One significant advantage of OpenFold -- beyond its active open-source development, its pytorch implementation (thus being more interoperable with common external ML libraries) -- is its release of training code. So if Odyssey wants to fine-tune predictions on a protein family of particular interest to Odyssey, OpenFold's your answer. (You all can actually join the OpenFold consortium -- which organizes not just OpenFold the existing project but diverse additional open source ML development directions -- for a small annual donation.) In some respects single-sequence models like OmegaFold have advantages -- you don't need a heterogenous compute environment, with a good GPU for inference but a lot of CPU cores for MSA precomputation. It's not a total panacea though; embedding in a large language model is a significant computation. On Thu, Sep 22, 2022 at 11:09 AM Chip Lesburg wrote:

Hi PHENIX users,

At this past summer’s Diffraction Methods GRC meeting https://www.grc.org/diffraction-methods-in-structural-biology-conference/202..., Tom Terwilliger presented the utility of incorporating AlphaFold2 predictions during structure fitting and rebuilding. I wonder if anyone has experience using that or an alternative such as OmegaFold https://www.biorxiv.org/content/10.1101/2022.07.21.500999v1 or OpenFold https://github.com/aqlaboratory/openfold? Are any of these easier or harder to get set up?

Chip

Charles Lesburg

*Vice President, Structural Biology* *M* 908-265-2064 301 Binney Street, Cambridge, MA 02142 *odysseytx.com http://odysseytx.com*

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