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APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Hyun Park, Parth Patel, Roland Haas, E. A. Huerta
Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
arXiv:2308.07954 [q-bio.BM], (15 Aug 2023)

@misc{park2023apace,

   title={APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics},

   author={Hyun Park and Parth Patel and Roland Haas and E. A. Huerta},

   year={2023},

   eprint={2308.07954},

   archivePrefix={arXiv},

   primaryClass={q-bio.BM}

}

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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 supercomputer, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 200 ensembles, distributed across 50 nodes in Delta, equivalent to 200 A100 NVIDIA GPUs, we found that APACE is up to two orders of magnitude faster than off-the-shelf 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.
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