Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F

In silico design and in vitro assessment of anti-Helicobacter pylori compounds as potential small-molecule arginase inhibitors

Related to a variety of gastrointestinal disorders ranging from gastric ulcer to gastric adenocarcinoma, the infection caused by the gram-negative bacteria Helicobacter pylori (H. pylori) poses as a great threat to human health; hence, the search for new treatments is a global priority. The H. pylori arginase (HPA) protein has been widely studied as one of the main virulence factors of this bacterium, being involved in the prevention of nitric oxide-mediated bacterial cell death, which is a central component of innate immunity. Given the growing need for the development of new drugs capable of combating the infection by H. pylori, the present work describes the search for new HPA inhibitors, using virtual screening techniques based on molecular docking followed by the evaluation of the proposed modes of interaction at the HPA active site. In vitro studies of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), followed by cytotoxicity activity in gastric adenocarcinoma and non-cancer cells, were performed. The results highlighted compounds 6, 11, and 13 as potential inhibitors of HPA; within these compounds, the results indicated 13 presented an improved activity toward H. pylori killing, with MIC and MBC both at 64 µg/mL. Moreover, compound 13 also presented a selectivity index of 8.3, thus being more selective for gastric adenocarcinoma cells compared to the commercial drug cisplatin. Overall, the present work demonstrates the search strategy based on in silico and in vitro techniques is able to support the rational design of new anti-H. pylori drugs.

Synthesis, biological evaluation and molecular docking studies of novel 1,2,3-triazole-quinazolines as antiproliferative agents displaying ERK inhibitory activity

ERK1/2 inhibitors have attracted special attention concerning the ability of circumventing cases of innate or log-term acquired resistance to RAF and MEK kinase inhibitors. Based on the 4-aminoquinazoline pharmacophore of kinases, herein we describe the synthesis of 4-aminoquinazoline derivatives bearing a 1,2,3-triazole stable core to bridge different aromatic and heterocyclic rings using copper-catalysed azide-alkyne cycloaddition reaction (CuAAC) as a Click Chemistry strategy. The initial screening of twelve derivatives in tumoral cells (CAL-27, HN13, HGC-27, and BT-20) revealed that the most active in BT-20 cells (25a, IC₅₀ 24.6 μM and a SI of 3.25) contains a more polar side chain (sulfone). Furthermore, compound 25a promoted a significant release of lactate dehydrogenase (LDH), suggesting the induction of cell death by necrosis. In addition, this compound induced G0/G1 stalling in BT-20 cells, which was accompanied by a decrease in the S phase. Western blot analysis of the levels of p-STAT3, p-ERK, PARP, p53 and cleaved caspase-3 revealed p-ERK1/2 and p-STA3 were drastically decreased in BT-20 cells under 25a incubation, suggesting the involvement of these two kinases in the mechanisms underlying 25a-induced cell cycle arrest, besides loss of proliferation and viability of the breast cancer cell. Molecular docking simulations using the ERK-ulixertinib crystallographic complex showed compound 25a could potentially compete with ATP for binding to ERK in a slightly higher affinity than the reference ERK1/2 inhibitor. Further in silico analyses showed comparable toxicity and pharmacokinetic profiles for compound 25a in relation to ulixertinib.

QSAR Modeling of SARS-CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2

The main protease (M^pro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) M^pro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC₅₀ of 8.9 μM), proglumetacin (tested twice independently, with AC₅₀ of 8.9 μM and 12.5 μM), and sufugolix (AC₅₀ 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).

1,2,3-Triazole tethered 2-mercaptobenzimidazole derivatives: design, synthesis and molecular assessment toward C6 glioma cell line

Aim: Glioblastoma multiforme (GBM) is an aggressive cancer with very limited clinical therapies. Herein, we have designed novel mercaptobenzimidazole derivatives (1-7) as multitarget antineoplastic drugs and assessed their antiproliferative profiles on an experimental model for GBM, the C6 glioma line. Results: The target compounds were synthesized in few steps with reasonable yields (33-90%). Compounds 1 (∼18 μM) and 4 (∼20 μM) showed dose-dependent antiproliferative effects on C6 glioma and significantly increased early apoptosis, but only 4 disrupted the cell cycle progression and did not induce autophagy. Docking simulations suggested these compounds as dual kinase and colchicine binding site inhibitors. Conclusion: In spite of the limited selective toxicity, 4 hold the potential to be further optimized for the treatment of GBM.

Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models

Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).

Logistic Regression of Ligands of Chemotaxis Receptors Offers Clues about Their Recognition by Bacteria

Because of relative simplicity of signal transduction pathway, bacterial chemotaxis sensory systems have been expected to be applied to biosensor. Tar and Tsr receptors mediate chemotaxis of Escherichia coli and have been studied extensively as models of chemoreception by bacterial two-transmembrane receptors. Such studies are typically conducted using two canonical ligands: l-aspartate for Tar and l-serine for Tsr. However, Tar and Tsr also recognize various analogs of aspartate and serine; it remains unknown whether the mechanism by which the canonical ligands are recognized is also common to the analogs. Moreover, in terms of engineering, it is important to know a single species of receptor can recognize various ligands to utilize bacterial receptor as the sensor for wide range of substances. To answer these questions, we tried to extract the features that are common to the recognition of the different analogs by constructing classification models based on machine-learning. We computed 20 physicochemical parameters for each of 38 well-known attractants that act as chemoreception ligands, and 15 known non-attractants. The classification models were generated by utilizing one or more of the seven physicochemical properties as descriptors. From the classification models, we identified the most effective physicochemical parameter for classification: the minimum electron potential. This descriptor that occurred repeatedly in classification models with the highest accuracies, This descriptor used alone could accurately classify 42/53 of compounds. Among the 11 misclassified compounds, eight contained two carboxyl groups, which is analogous to the structure of characteristic of aspartate analog. When considered separately, 16 of the 17 aspartate analogs could be classified accurately based on the distance between their two carboxyl groups. As shown in these results, we succeed to predict the ligands for bacterial chemoreceptors using only a few descriptors; single descriptor for single receptor. This result might be due to the relatively simple topology of bacterial two-transmembrane receptors compared to the G-protein-coupled receptors of seven-transmembrane receptors. Moreover, this distance between carboxyl groups correlated with the receptor binding affinity of the aspartate analogs. In view of this correlation, we propose a common mechanism underlying ligand recognition by Tar of compounds with two carboxyl groups.

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r²) of the regression models was 0.664 (for test sets). The 2B-3 (A_2Bvs A₃) model performed best with q² = 0.769 for training sets (10-fold cross-validation), and r² = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A_2Avs A₃) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity

A computational procedure to search for selective ligands for structurally related protein targets was developed and verified for serotonergic 5-HT7/5-HT1A receptor ligands. Starting from a set of compounds with annotated activity at both targets (grouped into four classes according to their activity: selective toward each target, not-selective and not-selective but active) and with an additional set of decoys (prepared using DUD methodology), the SVM (Support Vector Machines) models were constructed using a selective subset as positive examples and four remaining classes as negative training examples. Based on these four component models, the consensus classifier was then constructed using a data fusion approach. The combination of two approaches of data representation (molecular fingerprints vs. structural interaction fingerprints), different training set sizes and selection of the best SVM component models for consensus model generation, were evaluated to determine the optimal settings for the developed algorithm. The results showed that consensus models with molecular fingerprints, a larger training set and the selection of component models based on MCC maximization provided the best predictive performance.

Discovery of Natural Product-Derived 5-HT1A Receptor Binders by Cheminfomatics Modeling of Known Binders, High Throughput Screening and Experimental Validation

The human 5-hydroxytryptamine receptor subtype 1A (5-HT1A) is highly expressed in the raphe nuclei region and limbic structures; for that reason 5-HT1A has served as a promising target for treating human mood disorders and neurodegenerative diseases. We have developed binary quantitative structure-activity relationship (QSAR) models for 5- HT1A binding using data retrieved from the WOMBAT database and the k-Nearest Neighbor (kNN) machine learning method. A rigorous QSAR modeling and screening workflow had been followed, with extensive internal and external validation processes. The models' classification accuracies to discriminate 5-HT1A binders from the non-binders are as high as 96% for the external validation. These models were employed further to mine two major natural products screening libraries, i.e. TimTec Natural Product Library (NPL) and Natural Derivatives Library (NDL). In the end five screening hits were tested by radioligand binding assays with a success rate of 40%, and two Library compounds were confirmed to be binders at the μM concentration against the human 5-HT1A receptor. The combined application of rigorous QSAR modeling and model-based virtual screening presents a powerful means for profiling natural products compounds with important biomedical activities.

An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the “artificial enrichment” and “analogue bias” of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD.

Recent trends in statistical QSAR modeling of environmental chemical toxicity

Quantitative cheminformatics approaches such as QSAR modeling find growing applications in chemical risk assessment. Traditional methods rely on the use of calculated chemical descriptors of molecules and relatively small training sets. However, in recent years, there is a trend toward the increased use of in vitro biological testing approaches to reduce both the length of experimental studies and the animal use for chemical risk assessment. Furthermore, there is also much greater emphasis on model validation using external datasets to enable the reliable use of computational models as part of regulatory decision making. In this chapter, recent trends emphasizing the need for both careful curation of experimental data prior to model development and rigorous model validation are investigated. Furthermore, recent approaches to chemical toxicity prediction that employ both chemical descriptors and in vitro screening data for developing novel hybrid chemical/biological models are being reviewed. Examples of respective application studies that employ novel workflows for model developments are described and recent important efforts by several academic, nonprofit, and industrial groups to start placing both data and, especially, models in the public domain are discussed.

Why QSAR fails: an empirical evaluation using conventional computational approach

Although a number of pitfalls of QSAR have been corrected in the past decade, the reliability of QSAR models is still insufficient. The reason why QSAR fails is still under hot debate; our study attempts to address this topic from a practical and empirical perspective, evaluating two relatively large toxicological data sets using a typical combination of support vector machine (SVM) and genetic algorithm (GA). Our results suggest that the vast number of equivalent models to be chosen and the insufficient validation strategy are primarily responsible for the failure of many QSAR models. First, a method often produces much more equivalent models than we might expect, and the corresponding descriptor sets show little overlap, indicating the unreliability of the conventional approaches. Moreover, although external validation has been considered necessary, validation on an arbitrarily selected independent set is still insufficient to guarantee the true predictability of a QSAR model. Therefore, more effective training and validation strategies are demanded to enhance the reliability of QSAR models. The present study also demonstrates that combinatorial or ensemble models can greatly reduce the variance of equivalent models, and that models built with the most frequently selected descriptors used by the equivalent models seem to yield more promising performances.

Estimating persistence of brominated and chlorinated organic pollutants in air, water, soil, and sediments with the QSPR-based classification scheme

We have estimated degradation half-lives of both brominated and chlorinated dibenzo-p-dioxins (PBDDs and PCDDs), furans (PBDFs and PCDFs), biphenyls (PBBs and PCBs), naphthalenes (PBNs and PCNs), diphenyl ethers (PBDEs and PCDEs) as well as selected unsubstituted polycyclic aromatic hydrocarbons (PAHs) in air, surface water, surface soil, and sediments (in total of 1,431 compounds in four compartments). Next, we compared the persistence between chloro- (relatively well-studied) and bromo- (less studied) analogs. The predictions have been performed based on the quantitative structure-property relationship (QSPR) scheme with use of k-nearest neighbors (kNN) classifier and the semi-quantitative system of persistence classes. The classification models utilized principal components derived from the principal component analysis of a set of 24 constitutional and quantum mechanical descriptors as input variables. Accuracies of classification (based on an external validation) were 86, 85, 87, and 75% for air, surface water, surface soil, and sediments, respectively. The persistence of all chlorinated species increased with increasing halogenation degree. In the case of brominated organic pollutants (Br-OPs), the trend was the same for air and sediments. However, we noticed that the opposite trend for persistence in surface water and soil. The results suggest that, due to high photoreactivity of C-Br chemical bonds, photolytic processes occurring in surface water and soil are able to play significant role in transforming and removing Br-OPs from these compartments. This contribution is the first attempt of classifying together Br-OPs and Cl-OPs according to their persistence, in particular, environmental compartments.

A novel multilevel statistical method for the study of the relationships between multireceptorial binding affinity profiles and in vivo endpoints

The present work introduces a novel method for drug research based on the sequential building of linked multivariate statistical models, each one introducing a different level of drug description. The use of multivariate methods allows us to overcome the traditional one-target assumption and to link in vivo endpoints with drug binding profiles, involving multiple receptors. The method starts with a set of drugs, for which in vivo or clinical observations and binding affinities for potentially relevant receptors are known, and allows obtaining predictions of the in vivo endpoints highlighting the most influential receptors. Moreover, provided that the structure of the receptor binding sites is known (experimentally or by homology modeling), the proposed method also highlights receptor regions and ligand-receptor interactions that are more likely to be linked to the in vivo endpoints, which is information of high interest for the design of novel compounds. The method is illustrated by a practical application dealing with the study of the metabolic side effects of antipsychotic drugs. Herein, the method detects related receptors confirmed by experimental results. Moreover, the use of structural models of the receptor binding sites allows identifying regions and ligand-receptor interactions that are involved in the discrimination between antipsychotic drugs that show metabolic side effects and those that do not. The structural results suggest that the topology of a hydrophobic sandwich involving residues in transmembrane helices (TM) 3, 5, and 6, as well as the assembling of polar residues in TM5, are important discriminators between target/antitarget receptors. Ultimately, this will provide useful information for the design of safer compounds inducing fewer side effects.

Molecular modelling and QSAR analysis of some structurally diverse N-type calcium channel blockers

A quantitative structure-activity relationship (QSAR) analysis was performed on a data set of 104 molecules showing N-type calcium channel blocking activity. Several types of descriptors, including electrotopological, structural, thermodynamics and ADMET, were used to derive a quantitative relationship between N-type calcium channel blocking activity and structural properties. The genetic algorithm-based genetic function approximation (GFA) method of variable selection was used to generate the 2D-QSAR model. The model was established on a training set of 83 molecules, and validated by a test set of 21 molecules. The model was developed using five information-rich descriptors--Atype_C_24, Atype_N_68, Rotlbonds, S_sssN, and ADME_Solubility--playing an important role in determining N-type calcium channel blocking activity. For the best QSAR model (model 4), the statistics were r (2) = 0.798; q (2) = 0.769; n = 83 for the training set. This model was further validated using the leave-one-out (LOO) cross-validation approach, Fischer statistics (F), Y-randomisation test, and predictions based on the test data set. The resulting descriptors produced by QSAR model 4 were used to identify physico-chemical features relevant to N-type calcium channel blocking activity.

Substance-tailored testing strategies in toxicology: an in silico methodology based on QSAR modeling of toxicological thresholds and Monte Carlo simulations of toxicological testing

The design of toxicological testing strategies aimed at identifying the toxic effects of chemicals without (or with a minimal) recourse to animal experimentation is an important issue for toxicological regulations and for industrial decision-making. This article describes an original approach which enables the design of substance-tailored testing strategies with a specified performance in terms of false-positive and false-negative rates. The outcome of toxicological testing is simulated in a different way than previously published articles on the topic. Indeed, toxicological outcomes are simulated not only as a function of the performance of toxicological tests but also as a function of the physico-chemical properties of chemicals. The required inputs for our approach are QSAR predictions for the LOAELs of the toxicological effect of interest and statistical distributions describing the relationship existing between in vivo LOAEL values and results from in vitro tests. Our methodology is able to correctly predict the performance of testing strategies designed to analyze the teratogenic effects of two chemicals: di(2-ethylhexyl)phthalate and Indomethacin. The proposed decision-support methodology can be adapted to any toxicological context as long as a statistical comparison between in vitro and in vivo results is possible and QSAR models for the toxicological effect of interest can be developed.

Discovery of geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of quantitative structure-activity relationship modeling, virtual screening, and experimental validation

Geranylgeranylation is critical to the function of several proteins including Rho, Rap1, Rac, Cdc42, and G-protein gamma subunits. Geranylgeranyltransferase type I (GGTase-I) inhibitors (GGTIs) have therapeutic potential to treat inflammation, multiple sclerosis, atherosclerosis, and many other diseases. Following our standard workflow, we have developed and rigorously validated quantitative structure-activity relationship (QSAR) models for 48 GGTIs using variable selection k nearest neighbor (kNN), automated lazy learning (ALL), and partial least squares (PLS) methods. The QSAR models were employed for virtual screening of 9.5 million commercially available chemicals, yielding 47 diverse computational hits. Seven of these compounds with novel scaffolds and high predicted GGTase-I inhibitory activities were tested in vitro, and all were found to be bona fide and selective micromolar inhibitors. Notably, these novel hits could not be identified using traditional similarity search. These data demonstrate that rigorously developed QSAR models can serve as reliable virtual screening tools, leading to the discovery of structurally novel bioactive compounds.

Comparative docking study of anibamine as the first natural product CCR5 antagonist in CCR5 homology models

Anibamine, a novel pyridine quaternary alkaloid recently isolated from Aniba sp., has been found to effectively bind to the chemokine receptor CCR5 with an IC(50) at 1 microM in competition with (125)I-gp120, an HIV viral envelope protein binding to CCR5 with high affinity. Since CCR5, a G-protein-coupled receptor, is an essential coreceptor for the human immunodeficiency virus type I (HIV-1) entry to host cells, a CCR5 antagonist that inhibits the cellular entry of HIV-1 provides a new therapy choice for the treatment of HIV. Anibamine provides a novel structural skeleton that is remarkably different from all lead compounds previously identified as CCR5 antagonists. Here, we report comparative docking studies of anibamine with several other known CCR5 antagonists in two CCR5 homology models built based on the crystal structures of bovine rhodopsin and human beta(2)-adrenergic receptor. The binding pocket of anibamine has some common features shared with other high affinity CCR5 antagonists, suggesting that they may bind in similar binding sites and/or modes. At the same time, several unique binding features of anibamine were identified, and it will likely prove beneficial in future molecular design of novel CCR5 antagonists based on the anibamine scaffold.