Analysis, Modeling, and Target-Specific Predictions of Linear Peptides Inhibiting Virus Entry

Abstract

Antiviral peptides (AVPs) are bioactive peptides that exhibit the inhibitory activity against viruses through a range of mechanisms. Virus entry inhibitory peptides (VEIPs) make up a specific class of AVPs that can prevent envelope viruses from entering cells. With the growing number of experimentally verified VEIPs, there is an opportunity to use machine learning to predict peptides that inhibit the virus entry. In this paper, we have developed the first target-specific prediction model for the identification of new VEIPs using, along with the peptide sequence characteristics, the attributes of the envelope proteins of the target virus, which overcomes the problem of insufficient data for particular viral strains and improves the predictive ability. The model's performance was evaluated through 10 repeats of 10-fold cross-validation on the training data set, and the results indicate that it can predict VEIPs with 87.33% accuracy and Matthews correlation coefficient (MCC) value of 0.76. The model also performs well on an independent test set with 90.91% accuracy and MCC of 0.81. We have also developed an automatic computational tool that predicts VEIPs, which is freely available at https://dbaasp.org/tools?page=linear-amp-prediction.

Figure 1

TM-scores between all and reference envelope proteins.

Figure 2

Superposition of envelope proteins from (a) DENV-2 (Blue (PDB: 7KV8)) and JEV (Red (PDB: 5MV1)); (b) SARS-COV-2 (Blue (PDB: 7CZT)) and SARS-COV (Red (PDB: 6CRV)).

Figure 3

Comparison of the amino acid composition among VEIPs and non-VEIPs.

Figure 4

Comparison of the physicochemical characteristics among VEIPs and non-VEIPs.

Figure 5

Average accuracies of the best predictive model proposed by applying the Y-scrambling test with different shuffling percentages of the true activity.