Sören von Bülow's Avatar

Sören von Bülow

@sobuelow.bsky.social

Senior Researcher at Bind Research, London

440 Followers  |  236 Following  |  6 Posts  |  Joined: 03.10.2023  |  1.7501

Latest posts by sobuelow.bsky.social on Bluesky

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/...

20.10.2025 11:26 — 👍 89    🔁 37    💬 0    📌 0

Huge thanks to @lindorfflarsen.bsky.social and all the members of SBiNLab at the University of Copenhagen for three fantastic postdoctoral years!

02.09.2025 15:43 — 👍 4    🔁 0    💬 1    📌 0

I’m excited to share that I have started a new position as Senior Scientist, Biomolecular Simulation, at @bindresearch.org in London! We are creating experimental and computational tools and public datasets with the goal of making intrinsically disordered proteins druggable.

02.09.2025 15:43 — 👍 23    🔁 1    💬 2    📌 0

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

17.07.2025 08:54 — 👍 13    🔁 5    💬 0    📌 0

They mostly leverage things now

25.06.2025 10:12 — 👍 2    🔁 0    💬 1    📌 0

Congratulations! 🙌

23.05.2025 15:33 — 👍 4    🔁 0    💬 0    📌 0

Now published! Big congrats to first author @gginell.bsky.social

We are actively working improving/updating various aspects of FINCHES; don't hesitate to reach out if you run into issues, have questions.
www.science.org/doi/10.1126/...

23.05.2025 11:31 — 👍 120    🔁 46    💬 8    📌 2
Figure showing the architecture of the CALVADOS package.

Figure showing the architecture of the CALVADOS package.

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...

15.04.2025 07:08 — 👍 47    🔁 16    💬 2    📌 2
Preview
Max Planck Research Group Leader (W2) in Molecular Design We are looking for exceptional early-career scientists conducting computational research with a proven record of accomplishment. The primary focus of this call is on candidates proposing research on b...

Job alert: Join us in Mainz as

Max Planck Research Group Leader (W2) in Molecular Design

...and make your own research dreams happen on de novo design, generative models, proteins, materials ...
tinyurl.com/r2xjxnuk

@mpip-mainz.mpg.de

04.04.2025 16:00 — 👍 64    🔁 51    💬 1    📌 3

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...

03.04.2025 03:05 — 👍 27    🔁 6    💬 0    📌 0

Thanks to @lindorfflarsen.bsky.social and all authors for this wonderful project on predicting IDR phase separation from sequence!

Check out the published version (including added exp. data from @tanjamittag.bsky.social) and feel free to try out our webserver.

25.03.2025 18:23 — 👍 23    🔁 4    💬 0    📌 0
Figure from the paper illustrating sequence–ensemble–function relationships for disordered proteins. ML prediction (black) and design (orange) approaches are highlighted on the connecting arrows. Prediction of properties/functions from sequence (or vice versa, design) can include biophysics approaches via structural ensembles, or bioinformatics approaches via other hetero- geneous sources. The lower panels show examples of properties and functions of IDRs for predictions or design targets. ML, machine learning; IDRs, intrinsically disordered proteins and regions.

Figure from the paper illustrating sequence–ensemble–function relationships for disordered proteins. ML prediction (black) and design (orange) approaches are highlighted on the connecting arrows. Prediction of properties/functions from sequence (or vice versa, design) can include biophysics approaches via structural ensembles, or bioinformatics approaches via other hetero- geneous sources. The lower panels show examples of properties and functions of IDRs for predictions or design targets. ML, machine learning; IDRs, intrinsically disordered proteins and regions.

Our review on machine learning methods to study sequence–ensemble–function relationships in disordered proteins is now out in COSB

authors.elsevier.com/sd/article/S...
Led by @sobuelow.bsky.social and Giulio Tesei

12.03.2025 21:37 — 👍 91    🔁 27    💬 0    📌 1

CALVADOS 🤝 PEG

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

06.03.2025 22:03 — 👍 41    🔁 7    💬 2    📌 2
Table of Contents figure showing the CALVADOS-RNA model and a snapshot from a mixed protein-RNA condensate

Table of Contents figure showing the CALVADOS-RNA model and a snapshot from a mixed protein-RNA condensate

CALVADOS-RNA is now published
doi.org/10.1021/acs....

This is a simple model for flexible RNA that complements and works with the CALVADOS protein model. Work led by Ikki Yasuda who visited us from Keio University.

Try it yourself using our latest code for CALVADOS
github.com/KULL-Centre/...

26.02.2025 19:11 — 👍 67    🔁 20    💬 1    📌 0

Check out @rasmusnorrild.bsky.social's work with Alex Buell and Joe Rogers developing and using Condensate Partitioning by mRNA-Display to probe phase separation of ~100.000 sequences, and @sobuelow.bsky.social's simulations to support and analyse the experiments
www.biorxiv.org/content/10.1...

21.12.2024 16:32 — 👍 36    🔁 8    💬 1    📌 0

Meet the CALVADOS RNA model

Ikki Yasuda, Sören von Bülow & Giulio Tesei have parameterized a simple model for disordered RNA. Despite it's simplicity (no sequence, no base pairing) we find that it captures several phenomena that depend on the charge, stickiness and polymer properties of RNA 🧬🧶🧪

29.11.2024 08:25 — 👍 89    🔁 21    💬 4    📌 2

BONUS! If IDPs are your jam, check out an ever-expanding starter pack!
go.bsky.app/J23B51L

10.11.2024 19:38 — 👍 30    🔁 19    💬 13    📌 4

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...

25.09.2024 16:43 — 👍 8    🔁 2    💬 0    📌 1

“Trust me”

25.09.2024 10:26 — 👍 1    🔁 0    💬 0    📌 0

Happy to share work led by @sobuelow.bsky.social on prediction of phase separation of disordered proteins from sequence

We combined active learning and coarse-grained simulations to develop a machine learning model for quantitative predictions of IDR phase separation 🧬🧶
doi.org/10.1101/2024...

03.06.2024 20:06 — 👍 7    🔁 1    💬 1    📌 0

Updated version of our coarse-grained CALVADOS model 🍎

We show that a Calpha representation of the folded domains can give rise to too compact conformations of multi-domain proteins (MDPs), and that a centre-of-mass representation in the folded domains improves agreement with experiments. 🧬🧶

08.02.2024 09:02 — 👍 23    🔁 10    💬 1    📌 0

@sobuelow is following 20 prominent accounts