Extensive Consensus Docking Evaluation for Ligand Pose Prediction and Virtual Screening Studies

Watermelon: setup and validation of an in silico fragment-based approach

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

SMS121, a new inhibitor of CD36, impairs fatty acid uptake and viability of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common among children. AML is characterized by aberrant proliferation of myeloid blasts in the bone marrow and impaired normal hematopoiesis. Despite the introduction of new drugs and allogeneic bone marrow transplantation, patients have poor overall survival rate with relapse as the major challenge, driving the demand for new therapeutic strategies. AML patients with high expression of the very long/long chain fatty acid transporter CD36 have poorer survival and very long chain fatty acid metabolism is critical for AML cell survival. Here we show that fatty acids are transferred from human primary adipocytes to AML cells upon co-culturing. A drug-like small molecule (SMS121) was identified by receptor-based virtual screening and experimentally demonstrated to target the lipid uptake protein CD36. SMS121 reduced the uptake of fatty acid into AML cells that could be reversed by addition of free fatty acids and caused decreased cell viability. The data presented here serves as a framework for the development of CD36 inhibitors to be used as future therapeutics against AML.

VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Discovery of novel IDO1/TDO2 dual inhibitors: a consensus Virtual screening approach with molecular dynamics simulations, and binding free energy analysis

The pursuit of effective cancer immunotherapy drugs remains challenging, with overexpression of indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) allowing cancer cells to evade immune attacks. While several IDO1 inhibitors have undergone clinical testing, only three dual IDO1/TDO2 inhibitors have reached human trials. Hence, this study focuses on identifying novel IDO1/TDO2 dual inhibitors through consensus structure-based virtual screening (SBVS). ZINC15 natural products library was refined based on molecular descriptors, and the selected compounds were docked to the holo form IDO1 and TDO2 using two different software programs and ranked according to their consensus docking scores. The top-scoring compounds underwent in silico evaluations for pharmacokinetics, toxicity, CYP3A4 affinity, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations. Five compounds (ZINC00000079405/10, ZINC00004028612/11, ZINC00013380497/12, ZINC00014613023/13, and ZINC00103579819/14) were identified as potential IDO1/TDO2 dual inhibitors due to their high consensus docking scores, key residue interactions with the enzymes, favorable pharmacokinetics, and avoidance of CYP3A4 binding. MD simulations of the top three hits with IDO1 indicated conformational changes and compactness, while MM-GBSA analysis revealed strong binding free energy for compounds 10 (ΔG: -20.13 kcal/mol) and 11 (ΔG: -16.22 kcal/mol). These virtual hits signify a promising initial step in identifying candidates as supplementary therapeutics to immune checkpoint inhibitors in cancer treatment. Their potential to deliver potent dual inhibition of IDO1/TDO2, along with safety and favorable pharmacokinetics, makes them compelling. Validation through in vitro and in vivo assays should be conducted to confirm their activity, selectivity, and preclinical potential as holo IDO1/TDO2 dual inhibitors.Communicated by Ramaswamy H. Sarma.

ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery

Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement the HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose; however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches that combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking. In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening while also being designed for use in high-performance computing (HPC) environments.

Conformer Generation for Structure-Based Drug Design: How Many and How Good?

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.

Recombinant production, purification, and biochemical characterization of a novel L-lactate dehydrogenase from Bacillus cereus NRC1 and inhibition study of mangiferin

Lactate dehydrogenase (LDH, EC 1.1.1.27) is one of the vital glycolytic conditions, especially during anaerobic conditions. It is a significant diagnostic, prognostic, and monitoring biomarker parameter. A 950-bp DNA fragment containing the gene (LDH) encoding LDH was amplified from Bacillus cereus NRC1. The deduced amino acid sequence reveals that B. cereus LDH (Bc-LDH) is highly homologous to the LDHs of Bacillus organisms. All LDH enzymes have a significant degree of conservation in their active site and several additional domains with unidentified functions. The gene for LDH, which catalyzes lactate synthesis, was cloned, sequenced (accession number: LC706200.1), and expressed in Escherichia coli BL21 (DE3). In this investigation, Bc-LDH was purified to homogeneity with a specific activity of 22.7 units/mg protein and a molecular weight of 35 kDa. It works optimally at pH 8.0. The purified enzyme was inhibited by FeCl2, CuCl2, ZnCl2, and NiCl, whereas CoCl2 was found to boost the activity of Bc-LDH. The molecular docking of the 3D model of the Bc-LDH structure with a natural inhibitor, mangiferin, demonstrated excellent LDH inhibition, with a free binding energy of −10.2 kcal/mol. Moreover, mangiferin is a potent Bc-LDH inhibitor that inhibits Bc-LDH competitively and has one binding site with a Ki value of 0.075 mM. The LDH-mangiferin interaction exhibits a low RMSF value (>1.5 Å), indicating a stable contact at the residues. This study will pave the way for more studies to improve the understanding of mangiferin, which could be considered an intriguing candidate for creating novel and improved LDH inhibitors.

MetaDOCK: A Combinatorial Molecular Docking Approach

Molecular docking plays a major role in academic and industrial drug screening and discovery processes. Despite the availability of numerous docking software packages, there is a lot of scope for improvement for the docking algorithms in terms of becoming more reliable to replicate the experimental binding results. Here, we propose a combinatorial or consensus docking approach where complementary powers of the existing methods are captured. We created a meta-docking protocol by combining the results of AutoDock4.2, LeDock, and rDOCK programs as these are freely available, easy to use, and suitable for large-scale analysis and produced better performance on benchmarking studies. Rigorous benchmarking analyses were undertaken to evaluate the scoring, posing, and screening capability of our approach. Further, the performance measures were compared against one standard state-of-the-art commercial docking software, GOLD, and one freely available software, PLANTS. Performances of MetaDOCK for scoring, posing, and screening the protein-ligand complexes were found to be quite superior compared to the reference programs. Exhaustive molecular dynamics simulation and molecular mechanics Poisson-Boltzmann and surface area-based free energy estimation also suggest better energetic stability of the docking solutions produced by our meta-approach. We believe that the MetaDOCK approach is a useful packaging of the freely available software and provides a better alternative to the scientific community who are unable to afford costly commercial packages.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Exploring the antileishmanial activity of N1,N2-disubstituted-benzoylguanidines: synthesis and molecular modeling studies

In this report, we describe the synthesis and evaluation of nine N¹,N²-disubstituted-benzoylguanidines against promastigotes and amastigotes forms of Leishmania amazonensis. The derivatives 2g and 2i showed low IC₅₀ values against promastigote form (90.8 ± 0.05 µM and 68.4 ± 0.03 µM, respectively), low cytotoxicity profile (CC₅₀ 396 ± 0.02 µM and 857.9 ± 0.06 µM) for peritoneal macrophages cells and SI of 5.5 and 12.5, respectively. Investigations about the mechanism of action of 2g and 2i showed that both compounds cause mitochondrial depolarization, increase in ROS levels, and generation of autophagic vacuoles on free promastigotes forms. These compounds were also capable of reducing the number of infected macrophages with amastigotes forms (59.5% ± 0.08% and 98.1% ± 0.46%) and the number of amastigotes/macrophages (79.80% ± 0.05% and 96.0% ± 0.16%), through increasing induction of microbicide molecule NO. Additionally, ADMET-Tox in silico predictions showed drug-like features and free of toxicological risks. The molecular docking studies with arginase and gp63 showed that relevant intermolecular interactions could explain the experimental results. Therefore, these results reinforce that benzoylguanidines could be a starting scaffold for the search for new antileishmanial drugs.Communicated by Ramaswamy H. Sarma.

Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors

Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors.

Comparison of ATP-binding pockets and discovery of homologous recombination inhibitors

The ATP binding sites of many enzymes are structurally related, which complicates their development as therapeutic targets. In this work, we explore a diverse set of ATPases and compare their ATP binding pockets using different strategies, including direct and indirect structural methods, in search of pockets attractive for drug discovery. We pursue different direct and indirect structural strategies, as well as ligandability assessments to help guide target selection. The analyses indicate human RAD51, an enzyme crucial in homologous recombination, as a promising, tractable target. Inhibition of RAD51 has shown promise in the treatment of certain cancers but more potent inhibitors are needed. Thus, we design compounds computationally against the ATP binding pocket of RAD51 with consideration of multiple criteria, including predicted specificity, drug-likeness, and toxicity. The molecules designed are evaluated experimentally using molecular and cell-based assays. Our results provide two novel hit compounds against RAD51 and illustrate a computational pipeline to design new inhibitors against ATPases.

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.

Identification of potent inhibitors of NEK7 protein using a comprehensive computational approach

NIMA related Kinases (NEK7) plays an important role in spindle assembly and mitotic division of the cell. Over expression of NEK7 leads to the progression of different cancers and associated malignancies. It is becoming the next wave of targets for the development of selective and potent anti-cancerous agents. The current study is the first comprehensive computational approach to identify potent inhibitors of NEK7 protein. For this purpose, previously identified anti-inflammatory compound i.e., Phenylcarbamoylpiperidine-1,2,4-triazole amide derivatives by our own group were selected for their anti-cancer potential via detailed Computational studies. Initially, the density functional theory (DFT) calculations were carried out using Gaussian 09 software which provided information about the compounds' stability and reactivity. Furthermore, Autodock suite and Molecular Operating Environment (MOE) software's were used to dock the ligand database into the active pocket of the NEK7 protein. Both software performances were compared in terms of sampling power and scoring power. During the analysis, Autodock results were found to be more reproducible, implying that this software outperforms the MOE. The majority of the compounds, including M7, and M12 showed excellent binding energies and formed stable protein-ligand complexes with docking scores of - 29.66 kJ/mol and - 31.38 kJ/mol, respectively. The results were validated by molecular dynamics simulation studies where the stability and conformational transformation of the best protein-ligand complex were justified on the basis of RMSD and RMSF trajectory analysis. The drug likeness properties and toxicity profile of all compounds were determined by ADMETlab 2.0. Furthermore, the anticancer potential of the potent compounds were confirmed by cell viability (MTT) assay. This study suggested that selected compounds can be further investigated at molecular level and evaluated for cancer treatment and associated malignancies.

Expansion of the 4-(Diethylamino)benzaldehyde Scaffold to Explore the Impact on Aldehyde Dehydrogenase Activity and Antiproliferative Activity in Prostate Cancer

Aldehyde dehydrogenases (ALDHs) are overexpressed in various tumor types including prostate cancer and considered a potential target for therapeutic intervention. 4-(Diethylamino)benzaldehyde (DEAB) has been extensively reported as a pan-inhibitor of ALDH isoforms, and here, we report on the synthesis, ALDH isoform selectivity, and cellular potencies in prostate cancer cells of 40 DEAB analogues; three analogues (14, 15, and 16) showed potent inhibitory activity against ALDH1A3, and two analogues (18 and 19) showed potent inhibitory activity against ALDH3A1. Significantly, 16 analogues displayed increased cytotoxicity (IC₅₀ = 10–200 μM) compared with DEAB (>200 μM) against three different prostate cancer cell lines. Analogues 14 and 18 were more potent than DEAB against patient-derived primary prostate tumor epithelial cells, as single agents or in combination treatment with docetaxel. In conclusion, our study supports the use of DEAB as an ALDH inhibitor but also reveals closely related analogues with increased selectivity and potency.

VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity.

Computational strategies to identify new drug candidates against neuroinflammation

The even more increasing application of computational approaches in these last decades has deeply modified the process of discovery and commercialization of new therapeutic entities. This is especially true in the field of neuroinflammation, in which both the peculiar anatomical localization and the presence of the blood-brain barrier makeit mandatory to finely tune the candidates' physicochemical properties from the early stages of the discovery pipeline. The aim of this review is therefore to provide a general overview to the readers about the topic of neuroinflammation, together with the most common computational strategies that can be exploited to discover and design small molecules controlling neuroinflammation, especially those based on the knowledge of the three-dimensional structure of the biological targets of therapeutic interest. The techniques used to describe the molecular recognition mechanisms, such as molecular docking and molecular dynamics, will therefore be eviscerated, highlighting their advantages and their limitations. Finally, we report several case studies in which computational methods have been applied in drug discovery on neuroinflammation, focusing on the last decade's research.

CIFDock: A novel CHARMM-based flexible receptor-flexible ligand docking protocol

Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data.

Combination of pose and rank consensus in docking-based virtual screening: the best of both worlds

The use of high-throughput docking (HTD) in the drug discovery pipeline is today widely established. In spite of methodological improvements in docking accuracy (pose prediction), scoring power, ranking power, and screening power in HTD remain challenging. In fact, pose prediction is of critical importance in view of the pose-dependent scoring process, since incorrect poses will necessarily decrease the ranking power of scoring functions. The combination of results from different docking programs (consensus scoring) has been shown to improve the performance of HTD. Moreover, it has been also shown that a pose consensus approach might also result in database enrichment. We present a new methodology named Pose/Ranking Consensus (PRC) that combines both pose and ranking consensus approaches, to overcome the limitations of each stand-alone strategy. This approach has been developed using four docking programs (ICM, rDock, Auto Dock 4, and PLANTS; the first one is commercial, the other three are free). We undertook a thorough analysis for the best way of combining pose and rank strategies, and applied the PRC to a wide range of 34 targets sampling different protein families and binding site properties. Our approach exhibits an improved systematic performance in terms of enrichment factor and hit rate with respect to either pose consensus or consensus ranking alone strategies at a lower computational cost, while always ensuring the recovery of a suitable number of ligands. An analysis using four free docking programs (replacing ICM by Auto Dock Vina) displayed comparable results.

The new methodology named Pose/Ranking Consensus (PRC) combines both pose and ranking consensus strategies. It displays an enhanced performance in terms of enrichment factor and hit rate, ensuring the recovery of a suitable number of ligands.

dockECR: Open consensus docking and ranking protocol for virtual screening of small molecules

The development of open computational pipelines to accelerate the discovery of treatments for emerging diseases allows finding novel solutions in shorter periods of time. Consensus molecular docking is one of these approaches, and its main purpose is to increase the detection of real actives within virtual screening campaigns. Here we present dockECR, an open consensus docking and ranking protocol that implements the exponential consensus ranking method to prioritize molecular candidates. The protocol uses four open source molecular docking programs: AutoDock Vina, Smina, LeDock and rDock, to rank the molecules. In addition, we introduce a scoring strategy based on the average RMSD obtained from comparing the best poses from each single program to complement the consensus ranking with information about the predicted poses. The protocol was benchmarked using 15 relevant protein targets with known actives and decoys, and applied using the main protease of the SARS-CoV-2 virus. For the application, different crystal structures of the protease, and frames obtained from molecular dynamics simulations were used to dock a library of 79 molecules derived from previously co-crystallized fragments. The ranking obtained with dockECR was used to prioritize eight candidates, which were evaluated in terms of the interactions generated with key residues from the protease. The protocol can be implemented in any virtual screening campaign involving proteins as molecular targets. The dockECR code is publicly available at: https://github.com/rochoa85/dockECR.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Discovery of JMJD7 inhibitors with the aid of virtual screening and bioactivity evaluation

Jumonji-C (JmjC) domain-containing 7 (JMJD7), which is a 2-oxoglutarate (2OG)-dependent oxygenase, has been demonstrated to play an important role in the occurrence and development of a number of diseases, particularly cancer. Discovery of JMJD7 inhibitors is thus of great importance. Herein consensus docking/scoring strategy and bioactivity evaluation were used to identify JMJD7 inhibitors from various chemical databases. Seven active compounds were retrieved. The most potent compound, Cpd-3, showed an IC₅₀ value of 6.62 μM against JMJD7. Further biophysical assays confirmed that Cpd-3 could efficiently bind to JMJD7 in vitro. Flexible docking was used to predict the binding mode of Cpd-3 with JMJD7. In a cellular assay, Cpd-3 displayed good inhibitory activity against cancer cell lines expressing a high level of JMJD7. As far as we know, Cpd-3 is the first JMJD7 inhibitor reported so far. Overall, this study established a good starting point for drug discovery targeting JMJD7.

From Anti-infective Agents to Cancer Therapy: a Drug Repositioning Study Revealed a New Use for Nitrofuran Derivatives

BACKGROUND

The progression of ovarian cancer seems to be related to HDAC1, HDAC3 and HDAC6 activity. A possible strategy for improving therapies for treating ovarian carcinoma, minimizing the preclinical screenings, is the repurposing of already approved pharmaceutical products as inhibitors of these enzymes.

OBJECTIVE

This work was aimed to implement a computational strategy for identifying new HDAC inhibitors for ovarian carcinoma treatment among approved drugs.

METHOD

The CHEMBL database was used to construct training, test and decoys sets for performing and validating HDAC1, HDAC3 and HDAC6 3D-QSAR models obtained by using FLAP program. Docking and MD simulations were used in combination with the generated models to identify novel potential HDAC inhibitors. Cell viability assays and Western blot analyses were performed on normal and cancer cells for a direct evaluation of the anti-proliferative activity and an in vitro estimation of HDAC inhibition of the compounds selected through in silico screening.

RESULT

The best quantitative prediction was obtained for the HDAC6 3D-QSAR model. The screening of approved drugs highlighted a new potential use as HDAC inhibitors for some compounds, in particular nitrofuran derivatives, usually known for their antibacterial activity, and frequently used as antimicrobial adjuvant therapy in cancer treatment. Experimental evaluation of these derivatives highlighted a significant antiproliferative activity against cancer cell lines overexpressing HDAC6, and an increase in acetylated alpha-tubulin levels.

CONCLUSION

Experimental results support the hypothesis of a potential direct interaction of nitrofuran derivatives with HDACs. In addition to the possible repurposing of already approved drugs, this work suggests the nitro group as a new zinc binding group, able to interact with the catalytic zinc ion of HDACs.

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the benefits of this novel type of scoring functions (SFs). In this study, to benchmark the performance of target-specific MLSFs on a relatively unbiased dataset, the MLSFs trained from three representative protein-ligand interaction representations were assessed on the LIT-PCBA dataset, and the classical Glide SP SF and three types of ligand-based quantitative structure-activity relationship (QSAR) models were also utilized for comparison. Two major aspects in virtual screening campaigns, including prediction accuracy and hit novelty, were systematically explored. The calculation results illustrate that the tested target-specific MLSFs yielded generally superior performance over the classical Glide SP SF, but they could hardly outperform the 2D fingerprint-based QSAR models. Although substantial improvements could be achieved by integrating multiple types of protein-ligand interaction features, the MLSFs were still not sufficient to exceed MACCS-based QSAR models. In terms of the correlations between the hit ranks or the structures of the top-ranked hits, the MLSFs developed by different featurization strategies would have the ability to identify quite different hits. Nevertheless, it seems that target-specific MLSFs do not have the intrinsic attributes of a traditional SF and may not be a substitute for classical SFs. In contrast, MLSFs can be regarded as a new derivative of ligand-based QSAR models. It is expected that our study may provide valuable guidance for the assessment and further development of target-specific MLSFs.

Discovery of Monoacylglycerol Lipase (MAGL) Inhibitors Based on a Pharmacophore-Guided Virtual Screening Study

Monoacylglycerol lipase (MAGL) is an important enzyme of the endocannabinoid system that catalyzes the degradation of the major endocannabinoid 2-arachidonoylglycerol (2-AG). MAGL is associated with pathological conditions such as pain, inflammation and neurodegenerative diseases like Parkinson’s and Alzheimer’s disease. Furthermore, elevated levels of MAGL have been found in aggressive breast, ovarian and melanoma cancer cells. Due to its different potential therapeutic implications, MAGL is considered as a promising target for drug design and the discovery of novel small-molecule MAGL inhibitors is of great interest in the medicinal chemistry field. In this context, we developed a pharmacophore-based virtual screening protocol combined with molecular docking and molecular dynamics simulations, which showed a final hit rate of 50% validating the reliability of the in silico workflow and led to the identification of two promising and structurally different reversible MAGL inhibitors, VS1 and VS2. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent MAGL inhibitors.

Thiohydantoins as anti-leishmanial agents: n vitro biological evaluation and multi-target investigation by molecular docking studies

Leishmaniasis is a neglected tropical disease caused by protozoa of the genus Leishmania. The first-line treatment of this disease is still based on pentavalent antimonial drugs that have a high toxicity profile, which could induce parasitic resistance. Therefore, there is a critical need to discover more effective and selective novel anti-leishmanial agents. In this context, thiohydantoins are a versatile class of substances due to their simple synthesis and several biological activities. In this work, thiohydantoins 1a-l were evaluated in vitro for antileishmania activity. Among them, four derivatives (1c, 1e, 1h and 1l) showed promising IC₅₀ values around 10 µM against promastigotes forms of Leishmania amazonensis and low cytotoxicity profile for peritoneal macrophages cells. Besides, these compounds induce oxidative stress through an increase in ROS production and the labeling of annexin-V and propidium iodide, indicating that promastigotes were undergoing a late apoptosis-like process. Additionally, molecular consensual docking analysis was carried out against two important targets to L. amazonensis: arginase and trypanothione reductase enzymes. Docking results suggest that thiohydantoin ring could be a pharmacophoric group due to its binding affinity by hydrogens bond interactions with important amino acid residues at the active site of both enzymes. These results demonstrate that compounds 1c, 1e, 1h and 1l may are promising in future advance studies.Communicated by Ramaswamy H. Sarma.

Consensus Docking in Drug Discovery

Background:

Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets.

Methods:

In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose.

Results:

The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus.

Conclusion:

In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Discovery of a new ATP-citrate lyase (ACLY) inhibitor identified by a pharmacophore-based virtual screening study

ATP citrate lyase (ACLY) is an important enzyme that catalyzes the conversion of citrate to acetyl-CoA in normal cells, facilitating the de novo fatty acid synthesis. Lipids and fatty acids were found to be accumulated in different types of tumors, such as brain, breast, rectal and ovarian cancer, representing a great source of energy for cancer cell growth and metabolism. Since ACLY-mediated conversion of citrate to acetyl-CoA constitutes the basis for fatty acid synthesis, ACLY seems to be quite an unexplored and promising therapeutic target for anticancer drug design. A pharmacophore-based virtual screening (VS) protocol with the aid of hierarchical docking, consensus docking (CD), molecular dynamics (MD) simulations and ligand-protein binding free energy calculations led to the identification of compound VS1, which showed a moderate but promising inhibitory activity, demonstrating to be 2.5 times more potent than reference inhibitor 2-hydroxycitrate. These results validate the reliability of our VS workflow and pave the way for the design of novel and more potent ACLY inhibitors.Communicated by Ramaswamy H. Sarma.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Molecular Architect: A User-Friendly Workflow for Virtual Screening

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Development of a cheminformatics platform for selectivity analyses of carbonic anhydrase inhibitors

The selectivity for a specific human Carbonic Anhydrase (hCA) isoform is an important property a hCA inhibitor (CAI) should be endowed with, in order to constitute a valuable therapeutic tool for the treatment of a desired pathology. In this context, we developed a chemoinformatic platform that allows the analysis of the structure and selectivity profile of known CAIs reported in literature, with the aim of identifying structural motifs connected to ligand selectivity, thus providing useful guidelines for the design of novel ligands selective for the desired hCA isoform. The platform is able to perform ultrafast structure and selectivity analyses through ligand fingerprint similarity, with no need of structural information about the target receptor and ligands' binding mode. It is easily accessible to the non-expert user through the implementation of a KNIME Analytic Platform workflow and could be extended to analyze the selectivity profile of known ligands of different target proteins.

In silico evaluation of pesticides as potential modulators of human DNA methyltransferases

DNA methylations are carried out by DNA methyltransferases (DNMTs) that are key enzymes during gene expression. Many chemicals, including pesticides, have shown modulation of epigenetic functions by inhibiting DNMTs. In this work, human DNMTs were evaluated as a potential target for pesticides through virtual screening of 1038 pesticides on DNMT1 (3SWR) and DNMT3A (2QRV). Molecular docking calculations for DNMTs-pesticide complexes were performed using AutoDock Vina. Binding-affinity values and contact patterns were employed as selection criteria of pesticides as virtual hits for DNMTs. The best three DNMT-pesticides complexes selected according to their high absolute affinity values (kcal/mol), for both DNMT1 and DNMT3A, were flocoumafen (-12.5; -9.9), brodifacoum (-12.4; -8.4) and difenacoum (-12.1; -8.7). These chemicals belong to second-generation rodenticides. The most frequent predicted interacting residues for DNMT1-pesticide complexes were Trp1170A, Phe1145A, Asn1578A, Arg1574A and Pro1225A; whereas for DNMT3A those were Arg271B, Lys740A, and Glu303B. These results suggest that rodenticides used for pest control are potential DNMT ligands and therefore, may modulate DNA methylations. This finding has important environmental and clinical implications, as epigenetic pathways are critical in many biochemical processes leading to diseases.

Screening of different drug design tools to predict the mode of action of steroidal derivatives as anti-cancer agents

There is a pressing need to discover and develop novel drugs against cancer. With the new era of bioinformatics, which integrates different aspects, drug development has been tremendously improved. Recently, extensive research was directed towards the rational modification of steroid molecules against different disease especially cancer. Moreover, heterocyclic steroid derivatives have shown a lot of different biological activities such as antimicrobial, anti-inflammatory, and anti-cancer activities. Molecular docking methods can be used to explore how the steroid derivatives conformations can adopt within the binding sites of specific macromolecular targets involved in cancer progression. We conducted this study to investigate the accuracy of different molecular docking calculations using different steroidal molecular targets, and to define the most accurate one to study the mode of action of steroid derivatives as potential anti-cancer drugs. Our results revealed that the Dock6, PLANTS, AutoDock, GLIDE (SP and XP), and GOLD (ASP, Chemscore, and PLP) software were able to maintain the binding mode of the co-crystallized ligands inside their proteins by achieving RMSD values lower than two. Moreover, molecular docking study revealed that compound 4, and 5 are promising steroidal derivatives as anti-cancer drugs. Further on, the cytotoxic activity of the selected steroidal derivatives were tested against leukemia cell line using MTT assay. The results revealed that compound 4, and 5 were potential cytotoxic agents against THP-1 cells (IC₅₀s were 44.67 µM, and 46.77 µM, respectively), these results are in agreement with the molecular docking study.

Discovery of Novel µ-Opioid Receptor Inverse Agonist from a Combinatorial Library of Tetrapeptides through Structure-Based Virtual Screening

Morphine, oxycodone, fentanyl, and other µ-opioid receptors (MOR) agonists have been used for decades in antinociceptive therapies. However, these drugs are associated with numerous side effects, such as euphoria, addiction, respiratory depression, and adverse gastrointestinal reactions, thus, circumventing these drawbacks is of extensive importance. With the aim of identifying novel peptide ligands endowed with MOR inhibitory activity, we developed a virtual screening protocol, including receptor-based pharmacophore screening, docking studies, and molecular dynamics simulations, which was used to filter an in-house built virtual library of tetrapeptide ligands. The three top-scored compounds were synthesized and subjected to biological evaluation, revealing the identity of a hit compound (peptide 1) endowed with appreciable MOR inverse agonist effect and selectivity over δ-opioid receptors. These results confirmed the reliability of our computational approach and provided a promising starting point for the development of new potent MOR modulators.

Key Topics in Molecular Docking for Drug Design

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Virtual screening identifies a PIN1 inhibitor with possible antiovarian cancer effects

Peptidyl-prolyl cis-trans isomerase, NIMA-interacting 1 (PIN1) is a peptidyl-prolyl isomerase that binds phospho-Ser/Thr-Pro motifs in proteins and catalyzes the cis-trans isomerization of proline peptide bonds. PIN1 is overexpressed in several cancers including high-grade serous ovarian cancer. Since few therapies are effective against this cancer, PIN1 could be a therapeutic target but effective PIN1 inhibitors are lacking. To identify molecules with in vivo inhibitory effects on PIN1, we used consensus docking to model existing PIN1-ligand X-ray structures and to screen a chemical database for candidate inhibitors. Ten molecules were selected and tested in cellular assays, leading to the identification of VS10 that bound and inhibited PIN1. VS10 treatment reduced the viability of ovarian cancer cell lines by inducing proteasomal PIN1 degradation, without effects on PIN1 transcription, and also reduced the levels of downstream targets β-catenin, cyclin D1, and pSer473-Akt. VS10 is a selective PIN1 inhibitor that may offer new opportunities for treating PIN1-overexpressing tumors.

Discovery of an intrasubunit nicotinic acetylcholine receptor-binding site for the positive allosteric modulator Br-PBTC

Nicotinic acetylcholine receptor (nAChR) ligands that lack agonist activity but enhance activation in the presence of an agonist are called positive allosteric modulators (PAMs). nAChR PAMs have therapeutic potential for the treatment of nicotine addiction and several neuropsychiatric disorders. PAMs need to be selectively targeted toward certain nAChR subtypes to tap this potential. We previously discovered a novel PAM, (R)-7-bromo-N-(piperidin-3-yl)benzo[b]thiophene-2-carboxamide (Br-PBTC), which selectively potentiates the opening of α4β2*, α2β2*, α2β4*, and (α4β4)₂α4 nAChRs and reactivates some of these subtypes when desensitized (* indicates the presence of other subunits). We located the Br-PBTC-binding site through mutagenesis and docking in α4. The amino acids Glu-282 and Phe-286 near the extracellular domain on the third transmembrane helix were found to be crucial for Br-PBTC's PAM effect. E282Q abolishes Br-PBTC potentiation. Using (α4^E282Qβ2)₂α5 nAChRs, we discovered that the trifluoromethylated derivatives of Br-PBTC can potentiate channel opening of α5-containing nAChRs. Mutating Tyr-430 in the α5 M4 domain changed α5-selectivity among Br-PBTC derivatives. There are two kinds of α4 subunits in α4β2 nAChRs. Primary α4 forms an agonist-binding site with another β2 subunit. Accessory α4 forms an agonist-binding site with another α4 subunit. The pharmacological effect of Br-PBTC depends both on its own and agonists' occupancy of primary and accessory α4 subunits. Br-PBTC reactivates desensitized (α4β2)₂α4 nAChRs. Its full efficacy requires intact Br-PBTC sites in at least one accessory and one primary α4 subunit. PAM potency increases with higher occupancy of the agonist sites. Br-PBTC and its derivatives should prove useful as α subunit-selective nAChR PAMs.

Efficient ligand discovery from natural herbs by integrating virtual screening, affinity mass spectrometry and targeted metabolomics

Although natural herbs have been a rich source of compounds for drug discovery, identification of bioactive components from natural herbs suffers from low efficiency and prohibitive cost of the conventional bioassay-based screening platforms. Here we develop a new strategy that integrates virtual screening, affinity mass spectrometry (MS) and targeted metabolomics for efficient discovery of herb-derived ligands towards a specific protein target site. Herb-based virtual screening conveniently selects herbs of potential bioactivity whereas affinity MS combined with targeted metabolomics readily screens candidate compounds in a high-throughput manner. This new integrated approach was benchmarked on screening chemical ligands that target the hydrophobic pocket of the nucleoprotein (NP) of Ebola viruses for which no small molecule ligands have been reported. Seven compounds identified by this approach from the crude extracts of three natural herbs were all validated to bind to the NP target in pure ligand binding assays. Among them, three compounds isolated from Piper nigrum (HJ-1, HJ-4 and HJ-6) strongly promoted the formation of large NP oligomers and reduced the protein thermal stability. In addition, cooperative binding between these chemical ligands and an endogenous peptide ligand was observed, and molecular docking was employed to propose a possible mechanism. Taken together, we established a platform integrating in silico and experimental screening approaches for efficient discovery of herb-derived bioactive ligands especially towards non-enzyme protein targets.

First-of-its-kind STARD3 Inhibitor: In Silico Identification and Biological Evaluation as Anticancer Agent

STARD3 is a cellular protein that represents an attractive target for cancer therapy, being overexpressed in breast cancer and implied in the development of colorectal, gastric, and prostate cancers. Unfortunately, no STARD3 inhibitor has been identified yet. In this work, an in silico strategy was applied to predict a reliable binding mode of cholesterol into STARD3 and to develop a pharmacophore-based virtual screening protocol that allowed the identification of the first STARD3 inhibitor ever reported. The identified compound VS1 binds STARD3 with micromolar affinity (IC₅₀ = 35 μM) and shows antiproliferative activity in breast (MCF7 and MDA- MB-231) and colon (HCT-116) cancer cell lines in the same concentration range (IC₅₀ = 49.7-105.5 μM). Although VS1 has a moderate potency, we demonstrated that it specifically targets STARD3 in the cells and induces its degradation. Overall, the results confirm the reliability of the computational strategies herein applied and the identification of the first hit compound for the development of novel potent STARD3 inhibitors.

Importance of Incorporating Protein Flexibility in Molecule Modeling: A Theoretical Study on Type I1/2 NIK Inhibitors

NF-κB inducing kinase (NIK), which is considered as the central component of the non-canonical NF-κB pathway, has been proved to be an important target for the regulation of the immune system. In the past few years, NIK inhibitors with various scaffolds have been successively reported, among which type I^1/2 inhibitors that can not only bind in the ATP-binding pocket at the DFG-in state but also extend into an additional back pocket, make up the largest proportion of the NIK inhibitors, and are worthy of more attention. In this study, an integration protocol that combines molecule docking, MD simulations, ensemble docking, MM/GB(PB)SA binding free energy calculations, and decomposition was employed to understand the binding mechanism of 21 tricyclic type I^1/2 NIK inhibitors. It is found that the docking accuracy is largely dependent on the selection of docking protocols as well as the crystal structures. The predictions given by the ensemble docking based on multiple receptor conformations (MRCs) and the MM/GB(PB)SA calculations based on MD simulations showed higher linear correlations with the experimental data than those given by conventional rigid receptor docking (RRD) methods (Glide, GOLD, and Autodock Vina), highlighting the importance of incorporating protein flexibility in predicting protein–ligand interactions. Further analysis based on MM/GBSA demonstrates that the hydrophobic interactions play the most essential role in the ligand binding to NIK, and the polar interactions also make an important contribution to the NIK-ligand recognition. A deeper comparison of several pairs of representative derivatives reveals that the hydrophobic interactions are vitally important in the structural optimization of analogs as well. Besides, the H-bond interactions with some key residues and the large desolvation effect in the back pocket devote to the affinity distinction. It is expected that our study could provide valuable insights into the design of novel and potent type I^1/2 NIK inhibitors.

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

Computationally driven discovery of phenyl(piperazin-1-yl)methanone derivatives as reversible monoacylglycerol lipase (MAGL) inhibitors

Monoacylglycerol lipase (MAGL) is an attractive therapeutic target for many pathologies, including neurodegenerative diseases, cancer as well as chronic pain and inflammatory pathologies. The identification of reversible MAGL inhibitors, devoid of the side effects associated to prolonged MAGL inactivation, is a hot topic in medicinal chemistry. In this study, a novel phenyl(piperazin-1-yl)methanone inhibitor of MAGL was identified through a virtual screening protocol based on a fingerprint-driven consensus docking (CD) approach. Molecular modeling and preliminary structure-based hit optimization studies allowed the discovery of derivative 4, which showed an efficient reversible MAGL inhibition (IC₅₀ = 6.1 µM) and a promising antiproliferative activity on breast and ovarian cancer cell lines (IC₅₀ of 31-72 µM), thus representing a lead for the development of new and more potent reversible MAGL inhibitors. Moreover, the obtained results confirmed the reliability of the fingerprint-driven CD approach herein developed.

Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.

Using a Consensus Docking Approach to Predict Adverse Drug Reactions in Combination Drug Therapies for Gulf War Illness

Gulf War Illness (GWI) is a chronic multisymptom illness characterized by fatigue, musculoskeletal pain, and gastrointestinal and cognitive dysfunction believed to stem from chemical exposures during the 1990⁻1991 Persian Gulf War. There are currently no treatments; however, previous studies have predicted a putative multi-intervention treatment composed of inhibiting Th1 immune cytokines followed by inhibition of the glucocorticoid receptor (GCR) to treat GWI. These predictions suggest the use of specific monoclonal antibodies or suramin to target interleukin-2 and tumor necrosis factor α , followed by mifepristone to inhibit the GCR. In addition to this putative treatment strategy, there exist a variety of medications that target GWI symptomatology. As pharmaceuticals are promiscuous molecules, binding to multiple sites beyond their intended targets, leading to off-target interactions, it is key to ensure that none of these medications interfere with the proposed treatment avenue. Here, we used the drug docking programs AutoDock 4.2, AutoDock Vina, and Schrödinger's Glide to assess the potential off-target immune and hormone interactions of 43 FDA-approved drugs commonly used to treat GWI symptoms in order to determine their putative polypharmacology and minimize adverse drug effects in a combined pharmaceutical treatment. Several of these FDA-approved drugs were predicted to be novel binders of immune and hormonal targets, suggesting caution for their use in the proposed GWI treatment strategy symptoms.

Identification of Myricetin as an Ebola Virus VP35-Double-Stranded RNA Interaction Inhibitor through a Novel Fluorescence-Based Assay

Ebola virus (EBOV) is a filovirus that causes a severe and rapidly progressing hemorrhagic syndrome; a recent epidemic illustrated the urgent need for novel therapeutic agents because no drugs have been approved for treatment of Ebola virus. A key contribution to the high lethality observed during EBOV outbreaks comes from viral evasion of the host antiviral innate immune response in which viral protein VP35 plays a crucial role, blocking interferon type I production, first by masking the viral double-stranded RNA (dsRNA) and preventing its detection by the pattern recognition receptor RIG-I. Aiming to identify inhibitors of the interaction of VP35 with the viral dsRNA, counteracting the VP35 viral innate immune evasion, we established a new methodology for high-yield recombinant VP35 (rVP35) expression and purification and a novel and robust fluorescence-based rVP35-RNA interaction assay ( Z' factor of 0.69). Taking advantage of such newly established methods, we screened a small library of Sardinian natural extracts, identifying Limonium morisianum as the most potent inhibitor extract. A bioguided fractionation led to the identification of myricetin as the component that can inhibit rVP35-dsRNA interaction with an IC₅₀ value of 2.7 μM. Molecular docking studies showed that myricetin interacts with the highly conserved region of the VP35 RNA binding domain, laying the basis for further structural optimization of potent inhibitors of VP35-dsRNA interaction.