Rosetta does not directly benefit from modern hardware, such as GPU or distributed computing;
I looked up about Rosetta out of curiosity and saw in the release notes that there are attempts to support TF and GPU calculations through TF in the latest builds.
Rosetta 3.13
New tools and apps: trRosetta available in C++ Rosetta now. A TensorFlow build (extras=tensorflow or extras=tensorflow_gpu) supports this.
Perhaps you have a better vision for solving this.
Hi, thanks for the support!
It's true that Rosetta is now expanding and attempting to modernize (and standardize) the software. However, trRosetta is a separate application from Rosetta. trRosetta is the direct response to Deepmind's amazing AlphaFold results in CASP14: it uses machine learning models to learn from existing proteins and identify the folding patterns. It's an amazing revolution by itself: it will allow scientists to get a glimpse into the structure (and therefore the function) of unknown proteins simply by sequencing the genome.
However, it is not a direct tool for protein design by itself: the design space is (perhaps) even more gigantic that the conformational/folding space, and given the nature of "creating the not-yet-created", machine learning models struggle to find direct solutions. There are, however, modern tools, such as the SeqDes Model or the TorchANI model that attempt to shed some light into the inner-working of proteins and therefore offer a possible guide to high throughput computational design of proteins (I would love to get into more detail on this, both models are implemented in ProtoSyn.jl ahah).
The Rosetta suite itself is also undergoing a modernization effort, I'm sure GPU (and other sweet goodness) are coming to Rosetta and PyRosetta soon. Alas, I'm highly hopeful that Rosetta manages to implement all the tools we desperately need! Until then, I guess the best way to learn is by doing! So we'll keep coding ahah
Very nice. It seems like a lot of software tools are needed to make your protein design toolkit complete and of course, this project will allow you to experiment in those newer areas.
I am not from the biology field but your articles about this and observing how you optimize ML models in Julia to use GPU for high throughput would be interesting and educational for everyone.
This is highly experimental (as it should be in the academic environment). Hopefully someone more experienced than me gets curious enough to try new things out: that's how science is made! Cheers
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Quite an ambitious project. All the best.
I looked up about Rosetta out of curiosity and saw in the release notes that there are attempts to support TF and GPU calculations through TF in the latest builds.
Perhaps you have a better vision for solving this.
Hi, thanks for the support!
It's true that Rosetta is now expanding and attempting to modernize (and standardize) the software. However, trRosetta is a separate application from Rosetta. trRosetta is the direct response to Deepmind's amazing AlphaFold results in CASP14: it uses machine learning models to learn from existing proteins and identify the folding patterns. It's an amazing revolution by itself: it will allow scientists to get a glimpse into the structure (and therefore the function) of unknown proteins simply by sequencing the genome.
However, it is not a direct tool for protein design by itself: the design space is (perhaps) even more gigantic that the conformational/folding space, and given the nature of "creating the not-yet-created", machine learning models struggle to find direct solutions. There are, however, modern tools, such as the SeqDes Model or the TorchANI model that attempt to shed some light into the inner-working of proteins and therefore offer a possible guide to high throughput computational design of proteins (I would love to get into more detail on this, both models are implemented in ProtoSyn.jl ahah).
The Rosetta suite itself is also undergoing a modernization effort, I'm sure GPU (and other sweet goodness) are coming to Rosetta and PyRosetta soon. Alas, I'm highly hopeful that Rosetta manages to implement all the tools we desperately need! Until then, I guess the best way to learn is by doing! So we'll keep coding ahah
Very nice. It seems like a lot of software tools are needed to make your protein design toolkit complete and of course, this project will allow you to experiment in those newer areas.
I am not from the biology field but your articles about this and observing how you optimize ML models in Julia to use GPU for high throughput would be interesting and educational for everyone.
This is highly experimental (as it should be in the academic environment). Hopefully someone more experienced than me gets curious enough to try new things out: that's how science is made! Cheers