Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations

The Saccharomyces cerevisiae amino acid transporter Lyp1 has a broad substrate spectrum

The main mediators for the amino acid uptake in Saccharomyces cerevisiae are the permeases belonging to the yeast amino acid transporter family. Recently, we discovered that members of this family support growth on more amino acids than previously described. Here we study the substrate spectrum of Lyp1, the main transporter responsible for the uptake of lysine in yeast. We show that overexpressed Lyp1 supports growth on alanine, asparagine, leucine, methionine, phenylalanine, serine, and valine when these are provided as the sole source of nitrogen to a strain severely deficient for the uptake of amino acids. We show that alanine and serine compete with lysine for the common transport system, albeit with much lower affinity. Thus, Lyp1 has a much broader substrate spectrum than previously thought, which may be true for many amino acid transporters.

Robustly interrogating machine learning-based scoring functions: what are they learning?

MOTIVATION

Machine learning-based scoring functions (MLBSFs) have been found to exhibit inconsistent performance on different benchmarks and be prone to learning dataset bias. For the field to develop MLBSFs that learn a generalizable understanding of physics, a more rigorous understanding of how they perform is required.

RESULTS

In this work, we compared the performance of a diverse set of popular MLBSFs (RFScore, SIGN, OnionNet-2, Pafnucy, and PointVS) to our proposed baseline models that can only learn dataset biases on a range of benchmarks. We found that these baseline models were competitive in accuracy to these MLBSFs in almost all proposed benchmarks, indicating these models only learn dataset biases. Our tests and provided platform, ToolBoxSF, will enable researchers to robustly interrogate MLBSF performance and determine the effect of dataset biases on their predictions.

AVAILABILITY AND IMPLEMENTATION

https://github.com/guydurant/toolboxsf.

Identification of a Potent CDK8 Inhibitor Using Structure-Based Virtual Screening

Pulmonary fibrosis is excessive scarring of the lung tissues. Transforming growth factor-beta (TGF-β) has been implicated in pulmonary fibrosis due to its ability to induce the epithelial-to-mesenchymal transition (EMT) and promote epithelial cell migration. Cyclin-dependent kinase 8 (CDK8) can mediate the TGF-β signaling pathways and could function as an alternative therapeutic target for treating pulmonary fibrosis. Here, we performed a structure-based virtual screening campaign to identify CDK8 inhibitors from a library of 1.6 million compounds. The screening process ended with the identification of a novel CDK8 inhibitor, P162-0948 (IC50: 50.4 nM). An interaction analysis highlighted important CDK8-ligand interactions that support its binding and inhibitory activity. Testing against a panel of 60 different kinases demonstrated P162-0948 selectivity toward CDK8. Crucially, the inhibitor was found to be structurally novel when compared to known CDK8 inhibitors. Testing in A549 human alveolar epithelial cell lines showed that the P162-0948 can reduce cell migration and protein expression of EMT-related proteins. When P162-0948 was treated in cells at 5 μM, phosphorylation of Smad in the nucleus was reduced, which suggests disruption of the TGF-β/Smad signaling pathway. The identification of P162-0948 shows that it is not only potent, but its structural novelty can inform future design studies for potential therapeutics targeting pulmonary fibrosis.

DiffInt: A Diffusion Model for Structure-Based Drug Design with Explicit Hydrogen Bond Interaction Guidance

The design of drug molecules is a critical stage in the drug discovery process. The structure-based drug design has long played an important role in efficient development. Significant progress has been made in recent years in the generation of 3D molecules via deep generation models. However, while many existing models have succeeded in incorporating structural information on target proteins, they have not been able to address important interactions between protein and drug molecules, especially hydrogen bonds. In this study, we propose DiffInt as a novel structure-based approach that explicitly addresses interactions. The model naturally incorporates hydrogen bonds between protein and ligand molecules by treating them as pseudoparticles. The experimental results show that DiffInt reproduces hydrogen bonds, and the hydrogen binding energies significantly outperform those of existing models. To facilitate the use of our tool for generating new drug molecules based on any protein's three-dimensional structure, we have made the source code and trained model available on GitHub (https://github.com/sekijima-lab/DiffInt) under the MIT license, with the execution environment provided on Google Colab.

Accurate prediction of protein-ligand interactions by combining physical energy functions and graph-neural networks

We introduce an advanced model for predicting protein-ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein-ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein-ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein-ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model's efficiency and generalizability. The model's efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery.Scientific contributionOur work introduces a novel training strategy for a protein-ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.

Are the metal identity and stoichiometry of metal complexes important for colchicine site binding and inhibition of tubulin polymerization?

Quite recently we discovered that copper(II) complexes with isomeric morpholine-thiosemicarbazone hybrid ligands show good cytotoxicity in cancer cells and that the molecular target responsible for this activity might be tubulin. In order to obtain better lead drug candidates, we opted to exploit the power of coordination chemistry to (i) assemble structures with globular shape to better fit the colchicine pocket and (ii) vary the metal ion. We report the synthesis and full characterization of bis-ligand cobalt(III) and iron(III) complexes with 6-morpholinomethyl-2-formylpyridine 4N-(4-hydroxy-3,5-dimethylphenyl)-3-thiosemicarbazone (HL1), 6-morpholinomethyl-2-acetylpyridine 4N-(4-hydroxy-3,5-dimethylphenyl)-3-thiosemicarbazone (HL2), and 6-morpholinomethyl-2-formylpyridine 4N-phenyl-3-thiosemicarbazone (HL3), and mono-ligand nickel(II), zinc(II) and palladium(II) complexes with HL1, namely [CoIII(HL1)(L1)](NO3)2 (1), [CoIII(HL2)(L2)](NO3)2 (2), [CoIII(HL3)(L3)](NO3)2 (3), [FeIII(L2)2]NO3 (4), [FeIII(HL3)(L3)](NO3)2 (5), [NiII(L1)]Cl (6), [Zn(L1)Cl] (7) and [PdII(HL1)Cl]Cl (8). We discuss the effect of the metal identity and metal complex stoichiometry on in vitro cytotoxicity and antitubulin activity. The high antiproliferative activity of complex 4 correlated well with inhibition of tubulin polymerization. Insights into the mechanism of antiproliferative activity were supported by experimental results and molecular docking calculations.

Computational prediction for designing novel ketonic derivatives as potential inhibitors for breast cancer: A trade-off between drug likeness and inhibition potency

Unlike simple molecular screening, a combined hybrid computational methodology has been applied which includes quantum chemical methods, molecular docking, and molecular dynamics simulations to design some novel ketonic derivatives. The current study contains the derivatives of an experimental ligand which are designed as a trade-off between drug likeness and inhibition strength. We investigate the interaction of various newly designed ketonic compounds with the breast cancer receptor known as the Estrogen Receptor Alpha (ERα). The molecular structures of all newly designed ligands were studied quantum chemically in terms of their fully optimized structures, 3-D molecular orbital distributions, global chemical descriptors, molecular electrostatic potentials and energies of frontier molecular orbitals (FMOs). All ligands under study show good binding affinities with the ERα protein. The ligands CMR2 and CMR4 exhibit improved molecular docking interactions. The intermolecular interactions indicate that CMR4 demonstrates better hydrophobic and hydrogen bonding interactions with protein (ERα). Furthermore, molecular dynamics simulations were conducted on ligands and reference drugs interacting with the ERα protein over a time span of 120 nanoseconds. The molecular dynamics results are interpreted in terms of ligand-protein stability and flexible behaviour based on their respective values of RMSD, RMSF, H-bonds, the radius of gyration, and SASA graphs. To analyse ligand-protein interactions throughout the entire 120 ns trajectory, a more advanced MM/PBSA method is utilized, where six selected ligands (CMR1, CMR2, CMR3, CMR4, CMR5 and CMR9) illustrate promising results for inhibition of the ERα receptor as assessed through MM/BBSA analysis. The CMR9 has the highest MM/BBSA binding free energy (-14.46 kcal/mol). The ADMET analysis reveals that CMR4 has maximum intestinal absorption (6.68) and clearance rate (0.1). All the compounds are non-toxic and safe to use. These findings indicate the potential of involving different computational techniques to design the ligand structures and to study the ligand-protein interactions for better understanding and achieving more potent synthetic inhibitors for breast cancer.

Computational identification of potential modulators of heme-regulated inhibitor (HRI) for pharmacological intervention against sickle cell disease

Sickle cell disease (SCD) poses a significant health challenge and therapeutic approaches often target fetal hemoglobin (HbF) to ameliorate symptoms. Hydroxyurea, a current therapeutic option for SCD, has shown efficacy in increasing HbF levels. However, concerns about myelosuppression and thrombocytopenia necessitate the exploration of alternative compounds. Heme-regulated inhibitor (HRI) presents a promising target for pharmacological intervention in SCD due to its association with HbF modulation. This study screened compounds for their potential inhibitory functions against HRI. Small-molecule compounds from 17 folkloric plants were subjected to in silico screening against HRI. Molecular docking was performed, and free binding energy calculations were determined using molecular mechanics with generalized born and surface area (MMGBSA). Lead compounds were subjected to molecular dynamics simulation at 100 ns. Computational quantum mechanical modeling of the lead compounds was subsequently performed. We further examined the pharmacodynamics, pharmacokinetic and physiological properties of the identified compounds. Five potential HRI inhibitors, including kaempferol-3-(2G-glucosyrutinoside), epigallocatechin gallate, tiliroside, myricetin-3-O-glucoside and cannabiscitrin, with respective docking scores of -16.0, -12.17, -11.37, -11.56 and 11.07 kcal/mol, were identified. The MMGBSA analysis of the complexes yielded free-binding energies of -69.76, -71.17, -60.44, -53.55 and -55 kcal/mol, respectively. The identified leads were stable within HRI binding pocket for the duration of the 100 ns simulation. The study identified five phytoligands with potential inhibitory effects on HRI. This finding holds promise for advancing SCD treatment strategies. However, additional preclinical analyses are warranted to validate the chemotherapeutic properties of the lead compounds.

Deep reinforcement learning enables better bias control in benchmark for virtual screening

Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.

New Dual Inhibitors of Tyrosyl-DNA Phosphodiesterase 1 and 2 Based on Deoxycholic Acid: Design, Synthesis, Cytotoxicity, and Molecular Modeling

Deoxycholic acid derivatives containing various heterocyclic functional groups at C-3 on the steroid scaffold were designed and synthesized as promising dual tyrosyl-DNA phosphodiesterase 1 and 2 (TDP1 and TDP2) inhibitors, which are potential targets to potentiate topoisomerase poison antitumor therapy. The methyl esters of DCA derivatives with benzothiazole or benzimidazole moieties at C-3 demonstrated promising inhibitory activity in vitro against TDP1 with IC50 values in the submicromolar range. Furthermore, methyl esters 4d-e, as well as their acid counterparts 3d-e, inhibited the phosphodiesterase activity of both TDP1 and TDP2. The combinations of compounds 3d-e and 4d-e with low-toxic concentrations of antitumor drugs topotecan and etoposide showed significantly greater cytotoxicity than the compounds alone. The docking of the derivatives into the binding sites of TDP1 and TDP2 predicted plausible binding modes of the DCA derivatives.

Computational identification of potential modulators of heme-regulated inhibitor (HRI) for pharmacological intervention against sickle cell disease

Background

Sickle cell disease (SCD) poses a significant health challenge and therapeutic approaches often target fetal hemoglobin (HbF) to ameliorate symptoms. Hydroxyurea, a current therapeutic option for SCD, has shown efficacy in increasing HbF levels. However, concerns about myelosuppression and thrombocytopenia necessitate the exploration of alternative compounds. Heme-regulated inhibitor (HRI) presents a promising target for pharmacological intervention in SCD due to its association with HbF modulation. This study systematically screened compounds for their potential inhibitory functions against HRI.

Methods

Small-molecule compounds from 17 plants commonly utilized in traditional SCD management were subjected to in silico screening against HRI. Molecular docking was performed, and free binding energy calculations were determined using molecular mechanics with generalized born and surface area (MMGBSA). The lead compounds were subjected to molecular dynamics simulation at 100 ns. Computational quantum mechanical modelling of the lead compounds was subsequently performed. We further examined the pharmacodynamics, pharmacokinetic and physiological properties of the identified compounds.

Results

Five potential HRI inhibitors, including kaempferol-3-(2G-glucosyrutinoside), epigallocatechin gallate, tiliroside, myricetin-3-O-glucoside, and cannabiscitrin, with respective docking scores of -16.0, -12.17, -11.37, -11.56 and 11.07 kcal/mol, were identified. The MMGBSA analysis of the complexes yielded free-binding energies of -69.76, -71.17, -60.44, 53.55, and - 55 kcal/mol, respectively. The identified leads were stable within HRI binding pocket for the duration of 100 ns simulation.

Conclusions

The study successfully identified five phytoligands with potential inhibitory effects on HRI, opening avenues for their use as modulators of HbF in SCD patients. This finding holds promise for advancing treatment strategies in SCD. However, additional preclinical analyses are warranted to validate the chemotherapeutic properties of the lead compounds.

Screening of potential antiplasmodial agents targeting cysteine protease-Falcipain 2: a computational pipeline

The spread of antimalarial drug resistance is a substantial challenge in achieving global malaria elimination. Consequently, the identification of novel therapeutic candidates is a global health priority. Malaria parasite necessitates hemoglobin degradation for its survival, which is mediated by Falcipain 2 (FP2), a promising antimalarial target. In particular, FP2 is a key enzyme in the erythrocytic stage of the parasite's life cycle. Here, we report the screening of approved drugs listed in DrugBank using a computational pipeline that includes drug-likeness, toxicity assessments, oral toxicity evaluation, oral bioavailability, docking analysis, maximum common substructure (MCS) and molecular dynamics (MD) Simulations analysis to identify capable FP2 inhibitors, which are hence potential antiplasmodial agents. A total of 45 drugs were identified, which have positive drug-likeness, no toxic features and good bioavailability. Among these, six drugs showed good binding affinity towards FP2 compared to E64, an epoxide known to inhibit FP2. Notably, two of them, Cefalotin and Cefoxitin, shared the highest MCS with E64, which suggests that they possess similar biological activity as E64. In an investigation using MD for 100 ns, Cefalotin and Cefoxitin showed adequate protein compactness as well as satisfactory complex stability. Overall, these computational approach findings can be applied for designing and developing specific inhibitors or new antimalarial agents for the treatment of malaria infections.Communicated by Ramaswamy H. Sarma.

Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes and screening chemical libraries

Introduction

Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem.

Methods

In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis.

Results

The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 - 40 folds.

Conclusion

The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.

Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management

Molecular docking is a computational technique that predicts the binding affinity of ligands to receptor proteins. Although it has potential uses in nutraceutical research, it has developed into a formidable tool for drug development. Bioactive substances called nutraceuticals are present in food sources and can be used in the management of diseases. Finding their molecular targets can help in the creation of disease-specific new therapies. The purpose of this review was to explore molecular docking's application to the study of dietary supplements and disease management. First, an overview of the fundamentals of molecular docking and the various software tools available for docking was presented. The limitations and difficulties of using molecular docking in nutraceutical research are also covered, including the reliability of scoring functions and the requirement for experimental validation. Additionally, there was a focus on the identification of molecular targets for nutraceuticals in numerous disease models, including those for sickle cell disease, cancer, cardiovascular, gut, reproductive, and neurodegenerative disorders. We further highlighted biochemistry pathways and models from recent studies that have revealed molecular mechanisms to pinpoint new nutraceuticals' effects on disease pathogenesis. It is convincingly true that molecular docking is a useful tool for identifying the molecular targets of nutraceuticals in the management of diseases. It may offer information about how nutraceuticals work and support the creation of new therapeutics. Therefore, molecular docking has a bright future in nutraceutical research and has a lot of potentials to lead to the creation of brand-new medicines for the treatment of disease.

New 5-Hydroxycoumarin-Based Tyrosyl-DNA Phosphodiesterase I Inhibitors Sensitize Tumor Cell Line to Topotecan

Tyrosyl-DNA-phosphodiesterase 1 (TDP1) is an important enzyme in the DNA repair system. The ability of the enzyme to repair DNA damage induced by a topoisomerase 1 poison such as the anticancer drug topotecan makes TDP1 a promising target for complex antitumor therapy. In this work, a set of new 5-hydroxycoumarin derivatives containing monoterpene moieties was synthesized. It was shown that most of the conjugates synthesized demonstrated high inhibitory properties against TDP1 with an IC₅₀ in low micromolar or nanomolar ranges. Geraniol derivative 33a was the most potent inhibitor with IC₅₀ 130 nM. Docking the ligands to TDP1 predicted a good fit with the catalytic pocket blocking access to it. The conjugates used in non-toxic concentration increased cytotoxicity of topotecan against HeLa cancer cell line but not against conditionally normal HEK 293A cells. Thus, a new structural series of TDP1 inhibitors, which are able to sensitize cancer cells to the topotecan cytotoxic effect has been discovered.

In Silico Drug Design of Anti-Breast Cancer Agents

Cancer is a condition marked by abnormal cell proliferation that has the potential to invade or indicate other health issues. Human beings are affected by more than 100 different types of cancer. Some cancer promotes rapid cell proliferation, whereas others cause cells to divide and develop more slowly. Some cancers, such as leukemia, produce visible tumors, while others, such as breast cancer, do not. In this work, in silico investigations were carried out to investigate the binding mechanisms of four major analogs, which are marine sesquiterpene, sesquiterpene lactone, heteroaromatic chalcones, and benzothiophene against the target estrogen receptor-α for targeting breast cancer using Schrödinger suite 2021-4. The Glide module handled the molecular docking experiments, the QikProp module handled the ADMET screening, and the Prime MM-GB/SA module determined the binding energy of the ligands. The benzothiophene analog BT_ER_15f (G-score -15.922 Kcal/mol) showed the best binding activity against the target protein estrogen receptor-α when compared with the standard drug tamoxifen which has a docking score of -13.560 Kcal/mol. TRP383 (tryptophan) has the highest interaction time with the ligand, and hence it could act for a long time. Based on in silico investigations, the benzothiophene analog BT_ER_15f significantly binds with the active site of the target protein estrogen receptor-α. Similar to the outcomes of molecular docking, the target and ligand complex interaction motif established a high affinity of lead candidates in a dynamic system. This study shows that estrogen receptor-α targets inhibitors with better potential and low toxicity when compared to the existing market drugs, which can be made from a benzothiophene derivative. It may result in considerable activity and be applied to more research on breast cancer.

Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation

Ribosomal protein S6 kinase beta-1 (S6K1) is considered a potential target for the treatment of various diseases, such as obesity, type II diabetes, and cancer. Development of novel S6K1 inhibitors is an urgent and important task for the medicinal chemists. In this research, an effective ensemble-based virtual screening method, including common feature pharmacophore model, 3D-QSAR pharmacophore model, naïve Bayes classifier model, and molecular docking, was applied to discover potential S6K1 inhibitors from BioDiversity database with 29,158 compounds. Finally, 7 hits displayed considerable properties and considered as potential inhibitors against S6K1. Further, carefully analyzing the interactions between these 7 hits and key residues in the S6K1 active site, and comparing them with the reference compound PF-4708671, it was found that 2 hits exhibited better binding patterns. In order to further investigate the mechanism of the interactions between 2 hits and S6K1 at simulated physiological conditions, the molecular dynamics simulation was performed. The ΔG_bind energies for S6K1-Hit1 and S6K1-Hit2 were - 111.47 ± 1.29 and - 54.29 ± 1.19 kJ mol^-1, respectively. Furthermore, deep analysis of these results revealed that Hit1 was the most stable complex, which can stably bind to S6K1 active site, interact with all of the key residues, and induce H1, H2, and M-loop regions changes. Therefore, the identified Hit1 may be a promising lead compound for developing new S6K1 inhibitor for various metabolic diseases treatment.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Novel TDP1 Inhibitors: Disubstituted Thiazolidine-2,4-Diones Containing Monoterpene Moieties

Tyrosyl-DNA-phosphodiesterase 1 (TDP1) is a promising target for antitumor therapy; the use of TDP1 inhibitors with a topoisomerase 1 poison such as topotecan is a potential combination therapy. In this work, a novel series of 3,5-disubstituted thiazolidine-2,4-diones was synthesized and tested against TDP1. The screening revealed some active compounds with IC₅₀ values less than 5 μM. Interestingly, compounds 20d and 21d were the most active, with IC₅₀ values in the submicromolar concentration range. None of the compounds showed cytotoxicity against HCT-116 (colon carcinoma) and MRC-5 (human lung fibroblasts) cell lines in the 1-100 μM concentration range. Finally, this class of compounds did not sensitize cancer cells to the cytotoxic effect of topotecan.

Reducing false positive rate of docking-based virtual screening by active learning

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

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.

Latonduine-1-Amino-Hydantoin Hybrid, Triazole-Fused Latonduine Schiff Bases and Their Metal Complexes: Synthesis, X-ray and Electron Diffraction, Molecular Docking Studies and Antiproliferative Activity

A series of latonduine derivatives, namely 11-nitro-indolo[2,3-d]benzazepine-7-(1-amino-hydantoin) (B), triazole-fused indolo[2,3-d]benzazepine-based Schiff bases HL1 and HL2 and metal complexes [M(p-cymene)(HL1)Cl]Cl, where M = Ru (1), Os (2), and [Cu(HL2)Cl2] (3) were synthesized and characterized by spectroscopic techniques (UV–vis, 1H, 13C, 15N–1H HSQC NMR) and ESI mass spectrometry. The molecular structures of B and HL1 were confirmed by single-crystal X-ray diffraction, while that of 3 by electron diffraction of nanometer size crystalline sample. Molecular docking calculations of species B in the binding pocket of PIM-1 enzyme revealed that the 1-amino-hydantoin moiety is not involved in any hydrogen-bonding interactions, even though a good accommodation of the host molecule in the ATP binding pocket of the enzyme was found. The antiproliferative activity of organic compounds B, HL1 and HL2, as well as complexes 1–3 was investigated in lung adenocarcinoma A549, colon adenocarcinoma LS-174 and triple-negative breast adenocarcinoma MDA-MB-231 cells and normal human lung fibroblast cells MRC-5 by MTT assays; then, the results are discussed.

Docking-Based Prediction of Peptide Binding to MHC Proteins

Major histocompatibility complex (MHC) proteins are the most polymorphic and polygenic proteins in humans. They bind peptides, derived from cleavage of different pathogenic antigens, and are responsible for presenting them to T cells. The peptides recognized by the T cell receptors are denoted as epitopes and they trigger an immune response.In this chapter, we describe a docking protocol for predicting the peptide binding to a given MHC protein using the software tool GOLD. The protocol starts with the construction of a combinatorial peptide library used in the docking and ends with the derivation of a quantitative matrix (QM) accounting for the contribution of each amino acid at each peptide position.

Does trigonelline help skin tone? Molecular docking studies of trigonelline on the human tyrosinase, formulation, optimization, and characterization of an emulgel-containing Trigonella foenum-graecum L. fenugreek standardized hydroalcoholic extract

AIMS

This study aimed to perform molecular docking studies to identify possibilities of the inhibitory potential of the trigonelline present in fenugreek seeds on the human tyrosinase, standardize fenugreek extract, formulate, and characterize an emulgel-containing fenugreek extract-entrapped niosomal vesicles.

MATERIALS AND METHODS

The docking study was performed using AutoDock software. The extract was standardized by the RP-HPLC method. Emulgels containing fenugreek extract and fenugreek extract-entrapped niosomes were optimized by the D-optimal method. In vitro characterization and stability studies were also carried out.

RESULTS

The lowest energy docked poses of trigonelline on the human tyrosinase complex was calculated -5.8 kcal/mol. Also, in vitro assessment of the tyrosinase inhibitory effect of trigonelline and comparison of IC50 values of trigonelline and kojic acid revealed that the enzyme inhibition efficacy of trigonelline was stronger than that of kojic acid. Optimization led to emulgels with desired viscosity, droplet size, and spreadability values. The release study showed that trigonelline was released from the niosomes at a lower rate compared with extract containing emulgel. Permeation investigations revealed that trigonelline in niosomes has a higher ability to permeate through the skin.

CONCLUSION

In conclusion, in silico and in vitro studies have shown that trigonelline can be assumed as an appropriate candidate for developing new cosmetic preparations and nonionic surfactant vesicles help trigonelline to permeate through the skin to a higher extent. However, clinical trials should be performed to confirm these findings.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

Molecular Modeling of Allosteric Site of Isoform-Specific Inhibition of the Peroxisome Proliferator-Activated Receptor PPARγ

The peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and controls a number of gene expressions. The ligand binding domain (LBD) of PPARγ is large and involves two binding sites: orthosteric and allosteric binding sites. Increased evidence has shown that PPARγ is an oncogene and thus the PPARγ antagonists have potential as anticancer agents. In this paper, we use Glide Dock approach to determine which binding site, orthosteric or allosteric, would be a preferred pocket for PPARγ antagonist binding, though antidiabetic drugs such as thiazolidinediones (TZDs) bind to the orthosteric site. The Glide Dock results show that the binding of PPARγ antagonists at the allosteric site yielded results that were much closer to the experimental data than at the orthosteric site. The PPARγ antagonists seem to selectively bind to residues Lys265, Ser342 and Arg288 at the allosteric binding site, whereas PPARγ agonists would selectively bind to residues Leu228, Phe363, and His449, though Phe282 and Lys367 may also play a role for agonist binding at the orthosteric binding pocket. This finding will provide new perspectives in the design and optimization of selective and potent PPARγ antagonists or agonists.

DrugRep: an automatic virtual screening server for drug repurposing

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/.

Fisetin decreases the duration of ictal-like discharges in mouse hippocampal slices

There is an increasing interest in the biological and therapeutic effects of fisetin, a natural phenolic compound. Fisetin has affinity on some neuronal targets and may have the potential to modulate neuronal activity. In this study the effects of acute application of fisetin on synchronized events were evaluated electro-physiologically. Besides, interaction of fisetin with closely related channels were investigated in silico. Acute horizontal hippocampal slices were obtained from 32- to 36-day-old C57BL/6 mice. Extracellular field potentials were recorded from CA3 region of the hippocampus. Bath application of 4 aminopyridine (4AP, 100 µM) initiated ictal- and interictal-like synchronized epileptiform discharges in the brain slices. Fifty micromolar fisetin was applied to the recording chamber during the epileptiform activity. The duration and frequencies of both ictal-like and interictal-like activities were calculated from the electrophysiological records. Molecular docking was performed to reveal interaction of fisetin on GABA-A, NMDA, AMPA receptors, and HCN2 channel, which are neuronal structures directly involved in recorded activity. Although fisetin does not affect basal neuronal activity in brain slice, it reduced the duration of ictal-like discharges significantly. Molecular docking results indicated that fisetin has no effect on GABA-A, NMDA, and AMPA receptors. However, fisetin binds to the (5JON) HCN2 channel strongly with the binding energy of -7.66 kcal/mol. Reduction on the duration of 4AP-induced ictal-like discharges can be explained as HCN channels can cause an inhibitory effect via enhancing M-type K + channels which increase K outward currents.

Structure-based virtual screening discovers novel kidney-type glutaminase inhibitors

Glutaminase (GLS) serves a critical bioenergetic role for malignant tumor growth and has become a valuable therapeutic target for cancer treatment. Herein, we performed a structure-based virtual screening to discover novel GLS inhibitors and provide information for developing new GLS inhibitors. We identified critical pharmacological interactions in the GLS1 binding site by analyzing the known GLS1 inhibitors and selected potential inhibitors based on their docking score and pharmacological interactions. The inhibitory effects of compounds were further confirmed by enzymatic and cell viability assays. We treated colorectal cancer and triple-negative breast cancer cells with the selected candidates and measured the inhibitory efficacy of hit compounds on cell viability. In total, we identified three GLS1 inhibitors. The compounds identified from our structure-based virtual screening methodology exhibited great anticancer potential as a lead targeting glutamine metabolism.

Identification of NEK7 inhibitors: structure based virtual screening, molecular docking, density functional theory calculations and molecular dynamics simulations

NEK7 is a NIMA related-protein kinase that plays a crucial role in spindle assembly and cell division. Dysregulation of NEK7 protein leads to development and progression of different types of malignancies including colon and breast cancers. Therefore, NEK7 could be considered as an attractive target for anti-cancer drug discovery. However, few efforts have been made for the development of selective inhibitors of NIMA-related kinase but still no FDA approved drug is known to selectively inhibit the NEK7 protein. Dacomitinib and Neratinib are two Enamide derivatives that were approved for treatment against non-small cell lung cancer and breast cancer respectively. Drug repurposing is a time and cost-efficient method for re-evaluating the activities of previously authorized medications. Thus, the present research involves the repurposing of two FDA-approved medications via comprehensive in silico approach including Density functional theory (DFTs) studies which were conducted to determine the electronic properties of the Dacomitinib and Neratinib. Afterward, binding orientation of selected drugs inside NEK7 activation loop was evaluated through molecular docking approach. Selected drugs exhibited potential molecular interactions engaging important amino acid residues of active site. The docking score of Dacomitinib and Neratinib was -30.77 and -26.78 kJ/mol, respectively. The top ranked pose obtained from molecular docking was subjected to Molecular Dynamics (MD) Simulations for investigating the stability of protein-ligand complex. The RMSD pattern revealed the stability of protein-ligand complex throughout simulated trajectory. In conclusion, both drugs displayed inhibitory efficacy against NEK7 protein and provide a prospective therapy option for malignant malignancies linked with NEK7. Communicated by Ramaswamy H. Sarma.

Molecular Docking of the Terpenes in Gorgonian Corals to COX-2 and iNOS Enzymes as Anti-Inflammatory

Background:

Because the inflammatory pathway is triggered by the enzymes cyclooxygenase- 2 (COX-2) and inducible nitric oxide synthase (iNOS), inhibitors, such as nonsteroidal anti-inflammatory drugs (NSAIDs), are needed, although these have side effects. Therefore, the discovery and development of natural medicine as a lead compound are needed. The gorgonian corals have been reported to contain cyclic diterpenes with anti-inflammatory activities. The specific anti-inflammatory inhibitor potential has not been reported regarding these secondary metabolites, whether in COX-2 or iNOS. Thus, the in silico method is the right alternative.

Objective:

This study aimed to determine the potency of fifteen terpenes of the various gorgonian corals to COX-2 and iNOS enzymes as an anti-inflammatory.

Methods:

Molecular docking was performed using ChemDraw Ultra 12.0, Chem3D Pro 12.0, Biovia Discovery Studio 2016 Client®, Autodock Tools 4.2, prediction pharmacokinetics (Pre-ADMET), and oral administration (Lipinski rule of five).

Results:

Potential terpenes based on ΔG (kcal/mol) and Ki (nM) to COX-2 were gyrosanol B (-10,32; 27,15), gyrosanol A (-10,20; 33,57), echinolabdane A (-9,81; 64,76). Only nine terpenes were specific to COX-2 active sites, while for iNOS were palmonine F (-7.76; 2070), briarenol C (-7.55; 2910), and all test compounds binding to the iNOS active sites. Pre-ADMET prediction obtained that HIA was very excellent (70–100%), Caco-2 had moderate permeability (4–70 nm sec-1), and PPB had strong binding (> 90%). Eight terpenes qualified for the Lipinski rule of five.

Conclusion:

iNOS was a specific target for terpenes based on the free energy of binding (ΔG).

Monoterpene substituted thiazolidin-4-ones as novel TDP1 inhibitors: synthesis, biological evaluation and docking

Tyrosyl-DNA phosphodiesterase 1(TDP1) is a promising target for a new therapy in oncological disease as an adjunct to topoisomerase 1 (TOP1) drugs. In this paper, novel thiazolidin-4-one derivatives with a benzyl and monoterpene substituents were synthesized. Compounds with a monoterpene fragment attached via a phenyloxy linker were active against TDP1 with IC₅₀ values in the 1÷3 μM range, while direct attachment of monoterpene moiety to the thiazolidin-4-one fragment had no activity. Molecular modelling predicted two plausible binding modes of the active compounds both effectively blocking access to the catalytic site of TDP. At non-toxic concentrations the active ligands potentiated the efficacy of the TOP1 poison topotecan in human cervical cancer HeLa cells, but not in non-cancerous HEK293A cells.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Targeting Streptomyces-Derived Streptenol Derivatives against Gynecological Cancer Target PIK3CA: An In Silico Approach

Streptomyces is amongst the most amenable genera for biotechnological applications, and it is extensively used as a scaffold for drug development. One of the most effective therapeutic applications in the treatment of cancer is targeted therapy. Small molecule therapy is one of them, and it has gotten a lot of attention recently. Streptomyces derived compounds namely streptenols A, C, and F-I and streptazolin were subjected for ADMET property assessment. Our computational studies based on molecular docking effectively displayed the synergistic effect of streptomyces-derived compounds on the gynecological cancer target PIK3CA. These compounds were observed with the highest docking scores as well as promising intermolecular interaction stability throughout the molecular dynamic simulation. Molecular docking and molecular dynamic modeling techniques were utilized to investigate the binding mode stability of drugs using a pharmacophore scaffold, as well as physicochemical and pharmacokinetic aspects linked to alpelisib. With a root mean square fluctuation of the protein backbone of less than 0.7 nm, they demonstrated a steady binding mode in the target binding pocket. They have also prompted hydrogen bonding throughout the simulations, implying that the chemicals have firmly occupied the active site. A comprehensive study showed that streptenol D, streptenol E, streptenol C, streptenol G, streptenol F, and streptenol B can be considered as lead compounds for PIK3CA-based inhibitor design. To warrant the treatment efficacy against cancer, comprehensive computational research based on proposed chemicals must be assessed through in vitro studies.

Anticholesterol activity of ethanol extract of Ranti Hitam (Solanum blumei Nees ex Blume) Leaves: In vivo and In silico study

Ranti Hitam known as local name of (Solanum blumei Nees ex Blume) found in the Dairi, Sumatera Utara, Indonesia. It is used by the community as traditional medicine which contains of various phytochemical constituent of steroidal alkaloids of β2-solanin, diosgenin, flavonoids, saponins and tannins. The purpose of the study was to investigate the anticholesterol activity of the ethanol extract of (Solanum blumei Nees ex Blume) by in vivo and insilico methods. A number of 15 rats were divided into 5 treatment groups as in vivo high fat diet model, otherwise insilico study was carried out to determine the activity of main compound of S. blumei in inhibiting HMG Co-A reductase. The bioactive compounds of S. blumei, diosgenin (C26H39O4) and β2-solanine (C39H63NO11) showed inhibition activity to HMG-CoA reductase by in silico and invivo test and it was indicated that 2 bioactive compounds of S. blumei had anticholesterol activity.

Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA

The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF_1% and EF_5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF_1% and EF_5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF_1% and EF_5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.

Computational methods on food contact chemicals: Big data and in silico screening on nuclear receptors family

According to Eurostat, the EU production of chemicals hazardous to health reached 211 million tonnes in 2019. Thus, the possibility that some of these chemical compounds interact negatively with the human endocrine system has received, especially in the last decade, considerable attention from the scientific community. It is obvious that given the large number of chemical compounds it is impossible to use in vitro/in vivo tests for identifying all the possible toxic interactions of these chemicals and their metabolites. In addition, the poor availability of highly curated databases from which to retrieve and download the chemical, structure, and regulative information about all food contact chemicals has delayed the application of in silico methods. To overcome these problems, in this study we use robust computational approaches, based on a combination of highly curated databases and molecular docking, in order to screen all food contact chemicals against the nuclear receptor family in a cost and time-effective manner.

Identification and analysis of a selective DYRK1A inhibitor

The dysregulation of DYRK1A is implicated in many diseases such as cancer, diabetes, and neurodegenerative diseases. Alzheimer's disease is one of the most common neurodegenerative disease and has elevated interest in DYRK1A research. Overexpression of DYRK1A has been linked to the formation of tau aggregates. Currently, an effective therapeutic treatment that targets DYRK1A is lacking. A specific small-molecule inhibitor would further our understanding of the physiological role of DYRK1A in neurodegenerative diseases and could be presented as a possible therapeutic option. In this study, we identified pharmacological interactions within the DYRK1A active site and performed a structure-based virtual screening approach to identify a selective small-molecule inhibitor. Several compounds were selected in silico for enzymatic and cellular assays, yielding a novel inhibitor. A structure-activity relationship analysis was performed to identify areas of interactions for the compounds selected in this study. When tested in vitro, reduction of DYRK1A dependent phosphorylation of tau was observed for active compounds. The active compounds also improved tau turbidity, suggesting that these compounds could alleviate aberrant tau aggregation. Testing the active compound against a panel of kinases across the kinome revealed greater selectivity towards DYRK1A. Our study demonstrates a serviceable protocol that identified a novel and selective DYRK1A inhibitor with potential for further study in tau-related pathologies.

Highly Antiproliferative Latonduine and Indolo[2,3-c]quinoline Derivatives: Complex Formation with Copper(II) Markedly Changes the Kinase Inhibitory Profile

A series of latonduine and indoloquinoline derivatives HL¹–HL⁸ and their copper(II) complexes (1–8) were synthesized and comprehensively characterized. The structures of five compounds (HL⁶, [CuCl(L¹)(DMF)]·DMF, [CuCl(L²)(CH₃OH)], [CuCl(L³)]·0.5H₂O, and [CuCl₂(H₂L⁵)]Cl·2DMF) were elucidated by single crystal X-ray diffraction. The copper(II) complexes revealed low micro- to sub-micromolar IC₅₀ values with promising selectivity toward human colon adenocarcinoma multidrug-resistant Colo320 cancer cells as compared to the doxorubicin-sensitive Colo205 cell line. The lead compounds HL⁴ and 4 as well as HL⁸ and 8 induced apoptosis efficiently in Colo320 cells. In addition, the copper(II) complexes had higher affinity to DNA than their metal-free ligands. HL⁸ showed selective inhibition for the PIM-1 enzyme, while 8 revealed strong inhibition of five other enzymes, i.e., SGK-1, PKA, CaMK-1, GSK3β, and MSK1, from a panel of 50 kinases. Furthermore, molecular modeling of the ligands and complexes showed a good fit to the binding pockets of these targets.

Discovery of a novel cyclin-dependent kinase 8 inhibitor with an oxindole core for anti-inflammatory treatment

Chronic inflammation is an underlying cause in a number of diseases. Cyclin-dependent kinase 8 (CDK8) has been implicated as an inflammatory mediator, indicating its potential as an anti-inflammatory target. Herein, we performed structure-based virtual screening (SBVS) to identify novel CDK8 inhibitors. The pharmacological interactions for CDK8 were identified and incorporated into a SBVS protocol. Selected compounds were tested in enzymatic assays, and one compound was confirmed to be a CDK8 inhibitor with a 50% inhibitory concentration (IC₅₀) value of 1684.4 nM. Comparing structural analogs identified a compound, F059-1017, with greater potency (IC₅₀ 558.1 nM). When tested in cell lines, the compounds displayed low cytotoxicity. Cellular assays revealed that the identified CDK8 inhibitors can reduce phosphorylation and expression of signaling mediators associated with inflammation. In addition, results of kinase profiling showed that compound F059-1017 is selective towards CDK8. These findings suggest that the new inhibitors have great potential as lead compounds for developing novel anti-inflammatory therapeutics.

Emerione A, a novel fungal metabolite as an inhibitor of New Delhi metallo-β-lactamase-1, restores carbapenem susceptibility in carbapenem-resistant isolates

OBJECTIVES

Bacterial strains that produce New Delhi metal-β-lactamase 1 (NDM-1) are worldwide threats. It is still a challenging task to find a potent NDM-1 inhibitor for clinical practice.

METHODS

Molecular docking and virtual screening of an in-house fungal natural product database for NDM-1 inhibitors were performed. Based on the screening results, the affinity and inhibition analysis of potential NDM-1 inhibitors was determined using purified NDM-1. The efficacy of compounds in combination with four β-lactam antibiotics (meropenem, imipenem, ceftriaxone and ampicillin) was evaluated. The morphological transforms of K. pneumoniae ATCC BAA2146 after treatment with the compounds were visualized by transmission electron microscopy.

RESULTS

In silico screening led to the identification of four fungal products as potential NDM-1 inhibitors. Emerione A (1), a methylated polyketide with bicyclo[4.2.0]octene and 3,6-dioxabicyclo[3.1.0]hexane, has significant activity in cells (K_d = 11.8 ± 0.6 μM; IC₅₀ = 12.1 ± 0.9 μM) and potentiates the activity of meropenem against two kinds of NDM-1-producing Enterobacteriaceae. To the best of our knowledge, emerione A (1) is the second fungal metabolite reported to exhibit NMD-1 inhibitory activity. According to the structural novelty of our database, we also found a structural new compound, asperfunolone A (2), with potential NMD-1 inhibitory activity.

CONCLUSION

Considering the low toxicity characteristic of emerione A (1), it may be processed as a potential lead compound for anti-NDM-1 drug development.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

Drug Repurposing: Deferasirox Inhibits the Anti-Apoptotic Activity of Mcl-1

Introduction

With the aim of repositioning commercially available drugs for the inhibition of the anti-apoptotic myeloid cell leukemia protein, Mcl-1, implied in various cancers, five molecules, highlighted from a published theoretical screening, were selected to experimentally validate their affinity toward Mcl-1.

Results

A detailed NMR study revealed that only two of the five tested drugs, Torsemide and Deferasirox, interacted with Mcl-1. NMR data analysis allowed the complete characterization of the binding mode of both drugs to Mcl-1, including the estimation of their affinity for Mcl-1. Biological assays evidenced that the biological activity of Torsemide was lower as compared to the Deferasirox, which was able to efficiently and selectively inhibit the anti-apoptotic activity of Mcl-1. Finally, docking and molecular dynamics led to a 3D model for the Deferasirox:Mcl-1 complex and revealed the positioning of the drug in the Mcl-1 P2/P3 pockets as well as almost all synthetic Mcl-1 inhibitors. Interestingly, contrary to known synthetic Mcl-1 inhibitors which interact through Arg263, Deferasirox, establishes a salt bridge with Lys234.

Conclusion

Deferasirox could be a potential candidate for drug repositioning as Mcl-1 inhibitor.

A synergism of in silico and statistical approaches to discover new potential endocrine disruptor mycotoxins

Mycotoxins are secondary metabolites produced by pathogenic fungi. They are found in a variety of different products, such as spices, cocoa, and cereals, and they can contaminate fields before and/or after harvest and during storage. Mycotoxins negatively impact human and animal health, causing a variety of adverse effects, ranging from acute poisoning to long-term effects. Given a large number of mycotoxins (currently more than 300 are known), it is impossible to use in vitro/in vivo methods to detect the potentially harmful effects to human health of all of these. To overcome this problem, this work aims to present a new robust computational approach, based on a combination of in silico and statistical methods, in order to screen a large number of molecules against the nuclear receptor family in a cost and time-effective manner and to discover the potential endocrine disruptor activity of mycotoxins. The results show that a high number of mycotoxins is predicted as a potential binder of nuclear receptors. In particular, ochratoxin A, zearalenone, α- and β-zearalenol, aflatoxin B1, and alternariol have been shown to be putative endocrine disruptors chemicals for nuclear receptors.

An antigen display system of GEM nanoparticles based on affinity peptide ligands

Gram-positive enhancer matrix (GEM) nanoparticles are often used in mucosal immunity, preparation of subunit vaccines or as an immune adjuvant due to its good immunological activities in recent years. Here, we designed and screened out a high affinity peptide ligand PL23, which could specifically target the non-epitope region of Classic Swine Fever Virus (CSFV) E2 protein, by virtual screening technology, enzyme linked immunosorbent assay (ELISA) and surface plasmon resonance (SPR) test. The OD value of PL23 at 450 nm was reached 1.982, and the K_D value of it was 90.12 nM. Its binding capacity to protein was verified by SDS-PAGE as well. PL23 was subsequently conjugated to GEM nanoparticles by dehydration synthesis generating GEM-PL23 particles, and the GEM-PL-E2 particles were assembled after incubated with CSFV E2 protein. The cytotoxic test indicated that PL23, CSFV E2 protein, GEM nanoparticles, GEM-PL23 particles and GEM-PL-E2 particles were not toxic to cells and GEM nanoparticles could significantly promote the growth of APCs at high concentration for 1 h, p<0.001. In addition, GEM nanoparticles could promote the uptake of antigen by APCs. The cytokines tests suggested that GEM-PL-E2 particles could promote innate immune responses, regulate adaptive immune responses generated by T cells and APCs, and promote the differentiation and maturation of dendritic cells without producing inflammasomes. The results of immunological activity identification showed GEM-PL-E2 particles induced higher levels of both neutralizing antibodies and anti-CSFV antibodies than CSFV E2 protein in mice. This strategy provided a new, simpler, faster and cheaper method for assembling GEM nanoparticles, using an affinity peptide ligand replaced the protein anchor (PA), and provided a better application prospect for the application of GEM particles.

Structure-based virtual screening and biological evaluation of novel small-molecule BTK inhibitors

Bruton tyrosine kinase (BTK) is linked to multiple signalling pathways that regulate cellular survival, activation, and proliferation. A covalent BTK inhibitor has shown favourable outcomes for treating B cell malignant leukaemia. However, covalent inhibitors require a high reactive warhead that may contribute to unexpected toxicity, poor selectivity, or reduced effectiveness in solid tumours. Herein, we report the identification of a novel noncovalent BTK inhibitor. The binding interactions (i.e. interactions from known BTK inhibitors) for the BTK binding site were identified and incorporated into a structure-based virtual screening (SBVS). Top-rank compounds were selected and testing revealed a BTK inhibitor with >50% inhibition at 10 µM concentration. Examining analogues revealed further BTK inhibitors. When tested across solid tumour cell lines, one inhibitor showed favourable inhibitory activity, suggesting its potential for targeting BTK malignant tumours. This inhibitor could serve as a basis for developing an effective BTK inhibitor targeting solid cancers.

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.