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FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization.
Jiang, Jici; Pei, Hongdi; Li, Jiayu; Li, Mingxin; Zou, Quan; Lv, Zhibin.
Afiliação
  • Jiang J; College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
  • Pei H; College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
  • Li J; College of Life Science, Sichuan University, Chengdu 610065, China.
  • Li M; College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
  • Zou Q; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Lv Z; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
Brief Bioinform ; 25(2)2024 Jan 22.
Article em En | MEDLINE | ID: mdl-38366802
ABSTRACT
Anti-coronavirus peptides (ACVPs) represent a relatively novel approach of inhibiting the adsorption and fusion of the virus with human cells. Several peptide-based inhibitors showed promise as potential therapeutic drug candidates. However, identifying such peptides in laboratory experiments is both costly and time consuming. Therefore, there is growing interest in using computational methods to predict ACVPs. Here, we describe a model for the prediction of ACVPs that is based on the combination of feature engineering (FE) optimization and deep representation learning. FEOpti-ACVP was pre-trained using two feature extraction frameworks. At the next step, several machine learning approaches were tested in to construct the final algorithm. The final version of FEOpti-ACVP outperformed existing methods used for ACVPs prediction and it has the potential to become a valuable tool in ACVP drug design. A user-friendly webserver of FEOpti-ACVP can be accessed at http//servers.aibiochem.net/soft/FEOpti-ACVP/.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article