An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

I A, Banerjee S. Deciphering the Chemotherapeutic Role of the Aryl Hydrocarbon Receptor Antagonist Resveratrol against the High-Penetrance Genes of Triple-Negative Breast Cancer

In addition to several other malignancies, the ligand-activated aryl hydrocarbon receptor (AhR) signaling pathway has been found to enhance the risk of triple-negative breast cancer (TNBC). Many natural compounds of pharmaceutical importance are identified as antagonistic exogenous ligands of AhR. The expressional lack of hormone receptors coupled with adverse prognosis leads to the absence of molecular-targeted therapy in TNBC. Hence, discovering low-cost therapeutic alternatives involving the identification of effective biomarkers is an urgent necessity. This study investigates the binding mechanism of resveratrol, a dietary exogenous AhR ligand against the high-penetrance genes in TNBC, viz., PALB2, TP53, PTEN, STK11, BRCA1, and BRCA2. Post-pharmacokinetic evaluation, molecular docking revealed the binding energy scores of resveratrol against the six TNBC high-penetrance receptors. The results obtained from docking were confirmed by molecular dynamics simulation including principal component analysis, calculation of total interaction energy, and free-energy landscape computation. PALB2 emerged as a promising therapeutic receptor of resveratrol. Furthermore, the PALB2-resveratrol binding dynamics were evaluated against olaparib, an FDA-approved standardized TNBC inhibitor. Our study reveals comparatively better chemistry of PALB2-resveratrol than PALB2-olaparib. Considering the current surge in the discovery of precision medicine in biomarker-based cancer therapeutics, this study proposes PALB2-resveratrol as a unique drug-receptor combination thus awaiting validation through in vitro studies.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery

INTRODUCTION

For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

AREAS COVERED

End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

EXPERT OPINION

The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual screening

Resource- and time-consuming biological experiments are unavoidable in traditional drug discovery, which have directly driven the evolution of various computational algorithms and tools for drug-target interaction (DTI) prediction. For improving the prediction reliability, a comprehensive platform is highly expected as some previously reported webservers are small in scale, single-method, or even out of service. In this study, we integrated the multiple-conformation based docking, 2D/3D ligand similarity search and deep learning approaches to construct a comprehensive webserver, namely D3CARP, for target prediction and virtual screening. Specifically, 9352 conformations with positive control of 1970 targets were used for molecular docking, and approximately 2 million target-ligand pairs were used for 2D/3D ligand similarity search and deep learning. Besides, the positive compounds were added as references, and related diseases of therapeutic targets were annotated for further disease-based DTI study. The accuracies of the molecular docking and deep learning approaches were 0.44 and 0.89, respectively. And the average accuracy of five ligand similarity searches was 0.94. The strengths of D3CARP encompass the support for multiple computational methods, ensemble docking, utilization of positive controls as references, cross-validation of predicted outcomes, diverse disease types, and broad applicability in drug discovery. The D3CARP is freely accessible at https://www.d3pharma.com/D3CARP/index.php.

In silico identification and analysis of potential inhibitors for acid phosphatase, HppA from Helicobacter pylori

Helicobacter pylori is the most common cause of gastric ulcers and is associated with gastric cancer. The enzyme HppA of class C nonspecific acid phosphohydrolases (NSAPs) of H. pylori plays a crucial role in the electron transport chain. Herein, we report an in silico homology model of HppA consisting of a monomeric α + β model. A high throughput structure-based virtual screening approach yielded potential inhibitors against HppA with higher binding energies. Further analyses of molecular interaction maps and protein-ligand fingerprints, followed by molecular mechanics-generalized Born surface area (MM-GBSA) end point binding energy calculations of docked complexes, resulted in the detection of top binders/ligands. Our investigations identified potential substrate-competitive small molecule inhibitors of HppA, with admissible pharmacokinetic properties. These molecules may provide a starting point for developing novel therapeutic agents against H. pylori.

DOX_BDW: Incorporating Solvation and Desolvation Effects of Cavity Water into Nonfitting Protein-Ligand Binding Affinity Prediction

Accurate prediction of the protein-ligand binding affinity (PLBA) with an affordable cost is one of the ultimate goals in the field of structure-based drug design (SBDD), as well as a great challenge in the computational and theoretical chemistry. Herein, we have systematically addressed the complicated solvation and desolvation effects on the PLBA brought by the difference of the explicit water in the protein cavity before and after ligands bind to the protein-binding site. Based on the new solvation model, a nonfitting method at the first-principles level for the PLBA prediction was developed by taking the bridging and displaced water (BDW) molecules into account simultaneously. The newly developed method, DOX_BDW, was validated against a total of 765 noncovalent and covalent protein-ligand binding pairs, including the CASF2016 core set, Cov_2022 covalent binding testing set, and six testing sets for the hit and lead compound optimization (HLO) simulation. In all of the testing sets, the DOX_BDW method was able to produce PLBA predictions that were strongly correlated with the corresponding experimental data (R = 0.66-0.85). The overall performance of DOX_BDW is better than the current empirical scoring functions that are heavily parameterized. DOX_BDW is particularly outstanding for the covalent binding situation, implying the need for considering an electronic structure in covalent drug design. Furthermore, the method is especially recommended to be used in the HLO scenario of SBDD, where hundreds of similar derivatives need to be screened and refined. The computational cost of DOX_BDW is affordable, and its accuracy is remarkable.

Identifying potential ligand molecules EGFR mediated TNBC targeting the kinase domain-identification of customized drugs through in silico methods

Background and Purpose

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer in which three hormone receptors are negative. This work aimed at identifying customized potential molecules inhibiting epidermal growth factor receptor (EGFR) by exploring variants using the pharmacogenomics approaches.

Experimental approach

The pharmacogenomics approach has been followed to identify the genetic variants across the 1000 genomes continental population. Model proteins for the populations have been designed by including genetic variants in the reported positions. The 3D structures of the mutated proteins have been generated through homology modeling. The kinase domain present in the parent and the model protein molecules has been investigated. The docking study has been performed with the protein molecules against the kinase inhibitors evaluated by the molecular dynamic simulation studies. Molecular evolution has been performed to generate the potential derivatives of these kinase inhibitors suitable for the conserved region of the kinase domain. This study considered variants within the kinase domain as the sensitive region and remaining residues as the conserved region.

Findings/Results

The results reveal that few kinase inhibitors interact with the sensitive region. Among the derivatives of these kinase inhibitors molecules, the potential kinase inhibitor that interacts with the different population models has been identified.

Conclusions and implications

This study encompasses the importance of genetic variants in drug action as well as in the design of customized drugs. This research gives way to designing customized potential molecules inhibiting EGFR by exploring variants using the pharmacogenomics approaches.

Identification of a short ACE2-derived stapled peptide targeting the SARS-CoV-2 spike protein

The design and synthesis of a series of peptide derivatives based on a short ACE2 α-helix 1 epitope and subsequent [i - i+4] stapling of the secondary structure resulted in the identification of a 9-mer peptide capable to compete with recombinant ACE2 towards Spike RBD in the micromolar range. Specifically, SARS-CoV-2 Spike inhibitor screening based on colorimetric ELISA assay and structural studies by circular dichroism showed the ring-closing metathesis cyclization being capable to stabilize the helical structure of the 9-mer ³⁴HEAEDLFYQ⁴² epitope better than the triazole stapling via click chemistry. MD simulations showed the stapled peptide being able not only to bind the Spike RBD, sterically interfering with ACE2, but also showing higher affinity to the target as compared to parent epitope.

ChemFlow─From 2D Chemical Libraries to Protein-Ligand Binding Free Energies

The accurate prediction of protein-ligand binding affinities is a fundamental problem for the rational design of new drug entities. Current computational approaches are either too expensive or inaccurate to be effectively used in virtual high-throughput screening campaigns. In addition, the most sophisticated methods, e.g., those based on configurational sampling by molecular dynamics, require significant pre- and postprocessing to provide a final ranking, which hinders straightforward applications by nonexpert users. We present a novel computational platform named ChemFlow to bridge the gap between 2D chemical libraries and estimated protein-ligand binding affinities. The software is designed to prepare a library of compounds provided in SMILES or SDF format, dock them into the protein binding site, and rescore the poses by simplified free energy calculations. Using a data set of 626 protein-ligand complexes and GPU computing, we demonstrate that ChemFlow provides relative binding free energies with an RMSE < 2 kcal/mol at a rate of 1000 ligands per day on a midsize computer cluster. The software is publicly available at https://github.com/IFMlab/ChemFlow.

Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features

Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have been formulated. In this work, we hypothesized that the specific features of ligands, corresponding to interactions well appreciated by medicinal chemists (e.g., hydrogen bonds, hydrophobic and aromatic interactions), might be responsible, in part, for the remaining SF errors. The latter provides direction to efforts aimed at the rational and systematic improvement of SF accuracy. In this proof-of-concept work, we took a CASF-2016 coreset of 285 ligands as a basis for comparison and calculated the values of scores for a representative panel of SFs (including AutoDock 4.2, AutoDock Vina, X-Score, NNScore2.0, ΔVina RF20, and DSX). The residual error of linear correlation of each SF value, with the experimental values of affinity and activity, was then analyzed in terms of its correlation with the presence of the fragments responsible for certain medicinal chemistry defined interactions. We showed that, despite the fact that SFs generally perform reasonably, there is room for improvement in terms of better parameterization of interactions involving certain fragments in ligands. Thus, this approach opens a potential way for the systematic improvement of SFs without their significant complication. However, the straightforward application of the proposed approach is limited by the scarcity of reliable available data for ligand-receptor complexes, which is a common problem in the field.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities.

Phytochemical Characterization, Functional Nutrition, and Anti-Diabetic Potentials of Leptadenia hastata (pers) Decne Leaves: In Silico and In Vitro Studies

The geometrical increase in diabetes mellitus (DM) and the undesirable side effects of synthetic drugs have intensified efforts to search for an effective and safe anti-diabetic therapy. This study aimed to identify the antioxidant and anti-diabetic agents in the ethanol extract of Leptadenia hastata (EELH). The phytochemicals, antioxidant vitamins, and minerals present in EELH were determined using standard procedures to achieve this aim. Gas chromatography coupled with mass spectroscopy and flame ionization detector (GC-MS/GC-FID) was employed to identify bioactive compounds. An e-pharmacophore model was generated from the extra precision, and energy-minimized docked position of standard inhibitor, acarbose onto human pancreatic amylase (HPA, PDB-6OCN). It was used to screen the GC-MS/GC-FID library of compounds. The top-scoring compounds were subjected to glide XP-docking and prime MM-GBSA calculation with the Schrodinger suite-v12.4. The Adsorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction of the best-fit compounds was made using SwissADME and PROTOX-II webservers. Further validation of the docking results was performed with the in vitro analysis of the α-amylase and α-glucosidase inhibitory activities. EELH contains appreciable amounts of antioxidant and anti-diabetic phytoconstituents. The top-4 scoring compounds (rutin, epicatechin, kaempferol, and naringenin) from the EELH phytochemical library interacted with amino acid residues within and around the HPA active site. The ADMET prediction shows that epicatechin, kaempferol, and naringenin had favorable drug-likeness, pharmacokinetic properties, and a good safety profile. EELH demonstrated good inhibitory actions against α-amylase and α-glucosidase with 1C₅₀ values of 14.14 and 4.22 µg/mL, respectively. Thus, L hastata phytoconstituents are promising novel candidates for developing an anti-diabetic drug.

Screening of potent STAT3-SH2 domain inhibitors from JAK/STAT compound library through molecular dynamics simulation

The Signal Transducer and Activator of Transcription 3 (STAT3) protein is activated consistently in the tumor cells and thus studied as a potent target for cancer prevention. The TYR705-phosphorylated (pTyr) STAT3 forms a homo-dimer by binding to its recognition site in the Src Homology 2 (SH2) domain of another STAT3 monomer, causing cellular survival, proliferation, inflammation, and tumor invasion. Many inhibitors of STAT3-SH2 have recently been identified using both computational and experimental approaches. In this study, we used molecular docking, Absorption, Distribution, Metabolism, and Excretion/Toxicological (ADME/tox) and molecular dynamics modeling to examine binding affinities and specificities of 191 inhibitor drugs from the SELLECKCHEM database. The binding free energies of the inhibitors were calculated by Induced Fit Docking (IFD) prime energy. The binding hotspots of STAT3-SH2 were evaluated via binding energy decomposition and hydrogen bond distribution analysis, and the inhibitor compound's stability was assessed through MD simulation. (-)-Epigallocatechin gallate, Kaempferol-3-O-rutinoside, Picroside I, Saikosaponin D, and Ginsenoside Rk1 were found to be the top hit inhibitor compounds. They exhibited an exceptional docking score, a low binding free energy, interacted with the key amino acid residue, and showed significant ADME/tox moderation. These compounds were further proved to be favorable by their stability in an MD simulation run for 100 ns using GROMACS software. The inhibitors (-)-Epigallocatechin gallate, Kaempferol-3-O-rutinoside, and Saikosaponin D show improved stability in molecular dynamic modeling and are expected to have a significant STAT3-SH2 inhibitory effect against cancer.

Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.

Identification of Novel Nrf2 Activator via Protein-ligand Interactions as Remedy for Oxidative Stress in Diabetes Mellitus

Background:

Oxidative stress is a significant player in the pathogenesis of diabetes mellitus and the Kelch-like ECH-associated protein1/nuclear factor erythroid 2-related factor 2/antioxidant response element (Keap1/Nrf2/ARE) signaling pathway serves as the essential defense system to mitigate oxidative stress. Nrf2 is responsible for the mitigation of oxidative stress while Keap1 represses Nrf2’s activation upon binding. Identification of Nrf2 activators has started to pick up enthusiasm as they can be used as therapeutic agents against diabetes mellitus. One of the ongoing mechanisms in the activation of Nrf2 is to disrupt Keap1/Nrf2 protein-protein interaction. This study aimed at using computational analysis to screen natural compounds capable of inhibiting Keap1/Nrf2 protein-protein interaction.

Methods:

A manual curated library of natural compounds was screened against crystal structure of Keap1 using glide docking algorithm. Binding free energy of the docked complexes, and adsorption, digestion, metabolism and excretion (ADME) properties were further employed to identify the hit compounds. The bioactivity of the identified hit against Keap1 was predicted using quantitative structure-activity relationship (QSAR) model.

Results:

A total of 7 natural compounds (Compound 222, 230, 310, 208, 210, 229 and 205) identified from different medicinal plants were found to be potent against Keap1 based on their binding affinity and binding free energy. The internal validated model kpls_radial_30 with R2 of 0.9109, Q2 of 0.7287 was used to predict the compounds’ bioactivities. Compound 205 was considered as the ideal drug candidate because it showed moderation for ADME properties, had predicted pIC50 of 6.614 and obeyed Lipinski’s rule of five.

Conclusion:

This study revealed that Compound 205, a compound isolated from Amphipterygium adstringens is worth considering for further experimental analysis.

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

Molecular docking studies, molecular dynamics and ADME/tox reveal therapeutic potentials of STOCK1N-69160 against papain-like protease of SARS-CoV-2

SARS-CoV-2 is a new strain of Coronavirus that caused the pneumonia outbreak in Wuhan, China and has spread to over 200 countries of the world. It has received worldwide attention due to its virulence and high rate of infection. So far, several drugs have experimented against SARS-CoV-2, but the failure of these drugs to specifically interact with the viral protease necessitates urgent measure to boost up researches for the development of effective therapeutics against SARS-CoV-2. Papain-like protease (PLpro) of the viral polyproteins is essential for maturation and infectivity of the virus, making it one of the prime targets explored for SARS-CoV-2 drug design. This study was conducted to evaluate the efficacy of ~ 50,000 natural compounds retrieved from IBS database against COVID-19 PLpro using computer-aided drug design. Based on molecular dock scores, molecular interaction with active catalytic residues and molecular dynamics (MD) simulations studies, STOCK1N-69160 [(S)-2-((R)-4-((R)-2-amino-3-methylbutanamido)-3-(4-chlorophenyl) butanamido) propanoic acid hydrochloride] has been proposed as a novel inhibitor against COVID-19 PLpro. It demonstrated favourable docking score, the free energy of binding, interacted with key amino acid residues necessary for PLpro inhibition and also showed significant moderation for parameters investigated for ADME/tox (Adsorption, distribution, metabolism, excretion and toxicological) properties. The edge of the compound was further established by its stability in MD simulation conducted for 30 ns employing GROMACS software. We propose that STOCK1N-69160 is worth further investigation for preventing SARS-CoV-2.

Central residues of FSHβ (89-97) peptide are not critical for FSHR binding: Implications for peptidomimetic design

In our previous study, we had identified a 9-mer peptide (FSHβ (89-97)) derived from seat belt loop of human FSHβ and demonstrated its ability to function as FSHR antagonist in vivo. Structure analysis revealed that the four central residues ⁹¹STDC⁹⁴ within this peptide may not be critical for receptor binding. In the present study, ⁹¹STDC⁹⁴ residues were substituted with alanine to generate ΔFSHβ 89-97(⁹¹STDC⁹⁴/AAAA) peptide. Analogous to the parent peptide, ΔFSHβ 89-97(⁹¹STDC⁹⁴/AAAA) peptide inhibited binding of iodinated FSH to rat FSHR and reduced FSH-induced cAMP production. The peptide could impede granulosa cell proliferation leading to reduction in FSH-mediated ovarian weight gain in immature female rats. In these rats, peptide administration further downregulated androgen receptor and estrogen receptor-alpha expression and upregulated estrogen receptor-beta expression. The results indicate that substitution of ⁹¹STDC⁹⁴ with alanine did not significantly alter FSHR antagonist activity of FSHβ (89-97) peptide implying that these residues are not critical for FSH-FSHR interaction and can be replaced with non-peptidic moieties for development of more potent peptidomimetics.

Substrate Selectivity of Coumarin Derivatives by Human CYP1 Enzymes: In Vitro Enzyme Kinetics and In Silico Modeling

Of the three enzymes in the human cytochrome P450 family 1, CYP1A2 is an important enzyme mediating metabolism of xenobiotics including drugs in the liver, while CYP1A1 and CYP1B1 are expressed in extrahepatic tissues. Currently used CYP substrates, such as 7-ethoxycoumarin and 7-ethoxyresorufin, are oxidized by all individual CYP1 forms. The main aim of this study was to find profluorescent coumarin substrates that are more selective for the individual CYP1 forms. Eleven 3-phenylcoumarin derivatives were synthetized, their enzyme kinetic parameters were determined, and their interactions in the active sites of CYP1 enzymes were analyzed by docking and molecular dynamic simulations. All coumarin derivatives and 7-ethoxyresorufin and 7-pentoxyresorufin were oxidized by at least one CYP1 enzyme. 3-(3-Methoxyphenyl)-6-methoxycoumarin (19) was 7-O-demethylated by similar high efficiency [21-30 ML/(min·mol CYP)] by all CYP1 forms and displayed similar binding in the enzyme active sites. 3-(3-Fluoro-4-acetoxyphenyl)coumarin (14) was selectively 7-O-demethylated by CYP1A1, but with low efficiency [0.16 ML/(min mol)]. This was explained by better orientation and stronger H-bond interactions in the active site of CYP1A1 than that of CYP1A2 and CYP1B1. 3-(4-Acetoxyphenyl)-6-chlorocoumarin (20) was 7-O-demethylated most efficiently by CYP1B1 [53 ML/(min·mol CYP)], followed by CYP1A1 [16 ML/(min·mol CYP)] and CYP1A2 [0.6 ML/(min·mol CYP)]. Variations in stabilities of complexes between 20 and the individual CYP enzymes explained these differences. Compounds 14, 19, and 20 are candidates to replace traditional substrates in measuring activity of human CYP1 enzymes.

Three-Dimensional Proteome-Wide Scale Screening for the 5-Alpha Reductase Inhibitor Finasteride: Identification of a Novel Off-Target

Finasteride, a 5-alpha reductase (5α-R) inhibitor, is a widely used drug for treating androgen-dependent conditions. However, its use is associated with sexual, psychological, and physical complaints, suggesting that other mechanisms, in addition to 5α-R inhibition, may be involved. Here, a multidisciplinary approach has been used to identify potential finasteride off-target proteins. SPILLO-PBSS software suggests an additional inhibitory activity of finasteride on phenylethanolamine N-methyltransferase (PNMT), the limiting enzyme in formation of the stress hormone epinephrine. The interaction of finasteride with PNMT was supported by docking and molecular dynamics analysis and by in vitro assay, confirming the inhibitory nature of the binding. Finally, this inhibition was also confirmed in an in vivo rat model. Literature data indicate that PNMT activity perturbation may be correlated with sexual and psychological side effects. Therefore, results here obtained suggest that the binding of finasteride to PNMT might have a role in producing the side effects exerted by finasteride treatment.

Exploring the antidiabetic potential of compounds isolated from Anacardium occidentale using computational aproach: ligand-based virtual screening

Diabetes mellitus is becoming an important public health challenge worldwide and especially in developing nations. About 8.8 percent of the world adult population has been reported to have diabetes. Glutamine-fructose-6-phosphate amidotransferase 1 (GFAT1) catalyses the first committed step in the pathway for biosynthesis of hexosamines in mammals, and its inhibition has been thought to prevent hyperglycaemia. Dipeptidyl peptidase-4 (DPP-4), on the other hand, degrades hormone glucagon-like peptide-1 (GLP-1), an enzyme that plays a major role in the enhancement of glucose-dependent insulin secretion, making these two proteins candidate targets for diabetes. To find potential inhibitors of DPP-4 and GFAT1 from Anacardium occidentale using a computational approach, glide XP (extra precision) docking, Induced Fit Docking (IFD), Binding free energy of the compounds were determined against prepared crystal structure of DPP-4 and GFAT1 using the Maestro molecular interface of Schrödinger suites. The Lipinski's rule of five (RO5) and ADME properties of the compounds were assessed. Predictive models for both protein targets were built using AutoQSAR. This study identified 8 hit compounds. Most of these compounds passed the RO5 and were within the recommended range for defined ADME parameters. In addition, the predicted pIC50 for the hit compounds were promising. The results obtained from the present study can be used to design an antidiabetic drug.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00084-z.

Molecular dynamics and in silico mutagenesis on the reversible inhibitor-bound SARS-CoV-2 main protease complexes reveal the role of lateral pocket in enhancing the ligand affinity

The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and there is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (Mpro) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 Mpro that resulted in hundreds of X-ray crystal structures of ligand-bound Mpro complexes in the Protein Data Bank (PDB) describing the interactions of different fragment chemotypes within different sites of the Mpro. In this work, we performed rigorous molecular dynamics (MD) simulation of 62 reversible ligand-Mpro complexes in the PDB to gain mechanistic insights about their interactions at the atomic level. Using a total of over 3 µs long MD trajectories, we characterized different pockets in the apo Mpro structure, and analyzed the dynamic interactions and binding affinity of ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets of Mpro. Our analyses unraveled the role of a lateral pocket in the catalytic site in Mpro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in the lateral pocket towards ligand binding and affinity against Mpro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and Mpro dimerization in the ligand association with the target. Thus, comprehensive molecular-level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 Mpro.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Water Bridges Play a Key Role in Affinity and Selectivity for Malarial Protease Falcipain-2

Falcipain-2 (FP-2) is hemoglobinase considered an attractive drug target of Plasmodium falciparum. Recently, it has been shown that peptidomimetic nitriles containing a 3-pyridyl (3Pyr) moiety at P2 display high affinity and selectivity for FP-2 with respect to human cysteine cathepsins (hCats), outperforming other P2-Pyr isomers and analogs. Further characterization demonstrated that certain P3 variants of these compounds possess micromolar inhibition of parasite growth in vitro and no cytotoxicity against human cell lines. However, the structural determinants underlying the selectivity of the 3Pyr-containing nitriles for FP-2 remain unknown. In this work, we conduct a thorough computational study combining MD simulations and free energy calculations to decipher the bases of the selectivity of the aforementioned nitriles. Our results reveal that water bridges involving the nitrogen and one carboxyl oxygen of I85 and D234 of FP-2, respectively, and the nitrogen of the neutral 3Pyr moiety, which are either less prevalent or nonexistent in the other complexes, explain the experimental activity profiles. The presence of crystallographic waters close to the bridging water positions in the studied proteases strongly supports the occurrence of such interactions. Overall, our findings suggest that selective FP-2 inhibitors can be designed by promoting water bridge formation at the bottom of the S2 subsite and/or by introducing complementary groups that displace the bridging water.

Identification and in vivo validation of a 9-mer peptide derived from FSHβ with FSHR antagonist activity

FSH-FSHR interaction is critical for folliculogenesis, spermatogenesis and progression of several cancers. Therefore, FSHR is an attractive target for fertility regulation and cancer therapeutics. Based on homology and structural analysis of hFSH-FSHR(ECD) complex, a minimal continuous stretch within FSHβ seat-belt loop (FSHβ (89-97)) was identified to be crucial for FSHR interaction. The ability of FSHβ (89-97) peptide to neutralize FSHR activity was evaluated by a panel of in vitro and in vivo experiments. The synthetic peptide significantly inhibited binding of [¹²⁵I]-FSH to rat Fshr as well as FSH-induced cAMP production. In immature rats, FSHβ (89-97) peptide administration reduced FSH-mediated increase in ovarian weight. The peptide inhibited transition of follicles from pre-antral to antral stage and hindered the cell cycle progression of granulosa cells beyond G0/G1 phase. In adult rats, administration of the peptide inhibited estradiol synthesis and significantly perturbed folliculogenesis.

In Silico Drug Repurposing for SARS-CoV-2 Main Proteinase and Spike Proteins

The pandemic caused by SARS-CoV-2 is currently representing a major health and economic threat to humanity. So far, no specific treatment to this viral infection has been developed and the emergency still requires an efficient intervention. In this work, we used virtual screening to facilitate drug repurposing against SARS-CoV-2, targeting viral main proteinase and spike protein with 3000 existing drugs. We used a protocol based on a docking step followed by a short molecular dynamic simulation and rescoring by the Nwat-MMGBSA approach. Our results provide suggestions for prioritizing in vitro and/or in vivo tests of already available compounds.

Insight into glycogen synthase kinase-3β inhibitory activity of phyto-constituents from Melissa officinalis: in silico studies

Over activity of Glycogen synthase kinase-3β (GSK-3β), a serine/threonine-protein kinase has been implicated in a number of diseases including stroke, type II diabetes and Alzheimer disease (AD). This study aimed to find novel inhibitors of GSK-3β from phyto-constituents of Melissa officinalis with the aid of computational analysis. Molecular docking, induced-fit docking (IFD), calculation of binding free energy via the MM-GBSA approach and Lipinski's rule of five (RO5) were employed to filter the compounds and determine their druggability. Most importantly, the compounds pIC50 were predicted by machine learning-based model generated by AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best model obtained was Model kpls_desc_38 (R2 = 0.8467 and Q2 = 0.8069), and this external validated model was utilized to predict the bioactivities of the lead compounds. While a number of characterized compounds from Melissa officinalis showed better docking score, binding free energy alongside adherence to RO5 than co-cystallized ligand, only three compounds (salvianolic acid C, ellagic acid and naringenin) showed more satisfactory pIC50. The results obtained in this study can be useful to design potent inhibitors of GSK-3β.

Combined strategies in structure-based virtual screening

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

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants

With the potential of improving virtual screening outcome, MM-PB/GBSA has become a disputed method that requires extensive testing and tuning to provide the optimal results. One of the tuning factors is the internal or solute dielectric constant. We have applied three test sets with receptors of different categories and libraries from different sources to investigate the underlying issue related to this constant. We discovered that increasing internal dielectric value does not improve the virtual screening enrichment qualitatively. More interestingly, nonpolar and polar calculated energies act differently in libraries with different molecular weight distributions. From this work, the performance of MM-PBSA rescoring in virtual screening is more library- than receptor-dependent.

PeptoGrid-Rescoring Function for AutoDock Vina to Identify New Bioactive Molecules from Short Peptide Libraries

Peptides are promising drug candidates due to high specificity and standout safety. Identification of bioactive peptides de novo using molecular docking is a widely used approach. However, current scoring functions are poorly optimized for peptide ligands. In this work, we present a novel algorithm PeptoGrid that rescores poses predicted by AutoDock Vina according to frequency information of ligand atoms with particular properties appearing at different positions in the target protein’s ligand binding site. We explored the relevance of PeptoGrid ranking with a virtual screening of peptide libraries using angiotensin-converting enzyme and GABAB receptor as targets. A reasonable agreement between the computational and experimental data suggests that PeptoGrid is suitable for discovering functional leads.

In silico Prediction, Characterization, Molecular Docking, and Dynamic Studies on Fungal SDRs as Novel Targets for Searching Potential Fungicides Against Fusarium Wilt in Tomato

Vascular wilt of tomato caused by Fusarium oxysporum f.sp. lycopersici (FOL) is one of the most devastating diseases, that delimits the tomato production worldwide. Fungal short-chain dehydrogenases/reductases (SDRs) are NADP(H) dependent oxidoreductases, having shared motifs and common functional mechanism, have been demonstrated as biochemical targets for commercial fungicides. The 1,3,6,8 tetra hydroxynaphthalene reductase (T4HNR) protein, a member of SDRs family, catalyzes the naphthol reduction reaction in fungal melanin biosynthesis. We retrieved an orthologous member of T4HNR, (complexed with NADP(H) and pyroquilon from Magnaporthe grisea) in the FOL (namely; FOXG_04696) based on homology search, percent identity and sequence similarity (93% query cover; 49% identity). The hypothetical protein FOXG_04696 (T4HNR like) had conserved T-G-X-X-X-G-X-G motif (cofactor binding site) at N-terminus, similar to M. grisea (1JA9) and Y-X-X-X-K motif, as a part of the active site, bearing homologies with two fungal keto reductases T4HNR (M. grisea) and 17-β-hydroxysteroid dehydrogenase from Curvularia lunata (teleomorph: Cochliobolus lunatus PDB ID: 3IS3). The catalytic tetrad of T4HNR was replaced with ASN¹¹⁵, SER¹⁴¹, TYR¹⁵⁴, and LYS¹⁵⁸ in the FOXG_04696. The structural alignment and superposition of FOXG_04696 over the template proteins (3IS3 and 1JA9) revealed minimum RMSD deviations of the C alpha atomic coordinates, and therefore, had structural conservation. The best protein model (FOXG_04696) was docked with 37 fungicides, to evaluate their binding affinities. The Glide XP and YASARA docked complexes showed discrepancies in results, for scoring and ranking the binding affinities of fungicides. The docked complexes were further refined and rescored from their docked poses through 50 ns long MD simulations, and binding free energies (ΔG_bind) calculations, using MM/GBSA analysis, revealed Oxathiapiprolin and Famoxadone as better fungicides among the selected one. However, Famoxadone had better interaction of the docked residues, with best protein ligand contacts, minimum RMSD (high accuracy of the docking pose) and RMSF (structural integrity and conformational flexibility of docking) at the specified docking site. The Famoxadone was found to be acceptable based on in silico toxicity and in vitro growth inhibition assessment. We conclude that the FOXG_04696, could be employed as a novel candidate protein, for structure-based design, and screening of target fungicides against the FOL pathogen.