Application of a FLAP-Consensus Docking Mixed Strategy for the Identification of New Fatty Acid Amide Hydrolase Inhibitors

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.

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.

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.

Application of MM-PBSA Methods in Virtual Screening

Computer-aided drug design techniques are today largely applied in medicinal chemistry. In particular, receptor-based virtual screening (VS) studies, in which molecular docking represents the gold standard in silico approach, constitute a powerful strategy for identifying novel hit compounds active against the desired target receptor. Nevertheless, the need for improving the ability of docking in discriminating true active ligands from inactive compounds, thus boosting VS hit rates, is still pressing. In this context, the use of binding free energy evaluation approaches can represent a profitable tool for rescoring ligand-protein complexes predicted by docking based on more reliable estimations of ligand-protein binding affinities than those obtained with simple scoring functions. In the present review, we focused our attention on the Molecular Mechanics-Poisson Boltzman Surface Area (MM-PBSA) method for the calculation of binding free energies and its application in VS studies. We provided examples of successful applications of this method in VS campaigns and evaluation studies in which the reliability of this approach has been assessed, thus providing useful guidelines for employing this approach in VS.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

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.

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.

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.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors

Carbonic anhydrase II (CAII) is a zinc-containing metalloenzyme whose aberrant activity is associated with various diseases such as glaucoma, osteoporosis, and different types of tumors; therefore, the development of CAII inhibitors, which can represent promising therapeutic agents for the treatment of these pathologies, is a current topic in medicinal chemistry. Molecular docking is a commonly used tool in structure-based drug design of enzyme inhibitors. However, there is still a need for improving docking reliability, especially in terms of scoring functions, since the complex pattern of energetic contributions driving ligand–protein binding cannot be properly described by mathematical functions only including approximated energetic terms. Here we report a novel CAII-specific fingerprint-based (IFP) scoring function developed according to the ligand–protein interactions detected in the CAII-inhibitor co-crystal structures of the most potent CAII ligands. Our IFP scoring function outperformed the ability of Autodock4 scoring function to identify native-like docking poses of CAII inhibitors and thus allowed a considerable improvement of docking reliability. Moreover, the ligand–protein interaction fingerprints showed a useful application in the binding mode analysis of structurally diverse CAII ligands.

New Chromane-Based Derivatives as Inhibitors of Mycobacterium tuberculosis Salicylate Synthase (MbtI): Preliminary Biological Evaluation and Molecular Modeling Studies

Tuberculosis is the leading cause of death from a single infectious agent worldwide; therefore, the need for new antitubercular drugs is desperate. The recently validated target salicylate synthase MbtI is the first enzyme involved in the biosynthesis of mycobactins, compounds able to chelate iron, an essential cofactor for the survival of Mycobacterium tuberculosis in the host. Here, we report on the synthesis and biological evaluation of chromane-based compounds as new potential inhibitors of MbtI. Our approach successfully allowed the identification of a novel lead compound (1), endowed with a promising activity against this enzyme (IC₅₀ = 55 μM). Molecular modeling studies were performed in order to evaluate the binding mode of 1 and rationalize the preliminary structure-activity relationships, thus providing crucial information to carry out further optimization studies.

Binding investigation and preliminary optimisation of the 3-amino-1,2,4-triazin-5(2H)-one core for the development of new Fyn inhibitors

Fyn tyrosine kinase inhibitors are considered potential therapeutic agents for a variety of human cancers. Furthermore, the involvement of Fyn kinase in signalling pathways that lead to severe pathologies, such as Alzheimer's and Parkinson's diseases, has also been demonstrated. In this study, starting from 3-(benzo[d][1,3]dioxol-5-ylamino)-6-methyl-1,2,4-triazin-5(2H)-one (VS6), a hit compound that showed a micromolar inhibition of Fyn (IC₅₀ = 4.8 μM), we computationally investigated the binding interactions of the 3-amino-1,2,4-triazin-5(2H)-one scaffold and started a preliminary hit to lead optimisation. This analysis led us to confirm the hypothesised binding mode of VS6 and to identify a new derivative that is about 6-fold more active than VS6 (compound 3, IC₅₀ = 0.76 μM).

Discovery and development of novel salicylate synthase (MbtI) furanic inhibitors as antitubercular agents

We report on the virtual screening, synthesis, and biological evaluation of new furan derivatives targeting Mycobacterium tuberculosis salicylate synthase (MbtI). A receptor-based virtual screening procedure was applied to screen the Enamine database, identifying two compounds, I and III, endowed with a good enzyme inhibitory activity. Considering the most active compound I as starting point for the development of novel MbtI inhibitors, we obtained new derivatives based on the furan scaffold. Among the SAR performed on this class, compound 1a emerged as the most potent MbtI inhibitor reported to date (K_i = 5.3 μM). Moreover, compound 1a showed a promising antimycobacterial activity (MIC⁹⁹ = 156 μM), which is conceivably related to mycobactin biosynthesis inhibition.

Anticancer Activity of Euplotin C, Isolated from the Marine Ciliate Euplotes crassus, Against Human Melanoma Cells

Cutaneous melanoma is the most serious type of skin cancer, so new cytotoxic weapons against novel targets in melanoma are of great interest. Euplotin C (EC), a cytotoxic secondary metabolite of the marine ciliate Euplotes crassus, was evaluated in the present study on human cutaneous melanoma cells to explore its anti-melanoma activity and to gain more insight into its mechanism of action. EC exerted a marked cytotoxic effect against three different human melanoma cell lines (A375, 501Mel and MeWo) with a potency about 30-fold higher than that observed in non-cancer cells (HDFa cells). A pro-apoptotic activity and a decrease in melanoma cell migration by EC were also observed. At the molecular level, the inhibition of the Erk and Akt pathways, which control many aspects of melanoma aggressiveness, was shown. EC cytotoxicity was antagonized by dantrolene, a ryanodine receptor (RyR) antagonist, in a concentration-dependent manner. A role of RyR as a direct target of EC was also suggested by molecular modelling studies. In conclusion, our data provide the first evidence of the anti-melanoma activity of EC, suggesting it may be a promising new scaffold for the development of selective activators of RyR to be used for the treatment of melanoma and other cancer types.

Rational Development of MAGL Inhibitors

Hit identification and hit-to-lead optimization are key steps of the early drug discovery program. Starting from the X-ray crystal structure of the human monoacylglycerol lipase (hMAGL), we herein describe the computational and experimental procedures that we applied for identifying and optimizing a new active inhibitor of this target enzyme. A receptor-based virtual screening method is reported in details, together with enzymatic assays and a first round of hit optimization.

Development of terphenyl-2-methyloxazol-5(4H)-one derivatives as selective reversible MAGL inhibitors

Monoacylglycerol lipase is a serine hydrolase that plays a major role in the degradation of the endocannabinoid neurotransmitter 2-arachidonoylglycerol. A wide number of MAGL inhibitors are reported in literature; however, many of them are characterised by an irreversible mechanism of action and this behavior determines an unwanted chronic MAGL inactivation, which acquires a functional antagonism of the endocannabinoid system. The possible use of reversible MAGL inhibitors has only recently been explored, due to the lack of known compounds possessing efficient reversible inhibitory activities. In this work, we report a new series of terphenyl-2-methyloxazol-5(4H)-one derivatives characterised by a reversible MAGL-inhibition mechanism. Among them, compound 20b showed to be a potent MAGL reversible inhibitor (IC₅₀ = 348 nM) with a good MAGL/FAAH selectivity. Furthermore, this compound showed antiproliferative activities against two different cancer cell lines that overexpress MAGL.

Recent Advancements in Docking Methodologies

Nowadays molecular docking has become an important methodology in CADD (Computer-Aided Drug Design)-assisted drug discovery process. It is an important computational tool widely used to predict binding mode, binding affinity and binding free energy of a protein-ligand complex. The important factors responsible for accurate results in docking studies are correct binding site prediction, use of suitable small-molecule databases, consistent docking pose, high dock score with good MD (Molecular Dynamics), clarity whether the compound is an inhibitor or agonist, etc. However, still there are several limitations which make it difficult to obtain accurate results from docking studies. In this chapter, the main focus is on recent advancements in various aspects of molecular docking such as ligand sampling, protein flexibility, scoring functions, fragment docking, post-processing, docking into homology models and protein-protein docking.

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

Ligand-protein docking is one of the most common techniques used in virtual screening campaigns. Despite the large number of docking software available, there is still the need of improving the efficacy of docking-based virtual screenings. To date, only very few studies evaluated the possibility of combining the results of different docking methods to achieve higher success rates in virtual screening studies (consensus docking). In order to better understand the range of applicability of this approach, we carried out an extensive enriched database analysis using the DUD dataset. The consensus docking protocol was then refined by applying modifications concerning the calculation of pose consensus and the combination of docking methods included in the procedure. The results obtained suggest that this approach performs as well as the best available methods found in literature, confirming the idea that this procedure can be profitably used for the identification of new hit compounds.

Development and validation of a docking-based virtual screening platform for the identification of new lactate dehydrogenase inhibitors

The human muscle isoform of lactate dehydrogenase (hLDH5) is one of the key enzymes of the glycolytic process. It is overexpressed in metastatic cancer cells and is linked to the vitality of tumors in hypoxic conditions. With the aim of identifying new hLDH5 inhibitors, a fully automated docking-based virtual screening platform was developed by considering different protein conformations and the consensus docking strategy. In order to verify the reliability of the reported platform, a small database of about 10,000 compounds was filtered by using this method, and the top-ranked compounds were tested for their hLDH5 inhibition activity. Enzymatic assays revealed that, among the ten selected compounds, two proved to efficiently inhibit enzyme activity with IC₅₀ values in the micromolar range. These results demonstrate the validity of the methodologies we followed, encouraging the application of larger virtual screening studies and further refinements of the platform. Furthermore, the two active compounds herein described may be considered as interesting leads for the development of new and more efficient LDH inhibitors.