Glycans are key molecules in many physiological and pathological processes. As with other molecules, like proteins, visualization of the 3D structures of glycans adds valuable information for understanding their biological function. Hence, here we introduce Azahar, a computing environment for the creation, visualization and analysis of glycan molecules. Azahar is implemented in Python and works as a plugin for the well known PyMOL package (Schrodinger in The PyMOL molecular graphics system, version 1.3r1, 2010). Besides the already available visualization and analysis options provided by PyMOL, Azahar includes 3 cartoon-like representations and tools for 3D structure caracterization such as a comformational search using a Monte Carlo with minimization routine and also tools to analyse single glycans or trajectories/ensembles including the calculation of radius of gyration, Ramachandran plots and hydrogen bonds. Azahar is freely available to download from http://www.pymolwiki.org/index.php/Azahar and the source code is available at https://github.com/agustinaarroyuelo/Azahar. 相似文献
Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.Based on GNNs and active learning, DeepReac+ is designed as a universal framework for quantitative modeling of chemical reactions. It takes molecular structures as inputs directly and adapts to various prediction tasks with fewer training data.相似文献
The rapid adoption of microbial whole genome sequencing in public health, clinical testing, and forensic laboratories requires the use of validated measurement processes. Well-characterized, homogeneous, and stable microbial genomic reference materials can be used to evaluate measurement processes, improving confidence in microbial whole genome sequencing results. We have developed a reproducible and transparent bioinformatics tool, PEPR, Pipelines for Evaluating Prokaryotic References, for characterizing the reference genome of prokaryotic genomic materials. PEPR evaluates the quality, purity, and homogeneity of the reference material genome, and purity of the genomic material. The quality of the genome is evaluated using high coverage paired-end sequence data; coverage, paired-end read size and direction, as well as soft-clipping rates, are used to identify mis-assemblies. The homogeneity and purity of the material relative to the reference genome are characterized by comparing base calls from replicate datasets generated using multiple sequencing technologies. Genomic purity of the material is assessed by checking for DNA contaminants. We demonstrate the tool and its output using sequencing data while developing a Staphylococcus aureus candidate genomic reference material. PEPR is open source and available at https://github.com/usnistgov/pepr. 相似文献
This review (with 340 refs) focuses on methods for specific and sensitive detection of metabolites for diagnostic purposes, with particular emphasis on electrochemical nanomaterial-based sensors. It also covers novel candidate metabolites as potential biomarkers for diseases such as neurodegenerative diseases, autism spectrum disorder and hepatitis. Following an introduction into the field of metabolic biomarkers, a first major section classifies electrochemical biosensors according to the bioreceptor type (enzymatic, immuno, apta and peptide based sensors). A next section covers applications of nanomaterials in electrochemical biosensing (with subsections on the classification of nanomaterials, electrochemical approaches for signal generation and amplification using nanomaterials, and on nanomaterials as tags). A next large sections treats candidate metabolic biomarkers for diagnosis of diseases (in the context with metabolomics), with subsections on biomarkers for neurodegenerative diseases, autism spectrum disorder and hepatitis. The Conclusion addresses current challenges and future perspectives.
Graphical abstract This review focuses on the recent developments in electrochemical biosensors based on the use of nanomaterials for the detection of metabolic biomarkers. It covers the critical metabolites for some diseases such as neurodegenerative diseases, autism spectrum disorder and hepatitis.
Tubulin, which is the building block of microtubules, plays an important role in cell division. This critical role makes tubulin an attractive target for the development of chemotherapeutic drugs to treat cancer. Currently, there is no general binding assay for tubulin–drug interactions. The present work describes the application of the catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) assay to investigate the binding of colchicinoid drugs to αβ-tubulin dimers extracted from porcine brain. Proof-of-concept experiments using positive (ligands with known affinities) and negative (non-binders) controls were performed to establish the reliability of the assay. The assay was then used to screen a library of seven colchicinoid analogues to test their binding to tubulin and to rank their affinities.
The heat capacity and density of solutions of cadmium and mercury diiodides in methylpyrrolidone (MP) at 298.15 K were studied by calorimetry and densimetry. The data obtained are discussed in relation to the special features of solvation and complex formation in solutions of the salts. The standard partial molar heat capacities and volumes (
Complex formation ability and stability of both weak and super-weak acids was studied by mean of in silico determined thermodynamic data of the complexes. While weak acids act like Brønsted acids forming hydrogen bond type Brønsted complexes, super-weak acids form Lewis complexes via van der Waals interaction. Unlike in the former type, upon complexation, C-H distances changes insignificantly, yet the complex formation is energy driven in the terms of zero-point corrected Energies, ΔEzp < 0 kcal mol?1, which supports the Lewis complex formation, with the exception of CH4, an extremely “weak acid”.
This review (with 154 refs.) describes the current status of using molecularly imprinted polymers in the extraction and quantitation of illicit drugs and additives. The review starts with an introduction into some synthesis methods (lump MIPs, spherical MIPs, surface imprinting) of MIPs using illicit drugs and additives as templates. The next section covers applications, with subsections on the detection of illegal additives in food, of doping in sports, and of illicit addictive drugs. A particular focus is directed towards current limitations and challenges, on the optimization of methods for preparation of MIPs, their applicability to aqueous samples, the leakage of template molecules, and the identification of the best balance between adsorption capacity and selectivity factor. At last, the need for convincing characterization methods, the lack of uniform parameters for defining selectivity, and the merits and demerits of MIPs prepared using nanomaterials are addressed. Strategies are suggested to solve existing problems, and future developments are discussed with respect to a more widespread use in relevant fields.
Graphical abstract This review gives a comprehensive overview of the advances made in molecularly imprinting of polymers for use in the extraction and quantitation of illicit drugs and additives. Methods for syntheses, highlighted applications, limitations and current challenges are specifically addressed.
A chemical substance (CS) is instantiated in the material world by a number of quantities of such substance (QCSs), placed in different locations. A change of location implies a change in the net of relationships entertained by the QCS with the region wherein it is found. This fact entails changes of the ontological status of the CS, as this is not fully determined by the inherent features of the CS and includes a relevant relational contribution. In order to demonstrate this thesis, we have chosen to analyse the status of quantities of a same CS that are synchronically located in different spacetime regions: a synthetic lab, a lab where the QCS is turned into a material, an industrial plant, the market where the QCS gets a price and a dump waste where the QCS is discarded, respectively:
The use of first-order predicate logic, mereology and locative logic allows carrying out a regimentation process that highlights the ontological commitments implied by the formal expressions through which each element of the aforementioned series can be described. The presence of relational properties discloses the systemic nature of the CS instantiated within a spacetime region. The implications of such an aspect are discussed.
We have explored the collaborative network of the current American Society for Mass Spectrometry (ASMS) membership using bibliometric methods. The analysis shows that 4249 members are connected in a single, large, co-authorship graph, including the majority of the most published authors in the field of mass spectrometry. The map reveals topographical differences between university groups and national laboratories, and that the co-authors with the strongest links have long worked together at the same location. We have collected and summarized information on the geographical distribution of members, showing a high coverage of active researchers in North America and Western Europe. Looking at research fields, we could also identify a number of new or ‘hot’ topics among ASMS members. Interactive versions of the maps are available on-line at https://goo.gl/UBNFMQ (collaborative network) and https://goo.gl/WV25vm (research topics).
A simple solvent-free protocol for the preparation of flunixin, a potent non-narcotic, non-steroidal anti-inflammatory drugs is reported using boric acid as catalyst. Its salt, flunixin meglumine are then prepared under reflux in EtOH. This sustainable method are then extended for the synthesis of a series of 2-(arylamino) nicotinic acid derivatives. The present protocol combines non-hazardous neat conditions with associated benefits like excellent yield, straightforward workup, and use of readily available and safe catalyst in the absence of any solvent, which are important factors in the pharmaceutical industry. The pathway for catalytic activation of 2-chloronicotic acid with boric acid was also investigated using Gaussian 03 program package.
First results are reported using a simple, fast, and reproducible matrix-assisted ionization (MAI) sample introduction method that provides substantial improvements relative to previously published MAI methods. The sensitivity of the new MAI methods, which requires no laser, high voltage, or nebulizing gas, is comparable to those reported for MALDI-TOF and n-ESI. High resolution full acquisition mass spectra having low chemical background are acquired from low nanoliters of solution using only a few femtomoles of analyte. The limit-of-detection for angiotensin II is less than 50 amol on an Orbitrap Exactive mass spectrometer. Analysis of peptides, including a bovine serum albumin digest, and drugs, including drugs in urine without a purification step, are reported using a 1 μL zero dead volume syringe in which only the analyte solution wetting the walls of the syringe needle is used in the analysis.
Bisphenol A (BPA) is a synthetic chemical extensively used in many consumer products. It mimics estrogen activities and is related to developmental disorders and metabolic diseases. The current challenge of BPA detection is their low circulating levels at 0.1～10 ng/mL which is close to the detection limit of most of current analytical methods. In this report, we developed a simple, sensitive, and accurate liquid chromatography mass spectrometry (LCMS) method after 1-methylimidazole-2-sulfonyl chloride derivatization. The method significantly improves sensitivity 5～9-fold over dansyl derivatization and approximately 100-fold without derivatization.
A major challenge in glycomics is the characterization of complex glycan structures that are essential for understanding their diverse roles in many biological processes. We present a novel efficient computational approach, named GlycoDeNovo, for accurate elucidation of the glycan topologies from their tandem mass spectra. Given a spectrum, GlycoDeNovo first builds an interpretation-graph specifying how to interpret each peak using preceding interpreted peaks. It then reconstructs the topologies of peaks that contribute to interpreting the precursor ion. We theoretically prove that GlycoDeNovo is highly efficient. A major innovative feature added to GlycoDeNovo is a data-driven IonClassifier which can be used to effectively rank candidate topologies. IonClassifier is automatically learned from experimental spectra of known glycans to distinguish B- and C-type ions from all other ion types. Our results showed that GlycoDeNovo is robust and accurate for topology reconstruction of glycans from their tandem mass spectra.
Neuropeptides are important signaling molecules used by nervous systems to mediate and fine-tune neuronal communication. They can function as neurotransmitters or neuromodulators in neural circuits, or they can be released as neurohormones to target distant cells and tissues. Neuropeptides are typically cleaved from larger precursor proteins by the action of proteases and can be the subject of post-translational modifications. The short, mature neuropeptide sequences often entail the only evolutionarily reasonably conserved regions in these precursor proteins. Therefore, it is particularly challenging to predict all putative bioactive peptides through in silico mining of neuropeptide precursor sequences. Peptidomics is an approach that allows de novo characterization of peptides extracted from body fluids, cells, tissues, organs, or whole-body preparations. Mass spectrometry, often combined with on-line liquid chromatography, is a hallmark technique used in peptidomics research. Here, we used an acidified methanol extraction procedure and a quadrupole-Orbitrap LC-MS/MS pipeline to analyze the neuropeptidome of Caenorhabditis elegans. We identified an unprecedented number of 203 mature neuropeptides from C. elegans whole-body extracts, including 35 peptides from known, hypothetical, as well as from completely novel neuropeptide precursor proteins that have not been predicted in silico. This set of biochemically verified peptide sequences provides the most elaborate C. elegans reference neurpeptidome so far. To exploit this resource to the fullest, we make our in-house database of known and predicted neuropeptides available to the community as a valuable resource. We are providing these collective data to help the community progress, amongst others, by supporting future differential and/or functional studies.
Targeted residue screening requires the use of reference substances in order to identify potential residues. This becomes a difficult issue when using multi-residue methods capable of analyzing several hundreds of analytes. Therefore, the capability of in silico fragmentation based on a structure database (“suspect screening”) instead of physical reference substances for routine targeted residue screening was investigated. The detection of fragment ions that can be predicted or explained by in silico software was utilized to reduce the number of false positives. These “proof of principle” experiments were done with a tool that is integrated into a commercial MS vendor instrument operating software (UNIFI) as well as with a platform-independent MS tool (Mass Frontier). A total of 97 analytes belonging to different chemical families were separated by reversed phase liquid chromatography and detected in a data-independent acquisition (DIA) mode using ion mobility hyphenated with quadrupole time of flight mass spectrometry. The instrument was operated in the MSE mode with alternating low and high energy traces. The fragments observed from product ion spectra were investigated using a “chopping” bond disconnection algorithm and a rule-based algorithm. The bond disconnection algorithm clearly explained more analyte product ions and a greater percentage of the spectral abundance than the rule-based software (92 out of the 97 compounds produced ≥1 explainable fragment ions). On the other hand, tests with a complex blank matrix (bovine liver extract) indicated that the chopping algorithm reports significantly more false positive fragments than the rule based software.
Coumarins are the phytochemicals, which belong to the family of benzopyrone, that display interesting pharmacological properties. Several natural, synthetic and semisynthetic coumarin derivatives have been discovered in decades for their applicability as lead structures as drugs. Coumarin based conjugates have been described as potential AChE, BuChE, MAO and β-amyloid inhibitors. Therefore, the objective of this review is to focus on the construction of these pharmacologically important coumarin analogues with anti-Alzheimer’s activities, highlight their docking studies and structure–activity relationships based on their substitution pattern with respect to the selected positions on the chromen ring by emphasising on the research reports conducted in between year 1968 to 2017.
