Computational design of ligand-binding proteins with high affinity and selectivity.

The ability to design proteins with high affinity and selectivity for any given small molecule is a rigorous test of our understanding of the physiochemical principles that govern molecular recognition. Attempts to rationally design ligand-binding proteins have met with little success, however, and the computational design of protein-small-molecule interfaces remains an unsolved problem. Current approaches for designing ligand-binding proteins for medical and biotechnological uses rely on raising antibodies against a target antigen in immunized animals and/or performing laboratory-directed evolution of proteins with an existing low affinity for the desired ligand, neither of which allows complete control over the interactions involved in binding. Here we describe a general computational method for designing pre-organized and shape complementary small-molecule-binding sites, and use it to generate protein binders to the steroid digoxigenin (DIG). Of seventeen experimentally characterized designs, two bind DIG; the model of the higher affinity binder has the most energetically favourable and pre-organized interface in the design set. A comprehensive binding-fitness landscape of this design, generated by library selections and deep sequencing, was used to optimize its binding affinity to a picomolar level, and X-ray co-crystal structures of two variants show atomic-level agreement with the corresponding computational models. The optimized binder is selective for DIG over the related steroids digitoxigenin, progesterone and ß-oestradiol, and this steroid binding preference can be reprogrammed by manipulation of explicitly designed hydrogen-bonding interactions. The computational design method presented here should enable the development of a new generation of biosensors, therapeutics and diagnostics.

Improved Free-Energy Landscape Quantification Illustrated with a Computationally Designed Protein-Ligand Interaction.

Quantifying the energy landscape underlying protein-ligand interactions leads to an enhanced understanding of molecular recognition. A powerful yet accessible single-molecule technique is atomic force microscopy (AFM)-based force spectroscopy, which generally yields the zero-force dissociation rate constant (koff ) and the distance to the transition state (Δx≠ ). Here, we introduce an enhanced AFM assay and apply it to probe the computationally designed protein DIG10.3 binding to its target ligand, digoxigenin. Enhanced data quality enabled an analysis that yielded the height of the transition state (ΔG≠ =6.3±0.2 kcal mol-1 ) and the shape of the energy barrier at the transition state (linear-cubic) in addition to the traditional parameters [koff (=4±0.1×10-4  s-1 ) and Δx≠ (=8.3±0.1 Å)]. We expect this automated and relatively rapid assay to provide a more complete energy landscape description of protein-ligand interactions and, more broadly, the diverse systems studied by AFM-based force spectroscopy.

Bioluminescent sensor proteins for point-of-care therapeutic drug monitoring.

For many drugs, finding the balance between efficacy and toxicity requires monitoring their concentrations in the patient's blood. Quantifying drug levels at the bedside or at home would have advantages in terms of therapeutic outcome and convenience, but current techniques require the setting of a diagnostic laboratory. We have developed semisynthetic bioluminescent sensors that permit precise measurements of drug concentrations in patient samples by spotting minimal volumes on paper and recording the signal using a simple point-and-shoot camera. Our sensors have a modular design consisting of a protein-based and a synthetic part and can be engineered to selectively recognize a wide range of drugs, including immunosuppressants, antiepileptics, anticancer agents and antiarrhythmics. This low-cost point-of-care method could make therapies safer, increase the convenience of doctors and patients and make therapeutic drug monitoring available in regions with poor infrastructure.

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge.

Community Structure-Activity Resource (CSAR) conducted a benchmark exercise to evaluate the current computational methods for protein design, ligand docking, and scoring/ranking. The exercise consisted of three phases. The first phase required the participants to identify and rank order which designed sequences were able to bind the small molecule digoxigenin. The second phase challenged the community to select a near-native pose of digoxigenin from a set of decoy poses for two of the designed proteins. The third phase investigated the ability of current methods to rank/score the binding affinity of 10 related steroids to one of the designed proteins (pKd = 4.1 to 6.7). We found that 11 of 13 groups were able to correctly select the sequence that bound digoxigenin, with most groups providing the correct three-dimensional structure for the backbone of the protein as well as all atoms of the active-site residues. Eleven of the 14 groups were able to select the appropriate pose from a set of plausible decoy poses. The ability to predict absolute binding affinities is still a difficult task, as 8 of 14 groups were able to correlate scores to affinity (Pearson-r > 0.7) of the designed protein for congeneric steroids and only 5 of 14 groups were able to correlate the ranks of the 10 related ligands (Spearman-ρ > 0.7).

Improving the Catalytic Performance of an Artificial Metalloenzyme by Computational Design.

Artifical metalloenzymes combine the reactivity of small molecule catalysts with the selectivity of enzymes, and new methods are required to tune the catalytic properties of these systems for an application of interest. Structure-based computational design could help to identify amino acid mutations leading to improved catalytic activity and enantioselectivity. Here we describe the application of Rosetta Design for the genetic optimization of an artificial transfer hydrogenase (ATHase hereafter), [(η(5)-Cp*)Ir(pico)Cl] ⊂ WT hCA II (Cp* = Me5C5(-)), for the asymmetric reduction of a cyclic imine, the precursor of salsolsidine. Based on a crystal structure of the ATHase, computational design afforded four hCAII variants with protein backbone-stabilizing and hydrophobic cofactor-embedding mutations. In dansylamide-competition assays, these designs showed 46-64-fold improved affinity for the iridium pianostool complex [(η(5)-Cp*)Ir(pico)Cl]. Gratifyingly, the new designs yielded a significant improvement in both activity and enantioselectivity (from 70% ee (WT hCA II) to up to 92% ee and a 4-fold increase in total turnover number) for the production of (S)-salsolidine. Introducing additional hydrophobicity in the Cp*-moiety of the Ir-catalyst provided by adding a propyl substituent on the Cp* moiety yields the most (S)-selective (96% ee) ATHase reported to date. X-ray structural data indicate that the high enantioselectivity results from embedding the piano stool moiety within the protein, consistent with the computational model.

Protein production from HEK293 cell line-derived stable pools with high protein quality and quantity to support discovery research.

Antibody-based therapeutics and recombinant protein reagents are often produced in mammalian expression systems, which provide human-like post-translational modifications. Among the available mammalian cell lines used for recombinant protein expression, Chinese hamster ovary (CHO)-derived suspension cells are generally utilized because they are easy to culture and tend to produce proteins in high yield. However, some proteins purified from CHO cell overexpression suffer from clipping and display undesired non-human post translational modifications (PTMs). In addition, CHO cell lines are often not suitable for producing proteins with many glycosylation motifs for structural biology studies, as N-linked glycosylation of proteins poses challenges for structure determination by X-ray crystallography. Hence, alternative and complementary cell lines are required to address these issues. Here, we present a robust method for expressing proteins in human embryonic kidney 293 (HEK293)-derived stable pools, leading to recombinant protein products with much less clipped species compared to those expressed in CHO cells and with higher yield compared to those expressed in transiently-transfected HEK293 cells. Importantly, the stable pool generation protocol is also applicable to HEK293S GnTI- (N-acetylglucosaminyltransferase I-negative) and Expi293F GnTI- suspension cells, facilitating production of high yields of proteins with less complex glycans for use in structural biology projects. Compared to HEK293S GnTI- stable pools, Expi293F GnTI- stable pools consistently produce proteins with similar or higher expression levels. HEK293-derived stable pools can lead to a significant cost reduction and greatly promote the production of high-quality proteins for diverse research projects.

VERITAS: Harnessing the power of nomenclature in biologic discovery.

We are entering an era in which therapeutic proteins are assembled using building block-like strategies, with no standardized schema to discuss these formats. Existing nomenclatures, like AbML, sacrifice human readability for precision. Therefore, considering even a dozen such formats, in combination with hundreds of possible targets, can create confusion and increase the complexity of drug discovery. To address this challenge, we introduce Verified Taxonomy for Antibodies (VERITAS). This classification and nomenclature scheme is extensible to multispecific therapeutic formats and beyond. VERITAS names are easy to understand while drawing direct connections to the structure of a given format, with or without specific target information, making these names useful to adopt in scientific discourse and as inputs to machine learning algorithms for drug development.

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features.

Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA's recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches - one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features - to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, ρ, of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task (ρ= 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs.

Autonomous IL-36R signaling in neutrophils activates potent antitumor effector functions.

While the rapid advancement of immunotherapies has revolutionized cancer treatment, only a small fraction of patients derive clinical benefit. Eradication of large, established tumors appears to depend on engaging and activating both innate and adaptive immune system components to mount a rigorous and comprehensive immune response. Identifying such agents is a high unmet medical need, because they are sparse in the therapeutic landscape of cancer treatment. Here, we report that IL-36 cytokine can engage both innate and adaptive immunity to remodel an immune-suppressive tumor microenvironment (TME) and mediate potent antitumor immune responses via signaling in host hematopoietic cells. Mechanistically, IL-36 signaling modulates neutrophils in a cell-intrinsic manner to greatly enhance not only their ability to directly kill tumor cells but also promote T and NK cell responses. Thus, while poor prognostic outcomes are typically associated with neutrophil enrichment in the TME, our results highlight the pleiotropic effects of IL-36 and its therapeutic potential to modify tumor-infiltrating neutrophils into potent effector cells and engage both the innate and adaptive immune system to achieve durable antitumor responses in solid tumors.

Dioxygen activation in soluble methane monooxygenase.

The controlled oxidation of methane to methanol is a chemical transformation of great value, particularly in the pursuit of alternative fuels, but the reaction remains underutilized industrially because of inefficient and costly synthetic procedures. In contrast, methane monooxygenase enzymes (MMOs) from methanotrophic bacteria achieve this chemistry efficiently under ambient conditions. In this Account, we discuss the first observable step in the oxidation of methane at the carboxylate-bridged diiron active site of the soluble MMO (sMMO), namely, the reductive activation of atmospheric O(2). The results provide benchmarks against which the dioxygen activation mechanisms of other bacterial multicomponent monooxygenases can be measured. Molecular oxygen reacts rapidly with the reduced diiron(II) cen-ter of the hydroxylase component of sMMO (MMOH). The first spectroscopically characterized intermediate that results from this process is a peroxodiiron(III) species, P*, in which the iron atoms have identical environments. P* converts to a second peroxodiiron(III) unit, H(peroxo), in a process accompanied by the transfer of a proton, probably with the assistance of a residue near the active site. Proton-promoted O-O bond scission and rearrangement of the diiron core then leads to a diiron(IV) unit, termed Q, that is directly responsible for the oxidation of methane to methanol. In one section of this Account, we provide a detailed discussion of these processes, with particular emphasis on possible structures of the intermediates. The geometries of P* and H(peroxo) are currently unknown, and recent synthetic modeling chemistry has highlighted the need for further structural characterization of Q, currently assigned as a di(µ-oxo)diiron(IV) "diamond core." In another section of the Account, we discuss in detail proton transfer during the O(2) activation events. The role of protons in promoting O-O bond cleavage, thereby initiating the conversion of H(peroxo) to Q, was previously a controversial topic. Recent studies of the mechanism, covering a range of pH values and in D(2)O instead of H(2)O, confirmed conclusively that the transfer of protons, possibly at or near the active site, is necessary for both P*-to-H(peroxo) and H(peroxo)-to-Q conversions. Specific mechanistic insights into these processes are provided. In the final section of the Account, we present our view of experiments that need to be done to further define crucial aspects of sMMO chemistry. Here our goal is to detail the challenges that we and others face in this research, particularly with respect to some long-standing questions about the system, as well as approaches that might be used to solve them.

Multiple roles of component proteins in bacterial multicomponent monooxygenases: phenol hydroxylase and toluene/o-xylene monooxygenase from Pseudomonas sp. OX1.

Phenol hydroxylase (PH) and toluene/o-xylene monooxygenase (ToMO) from Pseudomonas sp. OX1 require three or four protein components to activate dioxygen for the oxidation of aromatic substrates at a carboxylate-bridged diiron center. In this study, we investigated the influence of the hydroxylases, regulatory proteins, and electron-transfer components of these systems on substrate (phenol; NADH) consumption and product (catechol; H(2)O(2)) generation. Single-turnover experiments revealed that only complete systems containing all three or four protein components are capable of oxidizing phenol, a major substrate for both enzymes. Under ideal conditions, the hydroxylated product yield was ∼50% of the diiron centers for both systems, suggesting that these enzymes operate by half-sites reactivity mechanisms. Single-turnover studies indicated that the PH and ToMO electron-transfer components exert regulatory effects on substrate oxidation processes taking place at the hydroxylase actives sites, most likely through allostery. Steady state NADH consumption assays showed that the regulatory proteins facilitate the electron-transfer step in the hydrocarbon oxidation cycle in the absence of phenol. Under these conditions, electron consumption is coupled to H(2)O(2) formation in a hydroxylase-dependent manner. Mechanistic implications of these results are discussed.

Oxidation reactions performed by soluble methane monooxygenase hydroxylase intermediates H(peroxo) and Q proceed by distinct mechanisms.

Soluble methane monooxygenase is a bacterial enzyme that converts methane to methanol at a carboxylate-bridged diiron center with exquisite control. Because the oxidizing power required for this transformation is demanding, it is not surprising that the enzyme is also capable of hydroxylating and epoxidizing a broad range of hydrocarbon substrates in addition to methane. In this work we took advantage of this promiscuity of the enzyme to gain insight into the mechanisms of action of H(peroxo) and Q, two oxidants that are generated sequentially during the reaction of reduced protein with O(2). Using double-mixing stopped-flow spectroscopy, we investigated the reactions of the two intermediate species with a panel of substrates of varying C-H bond strength. Three classes of substrates were identified according to the rate-determining step in the reaction. We show for the first time that an inverse trend exists between the rate constant of reaction with H(peroxo) and the C-H bond strength of the hydrocarbon examined for those substrates in which C-H bond activation is rate-determining. Deuterium kinetic isotope effects revealed that reactions performed by Q, but probably not H(peroxo), involve extensive quantum mechanical tunneling. This difference sheds light on the observation that H(peroxo) is not a sufficiently potent oxidant to hydroxylate methane, whereas Q can perform this reaction in a facile manner. In addition, the reaction of H(peroxo) with acetonitrile appears to proceed by a distinct mechanism in which a cyanomethide anionic intermediate is generated, bolstering the argument that H(peroxo) is an electrophilic oxidant that operates via two-electron transfer chemistry.

Characterization of iron dinitrosyl species formed in the reaction of nitric oxide with a biological Rieske center.

Reactions of nitric oxide with cysteine-ligated iron-sulfur cluster proteins typically result in disassembly of the iron-sulfur core and formation of dinitrosyl iron complexes (DNICs). Here we report the first evidence that DNICs also form in the reaction of NO with Rieske-type [2Fe-2S] clusters. Upon treatment of a Rieske protein, component C of toluene/o-xylene monooxygenase from Pseudomonas sp. OX1, with an excess of NO(g) or NO-generators S-nitroso-N-acetyl-D,L-pencillamine and diethylamine NONOate, the absorbance bands of the [2Fe-2S] cluster are extinguished and replaced by a new feature that slowly grows in at 367 nm. Analysis of the reaction products by electron paramagnetic resonance, Mössbauer, and nuclear resonance vibrational spectroscopy reveals that the primary product of the reaction is a thiolate-bridged diiron tetranitrosyl species, [Fe(2)(µ-SCys)(2)(NO)(4)], having a Roussin's red ester (RRE) formula, and that mononuclear DNICs account for only a minor fraction of nitrosylated iron. Reduction of this RRE reaction product with sodium dithionite produces the one-electron-reduced RRE, having absorptions at 640 and 960 nm. These results demonstrate that NO reacts readily with a Rieske center in a protein and suggest that dinuclear RRE species, not mononuclear DNICs, may be the primary iron dinitrosyl species responsible for the pathological and physiological effects of nitric oxide in such systems in biology.

Identification of protein-bound dinitrosyl iron complexes by nuclear resonance vibrational spectroscopy.

We have applied (57)Fe nuclear resonance vibrational spectroscopy (NRVS) to identify protein-bound dinitrosyl iron complexes. Intense NRVS peaks due to vibrations of the N-Fe-N unit can be observed between 500 and 700 cm(-1) and are diagnostic indicators of the type of iron dinitrosyl species present. NRVS spectra for four iron dinitrosyl model compounds are presented and used as benchmarks for the identification of species formed in the reaction of Pyrococcus furiosus ferredoxin D14C with nitric oxide.

Revisiting the mechanism of dioxygen activation in soluble methane monooxygenase from M. capsulatus (Bath): evidence for a multi-step, proton-dependent reaction pathway.

Stopped-flow kinetic investigations of soluble methane monooxygenase (sMMO) from M. capsulatus (Bath) have clarified discrepancies that exist in the literature regarding several aspects of catalysis by this enzyme. The development of thorough kinetic analytical techniques has led to the discovery of two novel oxygenated iron species that accumulate in addition to the well-established intermediates H(peroxo) and Q. The first intermediate, P*, is a precursor to H(peroxo) and was identified when the reaction of reduced MMOH and MMOB with O(2) was carried out in the presence of >or=540 microM methane to suppress the dominating absorbance signal due to Q. The optical properties of P* are similar to those of H(peroxo), with epsilon(420) = 3500 M(-1) cm(-1) and epsilon(720) = 1250 M(-1) cm(-1). These values are suggestive of a peroxo-to-iron(III) charge-transfer transition and resemble those of peroxodiiron(III) intermediates characterized in other carboxylate-bridged diiron proteins and synthetic model complexes. The second identified intermediate, Q*, forms on the pathway of Q decay when reactions are performed in the absence of hydrocarbon substrate. Q* does not react with methane, forms independently of buffer composition, and displays a unique shoulder at 455 nm in its optical spectrum. Studies conducted at different pH values reveal that rate constants corresponding to P* decay/H(peroxo) formation and H(peroxo) decay/Q formation are both significantly retarded at high pH and indicate that both events require proton transfer. The processes exhibit normal kinetic solvent isotope effects (KSIEs) of 2.0 and 1.8, respectively, when the reactions are performed in D(2)O. Mechanisms are proposed to account for the observations of these novel intermediates and the proton dependencies of P* to H(peroxo) and H(peroxo) to Q conversion.

Engineered Biosensors from Dimeric Ligand-Binding Domains.

Biosensors are important components of many synthetic biology and metabolic engineering applications. Here, we report a second generation of Saccharomyces cerevisiae digoxigenin and progesterone biosensors based on destabilized dimeric ligand-binding domains that undergo ligand-induced stabilization. The biosensors, comprising one ligand-binding domain monomer fused to a DNA-binding domain and another fused to a transcriptional activation domain, activate reporter gene expression in response to steroid binding and receptor dimerization. The introduction of a destabilizing mutation to the dimer interface increased biosensor dynamic range by an order of magnitude. Computational redesign of the dimer interface and functional selections were used to create heterodimeric pairs with further improved dynamic range. A heterodimeric biosensor built from the digoxigenin and progesterone ligand-binding domains functioned as a synthetic "AND"-gate, with 20-fold stronger response to the two ligands in combination than to either one alone. We also identified mutations that increase the sensitivity or selectivity of the biosensors to chemically similar ligands. These dimerizing biosensors provide additional flexibility for the construction of logic gates and other applications.

Computational Design of Ligand Binding Proteins.

The ability to design novel small-molecule binding sites in proteins is a stringent test of our understanding of the principles of molecular recognition, and would have many practical applications, in synthetic biology and medicine. Here, we describe a computational method in the context of the macromolecular modeling suite Rosetta to designing proteins with sites featuring predetermined interactions to ligands of choice. The required inputs for the method are a model of the small molecule and the desired interactions (e.g., hydrogen bonding, electrostatics, steric packing), and a set of crystallographic structures of proteins containing existing or predicted binding pockets. Constellations of backbones surrounding the putative pocket are searched for compatibility with the desired binding site conception using RosettaMatch and surrounding amino acid side chain identities are optimized using RosettaDesign. Validation of the design is performed using metrics that evaluate the interface energy of the predicted binding pose, the preformation of key binding site features in the apo-state, and the local compatibility of the designed sequence changes with the wild type backbone structure, and top-ranking candidate designs are generated for experimental validation. This approach can allow for the creation of novel binding sites and for the rational tuning of specificity for congeneric ligands by altering the programmed interactions by design, thus offering a general computational protocol for construction and modulation of protein-small molecule interfaces.

Single amino acid substitution in LC-CDR1 induces Russell body phenotype that attenuates cellular protein synthesis through eIF2α phosphorylation and thereby downregulates IgG secretion despite operational secretory pathway traffic.

Amino acid sequence differences in the variable region of immunoglobulin (Ig) cause wide variations in secretion outputs. To address how a primary sequence difference comes to modulate Ig secretion, we investigated the biosynthetic process of 2 human IgG2κ monoclonal antibodies (mAbs) that differ only by one amino acid in the light chain complementarity-determining region 1 while showing ∼20-fold variance in secretion titer. Although poorly secreted, the lower-secreting mAb of the 2 was by no means defective in terms of its folding stability, antigen binding, and in vitro biologic activity. However, upon overexpression in HEK293 cells, the low-secreting mAb revealed a high propensity to aggregate into enlarged globular structures called Russell bodies (RBs) in the endoplasmic reticulum. While Golgi morphology was affected by the formation of RBs, secretory pathway membrane traffic remained operational in those cells. Importantly, cellular protein synthesis was severely suppressed in RB-positive cells through the phosphorylation of eIF2α. PERK-dependent signaling was implicated in this event, given the upregulation and nuclear accumulation of downstream effectors such as ATF4 and CHOP. These findings illustrated that the underlining process of poor Ig secretion in RB-positive cells was due to downregulation of Ig synthesis instead of a disruption or blockade of secretory pathway trafficking. Therefore, RB formation signifies an end of active Ig production at the protein translation level. Consequently, depending on how soon and how severely an antibody-expressing cell develops the RB phenotype, the productive window of Ig secretion can vary widely among the cells expressing different mAbs.

Improving Binding Affinity and Selectivity of Computationally Designed Ligand-Binding Proteins Using Experiments.

The ability to de novo design proteins that can bind small molecules has wide implications for synthetic biology and medicine. Combining computational protein design with the high-throughput screening of mutagenic libraries of computationally designed proteins is emerging as a general approach for creating binding proteins with programmable binding modes, affinities, and selectivities. The computational step enables the creation of a binding site in a protein that otherwise does not (measurably) bind the intended ligand, and targeted mutagenic screening allows for validation and refinement of the computational model as well as provides orders-of-magnitude increases in the binding affinity. Deep sequencing of mutagenic libraries can provide insights into the mutagenic binding landscape and enable further affinity improvements. Moreover, in such a combined computational-experimental approach where the binding mode is preprogrammed and iteratively refined, selectivity can be achieved (and modulated) by the placement of specified amino acid side chain groups around the ligand in defined orientations. Here, we describe the experimental aspects of a combined computational-experimental approach for designing-using the software suite Rosetta-proteins that bind a small molecule of choice and engineering, using fluorescence-activated cell sorting and high-throughput yeast surface display, high affinity and ligand selectivity. We illustrated the utility of this approach by performing the design of a selective digoxigenin (DIG)-binding protein that, after affinity maturation, binds DIG with picomolar affinity and high selectivity over structurally related steroids.

A general strategy to construct small molecule biosensors in eukaryotes.

Biosensors for small molecules can be used in applications that range from metabolic engineering to orthogonal control of transcription. Here, we produce biosensors based on a ligand-binding domain (LBD) by using a method that, in principle, can be applied to any target molecule. The LBD is fused to either a fluorescent protein or a transcriptional activator and is destabilized by mutation such that the fusion accumulates only in cells containing the target ligand. We illustrate the power of this method by developing biosensors for digoxin and progesterone. Addition of ligand to yeast, mammalian, or plant cells expressing a biosensor activates transcription with a dynamic range of up to ~100-fold. We use the biosensors to improve the biotransformation of pregnenolone to progesterone in yeast and to regulate CRISPR activity in mammalian cells. This work provides a general methodology to develop biosensors for a broad range of molecules in eukaryotes.