id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-262748-v4xue7ha	Xu, Yongtao	Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine	2015-12-04		.txt	text/plain	4636	253	40	Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The predictive support vector machine model for selfderived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process. In view of the important role of E proteins in virus fusion process and common mechanism of action of self-derived peptides, we developed a SVM model to predict the antiviral activities of self-derived peptides using sequence-based statistical scores as input features. Because similar sequences are often associated with similar structure and function, the sequence-based property AVPalign would account for the activities of the self-derived peptide inhibitors which regulate the virus fusion by mimicking the binding to E proteins. The prominent performance of EAPscoring model indicates the sequence-based stability feature of self-derived peptides may reflect their potential of binding to E proteins so as to regulate the virus entry process.	./cache/cord-262748-v4xue7ha.txt	./txt/cord-262748-v4xue7ha.txt