Accurate prediction of protein beta-aggregation with generalized statistical potentials.
Bioinformatics
; 36(7): 2076-2081, 2020 04 01.
Article
em En
| MEDLINE
| ID: mdl-31904854
ABSTRACT
MOTIVATION Protein beta-aggregation is an important but poorly understood phenomena involved in diseases as well as in beneficial physiological processes. However, while this task has been investigated for over 50 years, very little is known about its mechanisms of action. Moreover, the identification of regions involved in aggregation is still an open problem and the state-of-the-art methods are often inadequate in real case applications. RESULTS:
In this article we present AgMata, an unsupervised tool for the identification of such regions from amino acidic sequence based on a generalized definition of statistical potentials that includes biophysical information. The tool outperforms the state-of-the-art methods on two different benchmarks. As case-study, we applied our tool to human ataxin-3, a protein involved in Machado-Joseph disease. Interestingly, AgMata identifies aggregation-prone residues that share the very same structural environment. Additionally, it successfully predicts the outcome of in vitro mutagenesis experiments, identifying point mutations that lead to an alteration of the aggregation propensity of the wild-type ataxin-3. AVAILABILITY AND IMPLEMENTATION A python implementation of the tool is available at https//bitbucket.org/bio2byte/agmata. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Doença de Machado-Joseph
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article