Tugce is a fourth-year Ph.D. student at the University of Birmingham and an enrichment student at the Turing. For her masters, she studied theoretical biophysics after graduating from Molecular Biology and Genetics with a double major in Physics. In her Ph.D. studies, she focuses on structural bioinformatics under the supervision of Peter Winn and Christopher Thomas. She is passionate about using computational techniques to gain a deeper understanding of biological concepts.
Antibiotic resistance is an emerging threat to public health. To deal with resistant bacteria, new antibiotics should be developed. However, we do not have a deep understanding of the working mechanisms of antibiotic-producing molecular machines (protein complexes) which stalls the discovery of new drugs. In her Ph.D. studies, Tugce aims to understand how these protein complexes, specifically polyketide synthases, work to accelerate the drug discovery process. She uses statistical approaches and machine learning algorithms to predict how the domains of these protein complexes interact with each other. In other words, she is working on the prediction of distances between monomers (amino acids) of any interacting domain pair to understand the overall structure and the dynamics of the complex. Further, she is using statistical analysis to detect co-evolved amino acid groups that are important for the structural integrity and/or some critical functions of the protein complex that provide the diversity in the chemical products.
In the Turing Institute, she aims to learn and apply advanced statistical methods and latest deep learning algorithms to improve her predictions and develop a tool that can be used to gain deeper insight about any multi-domain protein complex which can lead to more efficient experimental designs to generate new antibiotic candidates.