The Sledzieski Lab is opening in the Halıcıoğlu Data Science Institute at @ucsandiego.bsky.social in July 2027! 🥳🏄♂️
We're recruiting at all levels, and graduate applications are now open for both HDSI and the BISB program. Please share with anyone that might be interested!
sledzieskilab.org
*Job alert*: Postdoc position at the Flatiron Institute to work on biophysical inference and cryo-EM. ❄️ 🔬Applications are welcome! apply.interfolio.com/193799
One of the most revered and celebrated albums in music history was released on Sept. 28, 1976 — 50 years ago today.
Songs in the Key of Life tracks, ranked:
Ok comp. bio / bioinformatics / genomics friends. Let's talk about @recombconf.bsky.social and, specifically, the way in which the "proceedings" have evolved. This seems like something that we, as a community, should address.
RECOMB now has no "published" proceedings in the classic sense.
ʙɪɴᴅᴄʀᴀꜰᴛ2 is out, and we're not waiting for the paper. The full code drops today, free for academic and industry use.
We're releasing it early so you can start designing right now, and bring its full power to the current Adaptyv competition.
github.com/PacesaLab/Bi...
📣 The Singh Lab opens at Fox Chase Cancer Center in Spring 2027, where I will be starting as an Assistant Professor!📣
Excited to continue studying how protein biophysics shapes drug response.
I am hiring across all levels and disciplines! Apply on the "Join" page ( & spread the word!): singhlab.bio
Ad forthcoming, but I am hiring for a postdoc or staff scientist to advance related investigations of the virus-host conflict. This position is ~80-100% computational, but with opportunities for experimental work if desired. If interested, shoot me an email with your CV! Reposts appreciated! 15/15
The US built the greatest talent magnet ever, attracting the smart, skilled, and striving. They’d come to study, then stay and build things here, or leave with positive impressions and fruitful connections. Helped make the US wealthy and powerful.
Then America chose to throw it away. And for what?
I'm giving a poster on MIMIC at #ISMB2026 today (C-211)! Drop by and chat with me about multimodal modeling for DNA, RNA, and protein biology. Inference code + checkpoints + sample data are all now available on GitHub/HuggingFace. github.com/PolymathicAI...
Introducing MIMIC: a new foundation model trained natively across DNA, RNA and proteins. MIMIC is multimodal and generative: it can use structure, regulation, evolution, and experimental context to infer missing biology or design new sequences. 🧵⬇️
Can we program a kinase like a switch? Inspired by natural autoinhibitory complexes, we designed miniproteins against active- and inactive-like conformations of Focal Adhesion Kinase. Depending on the targeted state, the resulting binders either activated or inhibited the kinase.
I am thrilled to share that UC Berkeley and UCSF have launched a joint initiative in Computational Biomedicine!
cdss.berkeley.edu/news/uc-berk...
We will soon be recruiting new faculty and postdoctoral fellows. Please repost to help spread the word.
My go to example of this sort of thing is the study of weird thermophilic bacteria gave us PCR, upon which so much of modern biological research and its many applications (including drug discovery!) depend
People often think about risk as referring to (i): Can the project achieve its specific aims? But really what NIH should want is (ii): studies with low probability of huge long-term impact. Here, think of work on gila monster venoms (leading to GLP-1 meds) or bacterial immune systems (crispr)
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...
We told you it wouldn’t be a long wait. 👀 Even more predicted protein structures have been added to the #AlphaFold Database.
This time, the database has expanded to include heterodimers – protein complexes made up of two different proteins.
alphafold.ebi.ac.uk
Introducing MIMIC: a new foundation model trained natively across DNA, RNA and proteins. MIMIC is multimodal and generative: it can use structure, regulation, evolution, and experimental context to infer missing biology or design new sequences. 🧵⬇️
Introducing MIMIC: a new foundation model trained natively across DNA, RNA and proteins. MIMIC is multimodal and generative: it can use structure, regulation, evolution, and experimental context to infer missing biology or design new sequences. 🧵⬇️
This work was a huge collaboration and was only possible thanks to our amazing team: Siavash Golkar (lead), Irina Espejo Morales, Jake Kovalic, @minhuanli.bsky.social, Ksenia Sokolova,...
Introducing MIMIC: a new foundation model trained natively across DNA, RNA and proteins. MIMIC is multimodal and generative: it can use structure, regulation, evolution, and experimental context to infer missing biology or design new sequences. 🧵⬇️
MIMIC also treats experimental context as a first-class modality.
Given assay + cellular context in natural language, it predicts condition-specific RNA reactivity better than sequence-only baselines, improving downstream RNA structure modeling.
There are still exciting frontiers to explore. Data integration in biology requires working carefully with domain experts, but by providing a framework to integrate diverse modalities, MIMIC represents a step toward one generative model across molecular biology.
Introducing MIMIC: a new foundation model trained natively across DNA, RNA and proteins. MIMIC is multimodal and generative: it can use structure, regulation, evolution, and experimental context to infer missing biology or design new sequences. 🧵⬇️
Multimodal conditioning also improves design.
MIMIC doesn't just predict aberrant splicing, it can design around it. For a pathogenic mutation, it proposes corrective edits that suppress cryptic exon inclusion while keeping the disease-causing mutation fixed.
Protein design makes the multimodal advantage clear.
Backbone geometry and surface chemistry provide different, complementary constraints on protein function. Conditioning on both, MIMIC generates diverse, high-confidence sequences with strong in silico binding support.
Introducing MIMIC: a new foundation model trained natively across DNA, RNA and proteins. MIMIC is multimodal and generative: it can use structure, regulation, evolution, and experimental context to infer missing biology or design new sequences. 🧵⬇️
What does this training paradigm buy you?
MIMIC learns representations that are SOTA across both RNA and protein downstream benchmarks, and multimodal conditioning consistently improves sequence reconstruction in both nucleic-acid and amino-acid settings.
Another example is splicing. MIMIC does something most splice models can't: isoform-aware splice prediction. By conditioning on transcript boundaries, it can recover the full transcript-specific splice structure, not just score donor/acceptor sites in isolation.
Introducing MIMIC: a new foundation model trained natively across DNA, RNA and proteins. MIMIC is multimodal and generative: it can use structure, regulation, evolution, and experimental context to infer missing biology or design new sequences. 🧵⬇️
MIMIC is built so any subset of modalities can be observed, and any subset can be generated
Sequence → prediction is only one case
You can also go the other way: use structure, splicing, or assay context to constrain the sequences compatible with a biological state.
Biological data is complex, and training MIMIC required a new substrate.
We built LORE: an aligned multimodal dataset connecting nucleic acid, protein, evolutionary, structural, regulatory, and experimental/context signals within shared biomolecular states.
We just preprinted one of my favorite studies @FlatironInst
. I was lucky to be part of an amazing team studying the effects of rapid cooling to preserve samples in cryoEM. Read on to learn about the limits of cryoEM for biophysics and how to overcome them www.biorxiv.org/content/10.6...
Our review on ML for modeling conformational ensembles is out now in Current Opinion in Structural Biology (w/ @sonyahanson.bsky.social)! It's been exciting to follow all the progress in this field recently, and I'm equally excited to see where it goes!
www.sciencedirect.com/science/arti...
Annual reminder: if you’ve been accepted to multiple graduate programs and are still deciding, please let the ones you’re definitely not going to know as soon as possible!
-Someone who got into his PhD off the waitlist the day after the deadline
AlphaFold database has entered the era of complexes. Together with NVIDIA, DeepMind and EBI, we use ColabFold, OpenFold and MMseqs2-GPU to predict ~31 million complexes (homo & hetro-dimers) resulting in 1.8 million high-quality predictions
📄 research.nvidia.com/labs/dbr/ass...
🌐 alphafold.ebi.ac.uk
You asked, we listened. Millions of AI-predicted protein complex structures are now available in the #AlphaFold Database.
This spans homodimers from 20 of the most studied species, including humans, as well as the World Health Organization’s priority pathogens list.
www.ebi.ac.uk/about/news/t...
Had a lot of fun working on this with Darius, Christian, and Rohit. We address the problem of unpredictable scaling behavior for PLMs -- with a simple 2-line drop-in replacement for ESM2, you no longer need to worry that a smaller model might end up performing better!
🧵⬇️
Protein language models don't scale reliably: bigger doesn't always mean better for embeddings. What if what you need from ESM-15B is already inside ESM-8M?
Introducing Reverse Distillation, which flips the usual distillation script: small models anchor and decompose large ones
We have started a project trying to predic the interactions/structures of all yeast protein pairs using an AlphaFold pooling approach. We are making the current dataset open and we welcome collaborations.
www.evocellnet.com/2026/03/mapp...
Predicting protein-protein interactions (PPIs) at proteome scale can take months with co-folding models due to the massive all-vs-all comparisons required.
We are excited to announce FlashPPI, a contrastive learning framework that predicts proteome wide physical interfaces in minutes. 1/🧵
Additionally, I want to speak directly to Iranian New Yorkers: you are part of the fabric of this city — you are our neighbors, small business owners, students, artists, workers, and community leaders. You will be safe here.
A great start to the Biophysical Society Annual Meeting in San Francisco! Check out the various projects being presented on work from Flatiron Institute people! #bps2026
Introducing The Structural History of Eukarya (SHE): The first proteome-scale phylogeny constructed entirely from 3D structure.
We computed 300 trillion alignments across 1,542 species to map the tree of life. 🧵👇 (1/5)
Join us and the BME Department this Thursday for a joint seminar featuring @flatironinstitute.org’s @samsl.io! Learn more here: www.cs.jhu.edu/event/cs-bme...
My time in @martinsteinegger.bsky.social's group is ending, but I’m staying in Korea to build a lab at Sungkyunkwan University School of Medicine. If you or someone you know is interested in molecular machine learning and open-source bioinformatics, please reach out. I am hiring!
mirdita.org