Postdoc at Biozentrum, University of Basel. Applying AI to structural biology, specializing in pLMs and remote homology detection.
Lorenzo Pantolini
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Remote homology and protein design: two sides of the same coin. Instead of finding remote homologs, we used TEA to design completely de novo proteins, folding into desired TEA sequences.
I always love working with Jay, and “speed-running” this proof of concept was no exception.
Lorenzo Pantolini
It's finally out!
@lorenzopantolini.bsky.social and I are headed to @iclr-conf.bsky.social at Rio soon, with talks about this work at @gembioworkshop.bsky.social and LMRL workshops. Reach out to chat about representation learning for de novo protein design! 🫖
🚀 New paper in @natmethods.nature.com!
We present OpenStructure's powerful scoring capabilities, used to assess predictionsin CAMEO and CASP.
Read the full study here:
🔗 doi.org/10.1038/s415...
#StructuralBiology #Bioinformatics #OpenStructure #CASP #CAMEO #ProteinStructure
Now published in NSMB!
Paper: doi.org/10.1038/s415...
Full PDF: rdcu.be/fhBtI
Overview of additions since the preprint👇 (1/5)
Is #AI hitting a plateau in structure prediction? Help us find out at CASP17! 🧪🧬
Calling for Targets: Immune Complexes, protein - ligand complexes, RNA/DNA, conformational ensembles, membrane proteins, viral origins, and large complexes.
The Rule of Thumb: If AF3 can’t model it, we want it.
Now published in NSMB!
Paper: doi.org/10.1038/s415...
Full PDF: rdcu.be/fhBtI
Overview of additions since the preprint👇 (1/5)
Meet Stoic from @daniil-litvinov.bsky.social and @ninjani.bsky.social: embeddings to predict stoichiometry of protein complexes from sequence fast and accurately 🧬🧩💻🤩
www.biorxiv.org/content/10.6...
I'm excited to share *Stoic*, a method for fast and accurate protein complex stoichiometry prediction directly from sequence. Preprint: www.biorxiv.org/content/10.6... 🧵👇(1/10)
CAMEO-3D
Search with TEA 🍵 Against Many!
→ On the web: pickybinders.org/tea/steam
→ Locally: github.com/PickyBinders...
Feedback welcome!
Janani Durairaj (Jay)
Janani Durairaj (Jay)
www.biorxiv.org
A fun little idea that worked surprisingly well, using a structure-informed yet structure-independent alphabet for de novo protein design: www.biorxiv.org/content/10.6...
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This work introduces the Runs N’ Poses dataset for benchmarking deep learning methods on the protein–ligand complex prediction task. It shows that current methods rely on memorization, challenging the...
This work introduces the Runs N’ Poses dataset for benchmarking deep learning methods on the protein–ligand complex prediction task. It shows that current methods rely on memorization, challenging the...
A fun little idea that worked surprisingly well, using a structure-informed yet structure-independent alphabet for de novo protein design: www.biorxiv.org/content/10.6...
🧵(1/n)
Excited to share our latest preprint evaluating AlphaFold3, Boltz-1, Chai-1 and Protenix for predicting protein-ligand interactions, featuring our newly introduced benchmark dataset 🌹Runs N’ Poses🌹!
www.biorxiv.org/content/10.1...
🧵👇 (1/n)
Excited to share our latest preprint evaluating AlphaFold3, Boltz-1, Chai-1 and Protenix for predicting protein-ligand interactions, featuring our newly introduced benchmark dataset 🌹Runs N’ Poses🌹!
www.biorxiv.org/content/10.1...
🧵👇 (1/n)
Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with other molecules, especially small mo...
Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with other molecules, especially small mo...
Fresh from bioRxiv our latest work introducing The Embedded Alphabet (TEA), a powerful new representation for protein sequences obtained by discretising ESM2 embeddings into 20 characters.
Pre-print: www.biorxiv.org/content/10.1...
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Detecting remote homology with speed and sensitivity is crucial for tasks like function annotation and structure prediction. We introduce a novel approach using contrastive learning to convert protein...
