Detecting signals and responding to them are among the most fundamental abilities of living systems. This application seeks to develop computational and experimental design strategies to engineer new protein-based sensor/actuators responding to small molecules in cells. The key idea is to engineer small-molecule binding sites into heterodimeric protein-protein interfaces such that the protein-protein interaction becomes dependent on the small molecule. Each of the two protein partners will be linked to a fragment of a split reporter. A functional sensor then detects the presence of the small molecule by reporter complementation. In this fashion, the sensor output is in principle modular: different reporter fragments can be attached to the small-molecule sensor components and tested for either detection (for example split-GFP) or actuation (split-enzymes or gene expression). Computational design of such modular sensor/actuators via small molecule-induced dimerization would be a first. The central strategy to create starting activity is to transplant binding site geometries from liganded protein monomer structures into protein-protein interfaces, followed by computational remodeling of the interface. This approach has led to successful design of sensors that respond to farnesyl pyrophosphate (FPP), a central intermediate in synthesis pathways for therapeutic molecules, industrial chemicals, and fuels, and generated initial sensor activity for two additional targets.
Aim 1 seeks to address key shortcomings of computational design by developing methods to (i) improve designed binding site geometries; (ii) assess and restrict conformational variability of binding sites residues; (ii) recombine fragments from design ensembles in a computational equivalent of DNA shuffling; and (iv) improve ranking of designs.
Aim 2 will build an experimental platform to characterize and improve engineered sensors by testing design predictions using modular reporters in cells, screening computationally designed libraries, directed evolution to optimize sensor function, and in vitro biophysical characterization. These studies will produce (i) improved FPP sensors useful in metabolic engineering applications and (ii) new sensors for molecules that could be used to specifically activate protein-protein interactions controlling cellular signaling, building on preliminary data showing initial designed sensor activity. While small molecule-induced protein dimerization exists in nature and has been reengineered, these systems are limited to a few molecules that can be sensed, and often have undesired side effects. The methodology developed in this project could greatly broaden applications of small-molecule sensing and actuation. Because the modular approach allows characterization of many functional and non- functional designs, this project will also provide unique information on successes and limitations of design that is critical for methodological improvements. Ultimately, these studies will lead to advanced computational design methods while generating new tools to control cellular behavior in biological engineering applications and to probe basic and disease biology.

Public Health Relevance

This research develops new technologies to engineer sensors made out of biological components that can detect and respond to molecular signals in living cells and organisms. Such new biosensors have many practical applications in biomedical research and biological engineering, and will also help to advance our understanding of fundamental cellular processes by probing health and disease states. The software and technology developed in this project will be widely available to researchers to improve design of new biological functions.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Wehrle, Janna P
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University of California San Francisco
Schools of Pharmacy
San Francisco
United States
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Mavor, David; Barlow, Kyle A; Asarnow, Daniel et al. (2018) Extending chemical perturbations of the ubiquitin fitness landscape in a classroom setting reveals new constraints on sequence tolerance. Biol Open 7:
Barlow, Kyle A; Ó Conchúir, Shane; Thompson, Samuel et al. (2018) Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation. J Phys Chem B 122:5389-5399
Alford, Rebecca F; Leaver-Fay, Andrew; Jeliazkov, Jeliazko R et al. (2017) The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. J Chem Theory Comput 13:3031-3048
Mavor, David; Barlow, Kyle; Thompson, Samuel et al. (2016) Determination of ubiquitin fitness landscapes under different chemical stresses in a classroom setting. Elife 5:
Ó Conchúir, Shane; Barlow, Kyle A; Pache, Roland A et al. (2015) A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design. PLoS One 10:e0130433
Ollikainen, Noah; de Jong, René M; Kortemme, Tanja (2015) Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity. PLoS Comput Biol 11:e1004335