The broader impact/commercial potential of this I-Corps project spans chemical manufacturing, pharmaceutical development, and agricultural biotechnology. This technology helps protein engineers take a candidate protein and make it more robust to chemical or environmental perturbations. In one application, this technology could be used to extend the half-life of pharmaceutical candidates during development. In another application, it could enable substituting toxic and wasteful chemistries with suitable enzymatic chemistries. These algorithms may help drive new green chemistries presently inaccessible because although the necessary enzymes can perform a desired catalysis, they cannot stay folded long enough for commercial use. In a third application, the technology can help improve the shelf life of these critical drugs, thereby expanding the population they might benefit.
This I-Corps project leverages machine learning to accelerate protein engineering. Convolutional neural networks have recently become the preferred artificial intelligence (AI) solution to a number of computer vision challenges, yet their biological applications remain scarce. Proteins, strings of folded amino acids that drive most biological processes, are exponentially being crystallized to solve their three-dimensional structure. This project combines these two resources by training a 3D convolutional neural network that characterizes chemical environments unique to each of the 20 amino acids. The same neural network can then predict the amino acid best fitting a given environment.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
