Our Garnet paper is now out in final form: a protein and small molecule force field trained from scratch, with competitive results for binding free energy prediction. pubs.rsc.org/sc/article/d...

Joe Greener@jgreener64.bsky.social · 7mo ago

Check out our pre-print, where we train a protein and small molecule force field from scratch with a graph neural network. We show comparable performance to existing, manually-tuned force fields on a range of tasks including binding free energy prediction. (1/4) arxiv.org/abs/2603.16770

Happy to share our new preprint on MEM-CALVADOS! Work led by @rsaltutti.bsky.social extending CALVADOS to flexible proteins at lipid membrane interfaces. 🗞️ doi.org/10.64898/202... 🖥️ github.com/gitesei/MEM-... @vetenskapsradet.bsky.social

AF-CALVADOS is now published doi.org/10.1002/pro.... We combine AlphaFold and CALVADOS to simulate flexible multidomain proteins at scale: — Ensembles of >12000 full-length human proteins — Comparison of IDRs alone and I n context for >1500 TFs @sobuelow.bsky.social @kejohansson.bsky.social

Everything you wanted to know about the protein chemistry behind how amino-acid changes affect the cellular abundance of proteins from @tkschulze.bsky.social Effects of residue substitutions on the cellular abundance of proteins doi.org/10.7554/eLif...

Check out our pre-print, where we train a protein and small molecule force field from scratch with a graph neural network. We show comparable performance to existing, manually-tuned force fields on a range of tasks including binding free energy prediction. (1/4) arxiv.org/abs/2603.16770

Training a force field for proteins and small molecules from scratch

Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural networ...

arxiv.org

New paper from former PhD student @tkschulze.bsky.social on supervised learning of protein variant effects across large-scale mutagenesis datasets MAVE/DMS experiments provide large amounts of data for benchmarking variant effect predictors, but may be difficult to use in supervised learning. 1/5

We (@sobuelow.bsky.social & @kejohansson.bsky.social) tested AF-CALVADOS using the recently described PeptoneBench SAXS benchmark that contains SAXS data for >400 proteins with different amounts of order and disorder. The results look pretty good 😇 so we are sharing here while updating the preprint📝

Kresten Lindorff-Larsen@lindorfflarsen.bsky.social · 12mo ago

We (@sobuelow.bsky.social) developed AF-CALVADOS to integrate AlphaFold and CALVADOS to simulate flexible multidomain proteins at scale See preprint for: — Ensembles of >12000 full-length human proteins — Analysis of IDRs in >1500 TFs 📜 doi.org/10.1101/2025... 💾 github.com/KULL-Centre/...

🎙️ Next up Dec 2 in VESS! Thea Schulze (Lindorff-Larsen Lab): Predicting mutated protein abundance @tkschulze.bsky.social Taylor Mighell (Lehner Lab): Massive mutagenesis to understand GPCRs @taylor-mighell.bsky.social 🔗 More info at varianteffect.org/seminar-series @varianteffect.bsky.social

We (@sobuelow.bsky.social) developed AF-CALVADOS to integrate AlphaFold and CALVADOS to simulate flexible multidomain proteins at scale See preprint for: — Ensembles of >12000 full-length human proteins — Analysis of IDRs in >1500 TFs 📜 doi.org/10.1101/2025... 💾 github.com/KULL-Centre/...

bioRxiv Biophysics@biorxiv-biophys.bsky.social · 12mo ago

AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level https://www.biorxiv.org/content/10.1101/2025.10.19.683306v1

Arriën & Giulio's paper on A coarse-grained model for disordered proteins under crowded conditions (that is the CALVADOS PEG model) is now published in final form: dx.doi.org/10.1002/pro.... @asrauh.bsky.social @giuliotesei.bsky.social

Kresten Lindorff-Larsen@lindorfflarsen.bsky.social · 2y ago

CALVADOS 🤝 PEG Work from @asrauh.bsky.social on a simple model for polyethylene glycol to study the effects of crowding on IDPs

Led by @vvouts.bsky.social in @rhp-lab.bsky.social, we measured the degron potency of >200,000 30-residue tiles from >5,000 cytosolic human proteins and trained an ML model for degrons 📜 www.biorxiv.org/content/10.1... 🖥️ github.com/KULL-Centre/...

Rasmus Hartmann-Petersen@rhp-lab.bsky.social · last yr.

In collaboration with the @lindorfflarsen.bsky.social group we release our map of degrons in >5,000 human cytosolic proteins with >99% coverage. A machine learning model trained on the data identifies missense variants forming degrons in exposed & disordered regions. Work led by @vvouts.bsky.social.

Happy to share a walkthrough of the applications of our package for simulations using CALVADOS! Big thanks to @sobuelow.bsky.social, @lindorfflarsen.bsky.social, and the whole team for making this possible. Thrilled to mark this as my first last-author paper!

Kresten Lindorff-Larsen@lindorfflarsen.bsky.social · last yr.

Do you like CALVADOS but are not quite sure how to make it? We’ve got your back! @sobuelow.bsky.social & @giuliotesei.bsky.social—together with the rest of the team—describe our software for simulations using the CALVADOS models incl. recipes for several applications. 1/5 doi.org/10.48550/arX...

Do you like CALVADOS but are not quite sure how to make it? We’ve got your back! @sobuelow.bsky.social & @giuliotesei.bsky.social—together with the rest of the team—describe our software for simulations using the CALVADOS models incl. recipes for several applications. 1/5 doi.org/10.48550/arX...

Supervised training using data generated by multiplexed assays of variant effects is potentially very powerful, but is made difficult by assay- and protein-specific effects Here @tkschulze.bsky.social devised a strategy to take this into account while training models www.biorxiv.org/content/10.1...

bioRxiv Biophysics@biorxiv-biophys.bsky.social · 2y ago

Supervised learning of protein variant effects across large-scale mutagenesis datasets https://www.biorxiv.org/content/10.1101/2025.04.02.646878v1

New preprint w @tkschulze.bsky.social who analysed cellular abundance (VAMP-seq) data for ~32,000 variants of six proteins 🧪 We find that much of the variation can be explained and predicted by a burial-dependent substitution matrix Lots more goodies in the paper doi.org/10.1101/2024...

bioRxiv Biophysics@biorxiv-biophys.bsky.social · 2y ago

Effects of residue substitutions on the cellular abundance of proteins https://www.biorxiv.org/content/10.1101/2024.09.23.614650v1

Big congratulations to PhD student Thea K Schulze from our PRISM centre on being awarded the elite research travel grant by the Minister for Higher Education and Science, Christina Egelund, to support a stay at Berkeley to study protein variant effects www1.bio.ku.dk/nyheder/2024...