Douglas Barrick
Johns Hopkins University, MD·Biophysics
Proteins fold into precise shapes, stick to partners and carry out chemistry, but the rules linking a protein's amino-acid sequence to its stability and function are still only partly understood. Huge databases of protein sequences now make it possible to read these rules from evolution itself.
- Fields
Biophysics
Biochemistry
- Topics
protein stability
protein design
consensus design
- Grants
- 3 known grants, through Aug 2030
What they work on
- Design proteins that combine consensus (most-common-residue) sequences with covariance (residue pairs that change together), using a Potts statistical model, and measure their stability, binding and enzyme activity.
- Use singular value decomposition (SVD) to sort protein sequence alignments into clusters, then design and test sequences carrying each cluster's covariance pattern to predict functional differences.
- Pinpoint which consensus and covariance positions drive stability and activity by making single and multi-site point mutations, and test the non-additive stabilization seen in consensus designs.
- Benchmark how well AI-based protein design methods produce highly stable proteins, using the lab's set of designed consensus proteins as a test bed.
- Measure the assembly energy of the Notch transcription complex and its parts, to learn how disordered linkers couple binding regions and how disease mutations disrupt assembly.
- Determine interaction strengths between subunits of five tandem repeat proteins using Ising-model analysis, fluorescence and circular dichroism, x-ray and magnetic resonance structures, and computer simulations.
More about the research
The lab's projects share this goal from several angles. One uses sequence statistics to design unusually stable, working proteins. Another measures how the parts of protein assemblies hold together. A third dissects a signaling complex involved in development and disease.
Why it matters
Better rules for how sequence controls stability and function could improve protein design. That could help produce sturdier protein drugs, diagnostic tools and industrial enzymes, and help test and improve AI design tools. The Notch work could clarify what goes wrong in Notch-related human diseases, including how specific mutations break apart the signaling complex.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Thermodynamic and Functional Analysis of Consensus and Covariance in Proteins, and Bivalent Notch Complexes
NIH · Sep 2025 – Aug 2030
Heteropolymeric Semi-Autonomous Repeat Proteins: Coupling Energetics, Structure, and Function
NSF · May 2020 – Apr 2027
Consensus and Covariance Proteins: Stability, Cooperativity, Function, & Design
NIH · Mar 2005 – Aug 2026
Closest labs
Rama Ranganathan
University of Chicago, IL·Biophysics
Develop and experimentally validate a deep generative model that enables text-prompted engineering of diverse, functional artificial proteins.
Esteban Orellana Vinueza
Dartmouth College, NH·Biochemistry
Determine how chemical modifications alter the structure, dynamics and folding or misfolding of tRNA isodecoders, tRNAs that share an anticodon but differ elsewhere, using spectroscopy and molecular dynamics simulations.
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
Johns Hopkins University: 100 labs on PI Pages, $4.1B a year on research, 622 research doctorates a year.
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