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
Interested in how molecules and processes are organized and regulated in living cells | physics, math, engineering, and computation (mostly) for biology
shrinivaslab.com
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
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
October 10, 2025 at 6:16 PM
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
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
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!
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!
October 10, 2025 at 6:16 PM
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!
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!
Happy to share our latest in @natcomputsci.nature.com
led by (amazing) Ryan Krueger + colab w M. Brenner!
We introduce a framework to directly design intrinsically disordered proteins (IDPs) from physics-based simulations.
🧬 doi.org/10.1038/s435...
📰 www.mccormick.northwestern.edu/news/article...
led by (amazing) Ryan Krueger + colab w M. Brenner!
We introduce a framework to directly design intrinsically disordered proteins (IDPs) from physics-based simulations.
🧬 doi.org/10.1038/s435...
📰 www.mccormick.northwestern.edu/news/article...
October 10, 2025 at 6:16 PM
Happy to share our latest in @natcomputsci.nature.com
led by (amazing) Ryan Krueger + colab w M. Brenner!
We introduce a framework to directly design intrinsically disordered proteins (IDPs) from physics-based simulations.
🧬 doi.org/10.1038/s435...
📰 www.mccormick.northwestern.edu/news/article...
led by (amazing) Ryan Krueger + colab w M. Brenner!
We introduce a framework to directly design intrinsically disordered proteins (IDPs) from physics-based simulations.
🧬 doi.org/10.1038/s435...
📰 www.mccormick.northwestern.edu/news/article...
Led by the amazing Aidan Zentner, with contribs from Ethan Halingstad, and in collab with Cameron Chalk, Michael Brenner, @amurugan.bsky.social, and Erik Winfree.
For a more fun overview, see Erik's version of the abstract www.dna.caltech.edu/DNAresearch_... :) (2/2)
For a more fun overview, see Erik's version of the abstract www.dna.caltech.edu/DNAresearch_... :) (2/2)
September 22, 2025 at 9:38 PM
Led by the amazing Aidan Zentner, with contribs from Ethan Halingstad, and in collab with Cameron Chalk, Michael Brenner, @amurugan.bsky.social, and Erik Winfree.
For a more fun overview, see Erik's version of the abstract www.dna.caltech.edu/DNAresearch_... :) (2/2)
For a more fun overview, see Erik's version of the abstract www.dna.caltech.edu/DNAresearch_... :) (2/2)
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)
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)
September 22, 2025 at 9:38 PM
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)
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)
September 12, 2025 at 2:50 AM
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.
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.
September 4, 2025 at 2:44 AM
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.
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)
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)
September 4, 2025 at 2:44 AM
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)
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)