@NotinPascal

Research in AI for Protein Design @Harvard | Prev. CS PhD @UniofOxford, Maths & Physics @Polytechnique

 Boston
pascalnotin.com/
Joined September 2020
  • Tweets450
  • Following407
  • Followers1.4K
43Media
Binder design is nice and all, but here we have three agents sharing data and autonomously controlling a lab to design enzymes with shifted substrate scopes and high activity! @cobanbrooks @NotinPascal @romerolab1
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Coban Brooks
@cobanbrooksAug 17
I’m really excited to share our new preprint, out today, where we built a fully autonomous system for enzyme engineering — integrating a self-driving lab with a generative protein language model that worked together over a month to engineer substrate specificity in glycoside hydrolase (GH1) enzymes. biorxiv.org/content/10.64898… [1/n]
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Állan Ferrari@ajrferrariMay 13
Now available as its peer-reviewed version in Nature: nature.com/articles/s41586-0…
Állan Ferrari@ajrferrari25 Mar 2025
Thrilled to share our new paper where we introduce a multiplexed hydrogen–deuterium exchange MS (mHDX‑MS) method that can measure hundreds of protein domains’ conformational energy landscapes—all in a single experiment! biorxiv.org/content/10.1101/…

Large-scale discovery, analysis, and design of protein energy landscapes

All folded proteins continuously fluctuate between their low-energy native structures and higher energy conformations that can be partially or fully unfolded. These rare states influence protein...

biorxiv.org
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Jude Wells@_judewellsJan 22
Quite pleased to hear that one of my submitted proteins placed #5 out of 1200 that were tested in this lovely competition. The approach: pure rational design. Nice to see that human + Microsoft Word is still competitive with state-of-the-art AI methods 🙂
Adaptyv Bio
@adaptyvbioJan 22
The results of the Nipah Protein Design Competition are out! 🧬 1200 proteins experimentally validated (3x more than last year) 📈 99 novel binders against the target protein (a challenging tetramer with little prior work) 💪 26 single digit nM or better binders, with the best ones at single-digit picomolar affinity! All data now available open-source on Proteinbase! Let's take a look at the results ⬇️
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Kaiyi Jiang
@idmjkyJan 16
Super excited to share that I will join @Princeton @omenndarlingbio this Fall as an assistant professor. We combine syn bio and AI to program macromolecules and cells for therapeutics engineering and drug target discovery. jianglab.bioengineering.prin…

Jiang Lab

jianglab.bioengineering.princeton.edu
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New paper “Proteome-wide model for human disease genetics” is now live at Nature Genetics: rdcu.be/eRu7K popEVE (pop.evemodel.org) finds the needles in the haystacks of human genetic variation:
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Yarin@yaringal29 Oct 2025
Reminder - PhD applications for OATML are now open The first funding deadline is December 2 - candidates interested in developing Bayesian deep learning methodology, applications of ML, AI security, and understanding ML methodology are encouraged to apply More info: oatml.cs.ox.ac.uk/apply.html

How to Apply - OATML

The Oxford Applied and Theoretical Machine Learning Group (OATML) is a research group within the Department of Computer Science of the University of Oxford led by Prof Yarin Gal. We come from...

oatml.cs.ox.ac.uk
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Alex Tong@AlexanderTong73 Sep 2025
Thrilled to announce I'm starting as a Principal Investigator at #Aithyra in Vienna! We'll be developing generative models to understand cell biology and design proteins. I'm hiring PhDs, Postdocs, & Visiting Researchers! PhD applications by Sept 10: apply.cemm.at
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Our paper on generalizable antibody-antigen binding affinity prediction has been featured on the cover of the August Issue of @NatComputSci! 📔🎉
Nature Computational Science@NatComputSci25 Aug 2025
🚨Our August issue is now live and includes research on antibody-antigen binding, molecular screening for zeolite synthesis, psychological experiments with LLMs, and much more! nature.com/natcomputsci/volu…
Yellow and orange antibody binding to purple and blue membrane proteins

ALT Yellow and orange antibody binding to purple and blue membrane proteins

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I’ve thoroughly enjoyed reading two (VERY!) recent papers that model protein sequences by retrieving evolutionary information (dynamically) at inference time, and there's a lot to unpack! [1] arxiv.org/abs/2506.08954 [2] biorxiv.org/content/10.1101/… (1/n)
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nature
@Nature31 Jul 2025
A generative artificial-intelligence tool has designed a synthetic CRISPR system that successfully edits human DNA go.nature.com/3JadkRy
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1/5 Biological data is noisy, redundant, and ever-growing. 🗣️ In our new paper (first paper of my post doc!! ⚡️), we track model performance across 14 years of UniRef100 snapshots to ask: how does pLM performance scale with training data?
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AI expands the repertoire of CRISPR-associated proteins for genome editing @NatureNV preview by @NotinPascal nature.com/articles/d41586-0… @thisismadani profluent.bio/ @jeffruffolo @AadyotB et al nature.com/articles/s41586-0…
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1/4 🚀 Announcing the 2025 Protein Engineering Tournament. This year’s challenge: design PETase enzymes, which degrade the type of plastic in bottles. Can AI-guided protein design help solve the climate crisis? Let’s find out! ⬇️ #AIforBiology #ClimateTech #ProteinEngineering #OpenScience
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Patrick Schwab
@schwabpa20 Jun 2025
Save the date! Machine Learning for Drug Discovery (MLDD) is happening soon on Monday 30 June, 2025. MLDD aims to bring together ML for drug discovery experts, innovators, and enthusiasts from the machine learning, biotechnology and drug discovery domains in London, UK to converge, exchange ideas, and forge new paths in revolutionizing therapeutic development. This year, we are very fortunate to feature keynotes by: - Charlotte Brunne (EPFL) - John Chodera (Achira / MSKCC) - Kexin Huang (Stanford University) - Jacob Kimmel (NewLimit) - Marinka Zitnik (Harvard University) To accommodate the worldwide community, the event will be held fully remotely this year - a link to join and further information are provided at the MLDD website (link in thread below!) Looking forward to seeing you there!
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I wonder if the gym membership for ProteinGym covers RNAGym as well?? oh wait, it's all free and 100% open source!
Pascal Notin@NotinPascal18 Jun 2025
🚨 New paper 🚨 RNA modeling just got its own Gym! 🏋️ Introducing RNAGym, large-scale benchmarks for RNA fitness and structure prediction. 🧵 1/9
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What would our data landscapes look like if we could biochemically characterize 100s, 1000s, 10^n evolutionary sequences? Papers like this make strides towards that moonshot dream!! Such neat work from @MargauxPinney lab! science.org/doi/10.1126/scie…

Evolutionary-scale enzymology enables exploration of a rugged catalytic landscape

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. In this study, we leveraged emerging microfluidic...

science.org
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As sequence databases get bigger and more diverse, retrieval-based methods provide an interesting alternative to scaling successively bigger protein language models. (1/5)
Ruben Weitzman@ruben_weitzman11 Jun 2025
🚨ICML Paper Alert🚨 What if finding the right protein homologs wasn't a slow search, but a learned part of the model itself? We introduce 𝐏𝐫𝐨𝐭𝐫𝐢𝐞𝐯𝐞𝐫, an end-to-end framework that learns to retrieve the most useful homologs for self-supervised reconstruction! (1/12)
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End-to-end differentiable homology search for protein fitness prediction. @ruben_weitzman @lood_ml @yaringal @deboramarks @NotinPascal
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🚨 New in @ImmunityCP ! EVE-Vax, an AI model that anticipates future viral evolution and designs antigens to proactively test vaccines + therapeutics—before variants even emerge. We envision this work will help make future-proofed vaccines and therapeutics. 👇 (1/7)
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