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Reposted by Giulio Tesei

David De Sancho @daviddesancho.bsky.social · 5d

Check out our latest, where we investigate the molecular origin of differences between Lys and Arg and their roles in biomolecular #condensates

@dipcehu.bsky.social

www.biorxiv.org/content/10.1...

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Reposted by Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 6d

Integrative modelling of biomolecular dynamics

Time-dependent and -resolved experiments combined with computation provide a view on molecular dynamics beyond that available from static, ensemble-averaged experiments

Review w @dariagusew.bsky.social & Carl G Henning Hansen
doi.org/10.48550/arX...

$Figure 1 from the review. Caption: Comparison of a schematic example showing static, time-dependent, and time-resolved experiments illustrated by a protein folding process. (a) A static experiment measuring the observable O$_{\text{exp}}$ is shown, which can be modelled as a distribution of simulated values, O$_{\text{calc}}$, representing a conformational ensemble of folded and unfolded states. (b) Shows a time-dependent experiment, where the equilibrium dynamics of reversible folding gives rise to measured transition times $\tau_1$ and $\tau_2$. These can be modelled as equilibrium dynamics, illustrated by a free energy (FE) surface along a chosen degree of freedom (D.O.F.) (c) A time-resolved experiment probes a non-equilibrium process, where the system begins at $t_{0}$ in the folded state. During the observation time $t$ the protein unfolds until $t_{\text{max}}$. At each time point, a distinct ensemble average, O$_{\text{exp}}$, can be observed, reflecting the proteins changing structure. This evolution can be modelled as distributions of O$_{\text{calc}}$ at each time point. These are shown together with a FE surface.$

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Giulio Tesei @giuliotesei.bsky.social · 6d

I'm hiring for a PhD position at Malmö University, Sweden!

The project will focus on molecular modelling of proteins, lipids, and biomolecular condensates at cell membranes.

More details and application form: tinyurl.com/4zm92365

Please feel free to share!

@vetenskapsradet.bsky.social | @mau.se

Snapshot of a condensate near a lipid membrane with Swedish Research Council and Malmö University logos.

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Reposted by Giulio Tesei

Sarah Shammas @sarahshammas.bsky.social · 7d

Why are transcription factors disordered? Come join us in Oxford as a postdoc and we'll find out together!

Help publish 3 mature projects, AND develop cool new single molecule fluorescence binding assays!

biophysics
transcription
protein:DNA interactions

my.corehr.com/pls/uoxrecru...

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Giulio Tesei @giuliotesei.bsky.social · 19d

Excited to see our review now on arXiv, written together with @fpesce.bsky.social and @lindorfflarsen.bsky.social

doi.org/10.48550/arX...

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 21d

New review on computational design of intrinsically disordered proteins 🖥️🍝 by @giuliotesei.bsky.social @fpesce.bsky.social & 👴

doi.org/10.48550/arX...

Figure 3 from the paper with the caption: "Role of machine learning in de novo design of IDRs. (A) Machine-learning models can be trained on diverse data sources, from molecular dynamics simulations to annotations of cellular localization and protein structures from the Protein Data Bank. (B) Often implemented as neural networks using sequence-encoded features as input, these models can initially be trained on a limited region of sequence space as surrogate models. Through active learning, additional simulations are performed during the design campaign to generate new data, and the surrogate model is retrained on the expanded dataset to progressively improve its accuracy. (C) Machine-learning models have been developed to predict biophysical observables, biological annotations, and protein structures. When combined, machine-learning models can be used to identify a set of sequences that strike a trade-off between multiple design objectives, defining a Pareto front."

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Reposted by Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Jul 22

Our paper on:

A coarse-grained model for simulations of phosphorylated disordered proteins

(aka parameters for phospho-serine and -threonine for CALVADOS)

is now published in Biophysical Journal

authors.elsevier.com/a/1lTcE1SPTB...

@asrauh.bsky.social @giuliotesei.bsky.social & Gustav Hedemark

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Reposted by Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Jul 17

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

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Reposted by Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Apr 17

AlphaFold is amazing but gives you static structures 🧊

In a fantastic teamwork, @mcagiada.bsky.social and @emilthomasen.bsky.social developed AF2χ to generate conformational ensembles representing side-chain dynamics using AF2 💃

Code: github.com/KULL-Centre/...
Colab: github.com/matteo-cagia...

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Giulio Tesei @giuliotesei.bsky.social · Apr 15

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 · Apr 15

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...

Figure showing the architecture of the CALVADOS package.

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Reposted by Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Apr 15

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 Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Mar 25

Our paper on prediction of phase-separation propensities of disordered proteins from sequence is now published:
www.pnas.org/doi/10.1073/...

The paper has been substantially updated compared to the preprint including new experimental data and using the neural network to finetune CALVADOS. 1/n

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Reposted by Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Mar 21

CALVADOS now has parameters for phosphorylated amino acids

@asrauh.bsky.social @giuliotesei.bsky.social and Gustav Hedemark used a top-down approach in which we targeted experimental data to derive parameters or phosphorylated serine and threonine doi.org/10.1101/2025...

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Reposted by Giulio Tesei

Arriën Symon Rauh @asrauh.bsky.social · Mar 9

Excited to share our PEG model for disordered proteins in CALVADOS!

If you are interested in exploring the effects of a crowder on the global dimensions of an IDP or want to explore the phase separation behaviour of a more weakly PS-prone IDP, have a look at our preprint and give it a try.

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Mar 6

CALVADOS 🤝 PEG

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

Figure 3 from the paper showing how the phase separation propensity of WT and aromatic variants of the hnRNPA1-LCD responds linearly to PEG concentration. Panel C shows a "slab" simulation of A1-LCD with 0%, 5% and 10% PEG.

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Reposted by Giulio Tesei

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Mar 12

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

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.

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