Biology subjects

Lee, P.-W.

Publications and source records attributed to Lee, P.-W..

4 recordsLinked to original sources

bioRxiv2025

De novo design of phosphorylation-induced protein switches for synthetic signaling in cells

A hallmark of living systems is their ability to respond and adapt to many types of exogenous and endogenous cues. At the core of this capability lie the evolved mechanisms of cellular signaling which perform the relay of biochemical signals throughout the cell. Much of this core signaling functionality in living systems is afforded by the ability of proteins to change their chemical and structural "status", by populating distinct states in conformational landscapes, undergoing post-translational modifications that alter these populations, and dynamically interacting with other cellular partners. Despite the remarkable advances we have witnessed in computational protein design, it remains an outstanding challenge to rationally design many of these aspects that are at the core of biological function. Here, we set out to design a minimal signaling network triggered by phosphorylation, which relies on de novo components that sample low-populated conformational states to control a designed protein interaction. In these designed components we implemented principles of signal amplification, conformational heterogeneity and molecular recognition which are ubiquitously used by nature to sustain biological function in cells. The designed proteins were biochemically and structurally characterized and ultimately are functional in cell-based systems where they regulate the transcription of reporter proteins in a phosphorylation-dependent manner. Overall, this work lays the foundation for designing synthetic signaling cascades using de novo protein components, which could be important to enhance our understanding of natural systems and for relevant applications in synthetic biology.

bioengineering↗

bioRxiv2025

A T7 RNAP regulatory toolbox for cell-free network engineering and biosensing applications

T7 RNA polymerase is ubiquitously used in the fields of synthetic biology and biotechnology. Yet the ability to precisely and modularly regulate T7 RNAP remains surprisingly limited. Here, we developed a T7 RNAP regulatory toolbox consisting of programmable synthetic repressors, activators, and biosensors in a cell-free system. This toolbox enables the scalable design of T7 RNAP based gene regulatory networks and enables the rapid and sensitive detection of diverse biomolecules, including small-molecule drugs, antibodies, and proteins. By integrating a protein design pipeline, we generated biosensors using fully synthetic binders, demonstrating the potential for rapid development of novel protein-based sensors. We constructed a diagnostic cell-free system combining SARS-CoV-2 Spike protein sensing, gene regulatory based amplification, enzymatic amplification, and glucose based detection demonstrating the potential for point-of-care detection with high sensitivity. This work establishes a flexible and expandable framework for constructing gene circuits responsive to a wide range of biomolecules and demonstrates the potential for engineering point-of-care cell-free diagnostic assays.

synthetic biology↗

bioRxiv2024

Targeting protein-ligand neosurfaces using a generalizable deep learning approach

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, where protein-protein interactions are conditioned to small molecules. Here, we present a computational strategy for the design of proteins that target neosurfaces, i.e. surfaces arising from protein-ligand complexes. To do so, we leveraged a deep learning approach based on learned molecular surface representations and experimentally validated binders against three drug-bound protein complexes. Remarkably, surface fingerprints trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in deep learning approaches. The designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells.

biochemistry↗

bioRxiv2020

Foraging strategies under extreme events: Contrasting adaptations by benthic macrofauna to drastic biogeochemical disturbance

Extreme events caused by global change are increasingly affecting the oceans biogeochemical cycling and ecosystem functioning, but it is challenging to observe how food webs respond to rapid habitat disturbances. Benthic communities are particularly vulnerable because their habitats are easily affected by extreme events. Here, we examined how benthic macrofauna responded to a "near shutdown" of shallow marine hydrothermal vents, triggered by M5.8 earthquake and C5 typhoon events. Despite reduced vent fluxes, we shows that the endemic vent crab Xenograpsus testudinatus continued to rely on chemosynthetic sulfur bacteria rather than photosynthetic sources. We posit this obligate nutritional dependence caused a population decline of vent crabs. In contrast, the non-endemic mollusks exhibited much greater dietary plasticity with no detectable impact on the population. Our study based on naturally occurring extreme events exemplifies how specialist species in marine system are particularly vulnerable to the unprecedented evolutionary and environmental pressures exerted by human activities worldwide.

ecology↗