Postdoctoral positions available: computationally-driven immune engineering

antibody and antigen

Summer 2018. The Bailey-Kellogg lab is seeking talented and motivated postdoctoral fellows to join us in developing next-generation protein therapeutics: designing, analyzing, and engineering interactions between proteins and the immune system. The computational researchers in my lab work closely with experimental scientists in collaborating labs (particularly the Ackerman and Griswold labs) to put these methods to practical use. The labs are highly interactive, and interested researchers will be able to take advantage of "cross-over" opportunities bridging the approaches and applications.

In general, my lab is developing and applying computational methods to analyze and engineer proteins for use in the treatment of infectious diseases, cancer, and chronic disorders (see e.g., PMIDs 25568954, 26000749, and 28607051). While the protein universe manifests a wide range of functions with potential therapeutic utility, clinical translation faces numerous hurdles, which computationally-driven immune engineering is helping to overcome. For example, we have shown that by targeted mutagenic elimination of immunogenic "hot spots" in a potent anti-staph enzyme, we can produce variants that escape immune recognition and are more efficacious in treating infections (including drug-resistant strains).

We have recently been expanding our focus to include antibody:antigen interactions (e.g., PMIDs 29199956 and 29949961). We antipicate an additional position for a postdoc who will develop and apply computational methodologies to map and engineer such interactions. Research opportunities span the full spectrum from computationally-driven discovery of candidates, to analysis of rich sequence and interaction data, to design-based enhancement of existing proteins, all requiring new computational methods tightly integrated with experimental evaluation. The ideal candidate will have a strong background in computational structural biology, including both algorithm implementation and practical use. Experience in computational protein design methods would be helpful, but is not required and can be learned in context.

Please email Chris Bailey-Kellogg, cbk -at- cs.dartmouth.edu, your CV, statement of research background and interests, and contact information for 2-3 references.