PSEA: A phenotypic similarity ensemble approach for prioritizes candidate genes to aid mendelian disease diagnosis

Motivation: Next-generation sequencing is increasingly applied to the molecular diagnosis of genetic disorders. However, challenges for the interpretation of NGS data remain given the massive number of variants produced by NGS. Careful assessment is required to identify the most likely disease-causing variants that best match the patients' clinical phenotypes, which is highly experience-dependent and of low cost-effectiveness. Results: The human phenotype ontology (HPO) together with the information content (IC) are widely used for phenotypic similarity evaluation. Here, we introduce PSEA, a new phenotypic similarity evaluation tool capable of quantifying groups of HPO terms unbiasedly. By comparing with other methods, PSEA show optimal performance and show a higher tolerance to phenotypic noise or incompleteness. We also developed a web server for disease-causing gene prioritization and HPO-gene weighted linkage visualization. Availability: Source code and Web service are free available at https://github.com/zhonghua-wang/psea and https://phoenix.bgi.com/psea, respectively.

KNIME-based Analysis of Off-Target Effect of Drugs Related to The Molecular 2D Fingerprint

Purpose: Structurally similar molecules are likely to have similar biological activity. In this study, similarity searching based on molecular 2D fingerprint was performed to analyze off-target effects of drugs. The purpose of this study is to determine the correlation between the adverse effects and drug off-targets. Methods: A workflow was built using KNIME to run dataset preparation of twenty-nine targets from ChEMBL, generate molecular 2D fingerprints of the ligands, calculate the similarity between ligand sets, and compute the statistical significance using similarity ensemble approach (SEA). Tanimoto coefficients (Tc) are used as a measure of chemical similarity in which the values between 0.2 and 0.4 are the most common for the majority of ligand pairs and considered to be insignificant similar. Result: The majority of ligand sets are unrelated, as is evidenced by the intrinsic chemical differences and the classification of statistical significance based on expectation value. The rank-ordered expectation value of inter-target similarity showed a correlation with off-target effects of the known drugs. Conclusion: Similarity-searching using molecular 2D fingerprint can be applied to predict off-targets and correlate them to the adverse effects of the drugs. KNIME as an open-source data analytic platform is applicable to build a workflow for data mining of ChEMBL database and generating SEA statistical model.

Predicting the levels of orcinol glucoside during the treatment of osteoporosis by network pharmacology and molecular docking

Introduction: As a traditional Chinese medicine (TCM), Curculigo orchioides Gaertn. (Xianmao) has been widely used to treat bone-related diseases. However, the active components of this TCM, and the specific mechanisms by which it exerts effect, have yet to be elucidated. To identify potential targets for orcinol glucoside (OG), an active constituent of C. orchioides, during the treatment of osteoporosis (OP) by adopting a network pharmacology approach. Methods: First, we mined the Similarity ensemble approach (SEA), SwissTargetPrediction, DisGeNET, and Genecards databases were mined for data related to the prediction of OG- and OP-related targets. Next, we identified the common targets for OG and OP, and then used STRING software to create a protein-protein interaction (PPI) network. Then, we used topological analysis to identify which of the common targets were most significant. Then, we used the common significant targets and g:profiler to perform gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes ( KEGG) pathway enrichment analysis. Finally, we used molecular docking to predict the targets of OG that were most relevant to the treatment of OP and investigated the potential pharmacological mechanisms that might be involved. Results: In total, 130 potential targets of OG, and 4582 targets relevant to OP, were subjected to network analysis. There were 73 common targets; these identified the principal pathways linked to OP. In addition, topological analysis identified 14 key targets. Most of the predicted targets played crucial roles in the PI3K-AKT signaling pathway. Molecular docking identified ten core targets (VEGFA, IL6, EGFR, MAPK1, HRAS, CCND1, FGF2, IL2, MCL1 and CDK4), thus indicating that OG may promote osteoblast proliferation and differentiation by accelerating progression of the cell cycle.Conclusions: This research provides a theoretical base for identifying the specific potential mechanisms of OG in treatment of OP.

A simple representation of three-dimensional molecular structure

Statistical and machine learning approaches predict drug-to-target relationships from 2D small-molecule topology patterns. One might expect 3D information to improve these calculations. Here we apply the logic of the Extended Connectivity FingerPrint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the Extended Three-Dimensional FingerPrint (E3FP). By integrating E3FP with the Similarity Ensemble Approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20, and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP. Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442 - 0.637 kcal/mol/heavy atom.

Pharmacology of W-18 and W-15

ABSTRACTW-18 (1-(4-Nitrophenylethyl)piperidylidene-2-(4-chlorophenyl)sulfonamide)and W-15 (4-chloro-N-[1-(2-phenylethyl)-2-piperidinylidene]-benzenesulfonamide) represent two emerging drugs of abuse chemically related to the potent opioid agonist fentanyl (N-(1-(2-phenylethyl)-4-piperidinyl)-N-phenylpropanamide). Here we describe the comprehensive pharmacological profiles of W-18 and W-15. Although W-18 and W-15 have been described as having potent anti-nociceptive activity and are presumed to interact with opioid receptors, we found them to be without detectible opioid activity at μ, δ, κ and nociception opioid receptors in a variety of assays. We also tested W-18 and W-15 for activity as allosteric modulators at opioid receptors and found them devoid of significant positive or negative allosteric modulatory activity. Comprehensive profiling at essentially all the druggable G-protein coupled receptors in the human genome using the PRESTO-Tango platform revealed no significant activity. In silico predictions using the Similarity Ensemble Approach suggested activity for W-18 only weakly at H3-histamine receptors, which was not confirmed in radioligand binding studies. Weak activity at the sigma receptors and the peripheral benzodiazepine receptor were found for W-18 (Ki=271 nM); W-15 displayed weak antagonist activity at 5-HT2-family serotonin receptors. W-18 is extensively metabolized, but its metabolites also lack opioid activity. W-18 and W-15 did inhibit hERG binding suggesting possible cardiovascular side-effects with high doses. Thus although W-18 and W-15 have been suggested to be potent opioid agonists, our results reveal no significant activity at these or other known targets for psychoactive drugs.