Design and Characterization of a Generalist Biosensor for Indole Derivatives.

Transcription factor (TF)-based biosensors are useful synthetic biology tools for applications in a variety of areas of biotechnology. A major challenge of biosensor circuits is the limited repertoire of identified and well-characterized TFs for applications of interest, in addition to the challenge of optimizing selected biosensors. In this work, we implement the IclR family repressor TF TtgV from Pseudomonas putida DOT-T1E as an indole-derivative biosensor in Escherichia coli. We optimize the genetic circuit utilizing different components, providing insights into biosensor design and expanding on previous studies investigating this TF. We discover novel physiologically relevant ligands of TtgV, such as skatole. The broad specificity of TtgV makes it a useful target for directed evolution and protein engineering toward desired specificity. TtgV, as an indole-derivative biosensor, is a promising genetic component for the detection of compounds with biological activities relevant to health and the gut microbiome.

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors.

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection.

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.

Molecular mechanism of plasmid-borne resistance to sulfonamide antibiotics.

The sulfonamides (sulfas) are the oldest class of antibacterial drugs and inhibit the bacterial dihydropteroate synthase (DHPS, encoded by folP), through chemical mimicry of its co-substrate p-aminobenzoic acid (pABA). Resistance to sulfa drugs is mediated either by mutations in folP or acquisition of sul genes, which code for sulfa-insensitive, divergent DHPS enzymes. While the molecular basis of resistance through folP mutations is well understood, the mechanisms mediating sul-based resistance have not been investigated in detail. Here, we determine crystal structures of the most common Sul enzyme types (Sul1, Sul2 and Sul3) in multiple ligand-bound states, revealing a substantial reorganization of their pABA-interaction region relative to the corresponding region of DHPS. We use biochemical and biophysical assays, mutational analysis, and in trans complementation of E. coli ΔfolP to show that a Phe-Gly sequence enables the Sul enzymes to discriminate against sulfas while retaining pABA binding and is necessary for broad resistance to sulfonamides. Experimental evolution of E. coli results in a strain harboring a sulfa-resistant DHPS variant that carries a Phe-Gly insertion in its active site, recapitulating this molecular mechanism. We also show that Sul enzymes possess increased active site conformational dynamics relative to DHPS, which could contribute to substrate discrimination. Our results reveal the molecular foundation for Sul-mediated drug resistance and facilitate the potential development of new sulfas less prone to resistance.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection.

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

High-Throughput Chemical Screen Identifies a 2,5-Disubstituted Pyridine as an Inhibitor of Candida albicans Erg11.

Fungal infections contribute to over 1.5 million deaths annually, with Candida albicans representing one of the most concerning human fungal pathogens. While normally commensal in nature, compromise of host immunity can result in C. albicans disseminating into the human bloodstream, causing infections with mortality rates of up to 40%. A contributing factor to this high mortality rate is the limited arsenal of antifungals approved to treat systemic infections. The most widely used antifungal class, the azoles, inhibits ergosterol biosynthesis by targeting Erg11. The rise of drug resistance among C. albicans clinical isolates, particularly against the azoles, has escalated the need to explore novel antifungal strategies. To address this challenge, we screened a 9,600-compound subset of the University of Tokyo Core Chemical Library to identify molecules with novel antifungal activity against C. albicans. The most potent hit molecule was CpdLC-6888, a 2,5-disubstituted pyridine compound, which inhibited growth of C. albicans and closely-related species. Chemical-genetic, biochemical, and modeling analyses suggest that CpdLC-6888 inhibits Erg11 in a manner similar to the azoles despite lacking the canonical five-membered nitrogen-containing azole ring. This work characterizes the antifungal activity of a 2,5-disubstituted pyridine against C. albicans, supporting the mining of existing chemical collections to identify compounds with novel antifungal activity. IMPORTANCE Pathogenic fungi represent a serious but underacknowledged threat to human health. The treatment and management of these infections relies heavily on the use of azole antifungals, a class of molecules that contain a five-membered nitrogen-containing ring and inhibit the biosynthesis of the key membrane sterol ergosterol. By employing a high-throughput chemical screen, we identified a 2,5-disubstituted pyridine, termed CpdLC-6888, as possessing antifungal activity against the prominent human fungal pathogen Candida albicans. Upon further investigation, we determined this molecule exhibits azole-like activity despite being structurally divergent. Specifically, transcriptional repression of the azole target gene ERG11 resulted in hypersensitivity to CpdLC-6888, and treatment of C. albicans with this molecule blocked the production of the key membrane sterol ergosterol. Therefore, this work describes a chemical scaffold with novel antifungal activity against a prevalent and threatening fungal pathogen affecting human health, expanding the repertoire of compounds that can inhibit this useful antifungal drug target.

SARS-CoV-2 receptor-binding mutations and antibody contact sites.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations can impact infectivity, viral load, and overall morbidity/mortality during infection. In this analysis, we look at the mutational landscape of the SARS-CoV-2 receptor-binding domain, a structure that is antigenic and allows for viral binding to the host. We develop a bioinformatics platform and analyze 104 193 Global Initiative on Sharing All Influenza Data sequences acquired on 15 October 2020, with a majority of sequences (96%) containing point mutations. We report high frequency mutations with improved binding affinity to ACE2 including S477N, N439K, V367F, and N501Y and address the potential impact of RBD mutations on antibody binding. The high frequency S477N mutation is present in 6.7% of all SARS-CoV-2 sequences, co-occurs with D614G, and is currently present in 14 countries. To address RBD-antibody interactions, we take a subset of human-derived antibodies and define their interacting residues using PDBsum. Our analysis shows that RBD mutations were found in approximately 9% of our dataset, with some mutations improving RBD-ACE2 interactions. We also show that antibody-mediated immunity against SARS-CoV-2 enlists broad coverage of the RBD, with multiple antibodies targeting a variety of RBD regions. These data suggest that it is unlikely for neutralization/RBD antibody binding to be significantly impacted, as a whole, in the presence of RBD point mutations that conserve the RBD structure.