A simple assay for detection of small-molecule redox activity

In addition to selecting molecules of pharmacological interest, high-throughput screening campaigns often generate hits of undesirable mechanism, which cannot be exploited for drug discovery as they lead to obvious problems of specificity and developability. Examples of undesirable mechanisms are target alkylation/acylation and compound aggregation. Both types of "promiscuous" mechanisms have been described in the literature, as have methods for their detection. In addition to these mechanisms, compounds can also inhibit by oxidizing susceptible enzyme targets, such as metalloenzymes and cysteine-using enzymes. However, this redox phenomenon has been documented infrequently, and an easy method for detecting this behavior is missing. In this article, the authors describe direct proof of small-molecule oxidation of a cysteine protease by liquid chromatography/tandem mass spectrometry, develop a simple assay to predict this oxidizing behavior by compounds, and show the utility of this assay by demonstrating its ability to distinguish nuisance redox compounds from well-behaved inhibitors in 3 historical GlaxoSmithKline drug discovery efforts.     

AlphaC-conotoxin PrXA: a new family of nicotinic acetylcholine receptor antagonists

We have purified a novel paralytic peptide with 32 AA and a single disulfide bond from the venom of Conus parius, a fish-hunting species. The peptide has the following sequence: TYGIYDAKPOFSCAGLRGGCVLPONLROKFKE-NH2, where O is 4-trans-hydroxyproline. The peptide, designated alphaC-conotoxin PrXA (alphaC-PrXA), is the defining member of a new, structurally distinct family of Conus peptides. The peptide is a competitive nAChR antagonist; all previously characterized conotoxins that competitively antagonize nAChRs are structurally and genetically unrelated. (Most belong to the alpha- and alphaA-conotoxin families.) When administered to mice and fish in vivo, alphaC-PrXA caused paralysis and death. In electrophysiological assays, alphaC-PrXA potently antagonized mouse muscle nicotinic acetylcholine receptors (nAChRs), with IC50 values of 1.8 and 3.0 nM for the adult (alpha1beta1 epsilondelta subunits) and fetal (alpha1beta1 gammadelta subunits) muscle nAChR subtypes, respectively. When tested on a variety of ligand-gated and voltage-gated ion channels, alphaC-PrXA proved to be a highly specific inhibitor of the neuromuscular nAChR. The peptide competes with alpha-bungarotoxin for binding at the alpha/delta and alpha/gamma subunit interfaces of the nAChR, with higher affinity for the alpha/delta subunit interface. AlphaC-PrXA is strikingly different from the many conopeptides shown to be nicotinic antagonists; it is most similar in its general biochemical features to the snake toxins known as Waglerins.     

Purification of chaperonins

The availability of protein samples of sufficient quality and in sufficient quantity is a driving force in biology and biotechnology. Protein samples that are free of critical contaminants are required for specific assays. Large amounts of highly homogeneous and reproducible material are needed for crystallography and nuclear magnetic resonance studies of protein structure. Protein-based therapeutic factors used in human medicine must not contain any contaminants that might interfere with treatment. The roles played by molecular chaperones in protein folding and in many cellular processes make these proteins very attractive candidates as biochemical reagents, and the class of chaperones called chaperonins is one of the most important candidates. Methods for successfully purifying chaperonins are needed to advance the field of chaperonin-mediated protein folding. This article outlines the strategies and methods used to obtain pure chaperonin samples from different biological sources. The objective is to help new researchers obtain better quality samples of chaperonins from many new organisms.     

A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers

The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucleotide-specific molecule. Usually, 10 to 20 steps are required for SELEX to be completed. Throughout this process it is necessary to discriminate between true DNA aptamers and unspecified DNA-binding sequences. Thus, a novel machine learning-based approach was developed to support and simplify the early steps of the SELEX process, to help discriminate binding between DNA aptamers from those unspecified targets of DNA-binding sequences. An Artificial Intelligence (AI) approach to identify aptamers were implemented based on Natural Language Processing (NLP) and Machine Learning (ML). NLP method (CountVectorizer) was used to extract information from the nucleotide sequences. Four ML algorithms (Logistic Regression, Decision Tree, Gaussian Naïve Bayes, Support Vector Machines) were trained using data from the NLP method along with sequence information. The best performing model was Support Vector Machines because it had the best ability to discriminate between positive and negative classes. In our model, an Accuracy (A) of 0.995, the fraction of samples that the model correctly classified, and an Area Under the Receiving Operating Curve (AUROC) of 0.998, the degree by which a model is capable of distinguishing between classes, were observed. The developed AI approach is useful to identify potential DNA aptamers to reduce the amount of rounds in a SELEX selection. This new approach could be applied in the design of DNA libraries and result in a more efficient and faster process for DNA aptamers to be chosen during SELEX.     

Different neuroplasticity for task targets and distractors

Adult learning-induced sensory cortex plasticity results in enhanced action potential rates in neurons that have the most relevant information for the task, or those that respond strongly to one sensory stimulus but weakly to its comparison stimulus. Current theories suggest this plasticity is caused when target stimulus evoked activity is enhanced by reward signals from neuromodulatory nuclei. Prior work has found evidence suggestive of nonselective enhancement of neural responses, and suppression of responses to task distractors, but the differences in these effects between detection and discrimination have not been directly tested. Using cortical implants, we defined physiological responses in macaque somatosensory cortex during serial, matched, detection and discrimination tasks. Nonselective increases in neural responsiveness were observed during detection learning. Suppression of responses to task distractors was observed during discrimination learning, and this suppression was specific to cortical locations that sampled responses to the task distractor before learning. Changes in receptive field size were measured as the area of skin that had a significant response to a constant magnitude stimulus, and these areal changes paralleled changes in responsiveness. From before detection learning until after discrimination learning, the enduring changes were selective suppression of cortical locations responsive to task distractors, and nonselective enhancement of responsiveness at cortical locations selective for target and control skin sites. A comparison of observations in prior studies with the observed plasticity effects suggests that the non-selective response enhancement and selective suppression suffice to explain known plasticity phenomena in simple spatial tasks. This work suggests that differential responsiveness to task targets and distractors in primary sensory cortex for a simple spatial detection and discrimination task arise from nonselective increases in response over a broad cortical locus that includes the representation of the task target, and selective suppression of responses to the task distractor within this locus.     

Longitudinal association of maternal attempt to lose weight during the postpartum period and child obesity at age 3 years

The effect of maternal attempt to lose weight during the postpartum period on later child weight has not been explored. Among 1,044 mother-infant pairs in Project Viva, we estimated longitudinal associations of maternal attempt to lose weight during the postpartum period with child weight and adiposity at age 3 years and examined differences in associations by type of weight loss strategy used. Using covariate-adjusted linear and logistic regression models, we estimated associations before and after adjusting for maternal weight-related variables including prepregnancy BMI. At 6 months postpartum, 53% mothers were trying to lose weight. At age 3 years, mean (s.d.) child BMI z-score was 0.44 (1.01) and 8.9% of children were obese. Children whose mothers were trying to lose weight at 6 months postpartum had higher BMI z-scores (0.30 (95% confidence interval (CI) 0.18, 0.42)) and were more likely to be obese (3.0 (95% CI 1.6, 5.8)) at 3 years of age. Addition of maternal prepregnancy BMI to the models attenuated but did not eliminate the associations seen for BMI z-score (0.24 (95% CI 0.12, 0.36) and obesity (2.4 (95% CI 1.2, 4.7)). Attempting to lose weight by exercising alone was the only weight loss strategy that consistently predicted higher child BMI z-score (0.36 (95% CI 0.14, 0.58)) and odds of obesity (6.0 (95% CI 2.2, 16.5)) at age 3 years. In conclusion, we observed an association between maternal attempt to lose weight at 6 months postpartum, particularly through exercise alone, measured using a single item and child adiposity at age 3 years. This association should be thoroughly examined in future studies.