Development of machine learning methods for automated design of new biological functions in bacterial proteins.

Protein design has matured to an exciting and leading area in synthetic biology, as confirmed by the 2018
Chemistry Nobel Prize to Frances Arnold. However, significant challenges remains in developing effective methods to automate design choices. In particular, there is no method to perform rational design of protein function by modification of protein internal dynamics. This research project is aimed at combining machine learning techniques and biomolecular simulations to develop the first AI-based 'protein engineer'. The doctoral researcher will work to: 1) design and implement a machine learning predictor of the effect of mutations on protein dynamics; 2) integrate the predictor into a widely used biomolecular simulation engine to automate selection and testing of candidate mutations; 3) incorporate the integrated machine learning simulation engine into a software container for easy deployment on different architectures. The optimal candidate should have a BSc and/or MSc degree in computer science, computational biology, computational chemistry or bioinformatics. Background in software development in Python and Linux/UNIX is expected.