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Protein aggregation: in silico algorithms and applications.
Prabakaran, R; Rawat, Puneet; Thangakani, A Mary; Kumar, Sandeep; Gromiha, M Michael.
Afiliação
  • Prabakaran R; Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India.
  • Rawat P; Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India.
  • Thangakani AM; Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India.
  • Kumar S; Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceutical Inc., Ridgefield, CT USA.
  • Gromiha MM; Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu India.
Biophys Rev ; 13(1): 71-89, 2021 Feb.
Article em En | MEDLINE | ID: mdl-33747245
ABSTRACT
Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Biophys Rev Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Biophys Rev Ano de publicação: 2021 Tipo de documento: Article