Os cientistas que receberam o Prêmio Nobel de Química de 2017 foram homenageados pelo desenvolvimento de uma técnica chamada microscopia crioeletrônica, ou crio-EM. A tecnologia foi revolucionária porque permitiu aos cientistas ver a...
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)
Uma série de imagens crio-EM. As fotos em escala de cinza representam projeções 2D de múltiplas visualizações da estrutura de imagem anexada a uma proteína alvo; a imagem colorida ilustra a reconstrução 3D derivada de projeções 2D. Crédito: Roger Castells-Graells/UCLA
Os cientistas que receberam o Prêmio Nobel de Química de 2017 foram homenageados pelo desenvolvimento de uma técnica chamada microscopia crioeletrônica, ou crio-EM. A tecnologia foi revolucionária porque permitiu aos cientistas ver a estrutura atômica das moléculas biológicas em alta resolução.
Mas o crio-EM ainda tinha um problema: só era eficaz para gerar imagens de moléculas grandes .
Agora, os bioquímicos da UCLA, trabalhando com cientistas da indústria farmacêutica, desenvolveram uma solução que tornará possível ao crio-EM adquirir também imagens de alta qualidade de moléculas de proteínas menores . Os cientistas projetaram uma estrutura de proteína em forma de cubo de 20 nanômetros, chamada de andaime, com saliências rígidas em forma de tripé que mantêm as pequenas proteínas no lugar.
A estrutura pode ser removida digitalmente da imagem quando a imagem está sendo processada, deixando uma imagem 3D composta apenas das pequenas proteínas que os cientistas estão analisando.
As proteínas de pequeno e médio porte são um ponto importante para a pesquisa de potenciais novos medicamentos que poderão um dia ser usados para combater algumas das doenças humanas mais intratáveis. O avanço, que foi testado numa proteína que os cientistas estão a estudar para a sua possível utilização em tratamentos contra o cancro, pode ser personalizado para quase qualquer proteína pequena. Os pesquisadores esperam que a expansão das capacidades de imagem do crio-EM os ajude a identificar locais específicos nas proteínas que podem atingir para fins terapêuticos.
Um artigo sobre a nova pesquisa foi publicado no Proceedings of the National Academy of Sciences .
Na crio-EM, os cientistas usam um microscópio crioeletrônico para enviar um feixe de elétrons através de amostras congeladas de material, deixando para trás uma imagem de milhares de moléculas – como proteínas – na amostra. As moléculas são fotografadas exatamente como estão na amostra, produzindo milhares de fotografias 2D da molécula tiradas de diferentes ângulos. O processamento computacional reconcilia todas essas fotografias para formular uma imagem 3D correta – separando o fundo, agrupando imagens com orientações semelhantes e gerando uma imagem 3D de alta resolução de uma única molécula.
Mas quando se trata de obter imagens das mais pequenas moléculas de proteína, o seu tamanho minúsculo torna impossível determinar as suas orientações nas imagens, o que resulta em imagens de resolução relativamente baixa.
Em estudos anteriores, os cientistas tentaram resolver o problema ligando pequenas moléculas a estruturas maiores, mas essas experiências demonstraram que se as pequenas moléculas fossem fixadas de forma demasiado flexível, elas sobressaíriam da estrutura em diferentes ângulos e orientações – o que ainda produziria imagens desfocadas. .
"As imagens ficam borradas porque o computador não consegue criar uma imagem composta distinta quando não consegue determinar as orientações com precisão", disse Todd Yeates, ilustre professor emérito de bioquímica da UCLA, diretor interino do Instituto de Pesquisa do Departamento de Energia da UCLA. Genômica e Proteômica e autor correspondente do artigo.
No novo estudo, a estrutura criada pelos cientistas tem saliências em forma de tripé que capturam as proteínas e as mantêm firmemente no lugar, o que rendeu as imagens de maior resolução que pretendiam.
"Anexar rigidamente as pequenas moléculas a estruturas maiores cria partículas que são grandes o suficiente para serem visualizadas e que têm precisamente a mesma forma 3D", disse Yeates. "E a partir daí, o processo funciona normalmente para construir a imagem 3D de alta resolução."
Roger Castells-Graells, pesquisador de pós-doutorado da UCLA e principal autor do estudo, disse que os cientistas primeiro tentaram outro formato para o andaime antes de chegar à versão com saliências em forma de tripé.
"No início usamos um 'pau' apontado para fora e isso não funcionou tão bem", disse ele. "A nova estrutura tem saliências que apontam uma para a outra em trigêmeos - como tripés - que seguram a proteína com firmeza."
Os pesquisadores testaram sua estrutura tentando criar imagens de uma proteína chamada KRAS. O KRAS estimula a proliferação das células e está envolvido em cerca de 25% dos cânceres humanos ; é de particular interesse para os investigadores farmacêuticos porque a identificação de locais específicos na proteína que estão relacionados com as suas capacidades causadoras de cancro poderia ajudar os cientistas a conceber medicamentos que neutralizem a actividade nesses locais – o que poderia ser um caminho para o tratamento do cancro.
Usando o crio-EM e a estrutura que desenvolveram, a equipe liderada pela UCLA conseguiu observar a estrutura atômica do KRAS ligada a uma molécula de medicamento que está sendo estudada como parte de um potencial tratamento para o câncer de pulmão. Seu trabalho provou que a nova abordagem crio-EM estruturada pode esclarecer como as moléculas de medicamentos se ligam e inibem proteínas celulares como o KRAS, e pode ajudar a orientar o desenvolvimento de medicamentos mais eficazes.
As aplicações potenciais para o novo avanço não param nos medicamentos contra o câncer, disse Castells-Graells.
“Nossa estrutura é modular e pode ser montada em qualquer configuração para capturar e reter todos os tipos de pequenas moléculas de proteínas ”, disse ele.
Mais informações: Roger Castells-Graells et al, Determinação da estrutura Cryo-EM de pequenos alvos proteicos terapêuticos com resolução de 3 Å usando uma estrutura de imagem rígida, Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2305494120
Informações do jornal: Proceedings of the National Academy of Sciences