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Giulio Tesei

@giuliotesei.bsky.social

Assistant Prof. at Malmö University; modelling of intrinsically disordered regions, membrane-associated proteins, and biomolecular condensates. He/him

3 Posts132 Follower 186 Folgt

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

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...

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 4mo ago

Run an MD simulation of any protein in the AlphaFold Protein Structure Database using AF-CALVADOS Thanks to @sobuelow.bsky.social AF-CALVADOS is now on Colab colab.research.google.com/github/KULL-...

Kresten Lindorff-Larsen@lindorfflarsen.bsky.social · 8mo 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📝

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 5mo ago

We are hiring a postdoc in computational biophysics and machine learning studies of intrinsically disordered proteins We aim to study the function of IDPs by combining CG MD, ML and bioinformatics in collaboration with @tanjamittag.bsky.social & @rhp-lab.bsky.social tinyurl.com/REWIRE-PD

Postdoc position:Computational Biophysics of Disordered Proteins, Lindorff-Larsen group, University of Copenhagen, Denmark

https://tinyurl.com/REWIRE-PD
Deadline May 3rd, 2026

xavier salvatella @xsalvatella1.bsky.social · 5mo ago

Our work on how oligomerization can enable selectively targeting intrinsic disorder with small molecules is now out in Science Advances: www.science.org/doi/10.1126/...). Congratulations Stase Bielskute and @borjaml.bsky.social and thanks to the collaborators and funders !

Oligomerization enables the selective targeting of an intrinsically disordered region by a small molecule

The druggability of intrinsically disordered regions.

science.org

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 5mo ago

Community perspective: Toward a unified framework for determining conformational ensembles of disordered proteins 🍝 with framework for experimental data acquisition, computational ensemble generation & validation Led by @hamidrgh.bsky.social, Silvio Tosatto & Alex Monzon doi.org/10.1038/s415...

Arriën Symon Rauh @asrauh.bsky.social · 6mo ago

Really excited to share the latest work from my PhD with @giuliotesei.bsky.social and @lindorfflarsen.bsky.social!

Kresten Lindorff-Larsen@lindorfflarsen.bsky.social · 6mo ago

New preprint with work led by @asrauh.bsky.social in which we explore how double mutant cycles could be used to study molecular interactions in condensates, and highlight difficulties in extracting information about interactions from mutational experiments www.biorxiv.org/content/10.6...

Julian Streit @julianstreit.bsky.social · 7mo ago

New preprint with @invemichele.bsky.social, Sandro Bottaro, Kamil Tamiola, and @lindorfflarsen.bsky.social on transiently structured states of IDPs sampled in atomistic simulations with enhanced sampling and integrated with experimental data.

Kresten Lindorff-Larsen@lindorfflarsen.bsky.social · 7mo ago

Third preprint of the year is from @julianstreit.bsky.social who, with our collaborators at Peptone, show that multithermal On-the-fly Probability Enhanced Sampling (OPES) enables efficient generation of atomistic ensembles for disordered peptides and proteins 🍝 www.biorxiv.org/content/10.6...

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 6mo ago

Integrative structural biology by the sea The Jan 31st deadline for our FEBS advanced course on integrative structural biology is getting closer, so get your applications in for Lost In Integrative Vol. 2 probingbiomolecules2026.febsevents.org network.febs.org/posts/integr...

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 6mo ago

Looking for something to read over the weekend? Our review on computational design of intrinsically disordered proteins is now published in final form 🍝 With @giuliotesei.bsky.social & @fpesce.bsky.social doi.org/10.1016/j.sb...

Figure illustrating the role of machine learning in IDP design, showing both the training data, machine learning and applications

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 7mo ago

First preprint of the year, led by Junjie Zhu from Haifeng Chen’s lab Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex doi.org/10.64898/202...

bioRxiv Bioinfo@biorxiv-bioinfo.bsky.social · 7mo ago

Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex https://www.biorxiv.org/content/10.64898/2026.01.14.699584v1

Davide Mercadante @mercadantelab.bsky.social · 7mo ago

New preprint!🚨 We're finally able to sample secondary structure in coarse-grained simulations of IDPs! 🍝🍝🍝 With an AI-based prediction of NMR chemical shifts in CG simulations, we correct dynamics on the fly, making secondary structure emerge as dictated by expts😍! www.biorxiv.org/content/10.6... 🧵👇

Capturing secondary structure in coarse grained intrinsically disordered proteins with simulations driven by chemical shifts.

A major challenge when investigating intrinsically disordered proteins (IDPs) pertains to understanding how secondary structure formation across otherwise disordered ensembles, relates to function. Wh...

biorxiv.org

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 10mo 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/...

Figure showing the AF-CALVADOS restraining and simulation protocol based on AF2 structure, PAE and pLDDT

bioRxiv Biophysics@biorxiv-biophys.bsky.social · 10mo 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

Anna Duncan @drannaduncan.bsky.social · 10mo ago

Registration now open for the '3rd Annual Danish Workshop on Advanced Molecular Simulation' Join us in beautiful Aarhus for a 2-day workshop on biomolecular simulations! 9-10 December 2025 Link for registration: aias.au.dk/events/show/...

David De Sancho @daviddesancho.bsky.social · 10mo ago

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...

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 10mo ago

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.$

Giulio Tesei @giuliotesei.bsky.social · 10mo ago

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.

Sarah Shammas @sarahshammas.bsky.social · 10mo ago

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...

Giulio Tesei @giuliotesei.bsky.social · 11mo ago

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 · 11mo ago

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."

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

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

authors.elsevier.com

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

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...

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

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 · last yr.

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

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

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...

bioRxiv Biophysics@biorxiv-biophys.bsky.social · last yr.

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2 https://www.biorxiv.org/content/10.1101/2025.04.16.649219v1

Giulio Tesei @giuliotesei.bsky.social · last yr.

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...

Figure showing the architecture of the CALVADOS package.

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

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

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

bioRxiv Biophysics@biorxiv-biophys.bsky.social · last yr.

A coarse-grained model for simulations of phosphorylated disordered proteins https://www.biorxiv.org/content/10.1101/2025.03.19.644261v1

Arriën Symon Rauh @asrauh.bsky.social · last yr.

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 · last yr.

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

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

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.