfonso
Suarez-Barrera, Miguel Orlando
Rueda-Forero, Nohora Juliana
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Universidad de Santander
- Repositorio:
- Repositorio Universidad de Santander
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.udes.edu.co:001/6632
- Acceso en línea:
- https://doi.org/10.1177/1176934320924681
https://repositorio.udes.edu.co/handle/001/6632
- Palabra clave:
- Heuristics
Directed molecular evolution
Protein engineering
Bacillus thuringiensis
- Rights
- openAccess
- License
- © The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions
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Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling |
| title |
Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling |
| spellingShingle |
Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling Heuristics Directed molecular evolution Protein engineering Bacillus thuringiensis |
| title_short |
Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling |
| title_full |
Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling |
| title_fullStr |
Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling |
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Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling |
| title_sort |
Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling |
| dc.creator.fl_str_mv |
Pinzón-Reyes, Efraín Hernando Sierra-Bueno, Daniel Alfonso Suarez-Barrera, Miguel Orlando Rueda-Forero, Nohora Juliana |
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Pinzón-Reyes, Efraín Hernando Sierra-Bueno, Daniel Alfonso Suarez-Barrera, Miguel Orlando Rueda-Forero, Nohora Juliana |
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Biomol |
| dc.subject.proposal.eng.fl_str_mv |
Heuristics Directed molecular evolution Protein engineering Bacillus thuringiensis |
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Heuristics Directed molecular evolution Protein engineering Bacillus thuringiensis |
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Digital |
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2020 |
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2020-07-27 |
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2022-04-27T15:47:48Z |
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Pinzón-Reyes EH, Sierra-Bueno DA, Suarez-Barrera MO, Rueda-Forero NJ, Abaunza-Villamizar S, Rondón-Villareal P. Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling. Evolutionary Bioinformatics. January 2020. doi:10.1177/1176934320924681 |
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Scopus |
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Evolutionary Bioinformatics |
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© The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions |
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Atribución-NoComercial 4.0 Internacional (CC BY-NC 4.0) |
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14 p |
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Pinzón-Reyes, Efraín Hernandof32016a4-7947-488c-95ea-7917c3cd3944-1Sierra-Bueno, Daniel Alfonso28665535-87e0-45e4-95f0-f2ab67cdb48a-1Suarez-Barrera, Miguel Orlandof29bdf0a-38a9-4ff0-a936-6eca5b5e1e8a-1Rueda-Forero, Nohora Juliana8a2dca8e-5326-487a-924d-96760e98b2bd-1Biomol2022-04-27T15:47:48Z2022-04-27T15:47:48Z2020-07-27DigitalDirected evolution methods mimic in vitro Darwinian evolution, inducing random mutations and selective pressure in genes to obtain proteins with enhanced characteristics. These techniques are developed using trial-and-error testing at an experimental level with a high degree of uncertainty. Therefore, in silico modeling of directed evolution is required to support experimental assays. Several in silico approaches have reproduced directed evolution, using statistical, thermodynamic, and kinetic models in an attempt to recreate experimental conditions. Likewise, optimization techniques using heuristic models have been used to understand and find the best scenarios of directed evolution. Our study uses an in silico model named HeurIstics DirecteD EvolutioN, which is based on a genetic algorithm designed to generate chimeric libraries from 2 parental genes, cry11Aa and cry11Ba, of Bacillus thuringiensis. These genes encode crystal-shaped δ-endotoxins with 3 conserved domains. Cry11 toxins are of biotechnological interest because they have shown to be effective as biopesticides for disease-spreading vectors. With our heuristic model, we considered experimental parameters such as DNA fragmentation length, number of generations or simulation cycles, and mutation rate, to get characteristics of Cry11 chimeric libraries such as percentage of population identity, truncation of variants obtained from the presence of internal stop codons, percentage of thermodynamic diversity, and stability of variants. Our study allowed us to focus on experimental conditions that may be useful for the design of in vitro and in silico experiments of directed evolution with Cry toxins of 3 conserved domains. Furthermore, we obtained in silico libraries of Cry11 variants, in which structural characteristics of wild Cry families were observed in a review of a sample of in silico sequences. We consider that future studies could use our in silico libraries and heuristic computational models, as the one suggested here, to support in vitro experiments of directed evolution.Ciencias Exactas y Naturales14 papplication/pdfhttps://doi.org/10.1177/11769343209246811176-9343https://repositorio.udes.edu.co/handle/001/6632engNueva Zelanda14116Pinzón-Reyes EH, Sierra-Bueno DA, Suarez-Barrera MO, Rueda-Forero NJ, Abaunza-Villamizar S, Rondón-Villareal P. Generation of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling. Evolutionary Bioinformatics. January 2020. doi:10.1177/1176934320924681ScopusEvolutionary Bioinformatics© The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissionsinfo:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/http://purl.org/coar/access_right/c_abf2https://journals.sagepub.com/doi/pdf/10.1177/1176934320924681HeuristicsDirected molecular evolutionProtein engineeringBacillus thuringiensisGeneration of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational ModelingArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Todas las AudienciasPublicationORIGINALGeneration of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling.pdfGeneration of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling.pdfapplication/pdf234538https://repositorio.udes.edu.co/bitstreams/f9e8bd4b-d302-4d0d-956b-91a054911b8e/download45ef86464ce1ea2ca637dcc032939fa2MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-859https://repositorio.udes.edu.co/bitstreams/eca1ad6b-a1ad-404d-a641-5e4c0e2911af/download38d94cf55aa1bf2dac1a736ac45c881cMD52TEXTGeneration of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling.pdf.txtGeneration of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling.pdf.txtExtracted texttext/plain5https://repositorio.udes.edu.co/bitstreams/399ca64e-90c7-4626-ba37-c3f2f1c8f064/download5dbe86c1111d64f45ba435df98fdc825MD53THUMBNAILGeneration of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling.pdf.jpgGeneration of Cry11 Variants of Bacillus thuringiensis by Heuristic Computational Modeling.pdf.jpgGenerated Thumbnailimage/jpeg12235https://repositorio.udes.edu.co/bitstreams/eab7d4cf-2a4b-41be-8904-558c07b533c5/downloadf1a63f9ac663caa141bc6b5e7207e212MD54001/6632oai:repositorio.udes.edu.co:001/66322023-10-11 11:10:13.393https://creativecommons.org/licenses/by-nc/4.0/© The Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissionshttps://repositorio.udes.edu.coRepositorio Universidad de Santandersoporte@metabiblioteca.comTGljZW5jaWEgZGUgUHVibGljYWNpw7NuIFVERVMKRGlyZWN0cmljZXMgZGUgVVNPIHkgQUNDRVNPCgo= |