In his research, Dr. Bojar is working to overcome this obstacle and transform glycobiology into a discipline that is predictable, unlocking it for biomedical applications in the process. Dr. Bojar was the first researcher to devise, develop, and apply methods derived from deep learning and natural language processing to glycans, learning the rules and grammar of the “third language of life,” next to DNA and proteins. Taming the incredible complexity of glycans, these models now can extract functions from glycan sequences and aid in understanding the overarching roles of glycans in biology. Supported by the Branco Weiss Fellowship, Dr. Bojar now engages in the ambitious task of translating these methods to the biomedical realm by developing glycan-based antibiotics and antivirals.
As glycans can block viral cell entry and microbial cell adhesion, they represent a treasure trove of novel potential drugs. Constructing machine learning models to predict antibiotic and antiviral activity from glycan sequences will thus uniquely enable Dr. Bojar to leverage the diversity of glycans to design candidate glycan therapeutics. These glycans can then be synthesized, chemically or enzymatically, and tested for their activity to curb microbial growth or viral infectivity. Any information will be fed back to the models, facilitating a design-build-test cycle that will lead to a continuous improvement of the candidate glycan therapeutics. In the age of antibiotic resistance and global pandemics, novel solutions to these vexing problems are needed. With his work, Dr. Bojar envisions giving new impetus to drug development in these areas but also to our understanding of glycans in biological processes in general, and thereby to the understanding of ourselves as humans.
