ACVPICPred: Inhibitory activity prediction of anti-coronavirus peptides based on artificial neural network

Abstract

Peptides, as small molecular compounds, exhibit prominent advantages in the inhibition of coronaviruses due to their safety, efficacy, and specificity, holding great promise as drugs against coronaviruses. The rapid and efficient determination of the activity of anti-coronavirus peptides (ACovPs) can greatly accelerate the development of drugs for treating coronavirus-related diseases. Hence, we present ACVPICPred, a computational model designed to predict the inhibitory activity of ACovPs based on their sequences and structural information. By leveraging bioinformatics tools AlphaFold3 for structural predictions and several feature extraction methods, the model integrates both sequence and structural features to enhance prediction accuracy. To address the limitations of existing datasets, we employed data augmentation techniques, including the introduction of noise and the SMOGN, to improve the model robustness. The model’s performance was evaluated through five-fold cross-validation, achieving a Pearson correlation coefficient of 0.7668 (p < 0.05) and an R² of 0.5880 on the training dataset. Overall, in our study, compared to models that only use sequence features, models that combine structural features have achieved more robust results in various evaluation metrics. ACVPICPred is freely accessible at the following URL: http://i.uestc.edu.cn/acvpICPred/main/Main.php.

Keywords

• Anti-coronavirus peptides
• Inhibitory concentration
• Regression
• Artificial neural network