Analysis and Prediction of Highly Effective Antiviral Peptides Based on Random Forests

Abstract

The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficient, which exceeds the previous models. The results suggest that aggregation could be an important feature for identifying antiviral peptides. In addition, our analysis reveals the characteristics of the antiviral peptides such as the importance of lysine and the abundance of α-helical secondary structures.

AntiVPP 1.0: A portable tool for prediction of antiviral peptides

“…To date, the algorithm based on RF for the prediction of AVPs has been the one that has shown a better performance in the prediction of these molecules as reported in the literature [11,16,17]. The comparison of the performance measures obtained in our study, using the different algorithms, supports the previous results on the robustness of RF for AVP predictions [16], as shown in Table 1.…”

“…In this study, we evaluated the RF algorithm using new combinations of chemical-physical characteristics of the AVPs, obtaining an excellent model with the following performance measures during the validation phase: TPR = 0.87, SPC = 0.97, ACC = 0.93, and MCC = 0.87. In addition, we also confirmed the need to include the relative frequency for the improvement of AVP predictions as previously reported [16]. A comparison among the existing methods for the prediction of AVPs shows that AntiVPP 1.0 has the highest SPC.…”

“…For training of the model, the data set T544p+544n* was used (a total of 1088 peptides). 544p corresponds to a collection of 544 antiviral peptides with experimentally validated activity, while the 544n* are 544 non-experimental negative peptides, which has been https://doi.org T used in the development of prediction models of antiviral peptides [11,16]. For validation of the model, the independent data set V60p +60n* was selected, composed of 60 peptides with experimentally validated activity (V60p) and 60 negative non-experimental peptides (60n*) (a total of 120 peptides).…”

“…The first one is the AVPpred server, which uses a vector support machine (SVM) for its predictions [11]. The second method is based on Random Forest (RF) algorithm and the resulting model of this work showed a better performance in the prediction of AVPs than AVPpred [16]. However, this model has not software to carry out prediction tasks by researchers who are not related to the field of machine learning.…”

AVP‐IC50Pred: Multiple machine learning techniques‐based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)

“…Lately, antimicrobial peptides have been used to control different types of pathogens, particularly viruses which are a major cause of malaise and death in the world because of their high genetic variation, different routes of transmission, efficient replication, and the capability to persist in the host cells . Although there are several traditional antiviral nucleoside and non‐nucleoside analogues against few viruses, many of these drugs have undesirable toxic effects . Whereas, AVPs with natural amino acids have lesser toxicity and are easily eliminated from the body …”