Thea K. Schulze
@tkschulze.bsky.social
📤 101
📥 159
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Postdoc, Biomolecular Simulation, MRC Laboratory of Molecular Biology
reposted by
Thea K. Schulze
Joe Greener
about 1 month 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
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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...
https://arxiv.org/abs/2603.16770
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
about 2 months ago
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
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
4 months ago
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📝
add a skeleton here at some point
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reposted by
Thea K. Schulze
Jingyou Rao
6 months ago
🎙️ 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
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
6 months 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/...
add a skeleton here at some point
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
10 months ago
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
add a skeleton here at some point
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
12 months ago
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/...
add a skeleton here at some point
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reposted by
Thea K. Schulze
Giulio Tesei
about 1 year ago
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!
add a skeleton here at some point
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
about 1 year ago
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...
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
about 1 year ago
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...
add a skeleton here at some point
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
over 1 year ago
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...
add a skeleton here at some point
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reposted by
Thea K. Schulze
Kresten Lindorff-Larsen
about 2 years ago
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...
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