Summary
Gabriel Rocklin is an Assistant Professor and protein design researcher based in Chicago with nine years of postdoctoral and faculty experience developing high-throughput methods for protein biophysics and therapeutics. He leads a lab that combines de novo computational protein design with display selections, mass spectrometry proteomics, and next-generation sequencing to test thousands of variants in parallel and accelerate design-test-analyze cycles. His work addresses fundamental questions about how sequence and structure dictate folding stability, dynamics, and resistance to aggregation or degradation, and aims to build quantitative models that predict these phenotypes from genotype. Trained as a biophysicist with a PhD from UCSF and experience in the Baker lab at UW, he bridges rigorous free-energy calculation approaches with large-scale experimental pipelines. Unusually, his group pairs classical computational modeling with very high-throughput experimental selection to directly iterate and validate predictive models for therapeutic protein optimization.
9 years of coding experience
Bachelor of Arts (B.A.), Biology-Chemistry, History, Bachelor of Arts (B.A.), Biology-Chemistry, History at Claremont McKenna College
Doctor of Philosophy (Ph.D.), Biophysics, Doctor of Philosophy (Ph.D.), Biophysics at University of California, San Francisco