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A computational model for predicting fusion peptide of retroviruses

Paper ID Volume ID Publish Year Pages File Format Full-Text
14950 1362 2016 6 PDF Available
Title
A computational model for predicting fusion peptide of retroviruses
Abstract

•A novel computational model for predicting fusion peptide of retroviruses was proposed.•A software tool named FP_predict.exe has been developed.•A large number of new putative FPs of five typical retroviruses were predicted.•Property, motif and evolutionary relationship about FP were computed and discussed.

As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar.

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Keywords
Fusion peptide domain prediction; Hidden Markov Method; Similarity comparison
First Page Preview
A computational model for predicting fusion peptide of retroviruses
Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 61, April 2016, Pages 245–250
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering