@article{TEXTUAL,
      recid = {12890},
      author = {Park, Hyun and Patel, Parth and Haas, Roland and Huerta,  E. A.},
      title = {APACE: AlphaFold2 and advanced computing as a service for  accelerated discovery in biophysics},
      journal = {PNAS},
      address = {2024-06-24},
      number = {TEXTUAL},
      abstract = {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 AI tools we introduce  APACE, AlphaFold2 and advanced computing as a service, a  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.},
      url = {http://knowledge.uchicago.edu/record/12890},
}