Optimizing Scorpion Toxin Processing through Artificial Intelligence

Autores
Psenicnik, Adam; Ojanguren Affilastro, Andres Alejandro; Graham, Matthew R.; Hassan, Mohamed K.; Abdel Rahman, Mohamed A.; Sharma, Prashant P.; Santibáñez López, Carlos E.
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Scorpion toxins are relatively short cyclic peptides (<150 amino acids) that can disrupt theopening/closing mechanisms in cell ion channels. These peptides are widely studied for severalreasons including their use in drug discovery. Although improvements in RNAseq have greatlyexpedited the discovery of new scorpion toxins, their annotation remains challenging, mainly due totheir small size. Here, we present a new pipeline to annotate toxins from scorpion transcriptomesusing a neural network approach. This pipeline implements basic neural networks to sort amino acidsequences to find those that are likely toxins and thereafter predict the type of toxin represented bythe sequence. We anticipate that this pipeline will accelerate the classification of scorpion toxins inforthcoming scorpion genome sequencing projects and potentially serve a useful role in identifyingtargets for drug development.
Fil: Psenicnik, Adam. Western Connecticut State University; Estados Unidos
Fil: Ojanguren Affilastro, Andres Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia". Departamento de Invertebrados. Area de Entomologia; Argentina
Fil: Graham, Matthew R.. Eastern Connecticut State University; Estados Unidos
Fil: Hassan, Mohamed K.. Port Said University; Egipto
Fil: Abdel Rahman, Mohamed A.. Suez Canal University; Egipto
Fil: Sharma, Prashant P.. University Of Wisconsin-madison; Estados Unidos
Fil: Santibáñez López, Carlos E.. Western Connecticut State University; Estados Unidos
Materia
Phyton
RNAseq
Sodium channel toxins
Neural network
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/263490
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Optimizing Scorpion Toxin Processing through Artificial IntelligencePsenicnik, AdamOjanguren Affilastro, Andres AlejandroGraham, Matthew R.Hassan, Mohamed K.Abdel Rahman, Mohamed A.Sharma, Prashant P.Santibáñez López, Carlos E.PhytonRNAseqSodium channel toxinsNeural networkhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Scorpion toxins are relatively short cyclic peptides (<150 amino acids) that can disrupt theopening/closing mechanisms in cell ion channels. These peptides are widely studied for severalreasons including their use in drug discovery. Although improvements in RNAseq have greatlyexpedited the discovery of new scorpion toxins, their annotation remains challenging, mainly due totheir small size. Here, we present a new pipeline to annotate toxins from scorpion transcriptomesusing a neural network approach. This pipeline implements basic neural networks to sort amino acidsequences to find those that are likely toxins and thereafter predict the type of toxin represented bythe sequence. We anticipate that this pipeline will accelerate the classification of scorpion toxins inforthcoming scorpion genome sequencing projects and potentially serve a useful role in identifyingtargets for drug development.Fil: Psenicnik, Adam. Western Connecticut State University; Estados UnidosFil: Ojanguren Affilastro, Andres Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia". Departamento de Invertebrados. Area de Entomologia; ArgentinaFil: Graham, Matthew R.. Eastern Connecticut State University; Estados UnidosFil: Hassan, Mohamed K.. Port Said University; EgiptoFil: Abdel Rahman, Mohamed A.. Suez Canal University; EgiptoFil: Sharma, Prashant P.. University Of Wisconsin-madison; Estados UnidosFil: Santibáñez López, Carlos E.. Western Connecticut State University; Estados UnidosMDPI2024-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/263490Psenicnik, Adam; Ojanguren Affilastro, Andres Alejandro; Graham, Matthew R.; Hassan, Mohamed K.; Abdel Rahman, Mohamed A.; et al.; Optimizing Scorpion Toxin Processing through Artificial Intelligence; MDPI; Toxins; 16; 10; 10-2024; 1-122072-6651CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-6651/16/10/437info:eu-repo/semantics/altIdentifier/doi/10.3390/toxins16100437info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:42:12Zoai:ri.conicet.gov.ar:11336/263490instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:42:12.657CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimizing Scorpion Toxin Processing through Artificial Intelligence
title Optimizing Scorpion Toxin Processing through Artificial Intelligence
spellingShingle Optimizing Scorpion Toxin Processing through Artificial Intelligence
Psenicnik, Adam
Phyton
RNAseq
Sodium channel toxins
Neural network
title_short Optimizing Scorpion Toxin Processing through Artificial Intelligence
title_full Optimizing Scorpion Toxin Processing through Artificial Intelligence
title_fullStr Optimizing Scorpion Toxin Processing through Artificial Intelligence
title_full_unstemmed Optimizing Scorpion Toxin Processing through Artificial Intelligence
title_sort Optimizing Scorpion Toxin Processing through Artificial Intelligence
dc.creator.none.fl_str_mv Psenicnik, Adam
Ojanguren Affilastro, Andres Alejandro
Graham, Matthew R.
Hassan, Mohamed K.
Abdel Rahman, Mohamed A.
Sharma, Prashant P.
Santibáñez López, Carlos E.
author Psenicnik, Adam
author_facet Psenicnik, Adam
Ojanguren Affilastro, Andres Alejandro
Graham, Matthew R.
Hassan, Mohamed K.
Abdel Rahman, Mohamed A.
Sharma, Prashant P.
Santibáñez López, Carlos E.
author_role author
author2 Ojanguren Affilastro, Andres Alejandro
Graham, Matthew R.
Hassan, Mohamed K.
Abdel Rahman, Mohamed A.
Sharma, Prashant P.
Santibáñez López, Carlos E.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Phyton
RNAseq
Sodium channel toxins
Neural network
topic Phyton
RNAseq
Sodium channel toxins
Neural network
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Scorpion toxins are relatively short cyclic peptides (<150 amino acids) that can disrupt theopening/closing mechanisms in cell ion channels. These peptides are widely studied for severalreasons including their use in drug discovery. Although improvements in RNAseq have greatlyexpedited the discovery of new scorpion toxins, their annotation remains challenging, mainly due totheir small size. Here, we present a new pipeline to annotate toxins from scorpion transcriptomesusing a neural network approach. This pipeline implements basic neural networks to sort amino acidsequences to find those that are likely toxins and thereafter predict the type of toxin represented bythe sequence. We anticipate that this pipeline will accelerate the classification of scorpion toxins inforthcoming scorpion genome sequencing projects and potentially serve a useful role in identifyingtargets for drug development.
Fil: Psenicnik, Adam. Western Connecticut State University; Estados Unidos
Fil: Ojanguren Affilastro, Andres Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia". Departamento de Invertebrados. Area de Entomologia; Argentina
Fil: Graham, Matthew R.. Eastern Connecticut State University; Estados Unidos
Fil: Hassan, Mohamed K.. Port Said University; Egipto
Fil: Abdel Rahman, Mohamed A.. Suez Canal University; Egipto
Fil: Sharma, Prashant P.. University Of Wisconsin-madison; Estados Unidos
Fil: Santibáñez López, Carlos E.. Western Connecticut State University; Estados Unidos
description Scorpion toxins are relatively short cyclic peptides (<150 amino acids) that can disrupt theopening/closing mechanisms in cell ion channels. These peptides are widely studied for severalreasons including their use in drug discovery. Although improvements in RNAseq have greatlyexpedited the discovery of new scorpion toxins, their annotation remains challenging, mainly due totheir small size. Here, we present a new pipeline to annotate toxins from scorpion transcriptomesusing a neural network approach. This pipeline implements basic neural networks to sort amino acidsequences to find those that are likely toxins and thereafter predict the type of toxin represented bythe sequence. We anticipate that this pipeline will accelerate the classification of scorpion toxins inforthcoming scorpion genome sequencing projects and potentially serve a useful role in identifyingtargets for drug development.
publishDate 2024
dc.date.none.fl_str_mv 2024-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/263490
Psenicnik, Adam; Ojanguren Affilastro, Andres Alejandro; Graham, Matthew R.; Hassan, Mohamed K.; Abdel Rahman, Mohamed A.; et al.; Optimizing Scorpion Toxin Processing through Artificial Intelligence; MDPI; Toxins; 16; 10; 10-2024; 1-12
2072-6651
CONICET Digital
CONICET
url http://hdl.handle.net/11336/263490
identifier_str_mv Psenicnik, Adam; Ojanguren Affilastro, Andres Alejandro; Graham, Matthew R.; Hassan, Mohamed K.; Abdel Rahman, Mohamed A.; et al.; Optimizing Scorpion Toxin Processing through Artificial Intelligence; MDPI; Toxins; 16; 10; 10-2024; 1-12
2072-6651
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-6651/16/10/437
info:eu-repo/semantics/altIdentifier/doi/10.3390/toxins16100437
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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