In silico redesign and de novo design of anti-MUC1 monoclonal antibodies

Thesis / Dissertation


Permanent link to this Item
Journal Title
Link to Journal
Journal ISSN
Volume Title
Mucin 1 (MUC1) is a cellular membrane-tethered protein which is abnormally glycosylated and overexpressed in several epithelial cancers. This abnormal glycosylation is hypothesized to participate in the hyper-activation of signaling pathways which promote tumor growth and provides a suitable target for detection and treatment of epithelial cancers via antibodies. While anti-MUC1 monoclonal antibodies do exist, none have proven to be effective in clinical trials. Antibody fragments have been identified as possible therapeutic agents since their small molecular weight and improved selectivity allow for better targeting and tissue penetration. Antibodies which do not elicit strong immune responses have also been used in treatment by means of conjugation to cytotoxic drugs. The failure of anti-MUC1 antibodies in targeting MUC1 is due to the inability of majority of existing antibodies to specifically recognize and bind the cancer associated truncated sugar and MUC1 peptide simultaneously. Thus far only one antibody has been shown to bind the cancer associated sugar and peptide (SN-101) however, this is a murine derived structure which is not ideal for treatment in humans. In this thesis, MD, and related analyses (such as structural analysis and free energy calculations) will be used to investigate and optimize the binding of the antigen binding fragment (Fab) region of the SN-101 antibody to Tn-glycosylated MUC1. The Tn O-glycosylated MUC1 glycoprotein was targeted due to previous research indicating that this particular glycosylated variant of MUC1 had been found in increased concentrations in breast cancers. The Fab region was considered as this is the region involved in binding the antigen target. Further structural data is available in the form of an X-ray crystal structure for this region. The utility of MD in rational design of antibodies against glycopeptide antigens is demonstrated by this research. This is of particular interest due to the role glycopeptides play in the pathology of complex diseases, such as cancer and arthritis, as well as viral infections, such as HIV and COVID-19. The automation of this in silico workflow provides a rapid screening approach which could possibly lead to the development of more specific, targeted treatments for glycoprotein related diseases via antibody drug conjugates. This approach can also be used to improve cancer biomarker detection assays.