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.

中文翻译：

---

APACE：AlphaFold2 和高级计算作为加速生物物理学发现的服务


从氨基酸序列预测蛋白质 3D 结构是生物物理学中的一项巨大计算挑战，并且在从药物发现到基因组解释的稳健蛋白质结构预测算法中发挥着关键作用。 AlphaFold 等人工智能模型的出现正在彻底改变依赖于强大蛋白质结构预测算法的应用程序。为了最大限度地发挥这些人工智能工具的影响并简化其可用性，我们引入了 APACE、AlphaFold2 和高级计算即服务，这是一种计算框架，可以有效处理该人工智能模型及其 TB 大小的数据库，以在现代环境中进行加速蛋白质结构预测分析。超级计算环境。我们在 Delta 和 Polaris 超级计算机中部署了 APACE，并使用四种示例蛋白质（6AWO、6OAN、7MEZ 和 6D6U）量化了其精确蛋白质结构预测的性能。使用分布在 200 个 NVIDIA A100 GPU 上的多达 300 个整体，我们发现 APACE 比现成的 AlphaFold2 实现快两个数量级，将解决问题的时间从几周缩短到几分钟。这种计算方法可以很容易地与机器人实验室联系起来，以实现自动化和加速科学发现。