key: cord-0934357-wzicwmwn authors: Polanco, Carlos; Samaniego, José Lino; Castañón-González, Jorge Alberto; Buhse, Thomas title: Polar Profile of Antiviral Peptides from AVPpred Database date: 2014-07-04 journal: Cell Biochem Biophys DOI: 10.1007/s12013-014-0084-4 sha: ba0d7e6e2901282c0967e3feda8e6b92868b5c5a doc_id: 934357 cord_uid: wzicwmwn Diseases of viral origin in humans are among the most serious threats to health and the global economy. As recent history has shown the virus has a high pandemic potential, among other reasons, due to its ability to spread by air, hence the identification, investigation, containment, and treatment of viral diseases should be considered of paramount importance. In this sense, the bioinformatics research has focused on finding fast and efficient algorithms that can identify highly toxic antiviral peptides and to serve as a first filter, so that trials in the laboratory are substantially reduced. The work presented here contributes to this effort through the use of an algorithm already published by this team, called polarity index method, which identifies with high efficiency antiviral peptides from the exhaustive analysis of the polar profile, using the linear sequence of the peptide. The test carried out included all peptides in APD2 Database and 60 antiviral peptides identified by Kumar and co-workers (Nucleic Acids Res 40:W199–204, 2012), to build its AVPpred algorithm. The validity of the method was focused on its discriminating capacity so we included the 15 sub-classifications of both Databases. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12013-014-0084-4) contains supplementary material, which is available to authorized users. Within the antimicrobial peptides there is a group of particular importance, the antiviral peptides. These peptides are potential drugs to face the increased incidence of chronic viral infections caused by HIV and hepatitis [4, 5] ; for that reason there is a need to accelerate the development of synthetic antiviral peptides. Inclusive, the testing of vaccines against HIV and hepatitis [6, 7] that are under development offers no guarantee of success regardless of the millions of infected people. In counterpart, antiviral medications are not oriented to the most acute infections that cause serious diseases, such as hemorrhagic fever and cancer [8] [9] [10] ; they have limited effectiveness and serious side effects. Perhaps more importantly, the antiviral chemotherapy is producing a rapid development of drugresistant strains, as a result of the high rate of virus replication, due to the low resistance to replication [11] . In such a scenario, the work in proteomics and bioinformatics should focus on the generation of fast and robust algorithms [12, 13] identifying the antiviral action, from the linear or three-dimensional structure of the peptide. However, the simulation of the three-dimensional structure of the peptide is very complex, without taking into account other factors involved such as: The dynamics of the membrane and toxicity. In this work, we use the Polarity index method [14] , already published by our team to identify SCAAP [15] [16] [17] and which only uses the linear peptide sequence to identify the same group of antiviral peptides that were identified by AVPpred algorithm [2] . This method [14] generates an exhaustive analysis of the peptide polarity through its polarity matrix. In this sense, the method apparently does not consider other factors, but indirectly it does, because it requires being calibrated by ''a set of peptides'' that are characteristic of the profile. The identification of antiviral peptides performed by the polarity index method [14] [14] by the Table 1 , which represents the incidences of antiviral sequences with a unique pathogenic action. From Kumar and co workers [2] work about antiviral peptides, we took 60 validated and experimental peptides from 1,245 antiviral peptides. His work evaluated these 60 peptides with 25 physicochemical properties (Table 3) out of 144 properties from the same database and called it AAindex database [3] . These peptides were used to build the SVM AVPpred algorithm [2] , and we are using them here to validate the Polarity index method Polarity index method is an algorithm that determines the probable antiviral pathogen action of peptides by using the peptide polarity sequence. It was applied to the APD2 database and the experimental peptides from AVPpred algorithm [2] with the following results. Selected physicochemical properties to build the AVPpred algorithm by Kumar [2] . AAindex amino acid index database [3] . Polarity (4) physicochemical properties are directly or indirectly related to the polarity From the 25 physicochemical properties used to design the SVM AVPpred algorithm [2] , 21 are directly or indirectly (18/25 = 84 %) related to the polarity (Table 3, column Polarity with entries with figure 4) . Polarity index method had over 43/60 = 71 % efficiency detecting the 60 validated and experimental antiviral peptides from Kumar and coworkers [2] (Table 4 , entries 1-60 and Table 5 column AVPpred), and 1/1 = 100 % detecting the antiviral peptides from APD2 database ( Table 4, entry 61 and Table 5 , colum Virus). There are no coincidences between both excluding sets ( Table 4 , columns #1 and #2). When reviewing different databases of antimicrobial peptides, we detected a peptide with predominantly toxic action toward a pathogenic group; this allows us to assume that nature considers only small changes in the primary structure of the peptide to induce its possible pathogenic action. In that sense, the peptide linear structure plays an important role in the identification of its pathogenic action, when we use algorithms that use ''training sets'' with the desired profile. Although the physicochemical property called polarity is involved in most of the algorithms that predict anti-virus peptides, this method has an innovative aspect as it expresses the metric through a polarity matrix that includes 16 interactions. We see this matrix as a picture of the polar dynamics of the peptide. The polarity matrix clearly shows a pattern that led us to achieve an identification efficiency of 70 % on the AVPpred database. The same pattern also rejected other groups of peptides in APD2 database, with the exception of the anti-virus set. We assume that if we built the matrix with one digit, perhaps we did not have enough information to focus the method correctly. We believe the effectiveness of the polarity index method in terms of the computing resources required makes it suitable candidate for a more detailed analysis related to the subdomains of peptides. In this regards, we have initiated a comprehensive classification of the APD2 database gathering, from published manuscripts, the toxicity values of antimicrobial peptides and thus explore peptide sub-domains with specific and very toxic pathogenic action. Our team is working on this as we consider it of vital importance to strongly support basic scientific research. We have also published work where the same method is used to understand the profile of the ''first proteins'' from 4 billion years ago. For this task, we are using clusters of GPU coprocessors, which will allow the analysis of 15 amino acids in length peptides. Finally, within the antiviral peptide group, there are two sub-groups not approached in this work as they constitute by themselves an independent topic for the importance they Polarity index method matches for both APD2 [1] and AVPpred antiviral peptide groups [2] . Unique action peptides with pathogenic action against only one group. Multiple action peptides with pathogenic action against two or more groups. (%): Percentage of hits/total peptides have and the degree of subject matter expertise required: the influenza A type H1N1 and HIV peptides. Given their potential to provoke a world pandemic, these two groups of peptides concentrate the efforts of several research groups, as they can undoubtedly become a problem of enormous proportions. Our team is now directing the method toward the identification of these two sub-groups. 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