RESUMO
While bacteriophages have previously been used as a model system to understand thermal adaptation, most adapted genomes observed to date contain very few modifications and cover a limited temperature range. Here, we set out to investigate genome adaptation to thermal stress by adapting six populations of T7 bacteriophage virions to increasingly stringent heat challenges. Further, we provided three of the phage populations' access to a new genetic code in which Amber codons could be read as selenocysteine, potentially allowing the formation of more stable selenide-containing bonds. Phage virions responded to the thermal challenges with a greater than 10°C increase in heat tolerance and fixed highly reproducible patterns of non-synonymous substitutions and genome deletions. Most fixed mutations mapped to either the tail complex or to the three internal virion proteins that form a pore across the E. coli cell membrane during DNA injection. However, few global changes in Amber codon usage were observed, with only one natural Amber codon being lost. These results reinforce a model in which adaptation to thermal stress proceeds via the cumulative fixation of a small set of highly adaptive substitutions and that adaptation to new genetic codes proceeds only slowly, even with the possibility of potential phenotypic advantages.
RESUMO
Despite the promise of deep learning accelerated protein engineering, examples of such improved proteins are scarce. Here we report that a 3D convolutional neural network trained to associate amino acids with neighboring chemical microenvironments can guide identification of novel gain-of-function mutations that are not predicted by energetics-based approaches. Amalgamation of these mutations improved protein function in vivo across three diverse proteins by at least 5-fold. Furthermore, this model provides a means to interrogate the chemical space within protein microenvironments and identify specific chemical interactions that contribute to the gain-of-function phenotypes resulting from individual mutations.
Assuntos
Mutação com Ganho de Função/genética , Algoritmos , Aminoácidos/genética , Aprendizado Profundo , Aprendizado de Máquina , Redes Neurais de Computação , Engenharia de Proteínas/métodos , Proteínas/genéticaRESUMO
We have found that the overproduction of enzymes in bacteria followed by their lyophilization leads to 'cellular reagents' that can be directly used to carry out numerous molecular biology reactions. We demonstrate the use of cellular reagents in a variety of molecular diagnostics, such as TaqMan qPCR with no diminution in sensitivity, and in synthetic biology cornerstones such as the Gibson assembly of DNA fragments, where new plasmids can be constructed solely based on adding cellular reagents. Cellular reagents have significantly reduced complexity and cost of production, storage and implementation, features that should facilitate accessibility and use in resource-poor conditions.