Aaron Chevalier

Synthetic biology: engineering Escherichia coli to see light.

We have designed a bacterial system that is switched between different states by red light. The system consists of a synthetic sensor kinase that allows a lawn of bacteria to function as a biological film, such that the projection of a pattern of light on to the bacteria produces a high-definition (about 100 megapixels per square inch), two-dimensional chemical image. This spatial control of bacterial gene expression could be used to ‘print’ complex biological materials, for example, and to investigate signalling pathways through precise spatial and temporal control of their phosphorylation steps.

A Synthetic Genetic Edge Detection Program

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing

We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility.

A CMOS Electrochemical Impedance Spectroscopy (EIS) Biosensor Array

In this paper, we present a fully integrated biosensor 10 × 10 array in a standard complementary metal-oxide semiconducor process, which takes advantage of electrochemical impedance spectroscopy (EIS). We also show that this system is able to detect various biological analytes, such as DNA and proteins, in real time and without the need for molecular labels. In each pixel of this array, we implement a biocompatible Au electrode transducer and embedded sensor circuitry which takes advantage of the coherent detector to measure the impedance of the associated electrode-electrolyte interface. This chip is capable of concurrently measuring admittance values as small as 10-8 Ω-1 within the array with the detection dynamic range of more than 90 dB in the frequency range of 10 Hz-50 MHz.

A CMOS electrochemical impedance spectroscopy biosensor array for label-free biomolecular detection

Biosensors are one of the fundamental detection platforms in biotechnology. They take advantage of unique biomolecular interactions to capture and detect specific analytes on a surface. The detection versatility of biosensors has always been their key advantage and it has been demonstrated that they can detect almost any analyte such as DNA, proteins, metabolites, and even micro-organisms. However, the achievable SNR and detection DR of biosensors can be very low. This is due to the fact that the capturing processes in biosensors suffer from a significant amount of biological interference (i.e., non-specific bindings) and biochemical noise which typically necessitate the use of complex biochemical labeling processes and sophisticated detectors [1]. Hence, the main design challenge of biosensors is to increase the SNR and DR while minimizing the complexity of both the assay and the detector. Today, this is the main impediment in point-of-care (PoC) biosensors, particularly in high-performance applications such as molecular diagnostics and forensics.

Global analysis of protein folding using massively parallel design, synthesis, and testing.

Exploring structure space to understand stability Understanding the determinants of protein stability is challenging because native proteins have conformations that are optimized for function. Proteins designed without functional bias could give insight into how structure determines stability, but this requires a large sample size. Rocklin et al. report a high-throughput protein design and characterization method that allows them to measure thousands of miniproteins (see the Perspective by Woolfson et al.). Iterative rounds of design and characterization increased the design success rate from 6 to 47%, which provides insight into the balance of forces that determine protein stability. Science, this issue p. 168; see also p. 133 Thousands of computationally designed proteins quantify the global determinants of miniprotein stability. Proteins fold into unique native structures stabilized by thousands of weak interactions that collectively overcome the entropic cost of folding. Although these forces are “encoded” in the thousands of known protein structures, “decoding” them is challenging because of the complexity of natural proteins that have evolved for function, not stability. We combined computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for more than 15,000 de novo designed miniproteins, 1000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences. This analysis identified more than 2500 stable designed proteins in four basic folds—a number sufficient to enable us to systematically examine how sequence determines folding and stability in uncharted protein space. Iteration between design and experiment increased the design success rate from 6% to 47%, produced stable proteins unlike those found in nature for topologies where design was initially unsuccessful, and revealed subtle contributions to stability as designs became increasingly optimized. Our approach achieves the long-standing goal of a tight feedback cycle between computation and experiment and has the potential to transform computational protein design into a data-driven science.

A CMOS fluorescent-based biosensor microarray

Biosensors are one of the most important analytical tools in biotechnology today. These detection systems take advantage of the selective interaction and binding of certain biological molecules to identify and detect different analytes such as toxins, hormones, DNA strands, proteins, bacteria, etc. The fundamental advantage of array-based biosensors, which compensates for their limited SNR and detection dynamic range [1], is their capability to detect multiple analytes simultaneously in parallel [2]. Today, densely packed biosensor arrays (i.e., microarrays) that detect hundreds or even thousands of different analytes are an integral part of biotechnology.

Massively parallel de novo protein design for targeted therapeutics

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37–43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.

Engineering Escherichia coli to see light

We have designed a bacterial system that is switched between different states by red light. The system consists of a synthetic sensor kinase that allows a lawn of bacteria to function as a biological film, such that the projection of a pattern of light on to the bacteria produces a high-definition (about 100 megapixels per square inch), two-dimensional chemical image. This spatial control of bacterial gene expression could be used to ‘print’ complex biological materials, for example, and to investigate signalling pathways through precise spatial and temporal control of their phosphorylation steps.

Immobilizing affinity proteins to nitrocellulose: a toolbox for paper-based assay developers

To enable enhanced paper-based diagnostics with improved detection capabilities, new methods are needed to immobilize affinity reagents to porous substrates, especially for capture molecules other than IgG. To this end, we have developed and characterized three novel methods for immobilizing protein-based affinity reagents to nitrocellulose membranes. We have demonstrated these methods using recombinant affinity proteins for the influenza surface protein hemagglutinin, leveraging the customizability of these recombinant “flu binders” for the design of features for immobilization. The three approaches shown are: (1) covalent attachment of thiolated affinity protein to an epoxide-functionalized nitrocellulose membrane, (2) attachment of biotinylated affinity protein through a nitrocellulose-binding streptavidin anchor protein, and (3) fusion of affinity protein to a novel nitrocellulose-binding anchor protein for direct coupling and immobilization. We also characterized the use of direct adsorption for the flu binders, as a point of comparison and motivation for these novel methods. Finally, we demonstrated that these novel methods can provide improved performance to an influenza hemagglutinin assay, compared to a traditional antibody-based capture system. Taken together, this work advances the toolkit available for the development of next-generation paper-based diagnostics.