Course Description:

Proteins and nucleic acids have characteristic three-dimensional structures which equip them to play specific roles as, e.g., receptors, enzymes, transporters, antibodies, or cellular scaffolds. The number of experimentally determined structures of biological macromolecules in the worldwide protein databank (PDB) has increased substantially over the last years, and more than 200,000 biomolecular structures are currently available. By analyzing these structures, we not only obtain detailed insight into the way how biomolecules function, but also gain understanding about the design principles of proteins and the evolution of protein structure on a large scale.

To access, interpret, and manipulate structural data of biological macromolecules, computational methods have become an indispensable tool that enables researchers to solve diverse biological problems. For example, how do mutations in a gene affect a protein’s shape or the binding of a small-molecule substrate? How do membrane proteins couple chemical signals to the transport of small molecules or ions across the cell membrane? How can the affinity of an antibody recognizing a viral protein be increased to block virus entry into cells and prevent an infection?

The course will cover the theory, computational algorithms, data resources, and practical tools used for the study of biomolecular structure. Students will get introduced into AlphaFold, Rosetta, and Amber, comprehensive software packages used for a wide range of applications such as the prediction of protein structures with and without the aid of experimental data, the modeling of the interaction of proteins and peptides with each other and with small-molecule ligands, and the design of proteins with improved or completely new functionalities. Computational structural biology methods become increasingly important for the interpretation of biological findings (e.g., from genomics data) and the engineering of therapeutics and probe molecules in biomedical research. The course will include practical lab exercises that will consolidate the theoretical concepts learned and train students how to use AlphaFold, Rosetta, and Amber to perform advanced computational experiments.