Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models.
Microb Pathog
; 193: 106727, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38851362
ABSTRACT
Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in human. In recent years, an increasing number of strains of K. pneumoniae resistant to multiple antibiotics have emerged, posing a significant threat to public health. The protein function of this bacterium is not well known, thus a systematic investigation of K. pneumoniae proteome is in urgent need. In this study, the protein functions of this bacteria were re-annotated, and their function groups were analyzed. Moreover, three machine learning models were built to identify novel virulence factors. Results showed that the functions of 16 uncharacterized proteins were first annotated by sequence alignment. In addition, K. pneumoniae proteins share a high proportion of homology with Haemophilus influenzae and a low homology proportion with Chlamydia pneumoniae. By sequence analysis, 10 proteins were identified as potential drug targets for this bacterium. Our model achieved a high accuracy of 0.901 in the benchmark dataset. By applying our models to K. pneumoniae, we identified 39 virulence factors in this pathogen. Our findings could provide novel clues for the treatment of K. pneumoniae infection.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas Bacterianas
/
Genoma Bacteriano
/
Factores de Virulencia
/
Aprendizaje Automático
/
Klebsiella pneumoniae
Límite:
Humans
Idioma:
En
Revista:
Microb Pathog
Asunto de la revista:
DOENCAS TRANSMISSIVEIS
/
MICROBIOLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
China