I appreciated this article. These analogies always fall apart at some point (and the article points this out) because these are complex processes, but I appreciate these attempts to turn biology into a human problem, because at the very least, it'll have the effect of getting more people up the learning curve.
A review on protein language models
21–30 of 31 posts
Re: A review on protein language models
#22I am gobsmacked by how little programmers know about the underpinnings of actual human language. This guy sounds like a SoCal 1960s burnout going on about being one with language of the forest.
Unrelated to conversation. But Apoorva is a female name. So its probably she. :)
Actually while it's generally female, it's not always! It depends on the region of Indian continent / ethnic group, as the final "-a" in many Sanskrit-roots may or may not be dropped depending on the modern language of derivation. For example, I know several Telugu, Marathi, and Bengali males named Apoorva/Apurva.
That being said, it does appear that this instance of Apoorva is indeed a female name (and in general, Tamil names like this one don't keep the aforementioned "-a" in the masculine).
Re: A review on protein language models
#23I'm excited to see where this, and Alphafold go. I'm also interested in a different direction for modeling proteins: ab-initio. I am curious if we can get a good-enough simulation of charge density around atoms to simulation folding using electric force models. It seems that chemists are using very computationally-intense models (HF, Kohn-Sham DFT etc) where the wave function (and therefore charge density) is modeled…
It's still unclear to me that using QM to simulate protein folding or enzymatic activity is a worthwhile endeavor. Even highly approximate QM methods don't seem significantly better than classical force fields for recapitulating folding dynamics, and the actual amount of computational effort required would be astronomical. I would recommend against it simply because we know of better, more economical methods, to get at solutions we need.
Re: A review on protein language models
#24Earlier quoted context omitted.
Unrelated to conversation. But Apoorva is a female name. So its probably she. :)
Actually while it's generally female, it's not always! It depends on the region of Indian continent / ethnic group, as the final "-a" in many Sanskrit-roots may or may not be dropped depending on the modern language of derivation. For example, I know several Telugu, Marathi, and Bengali males named Apoorva/Apurva. That being said, it does appear that this instance of Apoorva is indeed a female name (and in general, T…
i am, in fact, a 'she' :)
Re: A review on protein language models
#25Earlier quoted context omitted.
The intersection between biology and computer science is by far my favorite topic. I wish I would have gone into bioinformatics after my CS masters degree. Both sciences really get the best out of the other.
Most bioinformaticians are biologists.
It depends on what type (and also whether you distinguish computational biology from bioinformatics). The people who create new algorithms for sequence assembly, protein folding, etc. tend to be computer scientists who got into biology. On the other hand, the people who analyze biological data computationally tend to be biologists who got into computing.
Re: A review on protein language models
#26I'm excited to see where this, and Alphafold go. I'm also interested in a different direction for modeling proteins: ab-initio. I am curious if we can get a good-enough simulation of charge density around atoms to simulation folding using electric force models. It seems that chemists are using very computationally-intense models (HF, Kohn-Sham DFT etc) where the wave function (and therefore charge density) is modeled…
interatomic potential is a promising scale and there's been some interesting recent developments using equivariant graph neural networks:
https://www.nature.com/articles/s41467-022-29939-5
https://arxiv.org/pdf/2206.07697
Re: A review on protein language models
#27It bums me out that the way to get rich via AI is to throw an OpenAI API key at a SaaS app, not solving fundamental hard science problems.
Re: A review on protein language models
#28I'm excited to see where this, and Alphafold go. I'm also interested in a different direction for modeling proteins: ab-initio. I am curious if we can get a good-enough simulation of charge density around atoms to simulation folding using electric force models. It seems that chemists are using very computationally-intense models (HF, Kohn-Sham DFT etc) where the wave function (and therefore charge density) is modeled…
I don't think DFTs will get better anytime soon. But here's something wild: Maybe it doesn't matter.
Run a DFT simulation of a protein with known structure melting. Time-reverse it. Train some sort of 3d convnet on the deltas at every point in the (wrong) melting curve.
Who cares if the DFT is wrong! The ml model will learn the rules of this fictional universe that uses the wrong rules to get the right thing.
Re: A review on protein language models
#29Earlier quoted context omitted.
Most bioinformaticians are biologists.
It depends on what type (and also whether you distinguish computational biology from bioinformatics). The people who create new algorithms for sequence assembly, protein folding, etc. tend to be computer scientists who got into biology. On the other hand, the people who analyze biological data computationally tend to be biologists who got into computing.
They are arguably computer scientists who do “bioinformatics” by building tools. Different skill set. Also not a large number of them.
Most bioinformatics jobs need domain specific bio knowledge.
Things will change even more as DNNs take over.
Re: A review on protein language models
#30Honestly I wish I learned more biology in college instead of cruising through a CS degree for a generic software engineering career. Now that I’ve gained more interest in bio, the doors have closed.