Rama Ranganathan
University of Chicago, IL·Biophysics
Proteins fold on their own into precise shapes. They bind partners, speed up chemical reactions, pass along signals and switch activity in response to other molecules, a process called allosteric regulation.
- Fields
Biophysics
Biochemistry
Computer Science
- Topics
protein design
protein engineering
protein evolution
- Grants
- 1 known grant, through Dec 2030
What they work on
- Develop and experimentally validate a deep generative model that enables text-prompted engineering of diverse, functional artificial proteins.
- Use experiments on protein dynamics to understand what information about protein function is encoded in generative models.
- Test hypotheses from sequence-based statistical models about how proteins achieve performance, robustness and adaptability, using forward evolution experiments in the lab.
More about the research
They do all this while tolerating random mutations and adapting as their environment changes. How evolution builds proteins that are high-performing, robust and adaptable at once is a central open question. Answering it would also make it easier to design new proteins.
Why it matters
Understanding how natural proteins are designed could make protein engineering more reliable. Experimentally validated generative models and a shared technology platform could help researchers create functional artificial proteins and apply model-driven approaches to other biological systems.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Yunan Luo
Georgia Institute of Technology, GA·Computer Science·Early-career
Develop machine learning methods that learn protein representations from amino acid sequence and 3D structure, in order to explain how composition and shape determine protein function.
Faez Ahmed
Massachusetts Institute of Technology, MA·Engineering·Early-career
Develop diffusion models, generative AI that builds outputs by removing noise step by step, that learn from the full history of design optimization runs rather than only final optimized designs, to generate more precise engineering designs.
Brian Kuhlman
University of North Carolina at Chapel Hill, NC·Biochemistry
Test whether a protein core mixing water-repelling and water-attracting amino acids yields a partly unfolded, water-containing cold state, using de novo designed cold unfolding proteins, biophysical measurements and molecular dynamics simulations.
The school
University of Chicago: 39 labs on PI Pages, $681M a year on research, 457 research doctorates a year.
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