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
Krishna Shrinivas
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Intrinsically disordered proteins (IDPs) flit between diverse shapes and interact in fuzzy ensembles whose collective properties shape function.
Q: Can we rationally design emergent properties of IDPs?
Our newest preprint takes a stab at this 🧵
biorxiv.org/content/10.6...
Krishna Shrinivas
Excited to share the first pre-print from our lab!!
Check it out here! www.biorxiv.org/content/10.6...
We found that many RNA-binding proteins canonically understood to regulate RNA processing can also function like transcription factors and cofactors to directly regulate transcription.
Hey, I wrote a thing about AI in astrophysics
ergosphere.blog/posts/the-ma...
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
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.
On AI agents, grunt work, and the part of science that isn't replaceable.
Design 3: localization in living cells 🔬
We designed IDPs to selectively enrich (client) or deplete (excluder) from FUS LC condensates & tested in living cells with @sneadlab.bsky.social.
Great work by co-first authors Neha Tyagi & Jackson Boodry, with Vita Chou & Wilton Snead!
Phase 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...
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
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
See our handy graphical abstract and article for free here:
bit.ly/4vsl845
Jon Henninger
Minas Karamanis
Krishna Shrinivas
Andriy Goychuk
Krishna Shrinivas
Krishna Shrinivas
Krishna Shrinivas
Krishna Shrinivas
Jon Henninger
🚨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...
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?
Nature Computational Science
Jon Henninger
How are biomolecular condensates spatially organized? As example, we focus on fibrillar centers producing ribosomal RNA in the nucleolus. Disrupting this function disrupts their patterning, and disrupting patterning disrupts function. Out in Cell Systems: authors.elsevier.com/c/1nA3C8YyDf... (1/6)
