The prediction obtained for our applicant is highly reliable, since the C-Score of the model is 1.42, and the cutoff value to consider a good prediction is -1.5. to be weighted by their immunogenicities. Motivated by these potential applications of constructing short weighted -superstrings to vaccine design, we approach this problem in two ways. First, we cIAP2 formalize the problem as a combinatorial optimization problem (in fact, as two polynomially equivalent problems) and develop an integer programming (IP) formulation for solving it optimally. Second, we describe a model that also takes into account good pairwise alignments of the obtained superstring with the input strings, and present a genetic algorithm that solves the problem approximately. We apply both algorithms to a set of 169 strings corresponding to the Nef protein taken from patiens infected with HIV-1. In the Mirodenafil dihydrochloride IP-based algorithm, we take the epitopes and the estimation of the immunogenicities from databases of experimental epitopes. In the genetic algorithm we take as candidate epitopes all 9-mers present in the 169 strings and estimate their immunogenicities using a public bioinformatics tool. Finally, we used several bioinformatic tools to evaluate the properties of the candidates generated by our method, which indicated that we can score Mirodenafil dihydrochloride high immunogenic -superstrings that at the same time present similar conformations to the Nef virus proteins. Introduction Infectious and transmissible diseases cause deaths of millions of people every year. The best immunological measures to prevent such diseases are vaccines. Therefore, the main efforts of immunologists are focused towards improving our predictions of effective epitopes that would confer protection against pathogens [1] and towards enhancing our ability to select appropriate epitopes for Mirodenafil dihydrochloride inclusion in an efficient vaccine [2]. Protective immunity requires humoral or cellular immunity depending on the pathogen. Humoral immunity implies the production of antibodies by B cells that interact with surface or secreted toxins of pathogens. Each antibody binds to an epitope, defined as the three-dimensional structure of amino acids that can be contacted by the variable region of an antibody. There are two types of B-cell epitopes: (i) linear or continuous epitopes, which are short peptides that correspond to a fragment of a protein, and (ii) conformational epitopes, composed of amino acids not contiguous in primary sequence of the protein but brought in close proximity within the folded 3D structure. The length of these Mirodenafil dihydrochloride epitopes is variable, ranging from 8 to 20 amino acids [3]. Cellular immunity depends on T-cell epitopes generated in other cell types, the antigen presenting cells (or APC) that generate linear epitopes from pathogen degradation or protein synthesis. These short linear amino acids generated from intracellular degraded or synthesized proteins from the microorganisms bind to two types of major histocompatibility complexes (MHC), class I MHC that attach epitopes of 8-9-mer lengths and class II MHC that fit epitopes of 12-15-mer lengths [4]. CD4+ T cells recognize class II MHC epitopes and CD8+ T cells recognize class I MHC epitopes in APC. Bionformatics methods that predict B-cell epitopes are based on certain correlations between some physicochemical properties of amino acids and the locations of linear B-cell epitopes with protein sequences [5]. Therefore, hydrophilicity, flexibility, turns, and solvent accessibility generated propensity scales for B-cell epitope prediction. However, propensity scale predictions have failed to predict B-cell epitopes since they are mainly based on fixed lengths and.

The prediction obtained for our applicant is highly reliable, since the C-Score of the model is 1