Understanding Macromolecules and their Interactions: Computational Methods and Experimental Insights

Abstract

Proteins are key building blocks of life, and hence crucial to understand. Proteins work together and with other molecules, like DNA and ligands, to control all biological processes happening in our bodies; from moving our muscles to digesting food from vision to hormone regulation. Both in health and disease. Hence, understanding these interactions at the atomic 3D level is crucial for developing new medicines. To aid the interpretation of the interactions between proteins and their interactors, this thesis introduces new tools. We enrich the existing PDB-REDO framework with improved description of DNA, and start with analysing halogen bonds. All aiming to helps scientists get access to accurate and as complete as possible 3D models of proteins and their partners. To be capable of handling thousands of protein samples to find ones that interact with a ligand and hence could be useful for developing new drugs, we developed X-FRAGtor. Thirdly, we present AlphaFill, which makes predicted protein models more complete by adding missing pieces, such as important vitamins or metals. All these tools are designed to help scientists study proteins fast, intuitively and accurately. Hereby we emphasize FAIR data practices, ensuring that these tools present findable, accessible, and reusable results by the scientific community. Using these tools and experimental techniques, the research zooms in on a rare type of DNA, base-J, found in parasites that cause diseases like sleeping sickness. We show how base-J interacts with J-DNA binding proteins, presenting new, experimentally validated models of their binding behaviour. Notably, it shows that JBP1 binds base-J far more strongly than JBP3, offering clues for potential therapeutic targets. Finally, we dived into the “CHAOS” of proteins. Herein we find that most human proteins are more flexible and messier than expected, like tangled strings that still perform important jobs. Together, these discoveries bring us closer to understanding life’s building blocks and could help scientists design new treatments for diseases in the future.