DrugCentral 2021 supports drug discovery and repositioning.

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

BiasNet: A Model to Predict Ligand Bias Toward GPCR Signaling.

Signaling bias is a feature of many G protein-coupled receptor (GPCR) targeting drugs with potential clinical implications. Whether it is therapeutically advantageous for a drug to be G protein biased or ß-arrestin biased depends on the context of the signaling pathway. Here, we explored GPCR ligands that exhibit biased signaling to gain insights into scaffolds and pharmacophores that lead to bias. More specifically, we considered BiasDB, a database containing information about GPCR biased ligands, and focused our analysis on ligands which show either a G protein or ß-arrestin bias. Five different machine learning models were trained on these ligands using 15 different sets of features. Molecular fragments which were important for training the models were analyzed. Two of these fragments (number of secondary amines and number of aromatic amines) were more prevalent in ß-arrestin biased ligands. After training a random forest model on HierS scaffolds, we found five scaffolds, which demonstrated G protein or ß-arrestin bias. We also conducted t-SNE clustering, observing correspondence between unsupervised and supervised machine learning methods. To increase the applicability of our work, we developed a web implementation of our models, which can predict bias based on user-provided SMILES, drug names, or PubChem CID. Our web implementation is available at: drugdiscovery.utep.edu/biasnet.

A comprehensive review of cytochrome P450 2E1 for xenobiotic metabolism.

Cytochrome P450 2E1 (CYP2E1) plays a vital role in drug-induced hepatotoxicity and cancers (e.g. lung and bladder cancer), since it is responsible for metabolizing a number of medications and environmental toxins to reactive intermediate metabolites. CYP2E1 was recently found to be the highest expressed CYP enzyme in human livers using a proteomics approach, and CYP2E1-related toxicity is strongly associated with its protein level that shows significant inter-individual variability related to ethnicity, age, and sex. Furthermore, the expression of CYP2E1 demonstrates regulation by extensive genetic polymorphism, endogenous hormones, cytokines, xenobiotics, and varying pathological states. Over the past decade, the knowledge of pharmacology, toxicology, and biology about CYP2E1 has grown remarkably, but the research progress has yet to be summarized. This study presents a timely systematic review on CYP2E1's xenobiotic metabolism, genetic polymorphism, and inhibitors, with the focus on their clinical relevance for the efficacy and toxicity of various CYP2E1 substrates. Moreover, several knowledge gaps have been identified towards fully understanding the potential interactions among different CYP2E1 substrates in clinical settings. Through in-depth analyses of these knowns and unknowns, we expect this review will aid in future drug development and improve management of CYP2E1 related clinical toxicity.

Targeting aberrant expression of Notch-1 in ALDH+ cancer stem cells in breast cancer.

We have previously reported that high aldehyde dehydrogenase (ALDH) enzyme activity in breast cancer cells results in breast cancer stem cell (BCSC) properties by upregualting Notch-1 and epithelial mesenchymal markers. This results in chemoresistance in breast cancer. Here, we examined the functional and clinical significance of ALDH expression by measuring the ALDH levels in breast cancer tissues by immunohistochemistry. There was a significantly higher ALDH expression in higher grade breast cancer tumor tissues (Grade- II and III) versus normal breast tissues. Injection of BCSC (ALDH+ and CD44+ /CD22- ) cells resulted in aggressive tumor growth in athymic mice versus ALDH- cells. The ALDH+ and CD44+ /CD22- tumors grow rapidly and are larger than ALDH- tumors which were slow growing and smaller. Molecularly, ALDH+ tumors expressed higher expression of Notch-1 and EMT markers than ALDH- tumors. Oral administration of the naturally occurring Psoralidin (Pso, 25 mg/kg of body weight) significantly inhibited the growth in ALDH+ and ALDH- tumors as well. Psoralidin inhibited Notch-1 mediated EMT activation in ALDH+ and ALDH- tumors-this confirms our in vitro findings. Our results suggest that Notch-1 could be an attractive target and inhibition of Notch-1 by Psoralidin may prevent pathogenesis of breast cancer as well as metastasis. © 2016 Wiley Periodicals, Inc.

BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments.

UNLABELLED: Rule-based models are analyzed with specialized simulators, such as those provided by the BioNetGen and NFsim open-source software packages. Here, we present BioNetFit, a general-purpose fitting tool that is compatible with BioNetGen and NFsim. BioNetFit is designed to take advantage of distributed computing resources. This feature facilitates fitting (i.e. optimization of parameter values for consistency with data) when simulations are computationally expensive. AVAILABILITY AND IMPLEMENTATION: BioNetFit can be used on stand-alone Mac, Windows/Cygwin, and Linux platforms and on Linux-based clusters running SLURM, Torque/PBS, or SGE. The BioNetFit source code (Perl) is freely available (http://bionetfit.nau.edu). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: bionetgen.help@gmail.com.

Looking Back, Looking Forward at Halogen Bonding in Drug Discovery.

Halogen bonding has emerged at the forefront of advances in improving ligand: receptor interactions. In particular the newfound ability of this extant non-covalent-bonding phenomena has revolutionized computational approaches to drug discovery while simultaneously reenergizing synthetic approaches to the field. Here we survey, via examples of classical applications involving halogen atoms in pharmaceutical compounds and their biological hosts, the unique advantages that halogen atoms offer as both Lewis acids and Lewis bases.

S···O and S···N Sulfur Bonding Interactions in Protein-Ligand Complexes: Empirical Considerations and Scoring Function.

Sulfur bonding interactions between organosulfur compounds and proteins were examined using crystal structures deposited to-date in the PDB. The data was analyzed as a function of sulfur-σ-hole-bonding (i.e., sulfur bonds) to main chain Lewis bases, viz. oxygen and nitrogen atoms of the backbone amide linkages. The analyses also included an examination of sulfur bonding to side chain Lewis bases (O, N, and S) and to the "non-classical" Lewis bases present in electron-rich aromatic amino acids as-well-as to donor-acceptor bond angle distributions. The interactions analyzed included those restricted to the sum of van der Waals radii of the respective atoms or to a distance of 4 Å. The surveyed data revealed that sulfur bonding tendencies (C-S-C bond angles) were impacted not only by steric effects but perhaps also by enthalpic features present in both the donor and acceptor participants. This knowledge is not only of fundamental interest but is also important in terms of materials and drug-design involving moieties incorporating the sulfur atom. Additionally, a new empirical scoring function was developed to address the anisotropy of sulfur in protein-ligand interactions. This newly developed scoring function is incorporated into AutoDock Vina molecular docking program and is valuable for modeling and drug design.

Pathogenic variants in TMEM184B cause a neurodevelopmental syndrome via alteration of metabolic signaling.

Transmembrane protein 184B (TMEM184B) is an endosomal 7-pass transmembrane protein with evolutionarily conserved roles in synaptic structure and axon degeneration. We report six pediatric patients who have de novo heterozygous variants in TMEM184B. All individuals harbor rare missense or mRNA splicing changes and have neurodevelopmental deficits including intellectual disability, corpus callosum hypoplasia, seizures, and/or microcephaly. TMEM184B is predicted to contain a pore domain, wherein many human disease-associated variants cluster. Structural modeling suggests that all missense variants alter TMEM184B protein stability. To understand the contribution of TMEM184B to neural development in vivo, we suppressed the TMEM184B ortholog in zebrafish and observed microcephaly and reduced anterior commissural neurons, aligning with patient symptoms. Ectopic TMEM184B expression resulted in dominant effects for K184E and G162R. However, in vivo complementation studies demonstrate that all other variants tested result in diminished protein function and indicate a haploinsufficiency basis for disease. Expression of K184E and other variants increased apoptosis in cell lines and altered nuclear localization of transcription factor EB (TFEB), a master regulator of lysosomal biogenesis, suggesting disrupted nutrient signaling pathways. Together, our data indicate that TMEM184B variants cause cellular metabolic disruption likely through divergent molecular effects that all result in abnormal neural development.

A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

Novel biotinylated lipid prodrugs of acyclovir for the treatment of herpetic keratitis (HK): transporter recognition, tissue stability and antiviral activity.

PURPOSE: Biotinylated lipid prodrugs of acyclovir (ACV) were designed to target the sodium dependent multivitamin transporter (SMVT) on the cornea to facilitate enhanced cellular absorption of ACV. METHODS: All the prodrugs were screened for in vitro cellular uptake, interaction with SMVT, docking analysis, cytotoxicity, enzymatic stability and antiviral activity. RESULTS: Uptake of biotinylated lipid prodrugs of ACV (B-R-ACV and B-12HS-ACV) was significantly higher than biotinylated prodrug (B-ACV), lipid prodrugs (R-ACV and 12HS-ACV) and ACV in corneal cells. Transepithelial transport across rabbit corneas indicated the recognition of the prodrugs by SMVT. Average Vina scores obtained from docking studies further confirmed that biotinylated lipid prodrugs possess enhanced affinity towards SMVT. All the prodrugs studied did not cause any cytotoxicity and were found to be safe and non-toxic. B-R-ACV and B-12HS-ACV were found to be relatively more stable in ocular tissue homogenates and exhibited excellent antiviral activity. CONCLUSIONS: Biotinylated lipid prodrugs demonstrated synergistic improvement in cellular uptake due to recognition of the prodrugs by SMVT on the cornea and lipid mediated transcellular diffusion. These biotinylated lipid prodrugs appear to be promising drug candidates for the treatment of herpetic keratitis (HK) and may lower ACV resistance in patients with poor clinical response.

Halogen interactions in protein-ligand complexes: implications of halogen bonding for rational drug design.

Halogen bonding interactions between halogenated ligands and proteins were examined using the crystal structures deposited to date in the PDB. The data was analyzed as a function of halogen bonding to main chain Lewis bases, viz. oxygen of backbone carbonyl and backbone amide nitrogen. This analysis also examined halogen bonding to side-chain Lewis bases (O, N, and S) and to the electron-rich aromatic amino acids. All interactions were restricted to van der Waals radii with respective atoms. The data reveals that while fluorine and chlorine have strong tendencies favoring interactions with the backbone Lewis bases at glycine, the trend is not restricted to the achiral amino acid backbone for larger halogens. Halogen side-chain interactions are not restricted to amino acids containing O, N, and S as Lewis bases. Electron-rich aromatic amino acids host a high frequency of halogen bonds as does Leu. A closer examination of the latter hydrophobic side chain reveals that the "propensity of interactions" of halogen ligands at this oily residue is an outcome of strong classical halogen bonds with Lewis bases in the vicinity. Finally, an examination of Θ1 (C-X···O and C-X···N) and Θ2 (X···O-Z and X···N-Z) angles reveals that very few ligands adopt classical halogen bonding angles, suggesting that steric and other factors may influence these angles. The data is discussed in the context of ligand design for pharmaceutical applications.

Discovery of new AKT1 inhibitors by combination of in silico structure based virtual screening approaches and biological evaluations.

Communicated by Ramaswamy H. Sarma.

Prediction of partial molar volumes of amino acids and small peptides: counting atoms versus topological indices.

Experimental data of partial molar volumes of amino acids and small peptides were compiled from several publications and enabled us to perform a predicative analysis based on quantitative structure-property relationships (QSPR). Based on the simplest level of the descriptors, the new method has high accuracy and was found to be more reliable when compared to the latter QSPR method based on topological indexes. Incorporation of isoelectric pH and 3-D solvent-accessible surface area parameters increased the predictability of the equation to a small extent. Cross-validation studies show that this method is successful in predicting the partial molar volumes of other noncoded amino acids, dipeptides, and diketopiperazine derivatives. This method is the beginning of new studies for larger peptides and proteins. It also can be suggested to be used for molecules that contain the same type of atoms as an amino acid.

REDIAL-2020: A suite of machine learning models to estimate Anti-SARS-CoV-2 activities.

Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (http://drugcentral.org/Redial). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (https://github.com/sirimullalab/redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.

An Improved Free Energy Perturbation FEP+ Sampling Protocol for Flexible Ligand-Binding Domains.

Recent improvements to the free energy perturbation (FEP) calculations, especially FEP+ , established their utility for pharmaceutical lead optimization. Herein, we propose a modified version of the FEP/REST (i.e., replica exchange with solute tempering) sampling protocol, based on detail studies on several targets by probing a large number of perturbations with different sampling schemes. Improved FEP+ binding affinity predictions for regular flexible-loop motions and considerable structural changes can be obtained by extending the prior to REST (pre-REST) sampling time from 0.24 ns/λ to 5 ns/λ and 2 × 10 ns/λ, respectively. With this new protocol, much more precise ∆∆G values of the individual perturbations, including the sign of the transformations and decreased error were obtained. We extended the REST simulations from 5 ns to 8 ns to achieve reasonable free energy convergence. Implementing REST to the entire ligand as opposed to solely the perturbed region, and also some important flexible protein residues (pREST region) in the ligand binding domain (LBD) has considerably improved the FEP+ results in most of the studied cases. Preliminary molecular dynamics (MD) runs were useful for establishing the correct binding mode of the compounds and thus precise alignment for FEP+ . Our improved protocol may further increase the FEP+ accuracy.

Discovery of GlyT2 Inhibitors Using Structure-Based Pharmacophore Screening and Selectivity Studies by FEP+ Calculations.

In recent years, mammalian Glycine transporter 2 (GlyT2) has emerged as a promising target for the development of compounds against chronic pain states. In our current work, we discovered a new set of promising hits that inhibit the glycine transporter at nano- and micromolar activity and have excellent selectivity over GlyT1 (as shown by in vitro studies) using a newly designed virtual screening (VS) protocol that combines a structure-based pharmacophore and docking screens with a success rate of 75%. Furthermore, the free energy perturbation calculations and molecular dynamics (MD) studies revealed the GlyT2 amino acid residues critical for the binding and selectivity of both Glycine and our Hit1 compound. The FEP+ results well-matched with the available literature mutational data proving the quality of the generated GlyT2 structure. On the basis of these results, we propose that our hit compounds may lead to new chronic pain agents to address unmet and challenging clinical needs.

Elucidation of the orientation of selected drugs with 2-hydroxylpropyl-ß-cyclodextrin using 2D-NMR spectroscopy and molecular modeling.

This project aims to study the nature of interaction and orientation of selected drugs such as dexamethorphan HBr (DXM), diphenhydramine HCl (DPH), and lidocaine HCl (LDC) inclusion complexes with hydroxyl-propyl ß-cyclodextrin (HP-ß-CD) using 1HNMR spectroscopy, 2D-NMR ROESY and molecular-modeling techniques. Freeze-drying technique was used to formulate the inclusion complexes between DXM, DPH and LDC with HP-ß-CD (1:1 M ratio) in solid state. Inclusion complex formation was initially characterized by Fourier transform-infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. Further characterization of inclusion complexes to determine the interaction of DXM, DPH and LDC with HP-ß-CD was performed using the 1HNMR spectroscopy, 2D-NMR ROESY and molecular modeling techniques. Inclusion complexes of DXM, DPH and LDC with HP-ß-CD were successfully prepared using the freeze-drying technique. Preliminary studies with FT-IR, DSC, XRD and SEM indicated the formation of inclusion complexes of DXM, DPH and LDC with HP-ß-CD at 1:1 M ratio. 1HNMR study showed a change in proton chemical shift upon complexation. 2D-NMR ROESY (two-dimensional) spectroscopy gave an insight into the spatial arrangement between the host and guest atoms. 2D-ROESY experiments further predicted the direction of orientation of guest molecules, indicating the probability that amino moieties of DXM, DPH and LDC are inside the hydrophobic HP-ß-CD cavity. Cross-peaks of inclusion complexes demonstrated intermolecular nuclear Overhauser effects (NOE) between the amino protons in DXM, DPH and LDC and H-atoms of HP-ß-CD. Molecular modeling studies further confirmed the NMR data, providing a structural basis of the individual complex formations. Microsecond time-level molecular dynamics and metadynamics simulations indicate much stronger binding of DXM to HP-ß-CD and more dynamic behavior for DPH and LDC. In particular, LDC can exhibit multiple binding modes, and even spent some time (∼1-2%) out of the carrier, proving the dynamic nature of the complex. To conclude, 2D-NMR and molecular dynamic simulations elucidate the formation of inclusion complexes and intermolecular interactions of DXM, DPH and LDC with HP-ß-CD.

AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina.

BACKGROUND: Halogen bonding has recently come to play as a target for lead optimization in rational drug design. However, most docking program don't account for halogen bonding in their scoring functions and are not able to utilize this new approach. In this study a new and improved halogen bonding scoring function (XBSF) is presented along with its implementation in the AutoDock Vina molecular docking software. This new improved program is termed as AutoDock VinaXB, where XB stands for the halogen bonding parameters that were added. RESULTS: XBSF scoring function is derived based on the X···A distance and C-X···A angle of interacting atoms. The distance term was further corrected to account for the polar flattening effect of halogens. A total of 106 protein-halogenated ligand complexes were tested and compared in terms of binding affinity and docking poses using Vina and VinaXB. VinaXB performed superior to Vina in the majority of instances. VinaXB was closer to native pose both above and below 2 Å deviation categories almost twice as frequently as Vina. CONCLUSIONS: Implementation of XBSF into AutoDock Vina has been shown to improve the accuracy of the docking result with regards to halogenated ligands. AutoDock VinaXB addresses the issues of halogen bonds that were previously being scored unfavorably due to repulsion factors, thus effectively lowering the output RMSD values.

Withaferin-A suppress AKT induced tumor growth in colorectal cancer cells.

The oncogenic activation of AKT gene has emerged as a key determinant of the aggressiveness of colorectal cancer (CRC); hence, research has focused on targeting AKT signaling for the treatment of advanced stages of CRC. In this study, we explored the anti-tumorigenic effects of withaferin A (WA) on CRC cells overexpressing AKT in preclinical (in vitro and in vivo) models. Our results indicated that WA, a natural compound, resulted in significant inhibition of AKT activity and led to the inhibition of cell proliferation, migration and invasion by downregulating the epithelial to mesenchymal transition (EMT) markers in CRC cells overexpressing AKT. The oral administration of WA significantly suppressed AKT-induced aggressive tumor growth in a xenograft model. Molecular analysis revealed that the decreased expression of AKT and its downstream pro-survival signaling molecules may be responsible for tumor inhibition. Further, significant inhibition of some important EMT markers, i.e., Snail, Slug, ß-catenin and vimentin, was observed in WA-treated human CRC cells overexpressing AKT. Significant inhibition of micro-vessel formation and the length of vessels were evident in WA-treated tumors, which correlated with a low expression of the angiogenic marker RETIC. In conclusion, the present study emphasizes the crucial role of AKT activation in inducing cell proliferation, angiogenesis and EMT in CRC cells and suggests that WA may overcome AKT-induced cell proliferation and tumor growth in CRC.

Identification of Novel Nitrosative Stress Inhibitors through Virtual Screening and Experimental Evaluation.

Nitrosative and oxidative stress, associated with the generation of excessive reactive nitrogen and oxygen radical species respectively, are thought to contribute to protein misfolding diseases which represent a group of neurodegenerative disorders that are characterized by protein aggregation and plaque formation. Curcumin, a polyphenolic compound, possesses diverse anti-inflammatory, antitumor, and antioxidant properties. Several studies have revealed that curcumin can reduce the oxidative/nitrosative stress and thereby decrease the neuronal attrition. However, curcumin has poor bioavailability and has raised several concerns focused on its limited clinical impact. The aim of this study was to find other compounds which can assist in decreasing nitrosative stress and possess enhanced bioavailability. Here, use of ß-lactoglobulin was examined as a vehicle to transport molecules to the gut. The Zinc database was searched using curcumin as reference and 6457 compounds were selected for the study. These compounds were docked to ß-lactoglobulin using Glide to find the best fit ligands. Our studies identified four compounds that bind to ß-lactoglobulin and scavenge NOx (free radicals) efficiently.