PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.
Structure
; 32(6): 838-848.e3, 2024 Jun 06.
Article
em En
| MEDLINE
| ID: mdl-38508191
ABSTRACT
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets Ssym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Mutação Puntual
/
Estabilidade Proteica
Limite:
Humans
Idioma:
En
Revista:
Structure
Assunto da revista:
BIOLOGIA MOLECULAR
/
BIOQUIMICA
/
BIOTECNOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China