  The prediction of protein 3D structure from amino acid sequence is a
computational grand challenge in biophysics, and plays a key role in robust
protein structure prediction algorithms, from drug discovery to genome
interpretation. The advent of AI models, such as AlphaFold, is revolutionizing
applications that depend on robust protein structure prediction algorithms. To
maximize the impact, and ease the usability, of these novel AI tools we
introduce APACE, AlphaFold2 and advanced computing as a service, a novel
computational framework that effectively handles this AI model and its TB-size
database to conduct accelerated protein structure prediction analyses in modern
supercomputing environments. We deployed APACE in the Delta and Polaris
supercomputers, and quantified its performance for accurate protein structure
predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up
to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE
is up to two orders of magnitude faster than off-the-self AlphaFold2
implementations, reducing time-to-solution from weeks to minutes. This
computational approach may be readily linked with robotics laboratories to
automate and accelerate scientific discovery.
