The antiviral peptides derived from enveloped proteins function by binding to their respective accessory proteins, disrupting forming of the trimer-of-hairpin and membrane fusion, and therefore inhibiting the virus infection. model displayed 92% prediction accuracy with the Matthews correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector BAN ORL 24 machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process. Introduction Fusion process is the initial step of viral infection, therefore targeting the fusion process represents a promising strategy in design of antiviral therapy [1]. The entry step involves fusion of the viral and the cellular receptor membranes, which is mediated by the viral envelope (E) proteins. There are three classes of envelope proteins [2]: Class I E proteins include influenza virus (IFV) hemagglutinin and retrovirus Human Immunodeficiency Virus 1 (HIV-1) gp41; Class II E proteins include a number of important human flavivirus pathogens such as Dengue virus (DENV), Japanese encephalitis FCGR3A virus (JEV), Yellow fever virus (YFV), West Nile virus (WNV), hepatitis C virus (HCV) and Togaviridae virus such as BAN ORL 24 alphavirus Semliki Forest virus (SFV); Class III E proteins include vesicular stomatitis virus (VSV), Herpes Simplex virus-1 (HSV-1) and Human cytomegalovirus (HCMV). Although the exact fusion mechanism remains elusive and the three classes of viral fusion proteins exhibit distinct structural folds, they may share a similar mechanism of membrane fusion [3]. A peptide derived from a protein-protein interface would inhibit the formation of that interface by mimicking the interactions with its partner proteins, and therefore may serve as a promising lead in drug discovery [4]. Enfuvirtide (T20), a peptide that mimicks the HR2 region of Class I HIV-1 gp41, is the first FDA-approved HIV-1 fusion drug that inhibits the entry process of virus infection [5C7]. Then peptides mimicking extended regions of the HIV-1 gp41 were also demonstrated as effective entry inhibitors [8, 9]. Furthermore, peptides derived from a distinct region of GB virus C E2 protein were found to interfere BAN ORL 24 with the very early events of the HIV-1 replication cycle [10]. Other successful examples of Class I peptide inhibitors include peptide inhibitors derived from SARS-CoV spike glycoprotein [11C13] and from Pichinde virus (PICV) envelope protein [14]. Recently, a peptide derived from the fusion initiation region of BAN ORL 24 the glycoprotein hemagglutinin (HA) in IFV, Flufirvitide-3 (FF-3) has progressed into clinical trial [15]. The success of developing the Class I peptide inhibitors into clinical use has triggered the interests in the design of inhibitors of the Class II and Class III E proteins. e.g. several hydrophobic peptides derived from the Class II DENV and WNV E proteins exhibited potent inhibitory activities [16C20]. In addition, a potent peptide inhibitor derived from the domain III of JEV glycoprotein and a peptide inhibitor derived from the stem region of Rift Valley fever virus (RVFV) glycoprotein were reported [21, 22]. Examples of the Class II peptide inhibitors of enveloped virus also include those derived from HCV E2 protein [23, 24] and from Claudin-1, a critical host factor in HCV entry [25]. Moreover, peptides derived from the Class III HSV-1 gB also exhibited antiviral activities [26C31], as well as those derived from HCMV gB [32]. Computational informatics plays an important role in predicting the activities of the peptides generated from combinatorial libraries. methods such as data mining, generic algorithm and vector-like analysis were reported to predict the antimicrobial activities of peptides [33C35]. In addition, quantitative structure-activity relationships (QSAR) [36C40] and artificial neural networks (ANN) were applied to predict the activities of peptides [41, 42]. Recently, a support vector machine (SVM) algorithm was employed to predict the antivirus activities using the physicochemical properties of general antiviral peptides [43]. However, the mechanism of action of antiviral peptides is different from antimicrobial peptides; in fact, various protein targets are involved in the virus infection. e.g. HIV-1 virus infection involves virus fusion, integration, reverse transcription and maturation, etc. Thus it is difficult to retrieve the common features from general antiviral BAN ORL 24 peptides to represent their antiviral activities. Virus fusion is mediated by E proteins. Although E proteins are highly divergent in sequence and structure, they share a common pathway of membrane fusion dynamics. i.e. E proteins experience significant conformational change to form a-trimer-of-hairpin, which drives the fusion of viral membrane and host membrane [44]. The antiviral peptides derived from enveloped proteins function by binding to their respective accessory proteins, disrupting forming of the trimer-of-hairpin and membrane fusion, and therefore inhibiting the.