A Combined Ligand- and Structure-Based Virtual Screening To Identify Novel NaV1.2 Blockers: In Vitro Patch Clamp Validation and In Vivo Anticonvulsant Activity.

Epilepsy is a neurological disorder characterized by recurrent seizures that arise from abnormal electrical activity in the brain. Voltage-gated sodium channels (NaVs), responsible for the initiation and propagation of action potentials in neurons, play a critical role in the pathogenesis of epilepsy. This study sought to discover potential anticonvulsant compounds that interact with NaVs, specifically, the brain subtype hNaV1.2. A ligand-based QSAR model and a docking model were constructed, validated, and applied in a parallel virtual screening over the DrugBank database. Montelukast, Novobiocin, and Cinnarizine were selected for in vitro testing, using the patch-clamp technique, and all of them proved to inhibit hNaV1.2 channels heterologously expressed in HEK293 cells. Two hits were evaluated in the GASH/Sal model of audiogenic seizures and demonstrated promising activity, reducing the severity of sound-induced seizures at the doses tested. The combination of ligand- and structure-based models presents a valuable approach for identifying potential NaV inhibitors. These findings may provide a basis for further research into the development of new antiseizure drugs for the treatment of epilepsy.

Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro.

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence. Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy -performed in a large and diverse chemolibrary- complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening. Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 µM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12-20 µM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7-45 µM). Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known "garbage in, garbage out" machine learning principle.

A combined ligand and target-based virtual screening strategy to repurpose drugs as putrescine uptake inhibitors with trypanocidal activity.

Chagas disease, also known as American trypanosomiasis, is a neglected tropical disease caused by the protozoa Trypanosoma cruzi, affecting nearly 7 million people only in the Americas. Polyamines are essential compounds for parasite growth, survival, and differentiation. However, because trypanosomatids are auxotrophic for polyamines, they must be obtained from the host by specific transporters. In this investigation, an ensemble of QSAR classifiers able to identify polyamine analogs with trypanocidal activity was developed. Then, a multi-template homology model of the dimeric polyamine transporter of T. cruzi, TcPAT12, was created with Rosetta, and then refined by enhanced sampling molecular dynamics simulations. Using representative snapshots extracted from the trajectory, a docking model able to discriminate between active and inactive compounds was developed and validated. Both models were applied in a parallel virtual screening campaign to repurpose known drugs as anti-trypanosomal compounds inhibiting polyamine transport in T. cruzi. Montelukast, Quinestrol, Danazol, and Dutasteride were selected for in vitro testing, and all of them inhibited putrescine uptake in biochemical assays, confirming the predictive ability of the computational models. Furthermore, all the confirmed hits proved to inhibit epimastigote proliferation, and Quinestrol and Danazol were able to inhibit, in the low micromolar range, the viability of trypomastigotes and the intracellular growth of amastigotes.

Identification of New Carbonic Anhydrase VII Inhibitors by Structure-Based Virtual Screening.

Human carbonic anhydrase VII (hCA VII) constitutes a promising molecular target for the treatment of epileptic seizures and other central nervous system disorders due to its almost exclusive expression in neurons. Achieving isoform selectivity is one of the main challenges for the discovery of new hCA inhibitors, since nonspecific inhibition may lead to tolerance and side effects. In the present work, we report the development of a molecular docking protocol based on AutoDock4Zn for the search of new hCA VII inhibitors by virtual screening. The docking protocol was applied to the screening of two sets of compounds: a ZINC15 subset of sulfur-containing structures and an in-house library consisting of synthetic and commercial candidates (including approved drugs). Five compounds were selected from the first screening campaign and three from the second one, and they were tested in vitro against the enzyme. Among the eight selected structures, four showed Ki values in the low nanomolar range. These confirmed hits include three approved drugs: meloxicam, piroxicam, and nitrofurantoin, which also showed good selectivity for hCA VII versus hCA II.

Structure-Based Virtual Screening Identifies Novobiocin, Montelukast, and Cinnarizine as TRPV1 Modulators with Anticonvulsant Activity In Vivo.

The transient receptor potential vanilloid 1 (TRPV1) receptor is a nonselective cation channel, known to be involved in the regulation of many important physiological and pathological processes. In the last few years, it has been proposed as a promising target to develop novel anticonvulsant compounds. However, thermoregulatory effects associated with the channel inhibition have hampered the path for TRPV1 antagonists to become marketed drugs. In this regard, we conducted a structure-based virtual screening campaign to find potential TRPV1 modulators among approved drugs, which are known to be safe and thermally neutral. To this end, different docking models were developed and validated by assessing their pose and score prediction powers. Novobiocin, montelukast, and cinnarizine were selected from the screening as promising candidates for experimental testing and all of them exhibited nanomolar inhibitory activity. Moreover, the in vivo profiles showed promising results in at least one of the three models of seizures tested.

iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules.

The clustering of small molecules implies the organization of a group of chemical structures into smaller subgroups with similar features. Clustering has important applications to sample chemical datasets or libraries in a representative manner (e.g., to choose, from a virtual screening hit list, a chemically diverse subset of compounds to be submitted to experimental confirmation, or to split datasets into representative training and validation sets when implementing machine learning models). Most strategies for clustering molecules are based on molecular fingerprints and hierarchical clustering algorithms. Here, two open-source in-house methodologies for clustering of small molecules are presented: iterative Random subspace Principal Component Analysis clustering (iRaPCA), an iterative approach based on feature bagging, dimensionality reduction, and K-means optimization; and Silhouette Optimized Molecular Clustering (SOMoC), which combines molecular fingerprints with the Uniform Manifold Approximation and Projection (UMAP) and Gaussian Mixture Model algorithm (GMM). In a benchmarking exercise, the performance of both clustering methods has been examined across 29 datasets containing between 100 and 5000 small molecules, comparing these results with those given by two other well-known clustering methods, Ward and Butina. iRaPCA and SOMoC consistently showed the best performance across these 29 datasets, both in terms of within-cluster and between-cluster distances. Both iRaPCA and SOMoC have been implemented as free Web Apps and standalone applications, to allow their use to a wide audience within the scientific community.

Novel Dimeric hHv1 Model and Structural Bioinformatic Analysis Reveal an ATP-Binding Site Resulting in a Channel Activating Effect.

The human voltage-gated proton channel (hHv1) is a highly selective ion channel codified by the HVCN1 gene. It plays a fundamental role in several physiological processes such as innate and adaptive immunity, insulin secretion, and sperm capacitation. Moreover, in humans, a higher hHv1 expression/function has been reported in several types of cancer cells. Here we report a multitemplate homology model of the hHv1 channel, built and refined as a dimer in Rosetta. The model was then subjected to extensive Gaussian accelerated molecular dynamics (GaMD) for enhanced conformational sampling, and representative snapshots were extracted by clustering analysis. Combining different structure- and sequence-based methodologies, we predicted a putative ATP-binding site located on the intracellular portion of the channel. Furthermore, GaMD simulations of the ATP-bound dimeric hHv1 model showed that ATP interacts with a cluster of positively charged residues from the cytoplasmic N and C terminal segments. According to the in silico predictions, we found that 3 mM intracellular ATP significantly increases the H+ current mediated by the hHv1 channel expressed in HEK293 cells and measured by the patch-clamp technique in an inside-out configuration (2.86 ± 0.63 fold over control at +40 mV). When ATP was added on the extracellular side, it was not able to activate the channel supporting the idea that the ATP-binding site resides in the intracellular face of the hHV1 channel. In a physiological and pathophysiological context, this ATP-mediated modulation could integrate the cell metabolic state with the H+ efflux, especially in cells where hHv1 channels are relevant for pH regulation, such as pancreatic ß-cells, immune cells, and cancer cells.

Homology Modeling and Molecular Dynamics Simulations of Trypanosoma cruzi Phosphodiesterase b1.

Cyclic nucleotide phosphodiesterases have been implicated in the proliferation, differentiation and osmotic regulation of trypanosomatids; in some trypanosomatid species, they have been validated as molecular targets for the development of new therapeutic agents. Because the experimental structure of Trypanosoma cruzi PDEb1 (TcrPDEb1) has not been solved so far, an homology model of the target was created using the structure of Trypanosoma brucei PDEb1 (TbrPDEb1) as a template. The model was refined by extensive enhanced sampling molecular dynamics simulations, and representative snapshots were extracted from the trajectory by combined clustering analysis. This structural ensemble was used to develop a structure-based docking model of the target. The docking accuracy of the model was validated by redocking and cross-docking experiments using all available crystal structures of TbrPDEb1, whereas the scoring accuracy was validated through a retrospective screen, using a carefully curated dataset of compounds assayed against TbrPDEb1 and/or TcrPDEb1. Considering the results from in silico validations, the model may be applied in prospective virtual screening campaigns to identify novel hits, as well as to guide the rational design of potent and selective inhibitors targeting this enzyme.

Strengths and Weaknesses of Docking Simulations in the SARS-CoV-2 Era: the Main Protease (Mpro) Case Study.

The scientific community is working against the clock to arrive at therapeutic interventions to treat patients with COVID-19. Among the strategies for drug discovery, virtual screening approaches have the capacity to search potential hits within millions of chemical structures in days, with the appropriate computing infrastructure. In this article, we first analyzed the published research targeting the inhibition of the main protease (Mpro), one of the most studied targets of SARS-CoV-2, by docking-based methods. An alarming finding was the lack of an adequate validation of the docking protocols (i.e., pose prediction and virtual screening accuracy) before applying them in virtual screening campaigns. The performance of the docking protocols was tested at some level in 57.7% of the 168 investigations analyzed. However, we found only three examples of a complete retrospective analysis of the scoring functions to quantify the virtual screening accuracy of the methods. Moreover, only two publications reported some experimental evaluation of the proposed hits until preparing this manuscript. All of these findings led us to carry out a retrospective performance validation of three different docking protocols, through the analysis of their pose prediction and screening accuracy. Surprisingly, we found that even though all tested docking protocols have a good pose prediction, their screening accuracy is quite limited as they fail to correctly rank a test set of compounds. These results highlight the importance of conducting an adequate validation of the docking protocols before carrying out virtual screening campaigns, and to experimentally confirm the predictions made by the models before drawing bold conclusions. Finally, successful structure-based drug discovery investigations published during the redaction of this manuscript allow us to propose the inclusion of target flexibility and consensus scoring as alternatives to improve the accuracy of the methods.

Citrus reticulata peel oil as an antiatherogenic agent: Hypolipogenic effect in hepatic cells, lipid storage decrease in foam cells, and prevention of LDL oxidation.

BACKGROUND AND AIMS: Hypercholesterolemia and oxidative stress are two of the most important risk factors for atherosclerosis. The aim of the present work was to evaluate mandarin (Citrus reticulata) peel oil (MPO) in cholesterol metabolism and lipid synthesis, and its antioxidant capacity. METHODS AND RESULTS: Incubation of hepatic HepG2 cells with MPO (15-60 µL/L) reduced cholesterogenesis and saponifiable lipid synthesis, demonstrated by [14C]acetate radioactivity assays. These effects were associated with a decrease in a post-squalene reaction of the mevalonate pathway. Molecular docking analyses were carried out using three different scoring functions to examine the cholesterol-lowering property of all the components of MPO against lanosterol synthase. Docking simulations proposed that minor components of MPO monoterpenes, like alpha-farnesene and neryl acetate, as well the major component, limonene and its metabolites, could be partly responsible for the inhibitory effects observed in culture assays. MPO also decreased RAW 264.7 foam cell lipid storage and its CD36 expression, and prevented low-density lipoprotein (LDL) lipid peroxidation. CONCLUSION: These results may imply a potential role of MPO in preventing atherosclerosis by a mechanism involving inhibition of lipid synthesis and storage and the decrease of LDL lipid peroxidation.

A structure-based approach towards the identification of novel antichagasic compounds: Trypanosoma cruzi carbonic anhydrase inhibitors.

Trypanosoma cruzi carbonic anhydrase (TcCA) has recently emerged as an interesting target for the design of new compounds to treat Chagas disease. In this study we report the results of a structure-based virtual screening campaign to identify novel and selective TcCA inhibitors. The combination of properly validated computational methodologies such as comparative modelling, molecular dynamics and docking simulations allowed us to find high potency hits, with KI values in the nanomolar range. The compounds also showed trypanocidal effects against T. cruzi epimastigotes and trypomastigotes. All the candidates are selective for inhibiting TcCA over the human isoform CA II, which is encouraging in terms of possible therapeutic safety and efficacy.