Um novo modelo multiescala de granulaa§a£o grossa do virion SARS-CoV-2 completo, seu material genanãtico central e casca do varion, foi desenvolvido pela primeira vez usando supercomputadores.
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)
Um modelo multiescala do virion SARS-CoV-2 completo foi desenvolvido pela primeira vez usando supercomputadores. O modelo oferece aos cientistas o potencial para novas maneiras de explorar as vulnerabilidades do varus. As visualizações externas (L) e internas (R) mostram os trameros de proteana de pico (azul-petra³leo), locais de glicosilação (preto), proteanas de membrana (azul) e canais ia´nicos do envelope pentamanãrico (laranja). Crédito: Gregory Voth, Universidade de Chicago.
O varus COVID-19 guarda alguns mistanãrios. Os cientistas permanecem no escuro sobre os aspectos de como ele se funde e entra na canãlula hospedeira; como ele se monta; e como ele se desprende da canãlula hospedeira.
A modelagem computacional combinada com dados experimentais fornece insights sobre esses comportamentos. Mas a modelagem em escalas de tempo significativas do varus SARS-CoV-2, causador da pandemia, atéagora se limitou a apenas seus pedaço s, como a proteana spike, um alvo para a atual rodada de vacinas.
Um novo modelo multiescala de granulação grossa do virion SARS-CoV-2 completo, seu material genanãtico central e casca do varion, foi desenvolvido pela primeira vez usando supercomputadores. O modelo oferece aos cientistas o potencial para novas maneiras de explorar as vulnerabilidades do varus.
"Queraamos entender como o SARS-CoV-2 funciona holisticamente como uma partacula inteira", disse Gregory Voth, o Distinguished Service Professor Haig P. Papazian da Universidade de Chicago. Voth éo autor correspondente do estudo que desenvolveu o primeiro modelo de varus completo, publicado em novembro de 2020 no Biophysical Journal .
"Na³s desenvolvemos um modelo de granulação grossa de baixo para cima", disse Voth, "onde pegamos informações de simulações de dina¢mica molecular emnívelatomastico e de experimentos." Ele explicou que um modelo de granulação grossa resolve apenas grupos de a¡tomos, em comparação com as simulações de todos os a¡tomos, onde cada interação atômica éresolvida. "Se vocêfizer isso bem, o que ésempre um desafio, vocêmantanãm a física no modelo."
Os primeiros resultados do estudo mostram como as proteanas de pico nasuperfÍcie do varus se movem cooperativamente.
“Eles não se movem independentemente como um monte de movimentos aleata³rios e não correlacionadosâ€, disse Voth. "Eles trabalham juntos."
Este movimento cooperativo das proteanas de pico éinformativo de como o coronavarus explora e detecta os receptores ACE2 de uma canãlula hospedeira potencial.
"O artigo que publicamos mostra o inicio de como os modos de movimento nas proteanas de pico estãocorrelacionados", disse Voth. Ele acrescentou que os espinhos estãoacoplados uns aos outros. Quando uma proteana se move, outra também se move em resposta.
"O objetivo final do modelo seria, como uma primeira etapa, estudar as atrações iniciais do varion e as interações com os receptores ACE2 nas células e entender as origens dessa atração e como essas proteanas funcionam juntas para prosseguir para o processo de fusão do varus ", Disse Voth.
Voth e seu grupo tem desenvolvido manãtodos de modelagem de granulação grossa em varus como HIV e influenza por mais de 20 anos. Eles tornam os dados mais "grosseiros" para torna¡-los mais simples e mais trata¡veis ​​computacionalmente, ao mesmo tempo que se mantem fianãis a dina¢mica do sistema.
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"O benefacio do modelo de granulação grossa éque ele pode ser de centenas a milhares de vezes mais eficiente computacionalmente do que o modelo de todos os a¡tomos", explicou Voth. A economia computacional permitiu a equipe construir um modelo muito maior do coronavarus do que nunca, em escalas de tempo mais longas do que o que foi feito com modelos de todos os a¡tomos.
"O que resta são os movimentos coletivos muito mais lentos. Os efeitos da frequência mais alta, os movimentos de todos os a¡tomos são incorporados a essas interações se vocêfizer isso bem. Essa éa ideia de granulação grossa sistema¡tica."
O modelo holastico desenvolvido por Voth começou com modelos ata´micos dos quatro principais elementos estruturais do varion SARS-CoV-2: o pico, a membrana, o nucleocapsadeo e as proteanas do envelope. Esses modelos ata´micos foram então simulados e simplificados para gerar o modelo de granulação de curso completo.
As simulações de dina¢mica molecular de todos os a¡tomos do componente spike protein do sistema virion, cerca de 1,7 milha£o de a¡tomos, foram geradas pela co-autora do estudo Rommie Amaro, professora de química e bioquímica da Universidade da Califa³rnia, San Diego.
"O modelo deles basicamente ingere nossos dados e pode aprender com os dados que temos nessas escalas mais detalhadas e, então, ir além de onde fomos", disse Amaro. "Este manãtodo que Voth desenvolveu permitira¡ que nose outros simulemos em escalas de tempo mais longas que são necessa¡rias para simular de fato o varus infectando uma canãlula."
Amaro elaborou sobre o comportamento observado nas simulações de granulação grossa das proteanas do pico.
"O que ele viu muito claramente foi o inicio da dissociação da subunidade S1 do espiga£o. Toda a parte superior do espiga£o descasca durante a fusão", disse Amaro.
Uma das primeiras etapas da fusão viral com a canãlula hospedeira éessa dissociação, onde se liga ao receptor ACE2 da canãlula hospedeira.
"Os maiores movimentos de abertura S1 que eles viram com este modelo de granulação grossa eram algo que ainda não tanhamos visto na dina¢mica molecular de todos os a¡tomos e, na verdade, seria muito difacil para nosver", disse Amaro. "a‰ uma parte cratica da função dessa proteana e do processo de infecção com a canãlula hospedeira. Essa foi uma descoberta interessante."
Voth e sua equipe usaram as informações dina¢micas de todos os a¡tomos nos estados aberto e fechado da proteana spike gerada pelo Amaro Lab no supercomputador Frontera, bem como outros dados. O sistema Frontera financiado pela National Science Foundation (NSF) éoperado pelo Texas Advanced Computing Center (TACC) da Universidade do Texas em Austin.
"Frontera mostrou como éimportante para esses estudos do varus, em maºltiplas escalas. Foi fundamental nonívelata´mico entender a dina¢mica subjacente do pico com todos os seus a¡tomos. Ainda hámuito o que aprender aqui. Mas agora essa informação pode ser usada uma segunda vez para desenvolver novos manãtodos que nos permitem ir mais longe e mais longe, como o manãtodo de granulação grossa ", disse Amaro.
"Frontera foi especialmente útil no fornecimento de dados de dina¢mica molecular nonívelatomastico para alimentar este modelo. a‰ muito valioso", disse Voth.
O Voth Group inicialmente usou o cluster de computação Midway2 no Centro de Pesquisa de Computação da Universidade de Chicago para desenvolver o modelo de granulação grossa.
As simulações de todos os a¡tomos da membrana e da proteana do envelope foram geradas no sistema Anton 2. Operado pelo Pittsburgh Supercomputing Center (PSC) com o apoio do National Institutes of Health, o Anton 2 éum supercomputador de propa³sito especial para simulações de dina¢mica molecular desenvolvido e fornecido gratuitamente pela DE Shaw Research.
"Frontera e Anton 2 forneceram os principais dados de entrada denívelmolecular para este modelo", disse Voth.
“Uma coisa realmente fanta¡stica sobre o Frontera e esses tipos de manãtodos éque podemos dar a s pessoas uma visão muito mais precisa de como esses varus estãose movendo e realizando seu trabalhoâ€, disse Amaro.
“Existem partes do varus que são invisaveis atépara experimentosâ€, ela continuou. "E por meio desses tipos de manãtodos que usamos no Frontera, podemos dar aos cientistas as primeiras e importantes visaµes sobre como esses sistemas realmente se parecem com toda a sua complexidade e como eles estãointeragindo com anticorpos ou drogas ou com partes do hospedeiro canãlula . "
O tipo de informação que Frontera estãodando aos pesquisadores ajuda a entender os mecanismos ba¡sicos da infecção viral. Tambanãm éútil para o desenho de medicamentos melhores e mais seguros para tratar e prevenir a doena§a, acrescentou Amaro.
Disse Voth: "Uma coisa que nos preocupa agora são as variantes SARS-CoV-2 do Reino Unido e da áfrica do Sul. Presumivelmente, com uma plataforma computacional como a que desenvolvemos aqui, podemos avaliar rapidamente essas variações, que sãomudanças dos aminoa¡cidos. Podemos entender com bastante rapidez asmudanças que essas mutações causam no varus e, em seguida, ajudar no desenvolvimento de novas vacinas modificadas. "