Michele Invernizzi
@invemichele.bsky.social
Computational physicist at https://peptone.io PhD @GroupParrinello, PostDoc @franknoe.bsky.social Disordered Proteins, AI for Science, Molecular Dynamics, Enhanced Sampling 🔗 https://scholar.google.com/citations?user=fnJktPAAAAAJ
Sampling IDPs is tough, but can be worth the effort! OPES multithermal made it a little easier for us, hopefully you'll find it useful as well
Julian Streit@julianstreit.bsky.social · 8mo agoNew 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.
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...
bioRxiv Biophysics@biorxiv-biophys.bsky.social · 8mo agoTransient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling https://www.biorxiv.org/content/10.64898/2026.01.17.700112v1
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/...
bioRxiv Biophysics@biorxiv-biophys.bsky.social · 12mo agoAF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level https://www.biorxiv.org/content/10.1101/2025.10.19.683306v1
We have a job opening at Peptone.io for an ML researcher. Come help us find new ways to understand and drug intrinsically disordered proteins (IDPs), it's a very interesting and important problem! Link in the reply ↓
Very interesting and powerful method! www.nature.com/articles/s43...
Everything everywhere all at once: a probability-based enhanced sampling approach to rare events - Nature Computational Science
A single semi-automatic enhanced sampling method for rare events, based on machine-learned committor functions, allows simultaneous sampling of reactive events, calculation of free energy and understa...
nature.com
The plan at FutureHouse has been to build scientific agents for discoveries. We’ve spent the last year researching the best way to make agents. We’ve made a ton of progress and now we’ve engineered them to be used at scale, by anyone. Free and on API.
Presenting one of my favorite manuscripts I've ever worked on: "Characterizing structural and kinetic ensembles of intrinsically disordered proteins using writhe" www.biorxiv.org/content/10.1... by Tommy Sisk, with a generative modeling component done in collaboration with @smnlssn.bsky.social
"De novo prediction of protein structural dynamics" I'll be presenting an overview of the field tomorrow at a workshop. Link to a PDF copy of the presentation: delalamo.xyz/assets/post_...
delalamo.xyz
Presenting our work on minimum energy path generation between two states for physical systems at the FPI Workshop at @ICLR tomorrow! Scaling up to solvated BPTI and observing the same conformational changes as long reference MD with six orders fewer force field evals! Drop by!
New preprint on arXiv! We propose a new technique to compute kinetic rates using multiple independent non-equilibrium (ratchet&pawl MD) simulations! We focused here on ligand unbinding kinetics, but this method can be applied to any situation where a reaction coordinate can be defined!
Kinetic rates calculation via non-equilibrium dynamics
This study introduces a novel computational approach based on ratchet-and-pawl molecular dynamics (rMD) for accurately estimating ligand dissociation kinetics in protein-ligand complexes. By integrati...
arxiv.org
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
BoltzDesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design by @yehlincho.bsky.social @martinpacesa.bsky.social @sokrypton.org 🧶🧬 www.biorxiv.org/content/10.1...
Wondering how to predict protein flexibility in a sec? No time to run MD simulations but want to go beyond pLDDT? Check out BBFlow arxiv.org/html/2503.05... Useful in particular for de novo designs. Led by Nico Wolf & Leif Seute, w Seva, Simon, and Jan. @mpip-mainz.mpg.de @hitsters.bsky.social
Small proteins can be more complex than they look! We know proteins fluctuate between different conformations- but by how much? How does it vary from protein to protein? Can highly stable domains have low stability segments? @ajrferrari.bsky.social experimentally tested >5,000 domains to find out!
Protein function often depends on protein dynamics. To design proteins that function like natural ones, how do we predict their dynamics? @hkws.bsky.social and I are thrilled to share the first big, experimental datasets on protein dynamics and our new model: Dyna-1! 🧵
Straight to the reading list: Training a machine learning model based on residues with missing NMR assignments as a proxy for protein motion
bioRxiv Biophysics@biorxiv-biophys.bsky.social · 2y agoLearning millisecond protein dynamics from what is missing in NMR spectra https://www.biorxiv.org/content/10.1101/2025.03.19.642801v1
Very excited for my first BSKY post. We present a new method, Loxodynamics, for exploring chemical and catalytic reaction space!
ChemRxiv Bot@chemrxivbot.bsky.social · 2y agoExploring Chemistry and Catalysis by Biasing Skewed Distributions via Deep Learning Authors: Zhikun Zhang, GiovanniMaria Piccini DOI: 10.26434/chemrxiv-2025-cvb1v
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
The paper describing our community effort to collect and organize #plumed tutorials has been published in the Journal of Chemical Physics, as part of the Michele Parrinello Festschrift! doi.org/10.1063/5.02...
PLUMED Tutorials: A collaborative, community-driven learning ecosystem
In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes
doi.org
As a peek toward where we're headed: Right now, CADD scientists are forced to use the same model week after week, even if new experimental data says the model is inaccurate. If we can fine- models, we can exploit that data to systematically improve our predictions week by week!
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 - 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
github.com
Frank Noe@franknoe.bsky.social · 2y agoSuper excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science. www.biorxiv.org/content/10.1...
I am hiring a postdoctoral scholar with a start date summer or fall 2025. Projects will be focused on thermodynamically consistent generative models, broadly defined. If you’re interested, please send a CV and one paragraph about why you think you’d be a good fit to rotskoff@stanford.edu
It’s been 20 years today since my first paper on intrinsically disordered proteins Mapping Long-Range Interactions in α-Synuclein using Spin-Label NMR and Ensemble Molecular Dynamics Simulations doi.org/10.1021/ja04... and I thought I would tell the somewhat random path that led to this paper. 1/n
Mapping Long-Range Interactions in α-Synuclein using Spin-Label NMR and Ensemble Molecular Dynamics Simulations
The intrinsically disordered protein α-synuclein plays a key role in the pathogenesis of Parkinson's disease (PD). We show here that the native state of α-synuclein consists of a broad distribution of...
doi.org
With today's report outlining risks on mirror life www.science.org/doi/full/10.... many have asked: Could mirror life survive in the wild? Yes. While mirror life in the wild could have some significant disadvantages (like finding food it can digest), they do not appear to be insurmountable: 🧵
Confronting risks of mirror life
Broad discussion is needed to chart a path forward.
science.org
In his book “The Nature of Statistical Learning” V. Vapnik wrote: “When solving a given problem, try to avoid a more general problem as an intermediate step”
See you at #NeurIPS2024, where we are presenting the latest Peptone.io paper @workshopmlsb.bsky.social www.mlsb.io/papers_2024/...
📢 New #preprint describing our community effort to share #plumed tutorials arxiv.org/abs/2412.03595 ! Explore the tutorials at www.plumed-tutorials.org
PLUMED Tutorials: a collaborative, community-driven learning ecosystem
In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, ef...
arxiv.org
Now something that is extremely hard to sample with all-atom MD: a big intrinsically disordered protein (IDP) like Complexin II. Different answers depending on MD forcefield. BioEmu - not traind on IDPs - looks reasonable, agrees with experimental evidence and is super fast.
Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science. www.biorxiv.org/content/10.1...