iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides.
Proteomics
; 24(9): e2300257, 2024 May.
Article
en En
| MEDLINE
| ID: mdl-38263811
ABSTRACT
With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http//mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Antivirales
/
Péptidos
/
Biología Computacional
/
Internalización del Virus
/
Aprendizaje Automático
/
SARS-CoV-2
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Proteomics
Asunto de la revista:
BIOQUIMICA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Taiwán