An Artificial Intelligence to Discover the Determinants of Microbiome-Transporter Interrelations

Mentored by Jens Meiler and Peter Stadler
at University of Leipzig, Interdisciplinary Centre for Bioinformatics

Cells use hundreds of active and passive transporters to import and export metabolites and signalling molecules. In particular the understanding of the metabolic interactions in microbial communities critically depends on the knowledge of the transport pathways. Carrier proteins proteins fall into diverse classes of membrane proteins. Despite their importance, knowledge of their inventory, structural diversity, and their payload is has remained fragmentary.

To goal of the project is to combine methods from comparative genomics, modelling of membrane protein structures with Alphafold and Rosetta, and machine learning classification into a comprehensive workflow to catalogue carrier proteins in procaryotes and to infer as much functional annotation as possible. The research plan calls for a tight integration of machine learning and AI methods bringing together evolutionary and structural biology.

Work environment

You will work at the Interdisciplinary Center for Bioinformatics in Leipzig and cooperate with the machine learning and AI experts at the scaDS.AI in Leipzig and Dresden.  You will have access to machine-learning HPC resources of the ScaDS.AI Center for Scalable Data Analytics and AI Dresden/Leipzig, offering state-of-the-art CPU (Intel and IBM POWER 9) and GPU (Nvidia A100 and V100) resources, as well as high-performance computing and storage systems for computational experiments.

Prerequisites

The candidate will need a solid background in bioinformatics or structural biology as well as proficiency in scientific programming and interest in data analysis. Prior expertise in machine learning and or AI techniques will be an advantage.

Further details on the requirements and application process can be found in SECAI's announcement for open PhD positions in 2024.