Understanding false positives in reporter gene assays: in silico chemogenomics approaches to prioritize cell-based HTS data

High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.     

Exploring the mechanism of Cremastra Appendiculata (SUANPANQI) against breast cancer by network pharmacology and molecular docking

BackgroundSUANPANQI, the pseudo phosphorous stem of Cremastra appendiculata, is one of the most well-known traditional Chinese medicine, which has been shown to inhibit tumorigenesis in various human cancers. However, the underlying mechanism of SUANPANQI treatment against breast cancer (BRCA) remains unclear. In this study. we aim to investigate the bioactive compounds and mechanisms of SUANPANQI in the treatment of BRCA based on network pharmacology and molecular docking.MethodsThe compounds were collected from previous research. SwissADME was used to screen bioactive compounds. The targets corresponding to SUANPANQI and BRCA were obtained using MalaCards and SwissTargetPrediction. SUANPANQI-related and BRCA-related targets were found and then overlapped to get intersections, which represented potential anti-BRCA targets of SUANPANQI. The Cytoscape software was used to construct bioactive compounds targeting the BRCA network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the targets was extracted from the metascape database, then conducted using the Cluster Profiler package in R software. Protein-Protein interaction (PPI) network was constructed using the STRING online database and analyzed using Cytoscape software. Pivotal genes were screened using the topological analysis, survival analysis, and pathological stage analysis. Molecular docking analysis was used to verify whether the bioactive compounds had a definite affinity with the pivotal targets.ResultsSixty-five bioactive compounds of SUANPANQI were involved with 225 predicted BRCA targets. Then, a compound-target network and a PPI network were constructed. The GO analysis and KEGG enrichment analysis suggested that SUANPANQI worked against BRCA via PI3K-Akt, Ras, FoxO, Rap1, and ErbB signaling pathways, etc. After topological analysis, survival analysis, and pathological stage analysis of the SUANPANQI potential targets against BRCA, 6 pivotal target genes (AR, HSP90AA1, MMP9, PGR, PTGS2, TNF) that were highly responsible for the therapeutic effects of SUANPANQI against BRCA were obtained. Molecular docking results showed that 6 bioactive compounds of SUANPANQI had strong binding efficiency with the 6 pivotal genes.ConclusionsThe present study clarifies the mechanism of SUANPANQI against BRCA through multiple targets and pathways, and provides evidence to support its clinical use.     

Emergence in Chemistry: Chemistry as the Embodiment of Emergence

The main aim of the paper is to reinforce the notion that emergence is a basic characteristic of the molecular sciences in general and chemistry in particular. Although this point is well accepted, even in the primary reference on emergence, the keyword emergence is rarely utilized by chemists and molecular biologists and chemistry textbooks for undergraduates. The possible reasons for this situation are discussed. The paper first re-introduces the concept of emergence based on very simple geometrical forms; and considers some simple chemical examples among low and high molecular weight compounds. On the basis of these chemical examples, a few interesting philosophical issues inherent to the field of emergence are discussed – again making the point that such examples, given their clarity and simplicity, permit one to better understand the complex philosophical issues. Thus, the question of predictability is discussed, namely whether and to what extent can emergent properties be predicted on the basis of the component’s properties; or the question of the explicability (a top down process). The relation between reductionism and emergentism is also discussed as well as the notion of downward causality and double causality (macrodeterminism); namely the question whether and to what extent the emergent properties of the higher hierarchic level affect the properties of the lower level components. Finally, the question is analyzed, whether life can be considered as an emergent property. More generally, the final point is made, that the re-introduction of the notion of emergence in chemistry, and in particular in the teaching, may bring about a deeper understanding of the meaning of chemical complexity and may bring chemistry closer to the humanistic areas of philosophy and epistemology. This revised version was published online in July 2006 with corrections to the Cover Date.     

Performance evaluation of the GastroPlusTM software tool for prediction of the toxicokinetic parameters of chemicals

ABSTRACT

The accurate prediction of toxicokinetic parameters arising from oral, dermal and inhalation routes of chemical exposure is a key element in chemical safety assessments. In this research, the physiologically based pharmacokinetic (PBPK) GastroPlus^TM software was evaluated against a series of chemicals for the prediction of toxicokinetic parameters. Overall, 67% of predicted intrinsic clearance (Clint) values were within 1- to 10-fold of empirical data for 463 compounds, and 87% of the predicted fraction unbounded in plasma (Fup) values were 1- to 3-fold of empirical data for 441 compounds. The r² (coefficient of determination) of predicted C_max (maximum plasma concentration) and AUC (Area Under Curve) values versus the corresponding empirical values from oral, inhalation and dermal exposures ranged from 0.04 to 0.92. Among the three exposures, the highest r² values, ranging from 0.80 to 0.92, were observed for oral exposure predictions, where 88% of the compounds had 1- to 10-fold differences between predicted and empirical values for C_max and AUC. The predicted plasma C_ss (steady-state plasma concentration) values were consistent with those C_ss values calculated by in vitro-to-in vivo extrapolation (IVIVE) approaches using experimental parameters. Based on the evaluation results, GastroPlus? can be used as a QSAR/PBPK tool for toxicokinetic parameter predictions.     

Identification of potential inhibitors against nuclear Dam1 complex subunit Ask1 of Candida albicans using virtual screening and MD simulations

Identification of hit compounds against specific target form the starting point for a drug discovery program. A consistent decline of new chemical entities (NCEs) in recent years prompted a challenge to explore newer approaches to discover potential hit compounds that in turn can be converted into leads, and ultimately drug with desired therapeutic efficacy. The vast amount of omics and activity data available in public databases offers an opportunity to identify novel targets and their potential inhibitors. State of the art in silico methods viz., clustering of compounds, virtual screening, molecular docking, MD simulations and MMPBSA calculations were employed in a pipeline to identify potential ‘hits’ against those targets as well whose structures, as of now, could only predict through threading approaches. In the present work, we have started from scratch, amino acid sequence of target and compounds retrieved from PubChem compound database, modeled it in such a way that led to the identification of possible inhibitors of Dam1 complex subunit Ask1 of Candida albicans. We also propose a ligand based binding site determination approach. We have identified potential inhibitors of Ask1 subunit of a Dam1 complex of C. albicans, which is required to prevent precocious spindle elongation in pre-mitotic phases. The proposed scheme may aid to find virtually potential inhibitors of other unique targets against candida.     

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE_DB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.     

Computer aided prediction of biological activity spectra: Evaluating versus known and predicting of new activities for thiazole derivatives

Computer aided prediction of biological activity spectra by the computer program PASS was applied to a set of 89 new thiazole derivatives. Experimentally tested activities (NSAID, local anaesthetic and antioxidant) coincide with the experiment in 70.8% cases, that exceeds significantly the random guess-work (～0.1%). Therefore, computer aided prediction using the Prediction of Activity Spectra for Substances (PASS) system (http://www.ibmh.msk.su/PASS) provides a reliable basis for planning of synthesis and experimental study for new compounds. New psychotropic activities are predicted for some compounds from the series under study. In particular, 7, 44 and 55 compounds likely have anxiolytic, anticonvulsant and cognition enhancer effects, respectively. Most of these compounds have the estimated values of probability to be active ( P a ) less than 60%. Therefore, if their activity will be confirmed by the experiment, they might occur to be New Chemical Entities.     

Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining

Molecular target identification is of central importance to drug discovery. Here, we developed a computational approach, named bioactivity profile similarity search (BASS), for associating targets to small molecules by using the known target annotations of related compounds from public databases. To evaluate BASS, a bioactivity profile database was constructed using 4296 compounds that were commonly tested in the US National Cancer Institute 60 human tumor cell line anticancer drug screen (NCI-60). Each compound was used as a query to search against the entire bioactivity profile database, and reference compounds with similar bioactivity profiles above a threshold of 0.75 were considered as neighbor compounds of the query. Potential targets were subsequently linked to the identified neighbor compounds by using the known targets of the query compound. About 45% of the predicted compound-target associations were successfully verified retrospectively, suggesting the possible application of BASS in identifying the targets of uncharacterized compounds and thus providing insight into the study of promiscuity and polypharmacology. Furthermore, BASS identified a significant fraction of structurally diverse compounds with similar bioactivities, indicating its feasibility of "scaffold hopping" in searching novel molecules against the target of interest.     

Novel dibenzosuberene substituted aroyl selenoureas: Synthesis,crystal structure,DFT, molecular docking and biological studies

Abstract

A series of aroyl selenourea dibenzosuberene (1–3) derivatives were synthesized and characterized by different analytical methods and single crystal X-ray crystallography. Quantum chemical computations were made using DFT to determine the structural and molecular properties of the compounds. The in vitro antibacterial action of the compounds was evaluated against chosen gram-negative (Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli), and gram-positive (Bacillus subtilis, Staphylococcus aureus, and Staphylococcus epidermidis) bacteria for their antifungal activity against Curvularia lunata, Penicillium notatum, and Aspergillus niger. Using molecular docking studies, the binding modes were understood along with the mechanism in opposing the target protein MurB.     

Prenylated Flavonoid Glycosides with PCSK9 mRNA Expression Inhibitory Activity from the Aerial Parts of Epimedium koreanum

Phytochemical investigation on the n-BuOH-soluble fraction of the aerial parts of Epimedium koreanum using the PCSK9 mRNA monitoring assay led to the identification of four previously undescribed acylated flavonoid glycosides and 18 known compounds. The structures of new compounds were elucidated by NMR, MS, and other chemical methods. All isolated compounds were tested for their inhibitory activity against PCSK9 mRNA expression in HepG2 cells. Of the isolates, compounds 6, 7, 10, 15, and 17–22 were found to significantly inhibit PCSK9 mRNA expression. In particular, compound 7 was shown to increase LDLR mRNA expression. Thus, compound 7 may potentially increase LDL uptake and lower cholesterol levels in the blood.     

Preliminary studies of berberine and its semi-synthetic derivatives as a promising class of multi-target anti-parkinson agents

Parkinson’s disease (PD) is a neurodegenerative disorder bearing motor and nonmotor symptoms. The treatment today is symptomatical rather than preventive or curative and this leaves the field open for the search of both novel molecular targets and drug candidates. Interference with α-synuclein fibrillation, monoamine oxidase (MAO) inhibition, modulation of adenosine receptors and the inhibition of specific phosphodiesterase (PDE) isoforms are some of the currently pursued strategies. We synthesised and studied some semi-synthetic berberine derivatives using a set of in silico tools. We evaluated their drug-likeness and tested the compounds against a set of target proteins involved in the onset or progression of PD, with a particular attention to MAO-B. Preliminary in vitro assay on MAO-B confirmed our in silico predictions.     

A method of finding an informative set of molecular graph indices for the prediction of chemical compound properties

The method and algorithms for predicting properties of chemical compounds by their quantitative structural characteristics, in particular, molecular graph indices, are presented. The prediction procedure consists of establishing the priority of indices for training sample compounds, classifying control sample compounds in the Euclidean space of indices, and finding a locally optimum (informative) index set giving a least prediction error. The algorithms have been successfully tested using the BACC system (analysis and classification of biologically active compounds), created at the S. V. Sobolev Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences. Translated fromZhumal Strukturnoi Khimii, Vol. 38, No. 4, pp. 795–802, July–August, 1997.