The Problem:
Evolution produces systems that display remarkable structural and functional properties that can rival or exceed the performance of man-made systems. For example, protein molecules fold spontaneously into precise, well-ordered structures and can carry out specific binding, catalysis of difficult chemical reactions, and allosteric regulation. At a larger spatial scale, networks of proteins assemble to form metabolic and signaling systems that show efficient and selective processing of material and information. These performance characteristics can coexist with robustness to random perturbation and the capacity to adapt to new functional states as conditions of selection vary in the environment. The goal of our laboratory is to understand the design principles that underlie structure, function, and adaptation in biological systems. For maximal experimental and theoretical power, we focus at the atomic to cellular scale.
The Strategy:
Our approach is to break the problem down into three essential tasks: (1) to define the pattern of constraints that specify biological systems, (2) to determine the underlying physics, and (3) to understand the generative process that produces these (and not other) architectures. In other words, we wish to understand what nature has built, how it works, and why it is built the way it is. Answers to these core questions lie at the essence of understanding and engineering biological systems, and more fundamentally, to explain how they are even possible through the random, algorithmic process that we call evolution. Read more about the three core directions and about current problems being addressed in our laboratory... .
Direct visualization of electric-field-stimulated ion conduction in a potassium channel.
Direct visualization of electric-field-stimulated ion conduction in a potassium channel. Cell. 2025 Jan 09; 188(1):77-88.e15.
PMID: 39793560
Natural Language Prompts Guide the Design of Novel Functional Protein Sequences.
Natural Language Prompts Guide the Design of Novel Functional Protein Sequences. bioRxiv. 2024 Nov 11.
PMID: 39605414
Deep-learning-based design of synthetic orthologs of SH3 signaling domains.
Deep-learning-based design of synthetic orthologs of SH3 signaling domains. Cell Syst. 2024 Aug 21; 15(8):725-737.e7.
PMID: 39106868
Functional protein dynamics in a crystal.
Functional protein dynamics in a crystal. Nat Commun. 2024 Apr 15; 15(1):3244.
PMID: 38622111
BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules.
BioCARS: Synchrotron facility for probing structural dynamics of biological macromolecules. Struct Dyn. 2024 Jan; 11(1):014301.
PMID: 38304444
ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.
ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design. ACS Synth Biol. 2023 12 15; 12(12):3544-3561.
PMID: 37988083
Undersampling and the inference of coevolution in proteins.
Undersampling and the inference of coevolution in proteins. Cell Syst. 2023 03 15; 14(3):210-219.e7.
PMID: 36693377
Roadmap on biology in time varying environments.
Roadmap on biology in time varying environments. Phys Biol. 2021 05 17; 18(4).
PMID: 33477124
100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design.
100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design. ACS Macro Lett. 2021 03 16; 10(3):327-340.
PMID: 35549066
RNA sectors and allosteric function within the ribosome.
RNA sectors and allosteric function within the ribosome. Proc Natl Acad Sci U S A. 2020 08 18; 117(33):19879-19887.
PMID: 32747536