Computational prediction of short linear motifs from protein sequences

Short Linear Motifs (SLiMs) are functional protein microdomains that typically mediate interactions between a short linear region in one protein and a globular domain in another. SLiMs usually occur in structurally disordered regions and mediate low affinity interactions. Most SLiMs are 3-15 amino a...

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Detalles Bibliográficos
Autores: Edwards, Richard J., Palopoli, Nicolás
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/85303
Acceso en línea:http://hdl.handle.net/11336/85303
Access Level:acceso abierto
Palabra clave:Short linear motifs
SLIM
Motif discovery
Protein-protein interactions
Posttranslational modifications
Intrinsically disordered proteins
Regular expressions
Sequence profiles
Sequence motifs
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
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repository_id_str
spelling Computational prediction of short linear motifs from protein sequencesEdwards, Richard J.Palopoli, NicolásShort linear motifsSLIMMotif discoveryProtein-protein interactionsPosttranslational modificationsIntrinsically disordered proteinsRegular expressionsSequence profilesSequence motifshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Short Linear Motifs (SLiMs) are functional protein microdomains that typically mediate interactions between a short linear region in one protein and a globular domain in another. SLiMs usually occur in structurally disordered regions and mediate low affinity interactions. Most SLiMs are 3-15 amino acids in length and have 2-5 defined positions, making them highly likely to occur by chance and extremely difficult to identify. Nevertheless, our knowledge of SLiMs and capacity to predict them from protein sequence data using computational methods has advanced dramatically over the past decade. By considering the biological, structural, and evolutionary context of SLiM occurrences, it is possible to differentiate functional instances from chance matches in many cases and to identify new regions of proteins that have the features consistent with a SLiM-mediated interaction. Their simplicity also makes SLiMs evolutionarily labile and prone to independent origins on different sequence backgrounds through convergent evolution, which can be exploited for predicting novel SLiMs in proteins that share a function or interaction partner. In this review, we explore our current knowledge of SLiMs and how it can be applied to the task of predicting them computationally from protein sequences. Rather than focusing on specific SLiM prediction tools, we provide an overview of the methods available and concentrate on principles that should continue to be paramount even in the light of future developments. We consider the relative merits of using regular expressions or profiles for SLiM discovery and discuss the main considerations for both predicting new instances of known SLiMs, and de novo prediction of novel SLiMs. In particular, we highlight the importance of correctly modelling evolutionary relationships and the probability of false positive predictions.Fil: Edwards, Richard J.. University of New South Wales; Australia. University of Southampton; Reino UnidoFil: Palopoli, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Southampton; Reino UnidoSpringer2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/85303Edwards, Richard J.; Palopoli, Nicolás; Computational prediction of short linear motifs from protein sequences; Springer; Methods in molecular biology (Clifton, N.J.); 1268; 1-2015; 89-1411940-6029CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-1-4939-2285-7_6info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/protocol/10.1007%2F978-1-4939-2285-7_6info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T14:01:34Zoai:ri.conicet.gov.ar:11336/85303instacron: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:34982024-05-08 14:01:34.959CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Computational prediction of short linear motifs from protein sequences
title Computational prediction of short linear motifs from protein sequences
spellingShingle Computational prediction of short linear motifs from protein sequences
Edwards, Richard J.
Short linear motifs
SLIM
Motif discovery
Protein-protein interactions
Posttranslational modifications
Intrinsically disordered proteins
Regular expressions
Sequence profiles
Sequence motifs
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
title_short Computational prediction of short linear motifs from protein sequences
title_full Computational prediction of short linear motifs from protein sequences
title_fullStr Computational prediction of short linear motifs from protein sequences
title_full_unstemmed Computational prediction of short linear motifs from protein sequences
title_sort Computational prediction of short linear motifs from protein sequences
dc.creator.none.fl_str_mv Edwards, Richard J.
Palopoli, Nicolás
author Edwards, Richard J.
author_facet Edwards, Richard J.
Palopoli, Nicolás
author_role author
author2 Palopoli, Nicolás
author2_role author
dc.subject.none.fl_str_mv Short linear motifs
SLIM
Motif discovery
Protein-protein interactions
Posttranslational modifications
Intrinsically disordered proteins
Regular expressions
Sequence profiles
Sequence motifs
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
topic Short linear motifs
SLIM
Motif discovery
Protein-protein interactions
Posttranslational modifications
Intrinsically disordered proteins
Regular expressions
Sequence profiles
Sequence motifs
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
description Short Linear Motifs (SLiMs) are functional protein microdomains that typically mediate interactions between a short linear region in one protein and a globular domain in another. SLiMs usually occur in structurally disordered regions and mediate low affinity interactions. Most SLiMs are 3-15 amino acids in length and have 2-5 defined positions, making them highly likely to occur by chance and extremely difficult to identify. Nevertheless, our knowledge of SLiMs and capacity to predict them from protein sequence data using computational methods has advanced dramatically over the past decade. By considering the biological, structural, and evolutionary context of SLiM occurrences, it is possible to differentiate functional instances from chance matches in many cases and to identify new regions of proteins that have the features consistent with a SLiM-mediated interaction. Their simplicity also makes SLiMs evolutionarily labile and prone to independent origins on different sequence backgrounds through convergent evolution, which can be exploited for predicting novel SLiMs in proteins that share a function or interaction partner. In this review, we explore our current knowledge of SLiMs and how it can be applied to the task of predicting them computationally from protein sequences. Rather than focusing on specific SLiM prediction tools, we provide an overview of the methods available and concentrate on principles that should continue to be paramount even in the light of future developments. We consider the relative merits of using regular expressions or profiles for SLiM discovery and discuss the main considerations for both predicting new instances of known SLiMs, and de novo prediction of novel SLiMs. In particular, we highlight the importance of correctly modelling evolutionary relationships and the probability of false positive predictions.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/85303
Edwards, Richard J.; Palopoli, Nicolás; Computational prediction of short linear motifs from protein sequences; Springer; Methods in molecular biology (Clifton, N.J.); 1268; 1-2015; 89-141
1940-6029
CONICET Digital
CONICET
url http://hdl.handle.net/11336/85303
identifier_str_mv Edwards, Richard J.; Palopoli, Nicolás; Computational prediction of short linear motifs from protein sequences; Springer; Methods in molecular biology (Clifton, N.J.); 1268; 1-2015; 89-141
1940-6029
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/978-1-4939-2285-7_6
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/protocol/10.1007%2F978-1-4939-2285-7_6
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
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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 15,812429

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