The general research focus of the laboratory of Andrew J. Bordner, Ph.D., is computational structural biology.
Dr. Bordner and his team are developing new computational methods for modeling, predicting the structures and interactions between biomolecules, and applying these tools to discover novel reagents and therapeutics as well as better understand the molecular basis for diseases.
- Developing structure-based prediction methods to assess which peptides bind to a particular MHC allotype. MHC molecules fall into two classes: class I MHC, which primarily bind intracellular protein fragments (peptides), and class II MHC, which bind extracellular peptides. T-cells subsequently recognize non-self peptides and initiate an immune response. These peptide epitopes may be from atypically expressed proteins in cancer or viral proteins (for class I MHC) or from bacterial proteins (for class II MHC). The binding of such peptides to MHC is required for a T-cell immune response. The knowledge of which peptides bind to particular MHC types can be used as a basis for discovering vaccines against pathogens and cancer. However, the vast number of both MHC types and protein fragments precludes comprehensive experimental determination of peptide-MHC binding affinities. Computational methods, such as the ones being studied in Dr. Bordner's lab, can rapidly identify candidate epitopes for subsequent experimental verification.
- Developing computational methods for matching bone marrow transplant donors and recipients. Optimal outcomes in bone marrow transplant surgery depend on minimizing an adverse immune response, which is mainly determined by genetic mismatches in MHC proteins (see above). Dr. Bordner and his colleagues are developing a machine learning method for predicting transplant outcomes based on 3-D physiochemical features of the MHC proteins and clinical data. This is a joint project with Octavio E. Pajaro, M.D., Ph.D., and Vijayan Balan, M.D., both of Mayo Clinic.
- Predicting membrane protein structures and interactions. Many membrane proteins are medically important as approximately 40 percent of all drugs target membrane proteins, particularly G protein-coupled receptors (GPCRs). Computational methods for modeling GPCR structures are useful because only a few high-resolution experimental structures are available. Experimental evidence indicates that GPCRs often function as dimers or higher order oligomers. Dr. Bordner's lab is currently investigating general computational techniques for predicting membrane protein interactions and using these to design transmembrane peptides that block GPCR interactions. His team is working closely with experimental colleagues in this endeavor.
- Identifying disease-associated genome variants using protein structures. Proteins generally form stable complexes or transient interactions with other proteins, nucleic acids or small ligands in order to carry out their biological functions. Single nucleotide variants (SNVs) that occur within these functional sites can disrupt these interactions and contribute to disease. Dr. Bordner and his colleagues are studying the relative occurrence frequencies of disease-associated versus neutral SNVs in functional sites and plan to use this information to develop computational methods for classifying SNVs. This information can also be used to generate potential biochemical mechanisms for a particular disease that can be experimentally validated.
Significance to patient care
Because the research of Dr. Bordner and his team focuses on developing new computational techniques and applying them to biomedical problems, it can improve patient care in multiple therapeutic areas.
First, the ultimate goal of their work on computational immunology is to apply the new methods they have developed to discovering more effective cancer vaccines.
Second, another project aims to develop accurate computational methods for predicting the outcomes of bone marrow and liver transplant surgeries. These new methods could potentially improve donor-patient matches and thereby result in fewer complications with better overall outcomes.
Third, the project to develop new techniques to identify functional genome variants (described above) can help in interpreting genomics data and thereby aid in discovering personalized treatments for cancer.
Finally, Dr. Bordner is planning to work on several collaborative projects in early-stage drug discovery that can potentially lead to new cancer drugs.