Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods

Structure-based virtual screening aids the identification of glycosyltransferases in the biosynthesis of salidroside

Glycosylation plays an important role in the structural diversification of plant natural products. The identification of efficient glycosyltransferases is also a crucial step for the biosynthesis of valuable glycoside products. However, functional characterization of glycosyltransferases (GTs) from an extensive plant gene list is labour-intensive and challenging. Salidroside is a bioactive component derived from plants, widely utilized in the fields of food and medicine. Here, through transcriptome analysis and structure-based virtual screening, we identified two GTs that participated in the biosynthesis of salidroside from a rarely studied herbaceous plant, Astilbe chinensis. Ach15909 was found to possess high catalytic activity as evidenced by the determination of its catalytic parameters. The key residues that determine its catalytic activity were further determined. Additionally, Ach15909 shows a preference for substrates with a volume of <150 Å3, and replacing the interdomain linker region located between the N- and C-terminal domains of Ach15909 allows it to accept substrates that were previously not catalyzable. Overall, the structure-based virtual screening approach showed high efficiency and cost-effectiveness; the successful identification of GTs in salidroside glycosylation sheds light on uncovering additional plant biosynthesis enzymes in the forthcoming research.

Repositioning of Small Molecules through the Inverse Virtual Screening in silico Tool: Case of Benzothiazole-Based Inhibitors of Soluble Epoxide Hydrolase (sEH)

Computational techniques accelerate drug discovery by identifying bioactive compounds for specific targets, optimizing molecules with moderate activity, or facilitating the repositioning of inactive items onto new targets. Among them, the Inverse Virtual Screening (IVS) approach is aimed at the evaluation of one or a small set of molecules against a panel of targets for addressing target identification. In this work, a focused library of benzothiazole-based compounds was re-investigated by IVS. Four items, originally synthesized and tested on bromodomain-containing protein 9 (BRD9) but yielding poor binding, were critically re-analyzed, disclosing only a partial fit with 3D structure-based pharmacophore models, which, in the meanwhile, were developed for this target. Afterwards, these compounds were re-evaluated through IVS on a panel of proteins involved in inflammation and cancer, identifying soluble epoxide hydrolase (sEH) as a putative interacting target. Three items were subsequently confirmed as able to interfere with sEH activity, leading to inhibition percentages spanning from 70 % up to 30 % when tested at 10 μM. Finally, one benzothiazole-based compound emerged as the most promising inhibitor featuring an IC50 in the low micromolar range (IC50=6.62±0.13 μM). Our data confirm IVS as a predictive tool for accelerating the target identification and repositioning processes.

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes

In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.

Virtual Screening Process: A Guide in Modern Drug Designing

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Structural insights into the binding of zoledronic acid with RANKL via computational simulations

Zoledronic acid (ZOL) inhibits receptor activator of nuclear factor-κB ligand (RANKL) and reduces bone turnover. This plays an important role in the development of bisphosphonate-related osteonecrosis of the jaw (BRONJ). Previous reports have shown that ZOL binds to the enzyme farnesyl pyrophosphate synthase (FPPS) to block its activity. However, the mechanism of action of ZOL and its interaction with RANKL is still unclear. In this study, we confirmed that ZOL significantly suppressed the bone remodeling in ZOL-treated rats, investigated whether ZOL could bind to RANKL and examined the interactions between these molecules at the atomic level. Surface plasmon resonance (SPR) assay was performed to validate that ZOL could directly bind to RANKL in a dose dependent manner, and the equilibrium constant was calculated (K_D = 2.28 × 10⁻⁴ M). Then, we used molecular docking simulation to predict the binding site and analyze the binding characteristics of ZOL and RANKL. Through molecular dynamics simulation, we confirmed the stable binding between ZOL and RANKL and observed their dynamic interactions over time. Binding free energy calculations and its decomposition were conducted to obtain the binding free energy −70.67 ± 2.62 kJ/mol for the RANKL–ZOL complex. We identified the key residues of RANKL in the binding region, and these included Tyr217(A), Val277(A), Gly278(A), Val277(B), Gly278(B), and Tyr215(C). Taken together, our results demonstrated the direct interaction between ZOL and RANKL, indicating that the pharmacological action of ZOL might be closely related to RANKL. The design of novel small molecules targeting RANKL might reduce the occurrence of BRONJ.

Phytochemical Analysis of the Methanolic Extract and Essential Oil from Leaves of Industrial Hemp Futura 75 Cultivar: Isolation of a New Cannabinoid Derivative and Biological Profile Using Computational Approaches

Cannabis sativa L. is a plant belonging to the Cannabaceae family, cultivated for its psychoactive cannabinoid (Δ⁹-THC) concentration or for its fiber and nutrient content in industrial use. Industrial hemp shows a low Δ⁹-THC level and is a valuable source of phytochemicals, mainly represented by cannabinoids, flavones, terpenes, and alkaloids, with health-promoting effects. In the present study, we investigated the phytochemical composition of leaves of the industrial hemp cultivar Futura 75, a monoecious cultivar commercially used for food preparations or cosmetic purposes. Leaves are generally discarded, and represent waste products. We analyzed the methanol extract of Futura 75 leaves by HPLC and NMR spectroscopy and the essential oil by GC-MS. In addition, in order to compare the chemical constituents, we prepared the water infusion. One new cannabinoid derivative (1) and seven known components, namely, cannabidiol (2), cannabidiolic acid (3), β-cannabispirol (4), β-cannabispirol (5), canniprene (6), cannabiripsol (7), and cannflavin B (8) were identified. The content of CBD was highest in all preparations. In addition, we present the outcomes of a computational study focused on elucidating the role of 2α-hydroxy-Δ^3,7-cannabitriol (1), CBD (2), and CBDA (3) in inflammation and thrombogenesis.

Biological Profile of Two Gentiana lutea L. Metabolites Using Computational Approaches and In Vitro Tests

Natural products have been the main source of bioactive molecules for centuries. We tested the biological profile of two metabolites extracted from Gentiana lutea L. by means of computational techniques and in vitro assays. The two molecules (loganic acid and gentiopicroside) were tested in silico using an innovative technique, named Inverse Virtual Screening (IVS), to highlight putative partners among a panel of proteins involved in inflammation and cancer events. A positive binding with cyclooxygenase-2 (COX-2), alpha-1-antichymotrypsin, and alpha-1-acid glycoprotein emerged from the computational experiments and the outcomes from the promising interaction with COX-2 were confirmed by Western blot, highlighting the reliability of IVS in the field of the natural products.

Exploration of potential inhibitors for tuberculosis via structure-based drug design, molecular docking, and molecular dynamics simulation studies

Drug resistance in tuberculosis is major threat to human population. In the present investigation, we aimed to identify novel and potent benzimidazole molecules to overcome the resistance management. A series of 20 benzimidazole derivatives were examined for its activity as selective antitubercular agents. Initially, AutodockVina algorithm was performed to assess the efficacy of the molecules. The results are further enriched by redocking by means of Glide algorithm. The binding free energies of the compounds were then calculated by MM-generalized-born surface area method. Molecular docking studies elucidated that benzimidazole derivatives has revealed formation of hydrogen bond and strong binding affinity in the active site of Mycobacterium tuberculosis protein. Note that ARG308, GLY189, VAL312, LEU403, and LEU190 amino acid residues of Mycobacterium tuberculosis protein PrpR are involved in binding with ligands of benzimidazoles. Interestingly, the ligands exhibited same binding potential to the active site of protein complex PrpR in both the docking programs. In essence, the result portrays that benzimidazole derivatives such as 1p, 1q, and 1 t could be potent and selective antitubercular agents than the standard drug isoniazid. These compounds were then subjected to molecular dynamics simulation to validate the dynamics activity of the compounds against PrpR. Finally, the inhibitory behavior of compounds was predicted using a machine learning algorithm trained on a data collection of 15,000 compounds utilizing graph-based signatures. Overall, the study concludes that designed benzimidazoles can be employed as antitubercular agents. Indeed, the results are helpful for the experimental biologists to develop safe and non-toxic drugs against tuberculosis.

Inverse Virtual Screening for the rapid re-evaluation of the presumed biological safe profile of natural products. The case of steviol from Stevia rebaudiana glycosides on farnesoid X receptor (FXR)

Nonnutritive sweeteners (NNSs) are widely employed as dietary substitutes for classical sugars thanks to their safety profile and low toxicity. In this study, a re-evaluation of the biological effects of steviol (1), the main metabolite from Stevia rebaudiana glycosides, was performed using the Inverse Virtual Screening (IVS) target fishing computational approach. Starting from well-known pharmacological properties of Stevia rebaudiana glycosides, this computational tool was employed for predicting the putative interacting targets of 1 and, afterwards, of its five synthetic ester derivatives 2-6, accounting a large panel of proteins involved in cancer and inflammation events. Applying this methodology, the farnesoid X receptor (FXR) was identified as the putative target partner of 1-6. The predicted ligand-protein interactions were corroborated by transactivation assays, specifically disclosing the agonistic activity of 1 and the antagonistic activities of 2-6 on FXR. The reported results highlight the feasibility of IVS as a fast and potent tool for predicting the interacting targets of query compounds, addressing the re-evaluation of their bioactivity. In light of the obtained results, the presumably safe profile of known compounds, such as the case of steviol (1), is critically discussed.

Discovery of noscapine derivatives as potential β-tubulin inhibitors

Twenty novel 1,2,3-triazole noscapine derivatives were synthesized starting from noscapine by consecutive N-demethylation, reduction of lactone ring, N-propargylation and Huisgen 1,3-dipolar cycloaddition reaction. In order to select the most promising molecules to subject to further biophysical and biological evaluation, a molecular docking analysis round was performed using noscapine as reference compound. The molecules featuring docking predicted binding affinity better than that of noscapine were then subjected to MTT assay against MCF7 cell line. The obtained results disclosed that all the selected triazole derivatives exhibited a remarkably lower cell viability compared to noscapine in the range of 20 μM in 48 h. In an attempt to correlate the biological activity with the ability to bind tubulin, the surface plasmon resonance (SPR) assay was employed. Compounds 8a, 8h, 9c, 9f and 9j were able to bind tubulin with affinity constant values in the nanomolar range and higher if compared to noscapine. Integrating computational predictions and experimental evaluation, two promising compounds (8h and 9c) were identified, whose relevant cytotoxicity was supposed to be correlated with tubulin binding affinity. These findings shed lights onto structural modifications of noscapine toward the identification of more potent cytotoxic agents targeting tubulin.

Accelerating the repurposing of FDA-approved drugs against coronavirus disease-19 (COVID-19)

The recent release of the main protein structures belonging to SARS CoV-2, responsible for the coronavirus disease-19 (COVID-19), strongly pushed for identifying valuable drug treatments. With this aim, we show a repurposing study on FDA-approved drugs applying a new computational protocol and introducing a novel parameter called IVS_ratio. Starting with a virtual screening against three SARS CoV-2 targets (main protease, papain-like protease, spike protein), the top-ranked molecules were reassessed combining the Inverse Virtual Screening novel approach and MM-GBSA calculations. Applying this protocol, a list of drugs was identified against the three investigated targets. Also, the top-ranked selected compounds on each target (rutin vs. main protease, velpatasvir vs. papain-like protease, lomitapide vs. spike protein) were further tested with molecular dynamics simulations to confirm the promising binding modes, obtaining encouraging results such as high stability of the complex during the simulation and a good protein–ligand interaction network involving some important residues of each target. Moreover, the recent outcomes highlighting the inhibitory activity of quercetin, a natural compound strictly related to rutin, on the SARS-CoV-2 main protease, strengthened the applicability of the proposed workflow.

New computational protocol applied to a repurposing campaign against SARS-CoV-2.