State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage.
Proteomics
; 23(21-22): e2200292, 2023 Nov.
Article
em En
| MEDLINE
| ID: mdl-37401192
ABSTRACT
Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Proteínas
/
Mapeamento de Interação de Proteínas
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Proteomics
Assunto da revista:
BIOQUIMICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos