Naail Kashif-Khan

Making proteins in the lab and with AI 🔬💻

Wielder of petri dish and keyboard 🧫⌨️

Love heavy metal and the Arsenal 🎸🔴

[Biology+AI Daily @BiologyAIDaily]

Optimizing Molecular Glues Using Free Energy Perturbation and Cofolding Methods

1. This study presents a comprehensive evaluation of Free Energy Perturbation (FEP) and Boltz-2 for predicting the binding affinity of molecular glues to protein complexes. The results show that FEP outperforms Boltz-2 in terms of correlation and RMSE, highlighting the need for more accurate high-throughput methods.

2. Molecular glues are small molecules that induce protein-protein interactions, offering access to new biology and protein targets. However, their rational design and optimization are challenging due to the dynamic nature of their binding sites. This study addresses this challenge by providing a detailed comparison of computational methods.

3. The study assessed 93 compounds across six diverse target/effector complexes, yielding 140 unique protein-compound measurements. This large-scale evaluation provides valuable insights into the capabilities and limitations of FEP and Boltz-2 in the context of molecular glue optimization.

4. FEP demonstrated good absolute predictability with RMSE values within 0.3-1.25 kcal/mol and strong correlations, making it a valuable tool for molecular glue optimization despite its higher computational cost. In contrast, Boltz-2 exhibited poor absolute predictability and generally poor correlations.

5. The poor performance of Boltz-2 suggests it is not suitable for high-throughput screening of molecular glues. This highlights the need for more accurate, high-throughput machine learning methods for pre-FEP screening to accelerate the discovery of molecular glues.

6. The study underscores the importance of accurate computational methods in the challenging field of molecular glue optimization. The findings provide a foundation for future research aimed at developing more efficient and accurate tools for drug discovery.

📜Paper: https://t.co/tpexR9iyYu
#MolecularGlues #FreeEnergyPerturbation #Boltz2 #DrugDiscovery #ComputationalBiology

[Naail Kashif-Khan @NKhan212]

@liambai21 Yup folds fine with the ESM Atlas API, so must be the visualizer being difficult!

[Naail Kashif-Khan @NKhan212]

@liambai21 I was just testing a few of the example sequences shown, some of which are pretty long! But even trying with a 163aa sequence it still doesn't work for me, it does the loading animation and then gives just a blank space. I'll tinker around and maybe try a different browser!

[Naail Kashif-Khan @NKhan212]

@DdelAlamo This is so cool! Not only really interesting to see what pLMs are "thinking", but super visually satisfying too 👀

[Naail Kashif-Khan @NKhan212]

@DdelAlamo I always just assumed they were LLM generated using the text of the paper lol

[Naail Kashif-Khan @NKhan212]

@klausenhauser I guess that's my overall concern here - you can't trust (or even really know) the data that will go into these models, let alone anything else about them (no code or weights, no details on training or experiments) so who will trust these over something like ESM-2?

[Naail Kashif-Khan @NKhan212]

So Ginkgo has just announced its own protein language model - my first question is, why would anyone want to use a closed-source proprietary pLM over one of the open-source and better understood models already out there?

[SynBioBeta @SynBioBeta]

@Ginkgo’s protein language model, built on @googlecloud technology, offers unprecedented insights for researchers, accelerating the development of life-saving medicines.

#DrugDevelopment #ProteinLLM #AIinBiotech #GinkgoBioworks #GoogleCloud

https://t.co/zlzfPmf2fU

[Naail Kashif-Khan @NKhan212]

Why release the source code/weights yourself when skeptical scientists will reproduce it for you? 900 IQ plays from @GoogleDeepMind

[Sergey Ovchinnikov @sokrypton]

AlphaFold3 reproduced and params/code released. 🤩

[Naail Kashif-Khan @NKhan212]

Ah yes it's every protein scientist's favourite amino acid, the shiny golden orb one

[Naail Kashif-Khan @NKhan212]

@egor__marin @DdelAlamo Interesting...anyone wanna make some structure problems?

[Naail Kashif-Khan @NKhan212]

@KevinKaichuang It's so visually satisfying!

[Naail Kashif-Khan @NKhan212]

People training biological "foundation" models - beware!

[Eric Topol @EricTopol]

On GenAI model collapse with training sets of excessive #AI-generated content
https://t.co/rKEJZafF3v
https://t.co/AV73adEzZZ
@iliaishacked @yaringal @OATML_Oxford

[Naail Kashif-Khan @NKhan212]

@sokrypton This is super interesting, is there some code we can play with to experiment with this?

[Naail Kashif-Khan @NKhan212]

@jacopo_gab "ise" vs "ize" isn't always just about British vs American English https://t.co/Pl2aVUx68s

[Naail Kashif-Khan @NKhan212]

@pengzhangzhi1 @LTEnjoy Something just doesn't sit right with me - train an inverse folding model on AF2 structures (which we think might not be a great idea), and then train a big language model on those inverse folded sequences, and also on AF2 predicted structures

[Naail Kashif-Khan @NKhan212]

@pengzhangzhi1 @LTEnjoy This is exactly what I'm concerned about. ProteinMPNN is trained only on the PDB and has been widely experimentally validated. In contrast, ESM-IF is trained on the PDB and 12m AF2 structures and no one's got it to work in the literature yet.

[Naail Kashif-Khan @NKhan212]

@LTEnjoy Cool stuff! Perhaps not so surprising given that ESM3 is trained on a bunch of predicted structures and inverse folded sequences. I reckon there's a load of junk in that data and it's probably not as good as training on just the PDB which are all "real" structures

[Naail Kashif-Khan @NKhan212]

@julian_englert @diffuse_bio @EvoscaleAI This is really cool! Protein design is very shiny and flashy but the nitty gritty lab characterization is my favourite part :) Any ideas why the non-binder from the paper looked like it binds in your experiments?

[Naail Kashif-Khan @NKhan212]

@jakublala It also seems that the "open" model isn't actually the best or biggest one they trained so they've definitely nerfed the publicly available stuff to keep the best ones for commercial use I'd imagine

[Naail Kashif-Khan @NKhan212]

@jakublala Based on this snippet from their press release I think they're going to pull a DeepMind and keep some secret sauce to themselves for licensing to pharma for drug discovery. I believe the currently available model is non commercial use only too but have to double check