Desimunização de L-Asparaginase De Escherichia Coli Através de algoritmo genético

• Autor(a) principal: Pacheco, Débora Albuquerque de Lima
• Data de Publicação: 2019
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/71169
• topic: Imunogenicidade. Engenharia de proteínas. Leucemia linfoblástica aguda.

L-asparaginase is a therapeutic protein used in Acute Lymphoblastic Leukemia treatment, whose administration may cause severe side effects or result in protein neutralization due to the activation of the immune response. Adaptive immunity components, such as MHC II (Major Histocompatibility Complex class II), are responsible for presenting exogenous peptides processed by antigen presenting cells (APCs). Immunoinformatics tools can be used to predict the binding affinity between a peptide and MHC-II pocket, which result in peptide immunogenicity. In addition, deimmunization methods by epitope mutation may be employed in an attempt to improve therapeutic proteins efficacy and reduce its immunogenicity. In this work, a peptide-MHC II affinity prediction method, TEPITOPE, was applied to calculate the immunogenicity scores of Escherichia coli L-asparaginase epitopes, and this information was used in a Genetic Algorithm (AG) of protein deimunization. The Algorithm used a set of protein sequences and performed crossover and mutations at each cycle in the immunogenic regions of the protein, considering only neutral mutations (when ∆∆G between wild and mutant protein was less than 0,5 kcal/mol), substituting sequences of higher immunogenicity by those of lower. At the end of 30 applications, the algorithm was able to generate 953 stable variants with less immunogenicity than the original sequence. Of these, the sequences of three, four and five mutations with the lowest immunogenicity were analyzed. From this analysis it was possible to verify the mutations that contributed to the reduction of the immunogenicity and those that were necessary for the protein to remain stable. The GA can be applied to other protein sequences, with parameterization potential to vary the dynamics of the algorithm or to implement new functions that increase its efficiency.