Difference between revisions of "Team:Heidelberg/Software"

         To simplify the generation of desired functionalities <i>de novo</i> and enable evolutionary leaps, we created AiGEM, our Artificial Intelligence for Genetic Evolution Mimicking software suite. The heart of our software is DeeProtein, a deep neuronal network trained on ~10 million protein sequences and able to infer sequence-function relationships from raw sequence data with high accuracy. By interfacing DeeProtein with our genetic algorithm GAIA, we – for the first time - established a fully closed, <i>in silico</i> evolution cycle driven by an intelligent network. We used AiGEM to successfully modulate the catalytic efficiency of β-lactamases, thereby even producing variants which highly outperform the wild type enzyme. To demonstrate AiGEM’s ability for generating functionality de novo, we created a novel, highly efficient β-galactosidase purely by <i>in silico</i> evolution of a β-glucuronidase parental sequence. Finally, as part of our integrated human practices project, we implemented SafetyNet, a DeeProtein-based web application safeguarding directed evolution experiments.