A Molecular Dynamics-Shared Pharmacophore Approach to Boost Early-Enrichment Virtual Screening: A Case Study on Peroxisome Proliferator-Activated Receptor α

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Consensus screening for a challenging target: the quest for P-glycoprotein inhibitors

ATP-binding cassette (ABC) transporters are a large family of proteins involved in membrane transport of a wide variety of substrates. Among them, ABCB1, also known as MDR-1 or P-glycoprotein (P-gp), is the most characterized. By exporting xenobiotics out of the cell, P-gp activity can affect the ADME properties of several drugs. Moreover, P-gp has been found to mediate multidrug resistance in cancer cells. Thus, the inhibition of P-gp activity may lead to increased absorption and/or intracellular accumulation of co-administered drugs, enhancing their effectiveness. Using the human-mouse chimeric cryoEM 3D structure of the P-gp in the inhibitor-bound intermediate form (PDBID: 6qee), approximately 200 000 commercially available natural compounds from the ZINC database were virtually screened. To build a model able to discriminate between substrate and inhibitors, two datasets of compounds with known activity, including P-gp inhibitors, substrates, and inactive molecules were also docked. The best docking pose of selected substrates and inhibitors were used to generate 3D common feature pharmacophoric models that were combined with the Autodock Vina binding energy values to prioritize compounds for visual inspection. With this consensus approach, 13 potential candidates were identified and then tested for their ability to inhibit P-gp, using zosuquidar, a third generation P-gp inhibitor, as a reference drug. Eight compounds were found to be active with 6 of them having an IC50 lower than 5 μM in a membrane-based ATPase activity assay. Moreover, the P-gp inhibitory activity was also confirmed by two different cell-based in vitro methods. Both retrospective and prospective results demonstrate the ability of the combined structure-based pharmacophore modeling and docking-based virtual screening approach to predict novel hit compounds with inhibitory activity toward P-gp. The resulting chemical scaffolds could serve as inspiration for the optimization of novel and more potent P-gp inhibitors.

Virtual Screening Strategy and In Vitro Tests to Identify New Inhibitors of the Immunoproteasome

Immunoproteasome inhibition is a promising strategy for the treatment of hematological malignancies, autoimmune diseases, and inflammatory diseases. The design of non-covalent inhibitors of the immunoproteasome β1i/β5i catalytic subunits could be a novel approach to avoid the drawbacks of the known covalent inhibitors, such as toxicity due to off-target binding. In this work, we report the biological evaluation of thirty-four compounds selected from a commercially available collection. These hit compounds are the outcomes of a virtual screening strategy including a dynamic pharmacophore modeling approach onto the β1i subunit and a pharmacophore/docking approach onto the β5i subunit. The computational studies were first followed by in vitro enzymatic assays at 100 μM. Only compounds capable of inhibiting the enzymatic activity by more than 50% were characterized in detail using Tian continuous assays, determining the dissociation constant (K_i) of the non-covalent complex where K_i is also the measure of the binding affinity. Seven out of thirty-four hits showed to inhibit β1i and/or β5i subunit. Compound 3 is the most active on the β1i subunit with K_i = 11.84 ± 1.63 µM, and compound 17 showed K_i = 12.50 ± 0.77 µM on the β5i subunit. Compound 2 showed inhibitory activity on both subunits (K_i = 12.53 ± 0.18 and K_i = 31.95 ± 0.81 on the β1i subunit and β5i subunit, respectively). The induced fit docking analysis revealed interactions with Thr1 and Phe31 of β1i subunit and that represent new key residues as reported in our previous work. Onto β5i subunit, it interacts with the key residues Thr1, Thr21, and Tyr169. This last hit compound identified represents an interesting starting point for further optimization of β1i/β5i dual inhibitors of the immunoproteasome.

Telomerase: A prominent oncological target for development of chemotherapeutic agents

Telomerase is a ribonucleoprotein (RNP) responsible for the maintenance of chromosomal integrity by stabilizing telomere length. Telomerase is a widely expressed hallmark responsible for replicative immortality in 80-90% of malignant tumors. Cancer cells produce telomerase which prevents telomere shortening by adding telomeres sequences beyond Hayflick's limit; which enables them to divide uncontrollably. The activity of telomerase is relatively low in somatic cells and absent in normal cells, but the re-activation of this RNP in normal cells suppresses p53 activity which leads to the avoidance of senescence causing malignancy. Here, we have focused explicitly on various anti-telomerase therapies and telomerase-inhibiting molecules for the treatment of cancer. We have covered molecules that are reported in developmental, preclinical, and clinical trial stages as potent telomerase inhibitors. Apart from chemotherapy, we have also included details of immunotherapy, gene therapy, G-quadruplex stabilizers, and HSP-90 inhibitors. The purpose of this work is to discuss the challenges behind the development of novel telomerase inhibitors and to identify various perspectives for designing anti-telomerase compounds.

In Silico Design, Synthesis, and Biological Evaluation of Anticancer Arylsulfonamide Endowed with Anti-Telomerase Activity

Telomerase, a reverse transcriptase enzyme involved in DNA synthesis, has a tangible role in tumor progression. Several studies have evidenced telomerase as a promising target for developing cancer therapeutics. The main reason is due to the overexpression of telomerase in cancer cells (85–90%) compared with normal cells where it is almost unexpressed. In this paper, we used a structure-based approach to design potential inhibitors of the telomerase active site. The MYSHAPE (Molecular dYnamics SHared PharmacophorE) approach and docking were used to screen an in-house library of 126 arylsulfonamide derivatives. Promising compounds were synthesized using classical and green methods. Compound 2C revealed an interesting IC₅₀ (33 ± 4 µM) against the K-562 cell line compared with the known telomerase inhibitor BIBR1532 IC₅₀ (208 ± 11 µM) with an SI ~10 compared to the BALB/3-T3 cell line. A 100 ns MD simulation of 2C in the telomerase active site evidenced Phe494 as the key residue as well as in BIBR1532. Each moiety of compound 2C was involved in key interactions with some residues of the active site: Arg557, Ile550, and Gly553. Compound 2C, as an arylsulfonamide derivative, is an interesting hit compound that deserves further investigation in terms of optimization of its structure to obtain more active telomerase inhibitors

Immunoproteasome and Non-Covalent Inhibition: Exploration by Advanced Molecular Dynamics and Docking Methods

The selective inhibition of immunoproteasome is a valuable strategy to treat autoimmune, inflammatory diseases, and hematologic malignancies. Recently, a new series of amide derivatives as non-covalent inhibitors of the β1i subunit with K_i values in the low/submicromolar ranges have been identified. Here, we investigated the binding mechanism of the most potent and selective inhibitor, N-benzyl-2-(2-oxopyridin-1(2H)-yl)propanamide (1), to elucidate the steps from the ligand entrance into the binding pocket to the ligand-induced conformational changes. We carried out a total of 400 ns of MD-binding analyses, followed by 200 ns of plain MD. The trajectories clustering allowed identifying three representative poses evidencing new key interactions with Phe31 and Lys33 together in a flipped orientation of a representative pose. Further, Binding Pose MetaDynamics (BPMD) studies were performed to evaluate the binding stability, comparing 1 with four other inhibitors of the β1i subunit: N-benzyl-2-(2-oxopyridin-1(2H)-yl)acetamide (2), N-cyclohexyl-3-(2-oxopyridin-1(2H)-yl)propenamide (3), N-butyl-3-(2-oxopyridin-1(2H)-yl)propanamide (4), and (S)-2-(2-oxopyridin-1(2H)-yl)-N,4-diphenylbutanamide (5). The obtained results in terms of free binding energy were consistent with the experimental values of inhibition, confirming 1 as a lead compound of this series. The adopted methods provided a full dynamic description of the binding events, and the information obtained could be exploited for the rational design of new and more active inhibitors.

A Computer-Based Methodology to Design Non-Standard Peptides Potentially Able to Prevent HOX-PBX1-Associated Cancer Diseases

In the last decades, HOX proteins have been extensively studied due to their pivotal role in transcriptional events. HOX proteins execute their activity by exploiting a cooperative binding to PBX proteins and DNA. Therefore, an increase or decrease in HOX activity has been associated with both solid and haematological cancer diseases. Thus, inhibiting HOX-PBX interaction represents a potential strategy to prevent these malignancies, as demonstrated by the patented peptide HTL001 that is being studied in clinical trials. In this work, a computational study is described to identify novel potential peptides designed by employing a database of non-natural amino acids. For this purpose, residue scanning of the HOX minimal active sequence was performed to select the mutations to be further processed. According to these results, the peptides were point-mutated and used for Molecular Dynamics (MD) simulations in complex with PBX1 protein and DNA to evaluate complex binding stability. MM-GBSA calculations of the resulting MD trajectories were exploited to guide the selection of the most promising mutations that were exploited to generate twelve combinatorial peptides. Finally, the latter peptides in complex with PBX1 protein and DNA were exploited to run MD simulations and the ΔGbinding average values of the complexes were calculated. Thus, the analysis of the results highlighted eleven combinatorial peptides that will be considered for further assays.

In Silico Strategy for Targeting the mTOR Kinase at Rapamycin Binding Site by Small Molecules

Computer aided drug-design methods proved to be powerful tools for the identification of new therapeutic agents. We employed a structure-based workflow to identify new inhibitors targeting mTOR kinase at rapamycin binding site. By combining molecular dynamics (MD) simulation and pharmacophore modelling, a simplified structure-based pharmacophore hypothesis was built starting from the FKBP12-rapamycin-FRB ternary complex retrieved from RCSB Protein Data Bank (PDB code 1FAP). Then, the obtained model was used as filter to screen the ZINC biogenic compounds library, containing molecules derived from natural sources or natural-inspired compounds. The resulting hits were clustered according to their similarity; moreover, compounds showing the highest pharmacophore fit-score were chosen from each cluster. The selected molecules were subjected to docking studies to clarify their putative binding mode. The binding free energy of the obtained complexes was calculated by MM/GBSA method and the hits characterized by the lowest ΔG_bind values were identified as potential mTOR inhibitors. Furthermore, the stability of the resulting complexes was studied by means of MD simulation which revealed that the selected compounds were able to form a stable ternary complex with FKBP12 and FRB domain, thus underlining their potential ability to inhibit mTOR with a rapamycin-like mechanism.

Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.

Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-Based Pharmacophore Modeling/Docking Approach

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.

Targeting Nonsense: Optimization of 1,2,4-Oxadiazole TRIDs to Rescue CFTR Expression and Functionality in Cystic Fibrosis Cell Model Systems

Cystic fibrosis (CF) patients develop a severe form of the disease when the cystic fibrosis transmembrane conductance regulator (CFTR) gene is affected by nonsense mutations. Nonsense mutations are responsible for the presence of a premature termination codon (PTC) in the mRNA, creating a lack of functional protein. In this context, translational readthrough-inducing drugs (TRIDs) represent a promising approach to correct the basic defect caused by PTCs. By using computational optimization and biological screening, we identified three new small molecules showing high readthrough activity. The activity of these compounds has been verified by evaluating CFTR expression and functionality after treatment with the selected molecules in cells expressing nonsense–CFTR–mRNA. Additionally, the channel functionality was measured by the halide sensitive yellow fluorescent protein (YFP) quenching assay. All three of the new TRIDs displayed high readthrough activity and low toxicity and can be considered for further evaluation as a therapeutic approach toward the second major cause of CF.

Pharmacophore-Based Design of New Chemical Scaffolds as Translational Readthrough-Inducing Drugs (TRIDs)

Translational readthrough-inducing drugs (TRIDs) rescue the functional full-length protein expression in genetic diseases, such as cystic fibrosis, caused by premature termination codons (PTCs). Small molecules have been developed as TRIDs to trick the ribosomal machinery during recognition of the PTC. Herein we report a computational study to identify new TRID scaffolds. A pharmacophore approach was carried out on compounds that showed readthrough activity. The pharmacophore model applied to screen different libraries containing more than 87000 compounds identified four hit-compounds presenting scaffolds with diversity from the oxadiazole lead. These compounds have been synthesized and tested using the Fluc reporter harboring the UGA PTC. Moreover, the cytotoxic effect and the expression of the CFTR protein were evaluated. These compounds, a benzimidazole derivative (NV2899), a benzoxazole derivative (NV2913), a thiazole derivative (NV2909), and a benzene-1,3-disulfonate derivative (NV2907), were shown to be potential new lead compounds as TRIDs, boosting further efforts to address the optimization of the chemical scaffolds.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.

Rescuing the CFTR protein function: Introducing 1,3,4-oxadiazoles as translational readthrough inducing drugs

Nonsense mutations in the CFTR gene prematurely terminate translation of the CFTR mRNA leading to the production of a truncated protein that lacks normal function causing a more severe form of the cystic fibrosis (CF) disease. About 10% of patients affected by CF show a nonsense mutation. A potential treatment of this alteration is to promote translational readthrough of premature termination codons (PTCs) by Translational Readthrough Inducing Drugs (TRIDs) such as PTC124. In this context we aimed to compare the activity of PTC124 with analogues differing in the heteroatoms position in the central heterocyclic core. By a validated protocol consisting of computational screening, synthesis and biological tests we identified a new small molecule (NV2445) with 1,3,4-oxadiazole core showing a high readthrough activity. Moreover, we evaluated the CFTR functionality after NV2445 treatment in CF model systems and in cells expressing a nonsense-CFTR-mRNA. Finally, we studied the supramolecular interactions between TRIDs and CFTR-mRNA to assess the biological target/mechanism and compared the predicted ADME properties of NV2445 and PTC124.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.