Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum.

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra.

The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

Practical aspect of dimer adduct formation in small-molecule drug analysis with LC-MS/MS.

Aim: Since the MS/MS based detection of small-molecule drugs with poor or even no ion fragmentation is a challenge in bioanalysis, alternative MS/MS detection strategies were in focus of this study and applied in the field of forensic toxicology. Material & methods: Analyte quantification with liquid chromatography-tandem mass spectrometry of problematic drugs was studied by the application of dimer adduct formation and valproic acid (VPA) was used as a model drug. VPA adduct ions could be identified during infusion experiments and the VPA dimer adduct ion was optimized for the detection. Conclusion: Dimer adduct ion formation can be used as an effective way of VPA quantification in human serum. Further, the parallel detection of dimer adduct ions with other adduct ion types can be stated as advantage in LC-MS/MS analysis of problematic drugs.

Identification of Small Molecule Inhibitors against Staphylococcus aureus Dihydroorotase via HTS.

Drug-resistant Staphylococcus aureus is an imminent threat to public health, increasing the importance of drug discovery utilizing unexplored bacterial pathways and enzyme targets. De novo pyrimidine biosynthesis is a specialized, highly conserved pathway implicated in both the survival and virulence of several clinically relevant pathogens. Class I dihydroorotase (DHOase) is a separate and distinct enzyme present in gram positive bacteria (i.e., S. aureus, B. anthracis) that converts carbamoyl-aspartate (Ca-asp) to dihydroorotate (DHO)-an integral step in the de novo pyrimidine biosynthesis pathway. This study sets forth a high-throughput screening (HTS) of 3000 fragment compounds by a colorimetry-based enzymatic assay as a primary screen, identifying small molecule inhibitors of S. aureus DHOase (SaDHOase), followed by hit validation with a direct binding analysis using surface plasmon resonance (SPR). Competition SPR studies of six hit compounds and eight additional analogs with the substrate Ca-asp determined the best compound to be a competitive inhibitor with a KD value of 11 µM, which is 10-fold tighter than Ca-asp. Preliminary structure-activity relationship (SAR) provides the foundation for further structure-based antimicrobial inhibitor design against S. aureus.

MolDiscovery: learning mass spectrometry fragmentation of small molecules.

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

Investigation of candidate genes and mechanisms underlying obesity associated type 2 diabetes mellitus using bioinformatics analysis and screening of small drug molecules.

BACKGROUND: Obesity associated type 2 diabetes mellitus is a metabolic disorder ; however, the etiology of obesity associated type 2 diabetes mellitus remains largely unknown. There is an urgent need to further broaden the understanding of the molecular mechanism associated in obesity associated type 2 diabetes mellitus. METHODS: To screen the differentially expressed genes (DEGs) that might play essential roles in obesity associated type 2 diabetes mellitus, the publicly available expression profiling by high throughput sequencing data (GSE143319) was downloaded and screened for DEGs. Then, Gene Ontology (GO) and REACTOME pathway enrichment analysis were performed. The protein - protein interaction network, miRNA - target genes regulatory network and TF-target gene regulatory network were constructed and analyzed for identification of hub and target genes. The hub genes were validated by receiver operating characteristic (ROC) curve analysis and RT- PCR analysis. Finally, a molecular docking study was performed on over expressed proteins to predict the target small drug molecules. RESULTS: A total of 820 DEGs were identified between healthy obese and metabolically unhealthy obese, among 409 up regulated and 411 down regulated genes. The GO enrichment analysis results showed that these DEGs were significantly enriched in ion transmembrane transport, intrinsic component of plasma membrane, transferase activity, transferring phosphorus-containing groups, cell adhesion, integral component of plasma membrane and signaling receptor binding, whereas, the REACTOME pathway enrichment analysis results showed that these DEGs were significantly enriched in integration of energy metabolism and extracellular matrix organization. The hub genes CEBPD, TP73, ESR2, TAB1, MAP 3K5, FN1, UBD, RUNX1, PIK3R2 and TNF, which might play an essential role in obesity associated type 2 diabetes mellitus was further screened. CONCLUSIONS: The present study could deepen the understanding of the molecular mechanism of obesity associated type 2 diabetes mellitus, which could be useful in developing therapeutic targets for obesity associated type 2 diabetes mellitus.

Fragment Screening by NMR.

This chapter describes the use of NMR to screen a fragment library as part of a fragment-based lead discovery (FBLD) campaign. The emphasis is on the practicalities involved in fragment screening by NMR, with particular attention to the use of 1D ligand-observed 1H NMR experiments. An overview of the theoretical considerations underlying the choice of method and experimental configuration is given, along with a discussion of steps that can be taken in order to minimize the risk of experimental artifacts often associated with the identification of low-affinity interactions.

High-Throughput Screen for Inhibitors of the Type IV Pilus Assembly ATPase PilB.

The bacterial type IV pilus (T4P) is a prominent virulence factor in many significant human pathogens, some of which have become increasingly antibiotic resistant. Antivirulence chemotherapeutics are considered a promising alternative to antibiotics because they target the disease process instead of bacterial viability. However, a roadblock to the discovery of anti-T4P compounds is the lack of a high-throughput screen (HTS) that can be implemented relatively easily and economically. Here, we describe the first HTS for the identification of inhibitors specifically against the T4P assembly ATPase PilB in vitroChloracidobacterium thermophilum PilB (CtPilB) had been demonstrated to have robust ATPase activity and the ability to bind its expected ligands in vitro. We utilized CtPilB and MANT-ATP, a fluorescent ATP analog, to develop a binding assay and adapted it for an HTS. As a proof of principle, we performed a pilot screen with a small compound library of kinase inhibitors and identified quercetin as a PilB inhibitor in vitro Using Myxococcus xanthus as a model bacterium, we found quercetin to reduce its T4P-dependent motility and T4P assembly in vivo. These results validated our HTS as effective in identifying PilB inhibitors. This assay may prove valuable in seeking leads for the development of antivirulence chemotherapeutics against PilB, an essential and universal component of all bacterial T4P systems.IMPORTANCE Many bacterial pathogens use their type IV pili (T4P) to facilitate and maintain infection of a human host. Small chemical compounds that inhibit the production or assembly of T4P hold promise in the treatment and prevention of infections, especially in the era of increasing threats from antibiotic-resistant bacteria. However, few chemicals are known to have inhibitory or anti-T4P activity. Their identification has not been easy due to the lack of a method for the screening of compound collections or libraries on a large scale. Here, we report the development of an assay that can be scaled up to screen compound libraries for inhibitors of a critical T4P assembly protein. We further demonstrate that it is feasible to use whole cells to examine potential inhibitors for their activity against T4P assembly in a bacterium.

Morphological profiling of small molecules.

Profiling approaches such as gene expression or proteome profiling generate small-molecule bioactivity profiles that describe a perturbed cellular state in a rather unbiased manner and have become indispensable tools in the evaluation of bioactive small molecules. Automated imaging and image analysis can record morphological alterations that are induced by small molecules through the detection of hundreds of morphological features in high-throughput experiments. Thus, morphological profiling has gained growing attention in academia and the pharmaceutical industry as it enables detection of bioactivity in compound collections in a broader biological context in the early stages of compound development and the drug-discovery process. Profiling may be used successfully to predict mode of action or targets of unexplored compounds and to uncover unanticipated activity for already characterized small molecules. Here, we review the reported approaches to morphological profiling and the kind of bioactivity that can be detected so far and, thus, predicted.

Identification of Three Small Molecules That Can Selectively Influence Cellular Manganese Levels in a Mouse Striatal Cell Model.

Manganese (Mn) is a biologically essential metal, critical as a cofactor for numerous enzymes such a glutamine synthetase and kinases such as ataxia-telangiectasia mutated (ATM). Similar to other essential metals such as iron and zinc, proper levels of Mn need to be achieved while simultaneously being careful to avoid excess levels of Mn that can be neurotoxic. A lifetime of occupational exposure to Mn can often lead to a Parkinsonian condition, also known as "manganism", characterized by impaired gait, muscle spasms, and tremors. Despite the importance of its regulation, the mechanisms underlying the transport and homeostasis of Mn are poorly understood. Rather than taking a protein or gene-targeted approach, our lab recently took a high-throughput-screening approach to identify 41 small molecules that could significantly increase or decrease intracellular Mn in a neuronal cell model. Here, we report characterization of these small molecules, which we refer to as the "Mn toolbox". We adapted a Fura-2-based assay for measuring Mn concentration and for measuring relative concentrations of other divalent metals: nickel, copper, cobalt, and zinc. Of these 41 small molecules, we report here the identification of three that selectively influence cellular Mn but do not influence the other divalent metals tested. The patterns of activity across divalent metals and the discovery of Mn-selective small molecules has potential pharmacological and scientific utility.

Multiple reaction monitoring profiling (MRM profiling): Small molecule exploratory analysis guided by chemical functionality.

Small molecules, including metabolites and lipids, provide information on metabolic pathways and active biological processes in living organisms. They are often diagnostic of disease. Current exploratory methods for metabolomics and lipidomics mostly rely on separation using liquid or gas chromatography (LC or GC) coupled with mass spectrometers capable of acquiring high resolution data to generate an enormous data, but at the cost of lengthy processing and data acquisition. Even though many molecules can be identified and quantified by these methods, the laborious protocols for purification, identification, and validation limit the accessible sample chemical information. To improve the speed and efficiency of exploratory metabolomics and lipidomics, multiple reaction monitoring profiling (MRM profiling) has been developed. This strategy involves a three-stage workflow which starts by considering the metabolome as a collection of functional groups. The Discovery Stage interrogates a representative sample mixture for functional groups using the functional group specific precursor ion (Prec) scans and neutral loss (NL) scans. This experiment usually uses a triple quadrupole mass spectrometer without chromatography, i.e. by direct sample infusion. In the second Screening Stage, the main features seen in the Prec and NL scans are organized into lists of precursor ion/product ion transitions (MRMs) which are then used for the fast, specific, and sensitive interrogation of each individual sample. Data analysis by univariate and multivariate statistical methods is used to identify the most informative MRMs and so classify the individual samples. The compounds (biomarkers) which are responsible for the most informative MRMs in particular sample classes can be investigated in an optional third Identification Stage i.e. in a structural identification study. MRM profiling benefits from the much smaller number of functional groups compared to the number of individual metabolites existing in biological samples (where most metabolites are still unknown), resulting in acquisition of a much smaller data set and a shorter analysis time. The application of MRM Profiling to several biological and clinical problems is used to illustrate its features.

Discovery of a Novel Inhibitor of Human Purine Nucleoside Phosphorylase by a Simple Hydrophilic Interaction Liquid Chromatography Enzymatic Assay.

Human purine nucleoside phosphorylase (HsPNP) belongs to the purine salvage pathway of nucleic acids. Genetic deficiency of this enzyme triggers apoptosis of activated T-cells due to the accumulation of deoxyguanosine triphosphate (dGTP). Therefore, potential chemotherapeutic applications of human PNP inhibitors include the treatment of T-cell leukemia, autoimmune diseases and transplant tissue rejection. In this report, we present the discovery of novel HsPNP inhibitors by coupling experimental and computational tools. A simple, inexpensive, direct and non-radioactive enzymatic assay coupled to hydrophilic interaction liquid chromatography and UV detection (LC-UV using HILIC as elution mode) was developed for screening HsPNP inhibitors. Enzymatic activity was assessed by monitoring the phosphorolysis of inosine (Ino) to hypoxanthine (Hpx) by LC-UV. A small library of 6- and 8-substituted nucleosides was synthesized and screened. The inhibition potency of the most promising compound, 8-aminoinosine (4), was quantified through Ki and IC50 determinations. The effect of HsPNP inhibition was also evaluated in vitro through the study of cytotoxicity on human T-cell leukemia cells (CCRF-CEM). Docking studies were also carried out for the most potent compound, allowing further insights into the inhibitor interaction at the HsPNP active site. This study provides both new tools and a new lead for developing novel HsPNP inhibitors.

Advancing automation in Compound Management: a novel industrial process underpinning drug discovery.

Faced with ageing infrastructure and ever-increasing demands from hit discovery and lead optimisation functions, AstraZeneca has chosen to develop innovative technologies and process solutions to support the future of drug discovery. These include the miniaturisation of compound storage tubes for high-density storage and rapid access to the corporate collection for feeding samples to the predicted tripling number of high throughput screening (HTS) campaigns. The acoustically- compatible tubes also enable the first fully-acoustic plate production process for faster sample supply to screening with less waste and continued high quality. Operating at a smaller scale reduces compound synthesis, storage, and consumption, prompting miniaturisation of upstream chemistry and downstream biological assays, while offering a transformative and sustainable solution to many drug discovery issues applicable across the industry.

Method for Phenotypic Chemical Screening to Identify Cryptochrome Inhibitors.

After germination, plants determine their morphogenesis, such as hypocotyl elongation and cotyledon opening, by responding to various wavelengths of light (photomorphogenesis). Cryptochrome is a blue-light photoreceptor that controls de-etiolation, stomatal opening and closing, flowering time, and shade avoidance. Successful incorporation of these phenotypes as indicators into a chemical screening system results in faster selection of candidate compounds. Here, we describe phenotypic screening for the blue-light response of Arabidopsis thaliana seedling and the resulting process that clarifies that the compound obtained in the screening is an inhibitor of cryptochromes.

Whole-Seedling-Based Chemical Genetic Screens in Arabidopsis.

Forward genetics has been extremely powerful for dissecting biological pathways in various model organisms. However, it is limited by the fact that redundant gene families and essential genes cannot be readily uncovered through such methods. Chemical genetics, on the other hand, provides a valuable complementary approach to probe biological processes and is suitable for not only genetic model organisms but also genetically less tractable species. We describe here a high-throughput chemical genetic screening method simply based on plant growth and developmental phenotypes in Arabidopsis. It was successfully utilized to study plant immunity and can be easily adapted for dissecting other plant signal transduction pathways.

Identification of ABA Receptor Agonists Using a Multiplexed High-Throughput Chemical Screening.

Small molecules that can activate abscisic acid (ABA) receptors represent valuable probes to study ABA perception and signaling. Additionally, these compounds have the potential to be used in the field to counteract the negative effect of drought stress on plant productivity. The PYR/PYL ABA receptors, in their ligand-bound conformation, inactivate protein phosphatases 2C (PP2Cs), triggering physiological responses that are essential for plant adaptation to environmental stresses, including drought. Based on this ligand-induced PP2C inactivation mechanism, we have developed an in vitro assay for the identification of ABA-receptor agonists by high-throughput screening of chemical libraries. The assay allows simultaneous use of different ABA receptors, increasing the chances to find new agonists and eliminates the need for parallel screening. In this chapter, we describe detailed procedures for the identification of ABA agonists using this multiplexed assay in a medium- (96-well plates) or a high-throughput (384-well plates) setup.

A Luciferase Reporter Assay to Identify Chemical Activators of ABA Signaling.

Plant stress tolerance relies on intricate signaling networks that are not fully understood. Several plant hormones are involved in the adaptation to different environmental conditions. Abscisic acid (ABA) has an essential role in stress tolerance, especially in the adaptation to drought. During the last years, chemical genomics has gained attention as an alternative approach to decipher complex traits. Additionally, chemical-based strategies have been very useful to untangle genetic redundancy, which is hard to address by other approaches such as classical genetics. Here, we describe the use of an ABA-inducible luciferase (LUC) reporter line for the high-throughput identification of chemical activators of the ABA signaling pathway. In this assay, seven-day-old pMAPKKK18-LUC+ seedlings are grown on 96-well plates and treated with test compounds. Next, the activity of the LUC reporter is quantified semiautomatically by image analysis. Candidate compounds able to activate the reporter are thus identified and subjected to a secondary screen by analyzing their effect on ABA-related phenotypes (e.g., inhibition of seed germination). This assay is fast, high-throughput, nondestructive, semiquantitative and can be applied to any other luciferase reporter lines, making it ideal for forward chemical genetic screenings.

Silver nanoparticle in biosensor and bioimaging: Clinical perspectives.

Recent developments in nanotechnology promoted the production of nanomaterials with various shapes and sizes by utilizing interdisciplinary researches of biology, chemistry, and material science toward the clinical perspectives. In particular, gold and silver (Ag) are noble metals that exhibit tunable and unique plasmonic properties for the downstream applications. Ag exhibits higher thermal and electrical conductivities, and more efficient in the electron transfer than gold with sharper extinction bands. In addition, modified Ag nanoparticle is more stable in water and air. With all these above features, Ag is an attractive tool in various fields, including diagnosis, drug delivery, environmental, electronics, and as antimicrobial agent. In particular, applications of Ag nanoparticle in the fields of biosensor and imaging are prominent in recent days. Enhancing the specific detection of clinical markers with Ag nanoparticle has been proved by several studies. This review discussed the constructive application of Ag nanoparticle in biosensor and bioimaging for the detection of small molecule to larger whole cell in the perspectives of diagnosing diseases.