Krishna Shrinivas

Krishna Shrinivas

@shrinivaslab.bsky.social

Asst. prof at Northwestern ChBE Interested in how molecules and processes are organized and regulated in living cells | physics, math, engineering, and computation (mostly) for biology shrinivaslab.com

1.9K followers180 following50 posts

Pinned

This is now published in PNAS! And double-treat, we also made the cover :) www.pnas.org/doi/10.1073/...

Krishna Shrinivas@shrinivaslab.bsky.social

Preprint! Inspired by condensates that form on specific DNA, we ask: can we design multicomponent fluids to form distinct condensates on diff. surfaces? i.e. perform classification by condensation ⚛️ 💻 exploiting phase transitions beyond compartmentalization! arxiv.org/abs/2509.08100 (1/2)

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Design 2: emergent condensate properties 🫧 By jointly optimizing IDP networks, we designed multicomponent mixtures that form condensates with: • layered architectures (like the nucleolus) • compositional specificity (who's in/out) • RNA-dependent reorganization

Our key idea: use ML to invert emerging physics-based models (Dimer, FINCHES, RPA) for de novo IDP design. Our framework uses gradient-based sequence design for target properties and is: fast: proteome-scale (10,000 seq) < 1 min on GPU flexible: change loss, change property

Design 1: IDPs as complex physical signal processors 🧮 We designed IDPs that sense/compute over cues — a single sequence can act as: • threshold detector (phosphorylation counting) • bandpass filter (respond only to intermediate signals) • logic gate (XOR, OR, NAND) over 2 cues

Krishna Shrinivas reposted

See our handy graphical abstract and article for free here: bit.ly/4vsl845

Jon Henninger@jhenninger.bsky.social

Check out our new work in Cell Systems @cp-cellsystems.bsky.social! Much of the biochemistry in the cell is organized by biomolecular condensates, but what mechanisms control the distribution of a given condensate throughout the cell? And is this mesoscale organization important for function?

Our work made the cover! (and my very first one!) Congrats to co-authors as well as Ramanna Shrinivas for cover art and @natcomputsci.nature.com for editing/cover design.

Nature Computational Science@natcomputsci.nature.com

🚨Our May issue is now live, including a method to design dynamic unstructured proteins, a benchmark to evaluate spatial alignment methods for spatial transcriptomics, and much more! www.nature.com/natcomputsci... 📰Cover: www.nature.com/articles/s43...

Krishna Shrinivas reposted

Do different regions in the cell nucleus find each other by random motion or is there a directed component? In short: likely both. Below, I summarize a few predictions from a recently updated preprint from last June: doi.org/10.48550/arX.... (1/9) #biophysics #theory #active #chromatin #condensates

doi.orgExternally driven condensates show translation-induced polarization, directed coalescence, and anomalous diffusion in viscoelastic mediaPhase separation into compositionally and physically distinct domains is ubiquitous in (non)living matter ranging from alloys and emulsions to biomolecular condensates in cells. The organization of th...

Krishna Shrinivas reposted

Papers are like buses... You wait for ages, then two come along at once. Huge congrats to @bornanovak.bsky.social and @jefflotthammer.bsky.social for pushing and driving every aspect of this work, preprinted ~1 year ago to the day (Friday before BPS), now published! www.nature.com/articles/s41...

nature.comAccurate predictions of disordered protein ensembles with STARLING - NatureThe deep learning model STARLING can generate accurate ensembles of intrinsically disordered regions of proteins using only&nbsp;protein sequence as input.
Borna Novak@bornanovak.bsky.social

Excited to announce the newest member of the flock - STARLING (conSTruction of intrinsicAlly disoRdered proteins ensembles efficientLy vIa multi-dimeNsional Generative models). www.biorxiv.org/content/10.1...

Krishna Shrinivas reposted

Pleased to share the final version of this behemoth of a paper, now finally published. I guess I can retire now? www.nature.com/articles/s41... More functional data, many thousands of words removed, and a few other updates from last year's preprint.

nature.comSequence and chemical specificity define the functional landscape of intrinsically disordered regions - Nature Cell BiologyLangstein-Skora, Schmid, Huth et al. propose that intrinsically disordered region functionality can be driven by the interplay between linear binding motifs and contextual chemical characteristics suc...
Alex Holehouse@alexholehouse.bsky.social

We’re pleased to share our substantially updated (more below on that…) version of our manuscript exploring the relationship between conservation in sequence/function for intrinsically disordered regions (IDRs) www.biorxiv.org/content/10.1...

Krishna Shrinivas reposted

To probe gene-scale chromatin physics, we built 96-mer (20 kb) arrays with defined histone marks. Combining single-molecule tracking, AFM imaging, and developing in vitro Hi-C, we saw how specific modifications dictate chromatin structure and dynamics. www.science.org/doi/10.1126/...

science.orgGene-scale in vitro reconstitution reveals histone acetylation directly controls chromatin architectureReconstituting 20-kb chromatin shows that tuning acetylation alone reshapes its folding, dynamics, and contact domain formation.

Krishna Shrinivas reposted

1/ Excited to share our new study with @brumbaugh-lab.bsky.social, out in @natbiotech.nature.com! P-bodies selectively sequester RNAs encoding cell fate regulators, often from the preceding developmental stage. Releasing these RNAs can drive changes in cell identity. 🧵 www.nature.com/articles/s41...

nature.comSelective RNA sequestration in biomolecular condensates directs cell fate transitions - Nature BiotechnologyStem cell differentiation is controlled by manipulating RNA condensates.

Krishna Shrinivas reposted

The problem: AI tools like AlphaFold & ProteinMPNN accelerate design of stable protein folds by inverting the sequence-structure map. But IDPs don't have 1 shape - they occupy a huge ensemble of shapes. Physics simulations are good models to generate ensembles but hard to design/invert over!

Our framework: We bridge machine learning & statistical physics to directly invert molecular simulations to design IDPS and engineer examples that: 🌀 form loops & linkers with tuned flexibility ⚡ sense salt, temperature, or phosphorylation stimuli 🤝 bind disordered targets like FUS or Whi3

Krishna Shrinivas reposted

Our review on cell cycle – cell fate (de)coupling is out! doi.org/10.1242/dev.... Was a lot of fun writing this with Allon Klein, reading old papers(earliest from 1902), and speculating on why cell cycle progression is not necessary for differentiation across many many tissues and species. (1/3)

doi.orgCoupling and decoupling of the cell cycle from cell differentiation in developmentSummary: This Spotlight surveys investigations of the dependence of cellular differentiation on the cell cycle in animals. Strict dependence is uncommon. The decoupling of the cell cycle and different...

Krishna Shrinivas reposted

Krishna Shrinivas reposted

Preprint! Inspired by condensates that form on specific DNA, we ask: can we design multicomponent fluids to form distinct condensates on diff. surfaces? i.e. perform classification by condensation ⚛️ 💻 exploiting phase transitions beyond compartmentalization! arxiv.org/abs/2509.08100 (1/2)

arXiv physics.bio-ph Biological Physics@physicsbioph-bot.bsky.social

Aidan Zentner, Ethan V. Halingstad, Cameron Chalk, Michael P. Brenner, Arvind Murugan, Erik Winfree, Krishna Shrinivas: Information processing driven by multicomponent surface condensates https://arxiv.org/abs/2509.08100 https://arxiv.org/pdf/2509.08100 https://arxiv.org/html/2509.08100

Preprint! Inspired by condensates that form on specific DNA, we ask: can we design multicomponent fluids to form distinct condensates on diff. surfaces? i.e. perform classification by condensation ⚛️ 💻 exploiting phase transitions beyond compartmentalization! arxiv.org/abs/2509.08100 (1/2)

arXiv physics.bio-ph Biological Physics@physicsbioph-bot.bsky.social

Aidan Zentner, Ethan V. Halingstad, Cameron Chalk, Michael P. Brenner, Arvind Murugan, Erik Winfree, Krishna Shrinivas: Information processing driven by multicomponent surface condensates https://arxiv.org/abs/2509.08100 https://arxiv.org/pdf/2509.08100 https://arxiv.org/html/2509.08100

Krishna Shrinivas reposted

Our work highlighted in @science.org by L. Bryan Ray! www.science.org/doi/10.1126/...

Krishna Shrinivas@shrinivaslab.bsky.social

New in @pnas.org with @gladfelterlab.bsky.social @sneadlab.bsky.social ! 📢 Cells use condensates (dynamic compartments without membranes) to organize key reactions. Some condensates have core & shell layers… but how do such layers form? 🤔 👉 pnas.org/doi/10.1073/pnas.2504778122 (1/6)

Extra curiosities 🔍 •⁠ ⁠Across tissues & species, stoichiometries of NONO/FUS are conserved, hinting at evolutionary tuning. •⁠ ⁠Simulations by Mary Skillicorn in the lab also suggest important roles for co-transcriptional nucleation of paraspeckles for tuning paraspeckle size/number.

We combined in vitro assays of binding and condensation with bioinformatics to ask which parts of NEAT1 each protein preferred binding to. Surprise: core proteins (FUS, NONO) actually prefer the same shell RNA regions as the shell protein TDP-43! Everyone crowds into the same RNA zones. 🌀

Another surprise: core & shell proteins don’t mix well (they’re immiscible, like oil & water). Putting these observations together in simulations suggests 🖥️⚛️: competition for RNA + immiscibility naturally push proteins to form different layers, even if they individually like the same parts of RNA.

We use paraspeckles as a model to study this question. Paraspeckles are built around a non-coding RNA NEAT1 whose middle regions are in the core and 5’/3’ ends on the shell layer - each layer also recruiting different proteins. (2/6)

Prevailing model suggests: → “Core” proteins bind the middle of the NEAT1 RNA scaffold → “Shell” proteins bind the RNA ends This selective binding could, in principle, assemble layers - but has not been explicitly tested. So we set out to do this!

Krishna Shrinivas reposted

Krishna Shrinivas reposted

Krishna Shrinivas reposted

Our paper on single-cell learning is now published in @currentbiology.bsky.social! @wallaceucsf.bsky.social sciencedirect.com/science/arti...

sciencedirect.comA receptor-inactivation model for single-celled habituation in Stentor coeruleusThe single-celled ciliate Stentor coeruleus demonstrates habituation to mechanical stimuli, but the mechanism of learning in this single cell, which l…
Deepa Rajan, PhD@deeparajan.bsky.social

How can a single cell learn without a brain or nervous system? We explore this in our new preprint with @wallaceucsf.bsky.social! We discovered that central features of learning in single cells can be accounted for by a model based on receptor inactivation bit.ly/3CDukfS 🧵 1/n

Krishna Shrinivas reposted

In the nucleus, many intrinsically disordered proteins (IDPs) form condensates. What IDP sequence features drive this behavior? We developed CondenSeq, a high-throughput approach to measure nuclear condensate formation, and applied it to ~14,000 IDPs to find out! rdcu.be/eq975

rdcu.beCharacterizing protein sequence determinants of nuclear condensates by high-throughput pooled imaging with CondenSeqNature Methods - CondenSeq is an imaging-based, high-throughput platform for characterizing condensate formation within the nuclear environment, uncovering the protein sequence features that...

Krishna Shrinivas reposted

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

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2 https://www.biorxiv.org/content/10.1101/2025.04.16.649219v1

Krishna Shrinivas reposted

Krishna Shrinivas reposted

To all people who value science as a source of truth, cures for diseases, and hope for our climate and future, here are 2 hard truths: 1) our current research ecosystem is already strained and very fragile; 2) If it gets broken it will take a generation or longer to repair. Here’s why: 1/n

Krishna Shrinivas reposted

The answer is yes! Come hear about STARLING this afternoon at the Biophysical Society IDP subgroup symposium! Thread to follow later today... www.biorxiv.org/content/10.1...

biorxiv.orgAccurate predictions of conformational ensembles of disordered proteins with STARLINGIntrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and play critical roles in virtually every eukaryotic cellular process. In contrast to folded pr...
Alex Holehouse@alexholehouse.bsky.social

Heading out to LA for the 2025 biophysical society meeting! The big question: Can we submit the preprint and get it online before my talk tomorrow?!?! WHO KNOWS!?

Krishna Shrinivas reposted

Super excited to finally share PANCS-Binders: our group's decade-long quest to accelerate protein binder discovery. TLDR: PANCS-binders is fast (2 days), cheap (pennies), has extremely high fidelity (low false positive and negatives), and high-throughput. 1/n