College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
College of Life Science, Sichuan University, Chengdu 610065, China.
Brief Bioinform. 2024 Jan 22;25(2). doi: 10.1093/bib/bbae037.
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/.
抗病毒肽（ACVPs）代表了一种抑制病毒与人细胞吸附和融合的相对新颖方法。一些基于肽的抑制剂作为有前途的潜在治疗候选药物显示出了希望。然而，在实验室实验中鉴定这些肽既昂贵又耗时。因此，人们越来越感兴趣地使用计算方法来预测 ACVPs。在这里，我们描述了一种基于特征工程（FE）优化和深度表示学习相结合的 ACVP 预测模型。FEOpti-ACVP 使用两个特征提取框架进行了预训练。在下一步中，测试了几种机器学习方法来构建最终算法。最终版本的 FEOpti-ACVP 优于用于 ACVPs 预测的现有方法，它有可能成为 ACVP 药物设计中的有价值工具。FEOpti-ACVP 的用户友好型网络服务器可在 http://servers.aibiochem.net/soft/FEOpti-ACVP/ 访问。
