Ragothaman Yennamalli

AAAAAAAAAAAAAAAAAHHH

The media frenzy and the way the scientific community reacted...fresh in my memory. The retraction is too late.

“With these considerations, we expect to fund through the 4th percentile.”

There it is, in black and white, the destruction of cancer research in the US.

hits closer to home...

🚨🚨🚨
Preprint time from Yennamalli Lab!
Here is the preprint that describes the Metalloproteome of Deinococcus indicus DR1
Let us know your feedback!
bsky.app/profile/bior...

🎉Today is the day!!!! 🙌
Based on last year's feedback out proposal for this year's @iscb.bsky.social #ISMB2025 tutorial was accepted and in the next few hours along with the same team members, the session will start.
Looking forward to one more super-duper tutorial! 🥳

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The work was supported by extramural grant from Department of Biotechnology, Govt. of India and support from SASTRA University. 5/5
Dr. Madhuprakash Jogi from University of Hyderabad is the collaborator in this project!

Have fun predicting and give us valuable feedback!!!

GitHub - raghuyennamalli/PreDSLpmo_v2: Updated repository for PreDSLpmo Updated repository for PreDSLpmo. Contribute to raghuyennamalli/PreDSLpmo_v2 development by creating an account on GitHub.

Datasets, code, and trained models are available at: github.com/raghuyennama...
4/5

Meet the team behind the webserver!
3/5

This is a follow up of the preprint and the associated thread.
bsky.app/profile/stru...
2/5

🚨🚨🚨
Yennamalli Lab is proud to announce another web-based resource predlpmo.in - an online web server for automated annotation of Lytic Polysaccharide Monooxygenases. A 🧵 1/5

A rollercoaster ride indeed! 🎢 Sorry to watch all this afar and see dear friends and acquaintances having a super hard time to do science.

Does anyone have access to this strain? $431+ship is insanely outside my budget, but I need to borrow some genes and don't feel like isolating wild tricho rn. Alternative cellulolytic species in Trichoderma could work too. TYIA!

www.atcc.org/products/56765

This is heartbreaking!

Explore 3-D models in your browser, download PDB files. Dive in now: deinococcus.in 🔗 #Proteomics #Metallomics #OpenData

🔬 New insight: our Deinococcus indicus DR1 atlas uncovers >100 previously uncharted proteins that may bind or move As, Cr, Co, Cu, Fe, Mn, Mo, Ni & Zn—hinting at a far-ranging metallome beyond the classic ars operon. ⚙️🧪 #Metallomics 4/5

Brief Stats:
📊 4128 proteins modelled with #AlphaFold
🔸 2145 structures at pLDDT ≥ 90
🔸 CATH / InterPro domains assigned to 2 735 sequences.
3/5

This work is a long standing collaboration between Yennamalli Lab and Dr. Richa Priyadarshini’s group at Shiv Nadar University. 2/5

🚨🚨🚨
Yennamalli Lab is proud to announce a newly launched website: deinococcus.in — a browsable, downloadable atlas of Deinococcus indicus DR1, the bacterium that shrugs off radiation ☢️ and arsenic 🧪. 1/5

YouTube video by Nerdwriter1 He Painted Bugs Like Jewels — And Changed Science Forever

What a find today morning!

He Painted Bugs Like Jewels — And Changed Science Forever youtu.be/2Tn6xnOnBFI?...

Send some of that mojo this side of the world. Multiple rejections in the last one month.

if it is already in public domain, why the threats? Science works when it is Open-Shareable science.

Machine learning-based structural classification of lytic polysaccharide monooxygenases Lytic polysaccharide monooxygenase (LPMO) is a copper-dependent redox enzyme and according to CAZy is classified either as cellulolytic or chitinolytic. According to CAZy, there are eight families of ...

The preprint is here 👉 biorxiv.org/content/10.1...
Completely worked by undergraduate student Aathi Manikandan!
If you’re working in enzyme discovery, biofuels, or structural ML, we’d love to hear your thoughts.
Thanks for reading!
#LPMO #AlphaFold #Bioinformatics #MachineLearning #Biotech

This project started as a question:
Can few structural features alone tell us something useful about function?
Ofc—it can.
And this approach might generalize to other enzyme families too. 9/10

What does this mean for biologists?
— You can functionally annotate LPMOs even with novel or borderline sequences
— You can use structure to discover or repurpose enzymes
— You get a clearer idea of why an LPMO behaves the way it does 8/10

With these features, we trained several ML models.
🥇 Best performer? SVM with an RBF kernel — 98% accuracy
🥈 MLP (tanh + adam) also did well — 97% accuracy
These models can classify LPMOs just from structural fingerprints! 7/10

We also looked into regioselectivity (i.e., what position the enzyme targets on the polysaccharide chain).
Turns out, geometry and charge cues near the active site offer real predictive power. 6/10

We pulled together ~100 high-quality structures (experimental + AlphaFold) across all LPMO families.
Then we extracted features like:
— Surface depth
— Active site shape
— Electrostatic charge
— Global geometry
All using tools like PyMOL, Open3D, Bio3D. 5/10