Samuel Sledzieski

Samuel Sledzieski

@samsl.io

Incoming Assistant Professor @ucsandiego.bsky.social HDSI Formerly @flatironinstitute.org @csail.mit.edu @msftresearch.bsky.social @uconn.bsky.social Computational systems x structure biology | he/him | sledzieskilab.org | 👨🏼‍💻

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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

sledzieskilab.orgSledzieski LabThe Sledzieski Lab at UC San Diego develops computational methods to better understand molecular structures and interactions, from modeling complex structural dynamics to building whole-organism inter...

Samuel Sledzieski reposted

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:

Samuel Sledzieski reposted

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.

Samuel Sledzieski reposted

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...

Samuel Sledzieski reposted

📣 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

Samuel Sledzieski reposted

Samuel Sledzieski reposted

Replying to @jnoms.bsky.social

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

Samuel Sledzieski reposted

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...

github.comGitHub - PolymathicAI/MIMIC: A Generative Multimodal Model for BiomoleculesA Generative Multimodal Model for Biomolecules. Contribute to PolymathicAI/MIMIC development by creating an account on GitHub.

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. 🧵⬇️

Samuel Sledzieski reposted

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.

Samuel Sledzieski reposted

Samuel Sledzieski reposted

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

Jonathan Pritchard@jkpritch.bsky.social

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)

Samuel Sledzieski reposted

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. 🧵⬇️

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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. 🧵⬇️

View full thread

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. 🧵⬇️

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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. 🧵⬇️

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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. 🧵⬇️

View full thread

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.

Samuel Sledzieski reposted

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

Samuel Sledzieski reposted

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! 🧵⬇️

Rohit Singh@rohitsingh8080.bsky.social

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

Samuel Sledzieski reposted

Samuel Sledzieski reposted

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/🧵

Samuel Sledzieski reposted

Replying to @mayor.nyc.gov

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.

Samuel Sledzieski reposted

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

Samuel Sledzieski reposted

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)

Samuel Sledzieski reposted

Samuel Sledzieski reposted

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

mirdita.orgMirdita Lab - Laboratory for Computational Biology & Molecular Machine LearningMirdita Lab builds scalable bioinformatics methods.