CMSDG Seminar of November 2019 (To register)

Speaker: Gabriel Rocklin, Phd  (Pharmacology, Northwestern Univ.)

Title: The structural basis for protein energy landscapes in a de novo designed proteome

Location: Northwestern Univ (Ryan Hall, Room 4003)

Time: 11/12/2019 7pm-8pm (Dinner served at 630pm)


All proteins dynamically sample diverse folded, unfolded, and excited states with differing free energies. Although energy landscapes have been studied for decades, existing methods have been restricted to assaying one protein per sample, limiting the development of quantitative, global models of protein energy landscapes. We developed a new experimental approach to rapidly characterize hundreds to thousands of protein energy landscapes simultaneously by hydrogen exchange mass spectrometry. In this approach, the target protein library is expressed as a mixture from custom-synthesized DNA oligos, and individual intact proteins are resolved by LC-IMS-MS to measure the overall exchange at each timepoint. We applied this approach to examine the energy landscapes of over 1,000 de novo designed miniproteins (43 residues in length) and found wide variation in landscapes, even among designs with similar topologies. The size of our dataset enabled us to statistically analyze the structural origins of the varied landscapes, revealing how different interaction types modulate both stability and conformational fluctuations. Combining these new large-scale experiments with computational modeling should ultimately lead to a quantitative understanding of the structural determinants of protein energy landscapes. 


Gabriel is currently a Faculty Member, Center for Synthetic Biology. He received his PhD from UCSF in 2013 and postdoctoral training from U of Washington in 2019. He is working on development of high-throughput methods for protein biophysics and protein design, with a focus on protein therapeutics. Key questions include: How do protein sequence and structure determine folding stability, conformational dynamics, and resistance to aggregation/degradation-inducing stresses? Can we quantitatively predict these protein “phenotypes” from genotype (sequence) using computational modeling? How do we design protein therapeutics that optimize these phenotypes for a particular application? To answer these questions, his group combine large-scale de novo computational protein design with high-throughput methods such as display selections, mass spectrometry proteomics, and next-generation sequencing, enabling to test thousands of proteins in parallel. By combining these technologies, he and his colleagues seek to develop efficient “design-test-analyze” cycles, iterating the way to an improved, quantitative understanding of protein biophysics and more advanced protein therapeutics.