AI assisted protein interaction modeling, pioneered by AlphaFold and RosettaFold, has become more diverse both with respect to the programs that do it, and how users run these programs. In this talk, we will cover the programs that are supported at the University of Utah, namely Alphafold2, Alphafold3, Colabfold, Boltz1, RFDiffusion and other tools from the Baker lab, the choices we have made with their deployment, and our experiences with using them. With respect to the ways to run, we will go over the standard SLURM scripts to run Alphafold in two stages (CPU only MSA search, GPU accelerated inference), use Colabfold server for faster MSA search, and using Google Colab running on compute nodes for interactive modeling in a notebook interface. Attendees should leave this talk with ideas how to set up and support these tools and contacts to UofU staff for further questions.
Managing data at scale in high-performance computing (HPC) environments requires efficient storage and retrieval strategies. Automated tiered storage solutions enable seamless migration of aged data to lower-cost archival tiers while maintaining accessibility. Enriched metadata—spanning tagging, search, discovery, and data provenance—enhances data usability and long-term value. This approach not only optimizes storage costs but also empowers researchers with better data discovery and reuse. Real-world HPC use cases demonstrate how metadata-driven workflows streamline research, ensuring that critical datasets remain accessible and actionable over time.
