Project description:Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5alpha-androstan-3beta-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches.
Project description:G protein-coupled receptors (GPCRs) are a family of transmembrane proteins that act as major mediators of cellular signaling, and are the primary targets for a large portion of clinical therapeutics. Despite their critical role in biology and medicine, a large number of GPCRs are poorly understood, lacking validated ligands or potent synthetic modulators. Ligand-induced GPCR activation can be measured in cell-based assays to test hypotheses about ligand-receptor interactions or to evaluate efficacy of synthetic agonists or antagonists. However, the techniques necessary to develop and implement a cell-based assay to study a given receptor of interest are not commonplace in all laboratories. This chapter outlines methods to develop a cell-based assay to evaluate agonist-induced activation for a GPCR of interest, which can be useful to evaluate the effectiveness of predicted ligands. Examples of sample preparation protocols and data analysis are provided to help researchers from interdisciplinary fields, especially those in fields with relatively little molecular biology or cell culture experience.
Project description:G protein-coupled receptors (GPCRs) are central to how mammalian cells sense and respond to chemicals. Mammalian olfactory receptors (ORs), the largest family of GPCRs, mediate the sense of smell through activation by small molecules, though for most bonafide ligands, they have not been identified. Here, we introduce a platform to screen large chemical panels against multiplexed GPCR libraries using next-generation sequencing of barcoded genetic reporters in stably engineered human cell lines. We mapped 39 mammalian ORs against 181 odorants and identified 79 interactions that have not been reported to our knowledge, including ligands for 15 previously orphaned receptors. This multiplexed receptor assay allows the cost-effective mapping of large chemical libraries to receptor repertoires at scale.
Project description:The ability to accurately predict the binding site, binding pose, and binding affinity for ligand-RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand-RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand-RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand-RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA-ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 Å, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand-RNA binding with an ensemble of RNA conformations and the metal-ion effects.
Project description:Previously, G protein-coupled receptor (GPCR) agonists were tethered from polyamidoamine (PAMAM) dendrimers to provide high receptor affinity and selectivity. Here, we prepared GPCR ligand--dendrimer (GLiDe) conjugates from a potent adenosine receptor (AR) antagonist; such agents are of interest for treating Parkinson's disease, asthma, and other conditions. Xanthine amine congener (XAC) was appended with an alkyne group on an extended C8 substituent for coupling by Cu(I)-catalyzed click chemistry to azide-derivatized G4 (fourth-generation) PAMAM dendrimers to form triazoles. These conjugates also contained triazole-linked PEG groups (8 or 22 moieties per 64 terminal positions) for increasing water-solubility and optionally prosthetic groups for spectroscopic characterization and affinity labeling. Human AR binding affinity increased progressively with the degree of xanthine substitution to reach K(i) values in the nanomolar range. The order of affinity of each conjugate was hA(2A)AR > hA(3)AR > hA(1)AR, while the corresponding monomer was ranked hA(2A)AR > hA(1)AR ≥ hA(3)AR. The antagonist activity of the most potent conjugate 14 (34 xanthines per dendrimer) was examined at the G(i)-coupled A(1)AR. Conjugate 14 at 100 nM right-shifted the AR agonist concentration--response curve in a cyclic AMP functional assay in a parallel manner, but at 10 nM (lower than its K(i) value), it significantly suppressed the maximal agonist effect in calcium mobilization. This is the first systematic probing of a potent AR antagonist tethered on a dendrimer and its activity as a function of variable loading.
Project description:MotivationG protein-coupled receptors (GPCRs) can selectively bind to many types of ligands, ranging from light-sensitive compounds, ions, hormones, pheromones and neurotransmitters, modulating cell physiology. Considering their role in many essential cellular processes, they are one of the most targeted protein families, with over a third of all approved drugs modulating GPCR signalling. Despite this, the large diversity of receptors and their multipass transmembrane architectures make the identification and development of novel specific, and safe GPCR ligands a challenge. While computational approaches have the potential to assist GPCR drug development, they have presented limited performance and generalization capabilities. Here, we explored the use of graph-based signatures to develop pdCSM-GPCR, a method capable of rapidly and accurately screening potential GPCR ligands.ResultsBioactivity data (IC50, EC50, Ki and Kd) for individual GPCRs were curated. After curation, we used the data for developing predictive models for 36 major GPCR targets, across 4 classes (A, B, C and F). Our models compose the most comprehensive computational resource for GPCR bioactivity prediction to date. Across stratified 10-fold cross-validation and blind tests, our approach achieved Pearson's correlations of up to 0.89, significantly outperforming previous methods. Interpreting our results, we identified common important features of potent GPCRs ligands, which tend to have bicyclic rings, leading to higher levels of aromaticity. We believe pdCSM-GPCR will be an invaluable tool to assist screening efforts, enriching compound libraries and ranking candidates for further experimental validation.Availability and implementationpdCSM-GPCR predictive models and datasets used have been made available via a freely accessible and easy-to-use web server at http://biosig.unimelb.edu.au/pdcsm_gpcr/.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
Project description:Machine learning models have emerged as powerful tools for drug discovery of lead compounds. Nevertheless, despite notable advances in model architectures, research on more reliable and physicochemical-based descriptors for molecules and proteins remains limited. To address this gap, we introduce the Fragment Integral Spectrum Descriptor (FISD), aimed at utilizing the spatial configuration and electronic structure information of molecules and proteins, as a novel physicochemical descriptor for virtual screening models. Validation demonstrates that the combination of FISD and a classical neural network model achieves performance comparable to that of complex models paired with conventional structural descriptors. Furthermore, we successfully predict and screen potential binding ligands for two given protein targets, showcasing the broad applicability and practicality of FISD. This research enriches the molecular and protein representation strategies of machine learning and accelerates the process of drug discovery.
Project description:Dynamic light scattering can be used to measure the diffusivity of a protein within a formulation. The dependence of molecular diffusivity on protein concentration (traditionally expressed in terms of the interaction parameter kD) is often used to infer whether protein-protein interactions are repulsive or attractive, resulting in solutions that are colloidally stable or unstable, respectively. However, a number of factors unrelated to intermolecular forces can also impact protein diffusion, complicating this interpretation. Here, we investigate the influence of multicomponent diffusion in a ternary protein-salt-water system on protein diffusion and kD in the context of Nernst-Planck theory. This analysis demonstrates that large changes in protein diffusivity with protein concentration can result even for hard-sphere systems in the absence of protein-protein interactions. In addition, we show that dynamic light scattering measurements of diffusivity made at low ionic strength cannot be reliably used to detect protein conformational changes. We recommend comparing experimentally determined kD values to theoretically predicted excluded-volume contributions, which will allow a more accurate assessment of protein-protein interactions.
Project description:MotivationDrug discovery has witnessed intensive exploration of predictive modeling of drug-target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug-target interactions with clinical outcomes: predicting genome-wide receptor activities or function selectivity, especially agonist versus antagonist, induced by novel chemicals. Two major obstacles compound the difficulty on this task: known data of receptor activity is far too scarce to train a robust model in light of genome-scale applications, and real-world applications need to deploy a model on data from various shifted distributions.ResultsTo address these challenges, we have developed an end-to-end deep learning framework, DeepREAL, for multi-scale modeling of genome-wide ligand-induced receptor activities. DeepREAL utilizes self-supervised learning on tens of millions of protein sequences and pre-trained binary interaction classification to solve the data distribution shift and data scarcity problems. Extensive benchmark studies on G-protein coupled receptors (GPCRs), which simulate real-world scenarios, demonstrate that DeepREAL achieves state-of-the-art performances in out-of-distribution settings. DeepREAL can be extended to other gene families beyond GPCRs.Availability and implementationAll data used are downloaded from Pfam (Mistry et al., 2020), GLASS (Chan et al., 2015) and IUPHAR/BPS and the data from reference (Sakamuru et al., 2021). Readers are directed to their official website for original data. Code is available on GitHub https://github.com/XieResearchGroup/DeepREAL.Supplementary informationSupplementary data are available at Bioinformatics online.
Project description:Measuring sensory sensitivity is important in studying development and developmental disorders. However, with children, there is a need to balance reliable but lengthy sensory tasks with the child's ability to maintain motivation and vigilance. We used simulations to explore the problems associated with shortening adaptive psychophysical procedures, and suggest how these problems might be addressed. We quantify how adaptive procedures with too few reversals can over-estimate thresholds, introduce substantial measurement error, and make estimates of individual thresholds less reliable. The associated measurement error also obscures group differences. Adaptive procedures with children should therefore use as many reversals as possible, to reduce the effects of both Type 1 and Type 2 errors. Differences in response consistency, resulting from lapses in attention, further increase the over-estimation of threshold. Comparisons between data from individuals who may differ in lapse rate are therefore problematic, but measures to estimate and account for lapse rates in analyses may mitigate this problem.