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Analyzing Protein Disorder with IUPred2A.
Erdos, Gábor; Dosztányi, Zsuzsanna.
Afiliación
  • Erdos G; Department of Biochemistry, MTA-ELTE Momentum Bioinformatics Research Group, ELTE Eötvös Loránd University, Budapest, Hungary.
  • Dosztányi Z; Department of Biochemistry, MTA-ELTE Momentum Bioinformatics Research Group, ELTE Eötvös Loránd University, Budapest, Hungary.
Curr Protoc Bioinformatics ; 70(1): e99, 2020 06.
Article en En | MEDLINE | ID: mdl-32237272
ABSTRACT
IUPred2A is a combined prediction tool designed to discover intrinsically disordered or conditionally disordered proteins and protein regions. Intrinsically disordered regions exist without a well-defined three-dimensional structure in isolation but carry out important biological functions. Over the years, various prediction methods have been developed to characterize disordered regions. The existence of disordered segments can also be dependent on different factors such as binding partners or environmental traits like pH or redox potential, and recognizing such regions represents additional computational challenges. In this article, we present detailed instructions on how to use IUPred2A, one of the most widely used tools for the prediction of disordered regions/proteins or conditionally disordered segments, and provide examples of how the predictions can be interpreted in different contexts. © 2020 The Authors. Basic Protocol 1 Analyzing disorder propensity with IUPred2A online Basic Protocol 2 Analyzing disordered binding regions using ANCHOR2 Support Protocol 1 Interpretation of the results Basic Protocol 3 Analyzing redox-sensitive disordered regions Support Protocol 2 Download options Support Protocol 3 REST API for programmatic purposes Basic Protocol 4 Using IUPred2A locally.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Proteínas / Biología Computacional Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Curr Protoc Bioinformatics Año: 2020 Tipo del documento: Article País de afiliación: Hungria

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Proteínas / Biología Computacional Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Curr Protoc Bioinformatics Año: 2020 Tipo del documento: Article País de afiliación: Hungria
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