|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|14950||1362||2016||6 صفحه PDF||سفارش دهید||دانلود رایگان|
• 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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Journal: Computational Biology and Chemistry - Volume 61, April 2016, Pages 245–250