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Overview Update: Computational Prediction of Intrinsic Disorder in Proteins.
Uversky, Vladimir N; Kurgan, Lukasz.
Afiliación
  • Uversky VN; Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida.
  • Kurgan L; Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia.
Curr Protoc ; 3(6): e802, 2023 Jun.
Article en En | MEDLINE | ID: mdl-37310199
There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aminoácidos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Curr Protoc Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aminoácidos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Curr Protoc Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos