Base de dados UML

Finalmente, com o objetivo de armazenar de forma eficiente e acessível os dados deste projeto referente aos epítopos e suas propriedades, bem como de informações ligadas a eles, foi desenhado uma base de dados. A figura 22 contêm a estrutura desta base de dados em linguagem UML (Unified Modeling Language) da estrutura deste banco de dados, desenhada especialmente para aceitar resultados de experimentos, estruturas e parâmetros de epítopos ou paratopos. Por consequência o uso deste banco de dados através do servidor SSH (Secure Shell) facilito varias pesquisas incluindo esta tese de doutorado de Benjamin Viart e o desenvolvimento do EPI-Peptide designer (Viart et al., 2016). Este banco de dados também armazena as informações dos epítopos de metaloproteases e neurotoxinas publicados em BMC Bionformatics através do congresso X-meeting do ano 2014 (Kozlova et al., 2015).

6. Conclusão

Nesta tese, descrevemos o desenvolvimento de um algoritmo baseado em conjuntos de dados capazes de identificar computacionalmente epítopos para células B dispostos linearmente na seqüência do antígeno. Mostramos que os principais métodos de mineração de dados possuem desempenhos similares, entretanto os dados de entrada e orientação dos modelos são ainda limitados. A árvore de decisão que utilizamos permitiu o entendimento das propriedades físico-químicas. Estes foram utilizados para classificar famílias de epítopos e discriminar de epítopos de não epítopos. Por conseqüência foi possível encontrar padrões que asseguraram a separação de grupos de epítopos baseados em famílias de proteínas e no tipo de animal usado para produzir anticorpos.

A árvore de decisão escolhida foi capaz de avaliar facilmente os descritores importantes durante a classificação permitindo gerar modelos computacionais confiáveis estatisticamente.

Escolhemos focar o modelo e nosso trabalho em metaloproteases, uma vez que é um dos grupos de proteínas relacionadas com a gravidade do envenenamento nos acidentes por serpentes. Utilizamos as proteínas Atr-I, BaP1 e Leuc-a, cujos epítopos foram previamente identificados, pelo método experimental de SPOT-Syntesis. Refinamos o modelo bioinformático Labimq, capaz de identificar EpiLCB corretamente nestas três proteínas. Este algoritmo mostrou melhor desempenho que outros mais usados disponíveis na web (ABCPred, Bepipred e TEPRF).

A proteína Atr-I foi escolhida para realizar a validação experimental de nosso algoritmo de predição. Duas sequências da proteína foram escolhidas, um epítopo identificado somente pelo método computacional e outra que não foi identificada como epítopo por nenhum método ate o momento. Os correspondentes peptídeos foram sintetizados e usados para produzir anticorpos em

camundongos. Uma vez que os anticorpos anti-epítopo neutralizou a atividade hemorrágica da proteína Atr-I, confirmamos a eficiência das previsões bioinformáticas.

O sucesso demonstrado durante a neutralização e eficácia de nossa metodologia computacional é que esta pode ser usada em outros venenos ou enzimas das famílias metaloproteases ou neurotoxinas.

7. Perspectivas

- Aprofundar a automatização desta metodologia e o refinamento dos dados, tornando possível a filtragem de todas as famílias de proteínas ou grupos, assim como observado em Pfam com o uso de clãs. O sucesso demonstrado durante a neutralização e eficácia de nossa metodologia computacional é que esta pode ser usada em outros venenos ou enzimas das famílias metaloproteases ou neurotoxinas.

- Explorar a possibilidade de usar os Clãs disponíveis em Pfam em cambio de famílias de proteínas.

- Aumentar os esforços para fornecer estes métodos via web.

- Explorar as diferencias entre EpiLCB e epítopos conformacionais usando a sequência de aminoácidos do antígeno.

- Desenhar um esquema de validação estatística de epítopos sobre um set de proteínas hipotéticas baseado em estáticas extraídas a partir da literatura para incrementar o numero de mostras permitindo assim resultados mais precisos.

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