Prediction of Disordered Regions in Proteins with Recurrent Neural Networks and Protein Dynamics.
J Mol Biol
; 434(12): 167579, 2022 06 30.
Article
en En
| MEDLINE
| ID: mdl-35469832
ABSTRACT
The role of intrinsically disordered protein regions (IDRs) in cellular processes has become increasingly evident over the last years. These IDRs continue to challenge structural biology experiments because they lack a well-defined conformation, and bioinformatics approaches that accurately delineate disordered protein regions remain essential for their identification and further investigation. Typically, these predictors use the protein amino acid sequence, without taking into account likely sequence-dependent emergent properties, such as protein backbone dynamics. Here we present DisoMine, a method that predicts protein'long disorder' with recurrent neural networks from simple predictions of protein dynamics, secondary structure and early folding. The tool is fast and requires only a single sequence, making it applicable for large-scale screening, including poorly studied and orphan proteins. DisoMine is a top performer in its category and compares well to disorder prediction approaches using evolutionary information. DisoMine is freely available through an interactive webserver at https//bio2byte.be/disomine/.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Redes Neurales de la Computación
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Análisis de Secuencia de Proteína
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Proteínas Intrínsecamente Desordenadas
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
J Mol Biol
Año:
2022
Tipo del documento:
Article
País de afiliación:
Bélgica