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

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.