Commercial Library Research Articles

Direct High-Throughput Screening Assay for mRNA Cap Guanine-N7 Methyltransferase Activity.

In eukaryotes, mature mRNA is formed through modifications of precursor mRNA, one of which is 5’ cap biosynthesis, involving RNA cap guanine‐N7 methyltransferase (N7‐MTase). N7‐MTases are also encoded by some eukaryotic viruses and facilitate their replication. N7‐MTase inhibitors have therapeutic potential, but their discovery is difficult because long RNA substrates are usually required for activity. Herein, we report a universal N7‐MTase activity assay based on small‐molecule fluorescent probes. We synthesized 12 fluorescent substrate analogues (GpppA and GpppG derivatives) varying in the dye type, dye attachment site, and linker length. GpppA labeled with pyrene at the 3’‐O position of adenosine acted as an artificial substrate with the properties of a turn‐off probe for all three tested N7‐MTases (human, parasite, and viral). Using this compound, a N7‐MTase inhibitor assay adaptable to high‐throughput screening was developed and used to screen synthetic substrate analogues and a commercial library. Several inhibitors with nanomolar activities were identified.

Analyses of Skin Secretions of Vipera ammodytes (Linnaeus, 1758) (Reptilia: Serpentes), with Focus on the Complex Compounds and Their Possible Role in the Chemical Communication.

Snakes rely heavily on chemical cues when foraging, searching for mates, etc. Snakes’ sex attractiveness pheromones comprise mainly heavy, semi-volatile compounds such as ketones. Here we investigated the composition of skin secretions of adult Vipera ammodytes (Linnaeus, 1758) individuals. The samples were analyzed by gas chromatography/mass spectrometry and the identification of the compounds was performed using commercial mass spectral libraries and retention times. The relative concentrations of all detected compounds were tested for significant differences between (1) male vs. female live individuals, (2) shed skin vs. live individuals, and (3) pre-reproductive vs. reproductive live individuals. We detected fifty-nine compounds of which six were ketones. Two ketones (2-pentacosanone and 2-heptacosanone) were present in many of the samples and thus may have an important role in the V. ammodytes chemical communication. We did not find significant differences between the relative concentrations of the compounds between male and female individuals (only three compounds are exceptions). Significant differences were found between extracts from shed skins and live individuals and between live pre-reproductive individuals and live reproductive individuals. The results of the study suggest that chemical communication in V. ammodytes involves less compounds in comparison to the known literature data for other species.

Design and Synthesis of 56 Shape-Diverse 3D Fragments.

Fragment‐based drug discovery is now widely adopted for lead generation in the pharmaceutical industry. However, fragment screening collections are often predominantly populated with flat, 2D molecules. Herein, we describe a workflow for the design and synthesis of 56 3D disubstituted pyrrolidine and piperidine fragments that occupy under‐represented areas of fragment space (as demonstrated by a principal moments of inertia (PMI) analysis). A key, and unique, underpinning design feature of this fragment collection is that assessment of fragment shape and conformational diversity (by considering conformations up to 1.5 kcal mol−1 above the energy of the global minimum energy conformer) is carried out prior to synthesis and is also used to select targets for synthesis. The 3D fragments were designed to contain suitable synthetic handles for future fragment elaboration. Finally, by comparing our 3D fragments with six commercial libraries, it is clear that our collection has high three‐dimensionality and shape diversity.

OpenFPGA: An Open-Source Framework for Agile Prototyping Customizable FPGAs

Demanded by ever-evolving data processing algorithms, field-programmable gate arrays (FPGAs) have become essential components of modern computing systems, thanks to their reconfigurable and distributed computing capabilities. However, FPGAs are among the very few integrated chips that still require long development cycles and high human efforts, even for industrial vendors. In this article, we introduce OpenFPGA, an open-source framework that can automate and significantly accelerate the development cycle of customizable FPGA architectures. OpenFPGA allows users to customize their FPGA architectures down to circuit-level details using a high-level architecture description language and autogenerate associated Verilog netlists which can be used in a backend flow to generate production-ready layouts. A generic Verilog-to-Bitstream generator is also provided, allowing end-users to implement practical applications on any FPGAs that OpenFPGA can support. Using OpenFPGA, we demonstrate less than 24-h layout generation of two FPGA fabrics, which are based on a Stratix-like architecture built with a commercial 12-nm standard cell library and 40-nm custom cells, respectively.

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology. Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >105 drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits. Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae. This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web. Graphical Abstract.

Identification of Enhancer-Promoter Contacts in Embryoid Bodies by Quantitative Chromosome Conformation Capture (4C)

During mammalian development, cell fates are determined through the establishment of regulatory networks that define the specificity, timing, and spatial patterns of gene expression. Embryoid bodies (EBs) derived from pluripotent stem cells have been a popular model to study the differentiation of the main three germ layers and to define regulatory circuits during cell fate specification. Although it is well-known that tissue-specific enhancers play an important role in these networks by interacting with promoters, assigning them to their relevant target genes still remains challenging. To make this possible, quantitative approaches are needed to study enhancer-promoter contacts and their dynamics during development. Here, we adapted a 4C method to define enhancers and their contacts with cognate promoters in the EB differentiation model. The method uses frequently cutting restriction enzymes, sonication, and a nested-ligation-mediated PCR protocol compatible with commercial DNA library preparation kits. Subsequently, the 4C libraries are subjected to high-throughput sequencing and analyzed bioinformatically, allowing detection and quantification of all sequences that have contacts with a chosen promoter. The resulting sequencing data can also be used to gain information about the dynamics of enhancer-promoter contacts during differentiation. The technique described for the EB differentiation model is easy to implement.

Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling.

Generative Topographic Mapping (GTM) can be efficiently used to visualize, analyze and model large chemical data. The GTM manifold needs to span the chemical space deemed relevant for a given problem. Therefore, the Frame set (FS) of compounds used for the manifold construction must well cover a given chemical space. Intuitively, the FS size must raise with the size and diversity of the target library. At the same time, the GTM training can be very slow or even becomes technically impossible at FS sizes of the order of 105 compounds – which is a very small number compared to today's commercially accessible compounds, and, especially, to the theoretically feasible molecules. In order to solve this problem, we propose a Parallel GTM algorithm based on the merging of “intermediate” manifolds constructed in parallel for different subsets of molecules. An ensemble of these subsets forms a FS for the “final” manifold. In order to assess the efficiency of the new algorithm, 80 GTMs were built on the FSs of different sizes ranging from 10 to 1.8 M compounds selected from the ChEMBL database. Each GTM was challenged to build classification models for up to 712 biological activities (depending on the FS size). With the novel parallel GTM procedure, we could thus cover the entire spectrum of possible FS sizes, whereas previous studies were forced to rely on the working hypothesis that FS sizes of few thousands of compounds are sufficient to describe the ChEMBL chemical space. In fact, this study formally proves this to be true: a FS containing only 5000 randomly picked compounds is sufficient to represent the entire ChEMBL collection (1.8 M molecules), in the sense that a further increase of FS compound numbers has no benefice impact on the predictive propensity of the above‐mentioned 712 activity classification models. Parallel GTM may, however, be required to generate maps based on very large FS, that might improve chemical space cartography of big commercial and virtual libraries, approaching billions of compounds

Strategy for identification of new psychoactive substances in illicit samples using attenuated total reflectance infrared spectroscopy

New strategy has been developed for the identification of novel psychoactive substances (NPS) in illicit samples. The methodology was based on the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-IR). First pass screening by ATR-IR allows known substances to be rapidly identified, while any non-matching samples are qualified by complementary analytical techniques and then feed back into the spectral libraries. Compounds’ identification by ATR-IR was based on the correlation coefficient value. After validation, developed strategy was successfully introduced into routine analysis. Thirty one NPS have been identified in forty five samples. One new opioid was identified and new ATR-IR spectra were acquired, not reported in commercial libraries yet. ATR-IR enabled distinguishing between closely related compounds, even structural isomers. It proved to be an easy and very reproducible tool for screening analysis of NPS, in less than one minute, with no sample destruction, even for very little amount of a sample. Developed and validated strategy may be introduced by other laboratories.

Creation and Curation of an Improved, Custom MALDI‐TOF Edwardsiella Library

Edwardsiella is a Gram‐negative rod, mannitol‐negative, hydrogen sulfide producing member of the Enterobacteriaceae family. E. tarda was the initial member of the Edwardsiella genus until E. ictaluri, E. hoshinae, E. piscicida, and E. anguillarum were identified as individual species distinct from E. tarda. The five known species of Edwardsiella are genetically and phenotypically similar to one another. We used Matrix‐Assisted Laser Desorption Ionization‐Time of Flight (MALDI‐TOF) mass spectrometry to create a library of spectral entries containing 5 E. tarda, 5E. ictaluri, 4 E. piscicida, and 5 E. anguillarum isolates. Nineteen Mass Spectral Profiles (MSPs) were assembled from 101 E. tarda, 139 E. ictaluri, 123 E. piscicida, and 112 E. anguillarum MALDI‐TOF spectra. This new library, named the UWSP Edwardsiella library, was challenged with the original 19 Edwardsiella isolates. The UWSP Edwardsiella library correctly identified 19 out of 19 isolates. In contrast, currently available commercial libraries identified the same 19 isolates as E. tarda. Scores generated with the current commercial library averaged just over 2.0. The UWSP Edwardsiella Library scores ranged between 2.50 to 2.75. After initial library creation, additional Edwardsiella isolates were obtained and used to challenge the UWSP Edwardsiella library. All 16 of those isolates were correctly identified with identification scores between 2.51 and 2.81. Here we report the creation of a custom Edwardsiella MALDI‐TOF library with improved ability to accurately differentiate and identify individual Edwardsiella species.Support or Funding InformationUWSP Office of Student Creative Activity and Research

DAD-FF: Hardening Designs by Delay-Adjustable D-Flip-Flop for Soft-Error-Rate Reduction

For the safety-critical applications such as biomedical and automobile electronics, the system failure induced by soft errors becomes a major issue of reliability. However, most of the commercial cell libraries do not include radiation-hardened components to build a safety-critical design. Therefore, a delay-adjustable D-flip-flop (DAD-FF) is proposed together with a design flow to construct a radiation-hardened system by automation. To enable such radiation-hardened design into the current design flow, DAD-FF is characterized as a general cell and compiled as a patch in the NanGate FreePDK45 bulk 45-nm open cell library, as an example. The experimental results show that DAD-FF is capable of reducing $1.3\times 10^{10}\text{X}$ soft errors with respect to the standard flip-flop (STD-FF) and resisting over 99.999997% strikes of heavy ions. Meanwhile, four radiation-hardened benchmark circuits are synthesized with DAD-FF cell, and further used to prove the effectiveness against soft errors compared to a prior work, built-in soft-error resilience (BISER), with 18% area and 40% timing improvement. To sum up, DAD-FF is elaborated from the modeling at the device-level to the validation at the system-level and exhibits its strong robustness to soft errors.

Field Programmable Gate Arrays for Enhancing the Speed and Energy Efficiency of Quantum Dynamics Simulations.

We present the first application of field programmable gate arrays (FPGAs) as new, customizable hardware architectures for carrying out fast and energy-efficient quantum dynamics simulations of large chemical/material systems. Instead of tailoring the software to fixed hardware, which is the typical case for writing quantum chemistry code for central processing units (CPUs) and graphics processing units (GPUs), FPGAs allow us to directly customize the underlying hardware (even at the level of specific electrical signals in the circuit) to give a truly optimized computational performance for quantum dynamics calculations. By offloading the most intensive and repetitive calculations onto an FPGA, we show that the computational performance of our real-time electron dynamics calculations can even exceed that of optimized commercial mathematical libraries running on high-performance GPUs. In addition to this impressive computational speedup, we show that FPGAs are immensely energy-efficient and consume 4 times less energy than modern GPU or CPU architectures. These energy savings are a practical and important metric for supercomputing centers (many of which exceed over $1 million in power costs alone), as exascale computing capabilities become more widespread and commonplace. Taken together, the implementation techniques and performance metrics of our study demonstrate that FPGAs could play a promising role in upcoming quantum chemistry and materials science applications, particularly for the acceleration and energy-efficient execution of quantum dynamics calculations.

Lipidomics-based assays coupled with computational approaches can identify novel phospholipase A2 inhibitors.

Phospholipase A2 (PLA2) enzymes play a major role in many diseases including the inflammatory cascade and specific potent small molecule inhibitors could be useful in studying their physiological role as well as for the development of drugs. In order to discover novel small molecule inhibitor platforms for members of the PLA2 superfamily of enzymes, we have applied computational approaches to determine the binding mode of potent inhibitors specific for particular PLA2s to the screening of chemical libraries. This has including the U.S. National Institutes of Health (NIH) National Cancer Institute (NCI) Diversity Set V and the ChemBridge commercial compound libraries. We have then subjected identified inhibitor structures to recently developed lipidomics based screening assays to determine the XI(50) and specificity of the identified compounds for specific PLA2s. Herein we review this approach and report the identity of initial hits for both the Group IVA cytosolic PLA2 and the Group VIA calcium-independent PLA2 that are worthy of further structural modification to develop novel platforms for inhibitor development.

Applicability of Supercritical fluid chromatography–Mass spectrometry to metabolomics. II–Assessment of a comprehensive library of metabolites and evaluation of biological matrices

In this work, the impact of biological matrices, such as plasma and urine, was evaluated under SFCHRMS in the field of metabolomics. For this purpose, a representative set of 49 metabolites were selected. The assessment of the matrix effects (ME), the impact of biological fluids on the quality of MS/MS spectra and the robustness of the SFCHRMS method were each taken into consideration. The results have highlighted a limited presence of ME in both plasma and urine, with 30% of the metabolites suffering from ME in plasma and 25% in urine, demonstrating a limited sensitivity loss in the presence of matrices. Subsequently, the MS/MS spectra evaluation was performed for further peak annotation. Their analyses have highlighted three different scenarios: 63% of the tested metabolites did not suffer from any interference regardless of the matrix; 21% were negatively impacted in only one matrix and the remaining 16% showed the presence of matrix-belonging compounds interfering in both urine and plasma. Finally, the assessment of retention times stability in the biological samples, has brought into evidence a remarkable robustness of the SFCHRMS method. Average RSD (%) values of retention times for spiked metabolites were equal or below 0.5%, in the two biological fluids over a period of three weeks.In the second part of the work, the evaluation of the Sigma Mass Spectrometry Metabolite Library of Standards containing 597 metabolites, under SFCHRMS conditions was performed. A total detectability of the commercial library up to 66% was reached. Among the families of detected metabolites, large percentages were met for some of them. Highly polar metabolites such as amino acids (87%), nucleosides (85%) and carbohydrates (71%) have demonstrated important success rates, equally for hydrophobic analytes such as steroids (78%) and lipids (71%). On the negative side, very poor performance was found for phosphorylated metabolites, namely phosphate-containing compounds (14%) and nucleotides (31%).

In silico MS/MS spectra for identifying unknowns: a critical examination using CFM-ID algorithms and ENTACT mixture samples

High-resolution mass spectrometry (HRMS) enables rapid chemical annotation via accurate mass measurements and matching of experimentally derived spectra with reference spectra. Reference libraries are generated from chemical standards and are therefore limited in size relative to known chemical space. To address this limitation, in silico spectra (i.e., MS/MS or MS2 spectra), predicted via Competitive Fragmentation Modeling-ID (CFM-ID) algorithms, were generated for compounds within the U.S. Environmental Protection Agency’s (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database (totaling, at the time of analysis, ~ 765,000 substances). Experimental spectra from EPA’s Non-Targeted Analysis Collaborative Trial (ENTACT) mixtures (n = 10) were then used to evaluate the performance of the in silico spectra. Overall, MS2 spectra were acquired for 377 unique compounds from the ENTACT mixtures. Approximately 53% of these compounds were correctly identified using a commercial reference library, whereas up to 50% were correctly identified as the top hit using the in silico library. Together, the reference and in silico libraries were able to correctly identify 73% of the 377 ENTACT substances. When using the in silico spectra for candidate filtering, an examination of binary classifiers showed a true positive rate (TPR) of 0.90 associated with false positive rates (FPRs) of 0.10 to 0.85, depending on the sample and method of candidate filtering. Taken together, these findings show the abilities of in silico spectra to correctly identify true positives in complex samples (at rates comparable to those observed with reference spectra), and efficiently filter large numbers of potential false positives from further consideration.Graphical abstract

Increasing the Accessibility for Characterizing Microplastics: Introducing New Application-Based and Spectral Libraries of Plastic Particles (SLoPP and SLoPP-E).

As smaller particle sizes are increasingly included in microplastic research, it is critical to chemically characterize microparticles to identify whether particles are indeed microplastics. To increase the accessibility of methods for characterizing microparticles via Raman spectroscopy, we created an application-based library of Raman spectroscopy parameters specific to microplastics based on color, morphology, and size. We also created two spectral libraries that are representative of microplastics found in environmental samples. Here, we present SLoPP, a spectral library of plastic particles, consisting of 148 reference spectra, including a diversity of polymer types, colors, and morphologies. To account for the effects of aging on microplastics and associated changes to Raman spectra, we present a spectral library of plastic particles aged in the environment (SLoPP-E). SLoPP-E includes 113 spectra, including a diversity of types, colors, and morphologies. The microplastics used to make SLoPP-E include environmental samples obtained across a range of matrices, geographies, and time. Our libraries increase the likelihood of spectral matching for a broad range of microplastics because our libraries include plastics containing a range of additives and pigments that are not generally included in commercial libraries. When used in combination with commercial libraries of over 24 000 spectra, 63% of the top 5 matches across all particles tested (product and environmental) are from SLoPP and SLoPP-E. These tools were developed to improve the accessibility of microplastics research in response to a growing and multidisciplinary field, as well as to enhance data quality and consistency.

PyISOLVER—A Fast Python OOP Implementation of LRDFIT Model

An inductance–resistance $(L- R)$ 2-D axisymmetric circuit model of a tokamak is constrained to fit combinations of diagnostic data. This model is widely used not only to reconstruct magnetohydrodynamics (MHD) equilibrium but also to predict time-dependent vacuum field evolution as part of TRANSP. However, the computational model was procedurally programmed with a commercial software platform which limits the reuse and distribution of this computational model and requires extra expense on the commercial software license. In this article, the original procedural program was ported from its commercial platform to an open-source Python platform and refactored into the object-oriented programming (OOP) style so that computational models could run independently on computers without calling any commercial software libraries. The new classes of the tokamak model contain the most frequently used methods and data structures from equilibrium fitting (EFIT) and LRDFIT. Currently, the model and diagnostic data are derived from national spherical torus experiment (NSTX)-U, however, a customized model could be easily developed for another tokamak. With Numba just-in-time (JIT) compiler, the speed of Python code is accelerated. The original deployment would take ~56 s to converge on a commercial software platform. The open-source Python version would take ~19 s with the same hardware to fit the same set of diagnostic data. Enabled by the OOP design pattern, the numerical algorithm could be further optimized without modification of any other part of code.

New classes of potent heparanase inhibitors from ligand-based virtual screening

Heparanase is a validated target in cancer therapy and a potential target for several inflammatory pathologies. A ligand-based virtual screening of commercial libraries was performed to expand the chemical space of small-molecule inhibitors. The screening was based on similarity with known inhibitors and was performed in several runs, starting from literature compounds and progressing through newly discovered inhibitors. Among the fifty-five tested compounds, nineteen had IC50 values lower than 5 µM and some showed remarkable potencies. Importantly, tere- and isophthalamides derivatives belong to new structural classes of heparanase inhibitors and some of them showed enzyme affinities (61 and 63, IC50 = 0.32 and 0.12 µM, respectively) similar to those of the most potent small-molecule inhibitors reported so far. Docking studies provided a comprehensive binding hypothesis shared by compounds with significant structural diversity. The most potent inhibitors reduced cell invasiveness and inhibited the expression of proangiogenic factors in tumour cell lines.

In Silico Design and Analysis of a Kinase-Focused Combinatorial Library Considering Diversity and Quality.

A structurally diverse, high-quality, and kinase-focused database plays a critical role in finding hits or leads in kinase drug discovery. Here, we propose a workflow for designing a virtual kinase-focused combinatorial library using existing structures. Based on the analysis of known protein kinase inhibitors (PKIs), detailed fragment optimization, fragment selection, fragment linking, and a molecular filtering scheme were defined. Quick recognition of core fragments that can possibly form dual hydrogen bonds with the hinge region of the ATP-pocket was proposed. Furthermore, three diversity and four quality metrics were chosen for compound library analysis, which can be applied to databases with over 30 million structures. Compared with 13 commercial libraries, our protocol demonstrates a special advantage in terms of good skeleton diversity, acceptable fingerprint diversity, balanced scaffold distribution, and high quality, which can work well not only on existing PKIs, but also on four chosen commercial libraries. Overall, the strategy can greatly facilitate the expansion of a desirable chemical space for kinase drug discovery.

COPMONENT COMPOSITION OF ROOT IN TWO SPECIES OPLOPANAX (ARALIACEAE)

The qualitative and quantitative composition of two species of Oplopanax root was studied by gas chromatography with mass spectrometric detection. Extraction of components from the investigated objects was carried out by extraction of dry ground raw material with 70% ethanol. For the analysis of polar compounds, the extracts were further derivatized to give the corresponding trimethylsilyl derivatives. Identification of components was carried out using commercial (NIST17, Wiley14) and own custom mass-spectrometer libraries. The percentage of components found was calculated using the areas of the corresponding chromatographic peaks. Found in these samples were 130 compounds of various classes: terpenes and their derivatives, alcohols, aldehydes, polyyns, polyenes, various acids and their derivatives, phenols, sterols, lignans, vitamins, nucleosides, glycosides and sugars. It has been established that the qualitative and quantitative composition of root extracts varies even within the same species. The presence of 26 compounds common to the two species of Oplopanax, including 12 acids, 3 polyyns, 6 terpenes and one representative each from the class polyenes, aldehydes, lignans, glycosides and sterols, was revealed. As a result of the comparative analysis, it was established that 4 compounds from the class of polyyns (falcarindiol acetate, oplopanediol acetate, oplopantriol A and oplopantriol B) are found only in the roots of O. horridus.

A surface-enhanced Raman spectroscopy database of 63 metabolites

Metabolomics, the study of metabolic profiles in a biological sample, has seen rapid growth due to advances in measurement technologies such as mass spectrometry (MS). While MS metabolite reference libraries have been generated for metabolomics applications, mass spectra alone are unable to unambiguously identify many metabolites in a sample; these unidentified compounds are typically annotated as “features”. Surface-enhanced Raman spectroscopy (SERS) is an interesting technology for metabolite identification based on vibrational spectra. However, no reports have been published that present SERS metabolite spectra from chemical libraries. In this paper, we demonstrate that an untargeted approach utilizing citrate-capped silver nanoparticles yields SERS spectra for 20% of 80 compounds chosen randomly from a commercial metabolite library. Furthermore, prescreening of the metabolites according to chemical functionality allowed for the efficient identification of samples within the library that yield distinctive SERS spectra under our experimental conditions. Last, we present a reference database of 63 metabolite SERS spectra for use as an identification tool in metabolomics studies; this set includes 30 metabolites that have not had previously published SERS spectra.