This webinar, brought to you by Twist Bioscience, will be hosted live and available on-demand.
Tuesday, July 14th, 2026
11:00 AM - 12:30 PM ET
Proteins have broad potential applications across medicine, materials science, and energy research, but designing proteins with predictable functional properties remains a major challenge. Many aspects of protein folding, stability, activity, and aggregation resistance are still difficult to model computationally, particularly across unexplored regions of sequence space. Advances in large-scale experimental screening are generating new opportunities to systematically characterize protein behavior and improve predictive modeling approaches.
In this webinar, brought to you by Twist Bioscience, Gabriel Rocklin, assistant professor of pharmacology at Northwestern University, Sara Volz, NIH NRSA postdoctoral fellow at Northwestern University, and Andra Campbell, National Defense Science & Engineering Graduate Fellow at Northwestern University, will discuss how megascale biophysical experiments are being integrated with machine learning to improve protein design. The session will examine approaches for generating and analyzing large protein datasets using oligo pools, DNA sequencing, and mass spectrometry. Speakers will also discuss how these methods support prediction of protein stability and aggregation resistance while enabling the development of functional enzymes across previously unexplored sequence space.
Topics to be covered
- Machine learning approaches for predicting protein stability and aggregation
- Megascale experimental methods for protein characterization
- Applications of oligo pools in biophysical screening workflows
- Integration of DNA sequencing and mass spectrometry in protein analysis
- Strategies for designing enzymes across unexplored sequence space
- Experimental approaches for improving aggregation resistance
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Gabriel Rocklin
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Sara Volz |
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Andra Campbell |
