ABSTRACT
A copper-catalyzed direct ortho-Csp2-H thiocyanation of free anilines has been developed. This method employs stable and non-toxic ammonium thiocyanate as a thiocyanation source, and tert-butyl hydroperoxide as the oxidant, enabling the synthesis of ortho-thiocyanated anilines with good yields and broad substrate tolerance. Hitherto, no reports have been found in the literature for the ortho-thiocyanation of aromatic amines, making this reaction an important breakthrough in synthetic organic chemistry.
ABSTRACT
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.
Subject(s)
Deep Learning , Nucleic Acids , Protein Binding , Proteins/chemistry , MutationABSTRACT
This article provides a comprehensive review and sequence-structure analysis of transcription regulator (TR) families, TetR and OmpR/PhoB, involved in specialized secondary metabolite (SSM) biosynthesis and resistance. Transcription regulation is a fundamental process, playing a crucial role in orchestrating gene expression to confer a survival advantage in response to frequent environmental stress conditions. This process, coupled with signal sensing, enables bacteria to respond to a diverse range of intra and extracellular signals. Thus, major bacterial signaling systems use a receptor domain to sense chemical stimuli along with an output domain responsible for transcription regulation through DNA-binding. Sensory and output domains on a single polypeptide chain (one component system, OCS) allow response to stimuli by allostery, that is, DNA-binding affinity modulation upon signal presence/absence. On the other hand, two component systems (TCSs) allow cross-talk between the sensory and output domains as they are disjoint and transmit information by phosphorelay to mount a response. In both cases, however, TRs play a central role. Biosynthesis of SSMs, which includes antibiotics, is heavily regulated by TRs as it diverts the cell's resources towards the production of these expendable compounds, which also have clinical applications. These TRs have evolved to relay information across specific signals and target genes, thus providing a rich source of unique mechanisms to explore towards addressing the rapid escalation in antimicrobial resistance (AMR). Here, we focus on the TetR and OmpR family TRs, which belong to OCS and TCS, respectively. These TR families are well-known examples of regulators in secondary metabolism and are ubiquitous across different bacteria, as they also participate in a myriad of cellular processes apart from SSM biosynthesis and resistance. As a result, these families exhibit higher sequence divergence, which is also evident from our bioinformatic analysis of 158 389 and 77 437 sequences from TetR and OmpR family TRs, respectively. The analysis of both sequence and structure allowed us to identify novel motifs in addition to the known motifs responsible for TR function and its structural integrity. Understanding the diverse mechanisms employed by these TRs is essential for unraveling the biosynthesis of SSMs. This can also help exploit their regulatory role in biosynthesis for significant pharmaceutical, agricultural, and industrial applications.
ABSTRACT
Actinobacteria produce numerous antibiotics and other specialized metabolites that have important applications in medicine and agriculture1. Diffusible hormones frequently control the production of such metabolites by binding TetR family transcriptional repressors (TFTRs), but the molecular basis for this remains unclear2. The production of methylenomycin antibiotics in Streptomyces coelicolor A3(2) is initiated by the binding of 2-alkyl-4-hydroxymethylfuran-3-carboxylic acid (AHFCA) hormones to the TFTR MmfR3. Here we report the X-ray crystal structure of an MmfR-AHFCA complex, establishing the structural basis for hormone recognition. We also elucidate the mechanism for DNA release upon hormone binding through the single-particle cryo-electron microscopy structure of an MmfR-operator complex. DNA binding and release assays with MmfR mutants and synthetic AHFCA analogues define the role of individual amino acid residues and hormone functional groups in ligand recognition and DNA release. These findings will facilitate the exploitation of actinobacterial hormones and their associated TFTRs in synthetic biology and in the discovery of new antibiotics.
Subject(s)
Anti-Bacterial Agents/biosynthesis , Furans/metabolism , Streptomyces coelicolor/metabolism , Apoproteins/chemistry , Apoproteins/metabolism , Apoproteins/ultrastructure , Bacterial Proteins/chemistry , Bacterial Proteins/classification , Bacterial Proteins/metabolism , Bacterial Proteins/ultrastructure , Cryoelectron Microscopy , Crystallography, X-Ray , DNA/chemistry , DNA/genetics , DNA/metabolism , DNA/ultrastructure , Furans/chemistry , Hormones/chemistry , Hormones/classification , Hormones/metabolism , Ligands , Models, Molecular , Peptides/metabolism , Repressor Proteins/chemistry , Repressor Proteins/classification , Repressor Proteins/metabolism , Repressor Proteins/ultrastructure , Signal Transduction , Streptomyces coelicolor/chemistry , Streptomyces coelicolor/genetics , Structure-Activity RelationshipABSTRACT
In Streptomycetes, tetracycline repressor family of transcription regulators (TetR-FTRs) controls various biological processes including antibiotic biosynthesis, cellular morphology and innate resistance. Here, we focus on understanding the structural basis of transcription regulation by CprB, a member of TetR-FTRs from S. coelicolor. CprB is implicated as a receptor of γ-butyrolactones, a class of quorum sensing molecules, responsible for initiating secondary metabolic pathways. In order to understand the molecular mechanism of DNA recognition, the X-ray structure of CprB in complex with its biological relevant operator sequence was solved to a resolution of 3.95Å. Furthermore, to refine and compliment the results, atomistic molecular dynamics simulations were carried out using the X-ray structure as the template. The studies reveal that CprB binds to DNA as dimer of dimers with this mode of interaction results in minimal distortion in the DNA, enabling these proteins to recognize multiple sequences with varying affinity. Another crucial finding from our simulation results was that the positively charged N-terminal arm of CprB brings extra stability to the protein-DNA complex by interacting with the minor-groove of the DNA and anchoring itself to the phosphate backbone. Corroborating electrophoretic mobility shift assay and fluorescence anisotropy experiments showed that the mutant ΔN6-CprB exhibited about 7-8 fold reduced DNA binding. Comparison with other TetR-FTRs reveals that this strategy is also employed by over 25% of TetR-FTRs, where N-terminal anchoring mechanism is used to enhance selectivity for a particular DNA sequence.
Subject(s)
Bacterial Proteins/chemistry , DNA-Binding Proteins/chemistry , Streptomyces coelicolor/chemistry , Trans-Activators/chemistry , Bacterial Proteins/metabolism , Binding Sites , Crystallography, X-Ray , Molecular Dynamics Simulation , Molecular Structure , Protein Stability , Trans-Activators/metabolismABSTRACT
Antibiotic production and resistance pathways in Streptomyces are dictated by the interplay of transcriptional regulatory proteins that trigger downstream responses via binding to small diffusible molecules. To decipher the mode of DNA binding and the associated allosteric mechanism in the sub-class of transcription factors that are induced by γ-butyrolactones, we present the crystal structure of CprB in complex with the consensus DNA element to a resolution of 3.25 Å. Binding of the DNA results in the restructuring of the dimeric interface of CprB, inducing a pendulum-like motion of the helix-turn-helix motif that inserts into the major groove. The crystal structure revealed that, CprB is bound to DNA as a dimer of dimers with the mode of binding being analogous to the broad spectrum multidrug transporter protein QacR from the antibiotic resistant strain Staphylococcus aureus. It was demonstrated that the CprB displays a cooperative mode of DNA binding, following a clamp and click model. Experiments performed on a subset of DNA sequences from Streptomyces coelicolor A3(2) suggest that CprB is most likely a pleiotropic regulator. Apart from serving as an autoregulator, it is potentially a part of a network of proteins that modulates the γ-butyrolactone synthesis and antibiotic regulation pathways in S. coelicolor A3(2).