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

蛋白质设计已经成熟到合成生物学中一个令人兴奋和领先的领域，2018年诺贝尔化学奖授予弗朗西斯·阿诺德证实了这一点。然而，在开发有效的方法来自动化设计选择方面仍然存在重大挑战。特别是，目前还没有通过修改蛋白质内部动力学来进行蛋白质功能的合理设计的方法。本研究项目旨在将机器学习技术和生物分子模拟相结合，开发出第一个基于人工智能的蛋白质工程师。这位博士生将致力于：1)设计和实现突变对蛋白质动力学影响的机器学习预测器；2)将预测器集成到广泛使用的生物分子模拟引擎中，以自动化候选突变的选择和测试；3)将集成的机器学习模拟引擎集成到软件容器中，以便在不同的架构上轻松部署。最佳应聘者应拥有计算机科学、计算生物学、计算化学或生物信息学学士和/或硕士学位。有Python和Linux/Unix软件开发背景者优先。

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.