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Darian

@darianyang.bsky.social
98 followers 229 following 2 posts

Computational Biophysics Postdoc at the University of Copenhagen with Kresten Lindorff-Larsen. Previously PhD in Biophysics at the University of Pittsburgh & Carnegie Mellon University. darianyang.github.io

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Reposted by Darian
Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 17/04/2025
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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Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 15/04/2025
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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MDDB @mddbeu.bsky.social · 04/04/2025
📢 Our article calling for a #FAIR database for #MolecularDynamics simulation data has now been peer-reviewed and published in @naturemethods.bsky.social 📖 Read it here: rdcu.be/ef6YX 📝 Support the statement: bit.ly/3zVS3qm #MDDB #FAIRdata #collaboration
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Darian @darianyang.bsky.social · 27/03/2025
And huge thanks to Ken Chiacchia and Jorge Salazar for highlighting our work! Check out their articles for a breakdown of the paper :) www.psc.edu/hiv-1-capsid... tacc.utexas.edu/news/latest-...
psc.edu
HIV Protein Switch May Help Virus Squeeze into Host Cell Nucleus
Simulations on Bridges-2 Help Pitt Team Visualize Rare, Transient Shape Change in Capsid Protein
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Darian @darianyang.bsky.social · 27/03/2025
The bulk of my thesis work was recently published! We used 19F NMR and weighted ensemble simulations among other methods to explore hidden dimer states of the HIV-1 capsid protein. If this sounds interesting to you, see the full paper here: www.pnas.org/doi/10.1073/...
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Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 25/03/2025
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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Dr. Fiona @fionachembot.bsky.social · 13/03/2025
Nevertheless, we persisted ❤️ 📣 NEW BIORXIV ALERT!! 🚨 Using WE MD, linguistic pathway clustering, dynamical network analyses, and HDXMS we reveal a hidden allosteric network within the SARS2 spike S1 domain and predict how the D614G mutation impacts this network! www.biorxiv.org/content/10.1...
biorxiv.org
D614G reshapes allosteric networks and opening mechanisms of SARS-CoV-2 spikes
The SARS-CoV-2 spike glycoprotein binds human epithelial cells and enables infection through a key conformational transition that exposes its receptor binding domain (RBD). Experimental evidence indic...
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Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 26/02/2025
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/...
Table of Contents figure showing the CALVADOS-RNA model and a snapshot from a mixed protein-RNA condensate
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Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · 21/02/2025
All-atom fixed backbone protein sequence design with FAMPNN @richardshuai.bsky.social Talal Widatalla @possuhuanglab.bsky.social @brianhie.bsky.social www.biorxiv.org/content/10.1...
FAMPNN architecture
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James Fraser @fraserlab.com · 18/02/2025
🚨 New preprint alert! 🚨 How does SARS-CoV-2 use as much of its genome as possible to evade our immune response? Our latest study dissects how Orf9b, which is encoded in an alternate reading frame from the N(ucleocapsid) protein, can regulate interferon signaling. www.biorxiv.org/content/10.1... 🧵👇
biorxiv.org
Coupled equilibria of dimerization and lipid binding modulate SARS Cov 2 Orf9b interactions and interferon response
Open Reading Frame 9b (Orf9b), an accessory protein of SARS-CoV and -2, is involved in innate immune suppression through its binding to the mitochondrial receptor Translocase of Outer Membrane 70 (Tom...
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Frank Noe @franknoe.bsky.social · 19/02/2025
The BioEmu-1 model and inference code are now public under MIT license!!! Please go ahead, play with it and let us know if there are issues. github.com/microsoft/bi...
github.com
GitHub - microsoft/bioemu: Inference code for scalable emulation of protein equilibrium ensembles with generative deep learning
Inference code for scalable emulation of protein equilibrium ensembles with generative deep learning - microsoft/bioemu
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John Chodera @jchodera.bsky.social · 19/02/2025
Everything is chaos, but I wanted to share some awesome recent science from the lab that hints at where the future of biomolecular simulation is headed: Foundation simulation models that can be fine-tuned to experimental free energy data to produce systematically more accurate predictions.
Figure 1 from arXiv preprint https://doi.org/10.1101/2025.01.06.631610

Fig. 1 Espaloma is an end-to-end differentiable molecular mechanics parameter assignment scheme for arbitrary organic molecules. Espaloma (extensible surrogate potential optimized by message-passing) is a modular approach for directly computing molecular mechanics force field parameters FFF from a chemical graph G such as a small molecule or biopolymer via a process that is fully differentiable in the model parameters FNN. In Stage 1, a graph neural network is used to generate continuous latent atom embeddings describing local chemical environments from the chemical graph. In Stage 2, these atom embeddings are transformed into feature vectors that preserve appropriate symmetries for atom, bond, angle, and proper/improper torsion inference via Janossy pooling.54 In Stage 3, molecular mechanics parameters are directly predicted from these feature vectors using feed-forward neural networks. This parameter assignment process is performed once per molecular species, allowing the potential energy to be rapidly computed using standard molecular mechanics or molecular dynamics frameworks thereafter. The collection of parameters FNN describing the espaloma model can be considered as the equivalent complete specification of a traditional molecular mechanics force field such as GAFF38,39/AM1-BCC55,56 in that it encodes the equivalent of traditional typing rules, parameter assignment tables, and even partial charge models. Reproduced from ref. 49 with permission from the Royal Society of Chemistry.
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Giulia Palermo @giuliapalermo.bsky.social · 08/02/2025
New paper from our lab @naturecomms.bsky.social! We reveal the dynamics and mechanism of target DNA traversal in #CRISPR Cas12a, a conundrum in the field! nature.com/articles/s41... #compchem We thank the amazing #HPC resources of PSC #Anton2 and SDSC
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Peter Škrinjar @peterskrinjar.bsky.social · 08/02/2025
Excited to share our latest preprint evaluating AlphaFold3, Boltz-1, Chai-1 and Protenix for predicting protein-ligand interactions, featuring our newly introduced benchmark dataset 🌹Runs N’ Poses🌹! www.biorxiv.org/content/10.1... 🧵👇 (1/n)
biorxiv.org
Have protein-ligand co-folding methods moved beyond memorisation?
Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins interact with other molecules, especially small mo...
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NPR @npr.org · 23/01/2025
An email obtained by NPR says NIH employees are subject to a travel freeze and offers of employment are being rescinded. Scientists worry about disruptions to critical research.
npr.org
National Institutes of Health cancels scientific meetings after Trump directives
An email obtained by NPR says NIH employees are subject to a travel freeze and offers of employment are being rescinded. Scientists worry about disruptions to critical research.
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Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · 15/01/2025
A framework for evaluating how well generative models of protein structure match the distribution of natural structures. @possuhuanglab.bsky.social www.biorxiv.org/content/10.1...
Generative models capture a biased set of protein structure spaceGenerative models do not capture the full expressivity of PDB structuresProtein structure embeddings reveal undersampled and de novo structure space
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Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 16/01/2025
How do proteins mis-fold? Paper led by Jacob Aunstrup from Alex Büll’s lab with MD simulations by Abigail Barclay, and key contributions from several others. We combined measurements of Φ-values with MD simulations to study the transition state for amyloid fibril growth doi.org/10.1038/s415...
Figure from the paper that illustrates the approach of probing the transition state for amyloid growth by experiments and simulations
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Timothy P. Jenkins @tpjenkins.bsky.social · 15/01/2025
🚨 Revolutionising Snakebite Treatments with AI-Designed Proteins 🐍 I'm proud to share our latest study published in hashtag#Nature, driven by Susana Vazquez Torres, and co-led by David Baker (Institute for Protein Design, University of Washington) and myself.
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mia @miarosenfeld.bsky.social · 17/12/2024
move over ligand RMSD < 2 Å 😤 ConfBench is on the scene! if you're interested in the evaluation of conformational accuracy of structure prediction methods, take a look at our first stab at a systematic conformational benchmark in the NP3 technical report below! 🧵 www.iambic.ai/post/np3-tec...
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