Class Of Substrates Research Articles

High-throughput computational investigation of protein electrostatics and cavity for SAM-dependent methyltransferases.

S-adenosyl methionine (SAM)-dependent methyl transferases (MTases) are a ubiquitous class of enzymes catalyzing dozens of essential life processes. Despite targeting a large space of substrates with diverse intrinsic reactivity, SAM MTases have similar catalytic efficiency. While understanding of MTase mechanism has grown tremendously through the integration of structural characterization, kinetic assays, and multiscale simulations, it remains elusive how these enzymes have evolved to fit the diverse chemical needs of their respective substrates. In this work, we performed a high-throughput molecular modeling analysis of 91 SAM MTases to better understand how their properties (i.e., electric field [EF] strength and active site volumes) help achieve similar catalytic efficiency toward substrates of different reactivity. We found that EF strengths have largely adjusted to make the target atom a better methyl acceptor. For MTases that target RNA/DNA and histone proteins, our results suggest that EF strength accommodates formal hybridization state and variation in cavity volume trends with diversity of substrate classes. Metal ions in SAM MTases contribute negatively to EF strength for methyl donation and enzyme scaffolds tend to offset these contributions.

Sensitive and reproducible on-chip SERS detection by side-polished fiber probes integrated with microfluidic chips

The combination of surface-enhanced Raman scattering (SERS) and microfluidic technology offers a promising solution to improve the reproducibility and reliability of SERS detection. Optical fiber SERS probes, as an important class of SERS substrates, provide a large SERS interaction area and flexible integration with microfluidic chips. In this article, a side-polished multimode fiber (SPMF) SERS probe is prepared by depositing Au nanorods on a planar polished fiber surface and integrating it into a microfluidic channel to form a glass-based microfluidic-SERS chip. The impact of SPMF residual thickness on SERS performance is thoroughly investigated, and the optimized residual thickness of 62 μm is determined by considering the evanescent wave intensity, transmission loss, and experimental feasibility. The optimized microfluidic-SERS chip shows low detection limits (as low as 10-8 M for MG) and good spectral reproducibility (RSD less than 10%) due to the large SERS interaction area provided by the SPMF SERS probe and the stable liquid flow in the microfluidic channel. The chip is furtherly used to detect pesticide and antibiotic residues in tap water, with the detection limits of 10-9 M for thiram and 10-6 M for levofloxacin. This work provides an effective way to realize highly sensitive and reproducible SERS detection, and may find important applications in environmental science, food safety, and biomedicine.

Physico-Chemical Quality and Physiological Profiles of Microbial Communities in Freshwater Systems of Mega Manila, Philippines

Studying the quality of freshwater systems and drinking water in highly urbanized megalopolises around the world remains a challenge. This article reports data on the quality of select freshwater systems in Mega Manila, Philippines. Water samples collected between 2020 and 2021 were analyzed for physico-chemical parameters and microbial community metabolic fingerprints, i.e., carbon substrate utilization patterns (CSUPs). The detection of arsenic, lead, cadmium, mercury, polyaromatic hydrocarbons (PAHs), and organochlorine pesticides (OCPs) was carried out using standard chromatography- and spectroscopy-based protocols. Physiological profiles were determined using the Biolog EcoPlate™ system. Eight samples were free of heavy metals, and none contained PAHs or OCPs. Fourteen samples had high microbial activity, as indicated by average well color development (AWCD) and community metabolic diversity (CMD) values. Community-level physiological profiling (CLPP) revealed that (1) samples clustered as groups according to shared CSUPs, and (2) microbial communities in non-drinking samples actively utilized all six substrate classes compared to drinking samples. The data reported here can provide a baseline or a comparator for prospective quality assessments of drinking water and freshwater sources in the region. Metabolic fingerprinting using CSUPs is a simple and cheap phenotypic analysis of microbial communities and their physiological activity in aquatic environments.

Radical Hydrocarboxylation of Unactivated Alkenes via Photocatalytic Formate Activation.

Herein we disclose a strategy to promote the hydrocarboxylation of unactivated alkenes using photochemical activation of formate salts. We illustrate that an alternative initiation mechanism circumvents the limitations of prior approaches and enables hydrocarboxylation of this challenging substrate class. Specifically, we found that accessing the requisite thiyl radical initiator without an exogenous chromophore eliminates major byproducts that have plagued attempts to exploit similar reactivity for unactivated alkene substrates. This redox-neutral method is technically simple to execute and effective across a broad range of alkene substrates. Feedstock alkenes, such as ethylene, are hydrocarboxylated at ambient temperature and pressure. A series of radical cyclization experiments indicate how the reactivity described in this report can be diverted by more complex radical processes.

A rising tide lifts all MBOATs: recent progress in structural and functional understanding of membrane bound O-acyltransferases.

Acylation modifications play a central role in biological and physiological processes. Across a range of biomolecules from phospholipids to triglycerides to proteins, introduction of a hydrophobic acyl chain can dramatically alter the biological function and cellular localization of these substrates. Amongst the enzymes catalyzing these modifications, the membrane bound O-acyltransferase (MBOAT) family occupies an intriguing position as the combined substrate selectivities of the various family members span all three classes of these biomolecules. MBOAT-dependent substrates are linked to a wide range of health conditions including metabolic disease, cancer, and neurodegenerative disease. Like many integral membrane proteins, these enzymes have presented challenges to investigation due to their intractability to solubilization and purification. However, over the last several years new solubilization approaches coupled with computational modeling, crystallography, and cryoelectron microscopy have brought an explosion of structural information for multiple MBOAT family members. These studies enable comparison of MBOAT structure and function across members catalyzing modifications of all three substrate classes, revealing both conserved features amongst all MBOATs and distinct architectural features that correlate with different acylation substrates ranging from lipids to proteins. We discuss the methods that led to this renaissance of MBOAT structural investigations, our new understanding of MBOAT structure and implications for catalytic function, and the potential impact of these studies for development of new therapeutics targeting MBOAT-dependent physiological processes.

Comparative Biochemical Studies of Disease-Associated Human Dicer Mutations on Processing of a Pre-microRNA and snoRNA.

Dicer is an RNase III enzyme that is responsible for the maturation of small RNAs such as microRNAs. As Dicer's cleavage products play key roles in promoting cellular homeostasis through the fine-tuning of gene expression, dysregulation of Dicer activity can lead to several human diseases, including cancers. Mutations in Dicer have been found to induce tumorigenesis and lead to the development of a rare pleiotropic tumor predisposition syndrome found in children and young adults called DICER1 syndrome. These patients harbor germline and somatic mutations in Dicer that lead to defective microRNA processing and activity. While most mutations occur within Dicer's catalytic RNase III domains, alterations within the Platform-PAZ (Piwi-Argonaute-Zwille) domain also cause loss of microRNA production. Using a combination of in vitro biochemical and cellular studies, we characterized the effect of disease-relevant Platform-PAZ-associated mutations on the processing of a well-studied oncogenic microRNA, pre-microRNA-21. We then compared these results to those of a representative from another Dicer substrate class, the small nucleolar RNA, snord37. From this analysis, we provide evidence that mutations within the Platform-PAZ domain result in differential impacts on RNA binding and processing, adding new insights into the complexities of Dicer processing of small RNA substrates.

SLCO family of organic anion transporting polypeptides in GtoPdb v.2023.1

The SLCO superfamily is comprised of the organic anion transporting polypeptides (OATPs). The 11 human OATPs are divided into 6 families and ten subfamilies based on amino acid identity. These proteins are located on the plasma membrane of cells throughout the body. They have 12 TM domains and intracellular termini, with multiple putative glycosylation sites. OATPs mediate the sodium-independent uptake of a wide range of amphiphilic substrates, including many drugs and toxins. Due to the multispecificity of these proteins, this guide lists classes of substrates and inhibitors for each family member. More comprehensive lists of substrates, inhibitors, and their relative affinities may be found in the review articles listed below.

A benthic substrate classification method for seabed images using deep learning: Application to management of deep‐sea coral reefs

Abstract Protecting deep‐sea coral‐based vulnerable marine ecosystems (VMEs) from human impacts, particularly bottom trawling, is a major conservation challenge in world oceans. Management processes for these ecosystems are weakened by key uncertainties that could be substantially addressed by having much greater volumes of quantitative image‐derived data that detail the distribution and abundance of coral reefs and the nature of impacts upon them. Considerably greater volumes of data could be available if the resource costs of image annotation are reduced. In this paper we propose a solution: a deep learning system capable of automatically identifying reef‐building stony corals amongst other seabed substrata in much larger volumes of seabed imagery than was previously possible. Using a previously annotated dataset, we trained a convolutional neural network on approximately 70,000 classified images (‘snips’) comprising six benthic substrate classes, including reef‐building stony coral—‘coral matrix’. Model performance improvements, chiefly by dataset cleaning, transfer learning and hyperparameter optimisation, resulted in the final trained model achieving validation accuracy of 98.19%. The classification was robust: benthic substrate types were accurately differentiated, and in some cases more consistently than was achieved by human annotators. Synthesis and applications. The availability of much larger volumes of automatically annotated image‐derived data will improve spatial management of impacts on coral‐based VMEs in the deep sea by (1) improved cross‐validation and performance of spatial models required to predict coral distribution and abundance over the large scales of managed areas, and (2) establishing empirical relationships between coral abundance on the seabed and coral bycatch landed during fishing operations.

Balancing the Seesaw in Mn-Catalyzed N-Heteroarene Hydrogenation: Mechanism-Inspired Catalyst Design for Simultaneous Taming of Activation and Transfer of H2

Recently, Mn-catalyzed hydrogenation reactions have gained significant interest due to their importance in various challenging transformations. Among several classes of organic substrates, including amides, ketones, carbamates, urea-derivatives, (cyclic) carbonates/polycarbonates, CO2, and CO2 derivatives, N-heteroarene hydrogenation using a Mn-based system is severely underdeveloped. Only a handful of reports are known in the literature, which mainly use expensive phosphine-based ligand systems for designing effective Mn catalysts. Mechanistically, for the known pincer Mn catalysts, the H2-activation step was facilitated by a typical metal–ligand bifunctional mechanism through the so-called “Mn-amino”/“Mn-amido” platform, while the H2-transfer step (in the form of hydride and proton transfer) was triggered by judiciously designed stereoelectronically tuned P- and/or N-donor ligands attached at the Mn center. Interestingly, these two steps, i.e., H2-activation and H2-transfer, often counteract owing to opposite stereoelectronic demands at the metal center, and the existing PNP-Mn and NNP-Mn catalysts indeed suffer from disbalanced energetics in the case of these two steps. With a rationally analyzed approach, herein, we show how these two crucial steps can be simultaneously tamed and their energetics can be balanced to optimize the kinetic and thermodynamic demand. Thus, the exchange of both the P and N arms in the PNP/NNP ligand framework with a better σ-donor, moderate π-acceptor, and sterically nonhindering planar ligands such as N-heterocyclic carbenes (NHCs), keeping the central amino/amido-based bifunctional H2-activating motif intact, facilitated both the critical steps in the catalytic cycle by reducing the corresponding activation barriers and balancing the thermodynamic stability of the Mn-hydride species (ΔG# H2-cleavage: 19.2 kcal/mol; ΔG# H2 (hydride/proton)-transfer: 22.2 kcal/mol; ΔG H2-cleavage step: −2.8 kcal/mol). Eventually, the bis-NHC-armed CNC-Mn pincer catalyst, designed through the mechanism-inspired approach, was proved apt in efficient catalytic hydrogenation of a variety of N-heteroarenes, under 10–60 bar of H2 pressure and 60–120 °C temperature within 6–12 h of reaction time.

Mechanism and Selectivity of Copper-Catalyzed Bromination of Distal C(sp3)-H Bonds.

Unactivated C(sp3)-H bonds are the most challenging substrate class for transition metal-catalyzed C-H halogenation. Recently, the Yu group [Liu, T.; Myers, M. C.; Yu, J. Q. Angew. Chem., Int. Ed.2017, 56 (1), 306-309] has demonstrated that a CuII/phenanthroline catalyst and BrN3, generated in situ from NBS and TMSN3 precursors, can achieve selective C-H bromination distal to a directing group. The current understanding of the mechanism of this reaction has left numerous questions unanswered. Here, we investigated the mechanism of Cu-catalyzed C(sp3)-H bromination with distal site selectivity using density functional theory calculations. We found that this reaction starts with the Br-atom transfer from BrN3 to the Cu center that occurs via a small energy barrier at the singlet-triplet state seam of crossing. In the course of this reaction, the presence of the N-H bond in the substrate is critically important and acts as a directing group for enhancing the stability of the catalyst-substrate interaction and for the recruitment of the substrate to the catalyst. The required C-centered radical substrate formation occurs via direct C-H dehydrogenation by the Cu-coordinated N3 radical, rather than via the previously proposed N-H bond dehydrogenation and then the 1,5-H transfer from the γ-(C-H) bond to the N-radical center pathway. The C-H bond activation by the azide radical is a regioselectivity-controlling step. The following bromination of the C-centered radical by the Cu-coordinated bromine completes the product formation. This reaction step is the rate-limiting step, occurs at the singlet-to-triplet state seam of the crossing point, and is exergonic.

Context-Driven Detection of Invertebrate Species in Deep-Sea Video

Each year, underwater remotely operated vehicles (ROVs) collect thousands of hours of video of unexplored ocean habitats revealing a plethora of information regarding biodiversity on Earth. However, fully utilizing this information remains a challenge as proper annotations and analysis require trained scientists’ time, which is both limited and costly. To this end, we present a Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), a benchmark suite and growing large-scale dataset to train, validate, and test methods for temporally localizing four underwater substrates as well as temporally and spatially localizing 59 underwater invertebrate species. DUSIA currently includes over ten hours of footage across 25 videos captured in 1080p at 30 fps by an ROV following pre-planned transects across the ocean floor near the Channel Islands of California. Each video includes annotations indicating the start and end times of substrates across the video in addition to counts of species of interest. Some frames are annotated with precise bounding box locations for invertebrate species of interest, as seen in Fig. 1. To our knowledge, DUSIA is the first dataset of its kind for deep sea exploration, with video from a moving camera, that includes substrate annotations and invertebrate species that are present at significant depths where sunlight does not penetrate. Additionally, we present the novel context-driven object detector (CDD) where we use explicit substrate classification to influence an object detection network to simultaneously predict a substrate and species class influenced by that substrate. We also present a method for improving training on partially annotated bounding box frames. Finally, we offer a baseline method for automating the counting of invertebrate species of interest.

Phylogenomic analyses across land plants reveals motifs and coexpression patterns useful for functional prediction in the BAHD acyltransferase family.

The BAHD acyltransferase family is one of the largest enzyme families in flowering plants, containing dozens to hundreds of genes in individual genomes. Highly prevalent in angiosperm genomes, members of this family contribute to several pathways in primary and specialized metabolism. In this study, we performed a phylogenomic analysis of the family using 52 genomes across the plant kingdom to gain deeper insights into its functional evolution and enable function prediction. We found that BAHD expansion in land plants was associated with significant changes in various gene features. Using pre-defined BAHD clades, we identified clade expansions in different plant groups. In some groups, these expansions coincided with the prominence of metabolite classes such as anthocyanins (flowering plants) and hydroxycinnamic acid amides (monocots). Clade-wise motif-enrichment analysis revealed that some clades have novel motifs fixed on either the acceptor or the donor side, potentially reflecting historical routes of functional evolution. Co-expression analysis in rice and Arabidopsis further identified BAHDs with similar expression patterns, however, most co-expressed BAHDs belonged to different clades. Comparing BAHD paralogs, we found that gene expression diverges rapidly after duplication, suggesting that sub/neo-functionalization of duplicate genes occurs quickly via expression diversification. Analyzing co-expression patterns in Arabidopsis in conjunction with orthology-based substrate class predictions and metabolic pathway models led to the recovery of metabolic processes of most of the already-characterized BAHDs as well as definition of novel functional predictions for some uncharacterized BAHDs. Overall, this study provides new insights into the evolution of BAHD acyltransferases and sets up a foundation for their functional characterization.

Reductive photoredox transformations of carbonyl derivatives enabled by strongly reducing photosensitizers.

Visible-light photoredox catalysis is well-established as a powerful and versatile organic synthesis strategy. However, some substrate classes, despite being attractive precursors, are recalcitrant to single-electron redox chemistry and thus not very amenable to photoredox approaches. Among these are carbonyl derivatives, e.g. ketones, aldehydes, and imines, which in most cases require Lewis or Brønsted acidic additives to activate via photoinduced electron transfer. In this work, we unveil a range of photoredox transformations on ketones and imines, enabled by strongly reducing photosensitizers and operating under simple, general conditions with a single sacrificial reductant and no additives. Specific reactions described here are umpolung C-C bond forming reactions between aromatic ketones or imines and electron-poor alkenes, imino-pinacol homocoupling reactions of challenging alkyl-aryl imine substrates, and γ-lactonization reactions of aromatic ketones with methyl acrylate. The reactions are all initiated by photoinduced electron transfer to form a ketyl or iminyl that is subsequently trapped.

Surface functionalized 3D printed metal structures as next generation recyclable SERS substrates.

Combining the design flexibility and rapid prototyping capabilities of additive manufacturing with photocatalytic and plasmonic functionalities is promising for the development of next-generation SERS applications such as point of care diagnostics and in situ monitoring of chemical reactions in fuels and chemical processing. Laser powder bed fusion (LPBF) is a well-matured additive manufacturing technique which generates metallic structures through localised melting and joining of metal powders using a laser. LPBF reduces material wastage during manufacturing, is applicable to a wide range of metals and alloys, and allows printing of complex internal structures. This feature article elaborates the use of soot templating, chemical vapour deposition and electroless plating techniques for grafting plasmonic and semiconductor nanoparticles on the surface of LPBF manufactured metallic substrates. The capability to fabricate different types of intricate metallic lattices using additive manufacturing is demonstrated and technical challenges in their adequate functionalization are elaborated. The developed methodology allows tailoring of the substrate structure, composition, morphology, plasmonic and photocatalytic activities and thus unveils a new class of recyclable SERS substrates.

Asymmetric Synthesis of Trifluoroethyl-Based, Chiral 3-Benzyloxy-1- and -2-Phenyl-quinazolinones of Biomedicinal Interest by Radical Type Cross-Coupling to Olefins

Several 2-substituted (H, Ph, and S-Me) and 1-substituted (H, Ph, and Bn), 3-hydroxy-1,3-quinazolin(di)ones were utilized for the first time as radical trapping agents in asymmetric 1,2-oxytrifluoromethylation of styrenes catalyzed by chiral vanadyl methoxide complexes bearing 3,5-disubstituted-N-salicylidene-t-leucinate templates. The effects of catalysts and solvents on the asymmetric induction were systematically examined. The best and complementary scenarios involved the use of vanadyl complexes V(O)-1 and V(O)-2, which bear 3-(2,5-dimethyl)phenyl-5-bromophenyl and 3-t-butyl-5-bromophenyl groups in an i-propanol solvent at ambient temperature. The corresponding (R)-cross-coupling products by V(O)-1 were obtained in 45-71% (for 2-substituted series) and 59-93% yields (for 1-substituted series) for p-/m-methylstyrenes and m-halo/CF3/CO2Me-styrenes in 38-63% ees (the best in 2-H case) and 60-84% ees (the best in 1-benzyl cases), respectively. The corresponding (S)-cross-coupling products by V(O)-2 were obtained in 28-55% (for 2-substituted series) and 45-72% yields (for 1-substituted series) for the same substrate class in 50-91% ees (85-91% ees in 2-phenyl cases) and 64-75% ees (up to 74-75% ees for each 1-H, Ph, and Bn cases), respectively. Theoretical calculations were carried out to explain the origin and extent of enantiocontrols. They both may serve as potential inhibitors of acetohydroxyacid synthase and epidermal growth factor receptor (EGFR) kinases.

An expeditious low-cost method for the acoustic characterization of seabeds in a Mediterranean coastal protected area

Posidonia oceanica meadows are ecosystem engineers which, despite their ecological relevance, are experiencing habitat fragmentation and area decrease. Cartography and information on the ecological status of these habitats is key to an effective maritime spatial planning and management for habitat conservation. In this work we apply an acoustic methodology to map benthic habitats (substrate and vegetation) in an archipelago of the Natura 2000 Network close to the coast of Murcia (SE Spain) where dense and sparse areas of P. oceanica, and patches of Cymodocea nodosa appear over a sandy and had bottom. The methodology uses dual frequency information (200 kHz and 38 kHz) acquired with a single-beam echosounder to develop a bathymetry, and performs sea bottom and vegetation supervised classifications, using video and scuba diver observations as groundtruthing data. Sea bottom was classified from acoustic features of the first and second 200 kHz echoes into 5 substrate classes using a random forest classifier: sand, fine sand, coarse sand, hard bottoms and hard bottoms with sandy patches. The vegetation was classified from features extracted from the "above-bottom" part of the echo (height and backscattering intensity) in both frequencies, resulting also in a 5 class classification: C. nodosa meadows, dense P. oceanica meadows, dispersed P. oceanica meadows, dense P. oceanica with sand patches, and no-vegetation; according to the random-forest Gini index, 38 kHz features were the most informational variables for this classification. The validation accuracies of both classifications were 85% (substrates) and 70% (vegetation), close to accuracies reported in the literature when using a similar number of classes. The results of this article (including bathymetric, and substrate and vegetation thematic maps), together with the acoustic methodology described and used, are contributions that can improve the continuous monitoring of Mediterranean seagrasses.

Direct deoxygenative borylation

Abstract Direct deoxygenative borylation is a highly enabling chemical transformation considering the attractive synthetic features of oxygenous feedstocks and organoboron compounds. Despite ranking among the synthetic ideality in different settings, such chemical space remained largely uncharted and underutilized until recent decades. This short review will summarize some key advances in the field of direct deoxy-borylation of alcohols, ethers, aldehydes, ketones, and carboxylic acids and organize these contributions based on substrate classes. In each representative, the general features, including reaction conditions, product scopes and mechanistic insights, will be highlighted and discussed.

One-Pot Enol Silane Formation–Allylation of Ketones Promoted by Trimethylsilyl Trifluoromethanesulfonate

AbstractKetones and related substrate classes undergo enol silane formation in the presence of trimethylsilyl trifluoromethanesulfonate (TMSOTf) and triethylamine, reaction conditions that also promote the in situ ionization of allyl propionates. When these two processes are performed in one pot, allylation of the ketone is observed in high yields. Aldehydes, esters, and thioesters also serve as enol silane precursors under these conditions. When unsymmetrical allyl cations are employed, regioselectivity depends upon the electronic and steric properties of the substituents.

A Phosphine-ß-diketiminate Nickel(I)-Complex for Small Molecule Activation.

A bis(diphenyl)-phosphine functionalized ß-diketimine ligand (PNac-H) was applied for the synthesis of a subvalent Ni(I) complex [PNac-Ni]. Here, the Ni(I) center is stabilized by a tetradentate PNNP-type pocket, forming a square planar coordination sphere. Subsequently, the Ni(I) complex was investigated with regard to its reactivity and the activation of small molecules. The reductive potential of Ni(I) enabled an activation of different substrate classes, such as CH2 X2 (X=Br, I), I2 or Ph2 E2 (E=S, Se). The ligand's design allows a stabilization of the reactive Ni(I) species while at the same time enabling activation processes due to a hemilabile coordination behavior and accessible axial coordination sites. The activation products have been characterized by single crystal X-ray diffraction, NMR and IR spectroscopy as well as elemental analysis.

Optimized Data Set and Feature Construction for Substrate Prediction of Membrane Transporters.

α-Helical transmembrane proteins termed membrane transporters mediate the passage of small hydrophilic substrate molecules across biological lipid bilayer membranes. Annotating the specific substrates of the dozens to hundreds of individual transporters of an organism is an important task. In the past, machine learning classifiers have been successfully trained on pan-organism data sets to predict putative substrates of transporters. Here, we critically examine the selection of an optimal data set of protein sequence features for the classification task. We focus on membrane transporters of the three model organisms Escherichia coli, Arabidopsis thaliana, and Saccharomyces cerevisiae, as well as human. We show that organism-specific classifiers can be robustly trained if at least 20 samples are available for each substrate class. If information from position-specific scoring matrices is included, such classifiers have F1 scores between 0.85 and 1.00. For the largest data set (A. thaliana), a 4-class classifier yielded an F-score of 0.97. On a pan-organism data set composed of transporters of all four organisms, amino acid and sugar transporters were predicted with an F1 score of 0.91.