A computational model for predicting fusion peptide of retroviruses. (April 2016) - Wu, Sijia; Han, Jiuqiang; Liu, Ruiling; Liu, Jun; Lv, Hongqiang
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 .