Presentation
MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization
DescriptionWe present a scalable, end-to-end workflow for protein design. By augmenting protein sequences with natural language descriptions of biochemical properties, we train generative models to preferentially align with protein fitness landscapes. Through complex experimental- and simulation-based observations, we integrate these measures as preferred parameters for generating new protein variants and demonstrate our workflow on five diverse supercomputers. We achieve >1 ExaFLOPS sustained performance in mixed precision on each supercomputer and a maximum sustained performance of 4.11 ExaFLOPS and peak performance of 5.57 ExaFLOPS. We establish the performance of our model on two tasks: (1) across a predetermined benchmark dataset of deep mutational scanning experiments to optimize the fitness-determining mutations in the yeast protein HIS7, and (2) in optimizing the design of the enzyme malate dehydrogenase to achieve lower activation barriers (and therefore increased catalytic rates) using simulation data. Our implementation thus sets high watermarks for multimodal protein design workflows.
Authors
Event Type
ACM Gordon Bell Finalist
TimeWednesday, 20 November 202410:30am - 11am EST
LocationB312-B313A
TP
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