A computational model for predicting fusion peptide of retroviruses. (April 2016)

• Record Type: Journal Article
• Authors: Wu, Sijia; Han, Jiuqiang; Liu, Ruiling; Liu, Jun; Lv, Hongqiang
• Is Part Of: Computational biology and chemistry. Volume 61(2016)
• Page Start: 245
• Page End: 250
• Publication Date: 2016-04
• Subjects: Fusion peptide domain prediction -- Hidden Markov Method -- Similarity comparison; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.02.013
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A novel computational model for predicting fusion peptide of retroviruses was proposed. A software tool namedFP_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. Abstract: 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 athttps://sourceforge.net/projects/fptool/files/?source=navbar .