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For-profit corporations that value protein engineering will beat a path to DeepMind's door ASAP, like pharmas. Protein conformation prediction is essential whe

For-profit corporations that value protein engineering will beat a path to DeepMind's door ASAP, like pharmas. Protein conformation prediction is essential when engineering new small-molecule drug compounds that must 'dock' with the specific proteins that regulate disease. Knowing how to create a protein with the precise shape to become biologically active has soaked up a lot of R&D funding toward pie-in-the-sky techniques that promise to advance that agenda (like quantum or DNA computing). If this method works as DeepMind says, it will immediately be adopted by every pharma to assess and tweak the shape of candidate proteins.

dekhn 6y ago
you give pharma too much credit. I had built a previous system to do something similar to this that produced excellent results and tried to give it away for free to Genentech, which ignored me. They said it didn't work for their purchasing department.
- TheRealPomax 6y ago
  I don't believe you, but I look forward to you showing proof of this with some links (and if you tried giving it for free, I assume you just open sourced the whole deal, so I look forward to a repo link or the like).
  - dekhn 6y ago
    I developed the Exacycle system at Google and used it to publish my work (I wrote that blog entry): https://ai.googleblog.com/2013/12/groundbreaking-simulations-by-google.html https://ai.googleblog.com/2013/12/groundbreaking-simulations... we offered the service for free to Genentech since I used to work there and knew they could probably use it to get some good publications. We didn't open source the distributed computing framework, but the underlying technology (Folding@Home) is based on gromacs, which is open source. It's the scale at which it ran, and the processing pipeline for filtering the results that had the real value.
- deeviant 6y ago
  I feel that the "produced excellent results" has a lot of unpack there. It obviously wasn't scoring 90+ in CASP. Actually, after reading your linked blog post, it's pretty obvious why they weren't exactly chomping on the bit: "To gain insights into the receptor’s dynamics, Kai performed detailed molecular simulations using hundreds of millions of core hours on Google’s infrastructure, generating hundreds of terabytes of valuable molecular dynamics data." Hmm, yes, hundreds of millions of hours of cpu time, hundreds of terabytes of data, who says no to that? It doesn't even seem seem to attack generalized protein folding in general. It really seems like the plan was, "let's attack this problem with a Google-sized firehose" rather than created a fundamentally different algorithm that had game-changing results. Comparing your system to AlphaFold seems like your really bending the truth here.
  - dekhn 6y ago
    If you note, the paper has an enormous number of citations from pharma, since modelling protein dynamics, rather than static structure, is key to understsanding ligand binding firehose. You can see another paper we published where attacking the problem with a firehose helped unlock a long-standing problem: https://pubmed.ncbi.nlm.nih.gov/24265211/ https://pubmed.ncbi.nlm.nih.gov/24265211/ in this case, showed that bond angles need to be 'free' to move rather than fully constrained,to build the most accurate models. This paper is also heavily cited amongst protein modellers. It is correct that the MD simulations don't directly work for CASP- in a sense, the results they produce directly disagree with CASP's mental model of protein structure and function.

dekhn 6y ago

you give pharma too much credit. I had built a previous system to do something similar to this that produced excellent results and tried to give it away for free to Genentech, which ignored me. They said it didn't work for their purchasing department.

TheRealPomax 6y ago
I don't believe you, but I look forward to you showing proof of this with some links (and if you tried giving it for free, I assume you just open sourced the whole deal, so I look forward to a repo link or the like).
- dekhn 6y ago
  I developed the Exacycle system at Google and used it to publish my work (I wrote that blog entry): https://ai.googleblog.com/2013/12/groundbreaking-simulations-by-google.html https://ai.googleblog.com/2013/12/groundbreaking-simulations... we offered the service for free to Genentech since I used to work there and knew they could probably use it to get some good publications. We didn't open source the distributed computing framework, but the underlying technology (Folding@Home) is based on gromacs, which is open source. It's the scale at which it ran, and the processing pipeline for filtering the results that had the real value.
deeviant 6y ago
I feel that the "produced excellent results" has a lot of unpack there. It obviously wasn't scoring 90+ in CASP. Actually, after reading your linked blog post, it's pretty obvious why they weren't exactly chomping on the bit: "To gain insights into the receptor’s dynamics, Kai performed detailed molecular simulations using hundreds of millions of core hours on Google’s infrastructure, generating hundreds of terabytes of valuable molecular dynamics data." Hmm, yes, hundreds of millions of hours of cpu time, hundreds of terabytes of data, who says no to that? It doesn't even seem seem to attack generalized protein folding in general. It really seems like the plan was, "let's attack this problem with a Google-sized firehose" rather than created a fundamentally different algorithm that had game-changing results. Comparing your system to AlphaFold seems like your really bending the truth here.
- dekhn 6y ago
  If you note, the paper has an enormous number of citations from pharma, since modelling protein dynamics, rather than static structure, is key to understsanding ligand binding firehose. You can see another paper we published where attacking the problem with a firehose helped unlock a long-standing problem: https://pubmed.ncbi.nlm.nih.gov/24265211/ https://pubmed.ncbi.nlm.nih.gov/24265211/ in this case, showed that bond angles need to be 'free' to move rather than fully constrained,to build the most accurate models. This paper is also heavily cited amongst protein modellers. It is correct that the MD simulations don't directly work for CASP- in a sense, the results they produce directly disagree with CASP's mental model of protein structure and function.