Integrating Ligand and Target-Driven Based Virtual Screening Approaches With in vitro Human Cell Line Models and Time-Resolved Fluorescence Resonance Energy Transfer Assay to Identify Novel Hit Compounds Against BCL-2

Predictive identification and design of potent inhibitors targeting resistance-inducing candidate genes from E. coli whole-genome sequences

Introduction: This work utilizes predictive modeling in drug discovery to unravel potential candidate genes from Escherichia coli that are implicated in antimicrobial resistance; we subsequently target the gidB, MacB, and KatG genes with some compounds from plants with reported antibacterial potentials. Method: The resistance genes and plasmids were identified from 10 whole-genome sequence datasets of E. coli; forty two plant compounds were selected, and their 3D structures were retrieved and optimized for docking. The 3D crystal structures of KatG, MacB, and gidB were retrieved and prepared for molecular docking, molecular dynamics simulations, and ADMET profiling. Result: Hesperidin showed the least binding energy (kcal/mol) against KatG (-9.3), MacB (-10.7), and gidB (-6.7); additionally, good pharmacokinetic profiles and structure-dynamics integrity with their respective protein complexes were observed. Conclusion: Although these findings suggest hesperidin as a potential inhibitor against MacB, gidB, and KatG in E. coli, further validations through in vitro and in vivo experiments are needed. This research is expected to provide an alternative avenue for addressing existing antimicrobial resistances associated with E. coli's MacB, gidB, and KatG.

Efficient, rapid, and high-yield synthesis of aryl Schiff base derivatives and their in vitro and in silico inhibition studies of hCA I, hCA II, AChE, and BuChE

This study reports a rapid and efficient synthesis of four novel aryl Schiff base derivatives. Biological activity and molecular modeling studies were conducted to evaluate the inhibitory effects of these compounds on human carbonic anhydrases (hCA) and cholinesterases. The results indicate that the triazole-ring-containing compounds have strong inhibitory effects on hCA I, hCA II, acetylcholinesterase (AChE), and butyrylcholinesterase (BuChE) targets. Besides comparing the Schiff bases synthesized in our study to reference molecules, we conducted in silico investigations to examine how these compounds interact with their targets. Our studies revealed that these compounds can occupy binding sites and establish interactions with crucial residues, thus inhibiting the functions of the targets. These findings have significant implications as they can be utilized to develop more potent compounds for treating the diseases that these target proteins play crucial roles in or to obtain drug precursors with enhanced efficacy.

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

Identifying Potential SOS1 Inhibitors via Virtual Screening of Multiple Small Molecule Libraries against KRAS-SOS1 Interaction

The RAS-MAPK signaling pathway, crucial for cell proliferation and differentiation, involves key proteins KRAS and SOS1. Mutations in the KRAS and SOS1 genes are implicated in various cancer types, including pancreatic, lung, and juvenile myelomonocytic leukemia. There is considerable interest in identifying inhibitors targeting KRAS and SOS1 to explore potential therapeutic strategies for cancer treatment. In this study, advanced in silico techniques were employed to screen small molecule libraries at this interface, leading to the identification of promising lead compounds as potential SOS1 inhibitors. Comparative analysis of the average binding free energies of these predicted potent compounds with known SOS1 small molecule inhibitors revealed that the identified compounds display similar or even superior predicted binding affinities compared to the known inhibitors. These findings offer valuable insights into the potential of these compounds as candidates for further development as effective anti-cancer agents.

Identification of natural product-based effective inhibitors of spleen tyrosine kinase (SYK) through virtual screening and molecular dynamics simulation approaches

Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase that plays an essential role in signal transduction across different cell types. In the context of allergy and autoimmune disorders, it is a crucial regulator of immune receptor signaling in inflammatory cells such as B cells, mast cells, macrophages, and neutrophils. Developing SYK kinase inhibitors has gained significant interest for potential therapeutic applications in neurological and cancer-related conditions. The clinical use of the most advanced SYK inhibitor, Fostamatinib, has been limited due to its unwanted side effects. Thus, a more targeted approach to SYK inhibition would provide a more comprehensive treatment window. In this study, we used a virtual screening approach to identify potential SYK inhibitors from natural compounds from the IMPPAT database. We identified two compounds, Isolysergic acid and Michelanugine, which showed strong affinity and specificity for the SYK binding pocket. All-atom molecular dynamics (MD) simulations were also performed to explore the stability, conformational changes, and interaction mechanism of SYK in complexes with the identified compounds. The identified compounds might have the potential to be developed into promising SYK inhibitors for the treatment of various diseases, including autoimmune disorders, cancer, and inflammatory diseases. This work aims to identify potential phytochemicals to develop a new protein kinase inhibitor for treating advanced malignancies by providing an updated understanding of the role of SYK.Communicated by Ramaswamy H. Sarma.

In silico design and cell-based evaluation of two dual anti breast cancer compounds targeting Bcl-2 and GPER

According to WHO statistics, breast cancer (BC) disease represents about 2.3 million diagnosed and 685,000 deaths globally. Regarding histological classification of BC, the Estrogen (ER) and Progesterone (PR) receptors negative-expression cancer, named Triple-Negative BC (TNBC), represents the most aggressive type of this disease, making it a challenge for drug discovery. In this context, our research group, applying a well-established Virtual Screening (VS) protocol, in addition to docking and molecular dynamics simulations studies, yielded two ligands identified as 6 and 37 which were chemically synthesized and evaluated on MCF-7 and MDA-MB-231 cancer cell lines. Strikingly, 37 assayed on MDA-MB-231 (a TNBC cell model) depicted an outstanding value of 18.66 μM much lower than 65.67 μM yielded by Gossypol Bcl-2 inhibitor whose main disadvantage is to produce multiple toxic effects. Highlighted above, enforce the premise of the computational tools to find new therapeutic options against the most aggressive forms of breast cancer, as the results herein showed.

A novel BH3 mimetic Bcl-2 inhibitor promotes autophagic cell death and reduces in vivo Glioblastoma tumor growth

Anti-apoptotic members of the Bcl-2 family proteins play central roles in the regulation of cell death in glioblastoma (GBM), the most malignant type of brain tumor. Despite the advances in GBM treatment, there is still an urgent need for new therapeutic approaches. Here, we report a novel 4-thiazolidinone derivative BH3 mimetic, BAU-243 that binds to Bcl-2 with a high affinity. BAU-243 effectively reduced overall GBM cell proliferation including a subpopulation of cancer-initiating cells in contrast to the selective Bcl-2 inhibitor ABT-199. While ABT-199 successfully induces apoptosis in high BCL2-expressing neuroblastoma SHSY-5Y cells, BAU-243 triggered autophagic cell death rather than apoptosis in GBM A172 cells, indicated by the upregulation of BECN1, ATG5, and MAP1LC3B expression. Lc3b-II, a potent autophagy marker, was significantly upregulated following BAU-243 treatment. Moreover, BAU-243 significantly reduced tumor growth in vivo in orthotopic brain tumor models when compared to the vehicle group, and ABT-199 treated animals. To elucidate the molecular mechanisms of action of BAU-243, we performed computational modeling simulations that were consistent with in vitro results. Our results indicate that BAU-243 activates autophagic cell death by disrupting the Beclin 1:Bcl-2 complex and may serve as a potential small molecule for treating GBM.

The Multistage Antimalarial Compound Calxinin Perturbates P. falciparum Ca2+ Homeostasis by Targeting a Unique Ion Channel

Malaria elimination urgently needs novel antimalarial therapies that transcend resistance, toxicity, and high costs. Our multicentric international collaborative team focuses on developing multistage antimalarials that exhibit novel mechanisms of action. Here, we describe the design, synthesis, and evaluation of a novel multistage antimalarial compound, 'Calxinin'. A compound that consists of hydroxyethylamine (HEA) and trifluoromethyl-benzyl-piperazine. Calxinin exhibits potent inhibitory activity in the nanomolar range against the asexual blood stages of drug-sensitive (3D7), multidrug-resistant (Dd2), artemisinin-resistant (IPC4912), and fresh Kenyan field isolated Plasmodium falciparum strains. Calxinin treatment resulted in diminished maturation of parasite sexual precursor cells (gametocytes) accompanied by distorted parasite morphology. Further, in vitro liver-stage testing with a mouse model showed reduced parasite load at an IC50 of 79 nM. A single dose (10 mg/kg) of Calxinin resulted in a 30% reduction in parasitemia in mice infected with a chloroquine-resistant strain of the rodent parasite P. berghei. The ex vivo ookinete inhibitory concentration within mosquito gut IC50 was 150 nM. Cellular in vitro toxicity assays in the primary and immortalized human cell lines did not show cytotoxicity. A computational protein target identification pipeline identified a putative P. falciparum membrane protein (Pf3D7_1313500) involved in parasite calcium (Ca2+) homeostasis as a potential Calxinin target. This highly conserved protein is related to the family of transient receptor potential cation channels (TRP-ML). Target validation experiments showed that exposure of parasitized RBCs (pRBCs) to Calxinin induces a rapid release of intracellular Ca2+ from pRBCs; leaving de-calcinated parasites trapped in RBCs. Overall, we demonstrated that Calxinin is a promising antimalarial lead compound with a novel mechanism of action and with potential therapeutic, prophylactic, and transmission-blocking properties against parasites resistant to current antimalarials.

Bioactive Phytoconstituents as Potent Inhibitors of Tyrosine-Protein Kinase Yes (YES1): Implications in Anticancer Therapeutics

Tyrosine-protein kinase Yes (YES1) belongs to the Tyrosine-protein kinase family and is involved in several biological activities, including cell survival, cell–cell adhesion, cell differentiation, and cytoskeleton remodeling. It is highly expressed in esophageal, lung, and bladder cancers, and thus considered as an attractive drug target for cancer therapy. In this study, we performed a virtual screening of phytoconstituents from the IMPPAT database to identify potential inhibitors of YES1. Initially, the molecules were retrieved on their physicochemical properties following the Lipinski rule of five. Then binding affinities calculation, PAINS filter, ADMET, and PASS analyses followed by an interaction analysis to select safe and clinically better hits. Finally, two compounds, Glabrene and Lupinisoflavone C (LIC), with appreciable affinities and a specific interaction towards the AlphaFold predicted structure of YES1, were identified. Their time-evolution analyses were carried out using an all-atom molecular dynamics (MD) simulation, principal component analysis, and free energy landscapes. Altogether, we propose that Glabrene and LIC can be further explored in clinical settings to develop anticancer therapeutics targeting YES1 kinase.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Screening of Clinically Approved and Investigation Drugs as Potential Inhibitors of SARS-CoV-2: A Combined in silico and in vitro Study

In the current study, we used 7922 FDA approved small molecule drugs as well as compounds in clinical investigation from NIH's NPC database in our drug repurposing study. SARS-CoV-2 main protease as well as Spike protein/ACE2 targets were used in virtual screening and top-100 compounds from each docking simulations were considered initially in short molecular dynamics (MD) simulations and their average binding energies were calculated by MM/GBSA method. Promising hit compounds selected based on average MM/GBSA scores were then used in long MD simulations. Based on these numerical calculations following compounds were found as hit inhibitors for the SARS-CoV-2 main protease: Pinokalant, terlakiren, ritonavir, cefotiam, telinavir, rotigaptide, and cefpiramide. In addition, following 3 compounds were identified as inhibitors for Spike/ACE2: Denopamine, bometolol, and rotigaptide. In order to verify the predictions of in silico analyses, 4 compounds (ritonavir, rotigaptide, cefotiam, and cefpiramide) for the main protease and 2 compounds (rotigaptide and denopamine) for the Spike/ACE2 interactions were tested by in vitro experiments. While the concentration-dependent inhibition of the ritonavir, rotigaptide, and cefotiam was observed for the main protease; denopamine was effective at the inhibition of Spike/ACE2 binding.

Binary-QSAR guided virtual screening of FDA approved drugs and compounds in clinical investigation against SARS-CoV-2 main protease

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the antiviral therapeutic activity binary QSAR model of the MetaCore/MetaDrug platform. Among 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 main protease (M^pro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docking scored compounds and following molecular mechanics generalized born surface area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivoxetil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 M^pro.

Transcription factor NF-κB as target for SARS-CoV-2 drug discovery efforts using inflammation-based QSAR screening model

NF-κB is a central regulator of immunity and inflammation. It is suggested that the inflammatory response mediated by SARS-CoV-2 is predominated by NF-κB activation. Thus, NF-κB inhibition is considered a potential therapeutic strategy for COVID-19. The aim of this study was to identify potential anti-inflammation lead molecules that target NF-κB using a quantitative structure-activity relationships (QSAR) model of currently used and investigated anti-inflammatory drugs as the basis for screening. We applied an integrated approach by starting with the inflammation-based QSAR model to screen three libraries containing more than 220,000 drug-like molecules for the purpose of finding potential drugs that target the NF-κB/IκBα p50/p65 (RelA) complex. We also used QSAR models to rule out molecules that were predicted to be toxic. Among screening libraries, 382 molecules were selected as potentially nontoxic and were analyzed further by short and long molecular dynamics (MD) simulations and free energy calculations. We have discovered five hit ligands with highly predicted anti-inflammation activity and nearly no predicted toxicities which had strongly favorable protein-ligand interactions and conformational stability at the binding pocket compared to a known NF-κB inhibitor (procyanidin B2). We propose these hit molecules as potential NF-κB inhibitors which can be further investigated in pre-clinical studies against SARS-CoV-2 and may be used as a scaffold for chemical optimization and drug development efforts.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Molecular Docking Study of Naturally Derived Flavonoids with Antiapoptotic BCL-2 and BCL-XL Proteins toward Ovarian Cancer Treatment

The naturally derived flavonoids are well known to have anticarcinogenic effects. Flavonoids could be an alternative strategy for ovarian cancer treatment, due to existing platinum-based drugs are reported to develop resistance with low survival rates. Inhibition of antiapoptotic proteins, namely B-cell lymphoma (Bcl-2) and B-cell lymphoma-extra large (Bcl-xl), is the key target to stimulate apoptosis process in cancer cells. This study aimed to determine the binding interaction of five naturally derived flavonoids (biochanin A, myricetin, apigenin, galangin, and fisetin) with potential antiapoptotic target proteins (Bcl-2 and Bcl-xl). The molecular docking study was conducted using AutoDock Vina program. The binding affinity and the presence of hydrogen bonds between the flavonoids and target proteins were predicted. Our findings showed that all the flavonoids showed better binding affinity with Bcl-xl than that of Bcl-2 proteins. The highest binding affinity was recorded in fisetin-Bcl-xl protein complex (-8.8 kcal/mol). Meanwhile, the other flavonoids docked with Bcl-xl protein showed binding affinities, ranging from -8.0 to -8.6 kcal/mol. A total of four hydrogen bonds, four hydrophobic contacts, and one electrostatic interaction were detected in the docked fisetin-Bcl-xl complex, explaining its high binding affinity with Bcl-xl. The present results indicate that all flavonoids could potentially serve as Bcl-xl protein inhibitors, which would consequently lead to apoptotic process in ovarian cancers.

Toward a Computational Ecotoxicity Assay

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.