(323e) Optimizing Antifreeze Protein Activity Via an Elitist Genetic Algorithm: A Comparison of High and Low Activity Variants
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Topical Conference: Applications of Data Science to Molecules and Materials
- Time: Tuesday, November 12, 2019 - 2:00pm-2:18pm
Given the existence of a suitably efficient fitness function, genetic algorithms offer a promising method for systematically optimizing the properties of biological materials. In our recent paper , we established a neural network based method for predicting the antifreeze activity of different proteins from molecular dynamics simulation. Using this method as our fitness function, we designed a simple, but effective, elitist genetic algorithm for selecting amino acid mutations that should increase the activity of native antifreeze proteins. We apply this algorithm to two antifreeze proteins: (1) a low activity Type III antifreeze protein from the winter flounder, and (2) a hyperactive antifreeze protein from the longhorn beetle. We show that the algorithm finds structures with a predicted freezing point depression 20 - 200% greater than that of the native protein. We hope that these results inspire further investigation of high activity antifreeze biologics that could someday be used in more effective tissue preservation.
 Kozuch, D. J., Stillinger, F. H., & Debenedetti, P. G. (2018). Combined molecular dynamics and neural network method for predicting protein antifreeze activity. Proceedings of the National Academy of Sciences, 115(52), 13252â13257