Computer-aided discovery of anti-HIV agents

Combating antimicrobial resistance in malaria, HIV and tuberculosis

Antimicrobial resistance poses a significant threat to the sustainability of effective treatments against the three most prevalent infectious diseases: malaria, human immunodeficiency virus (HIV) infection and tuberculosis. Therefore, there is an urgent need to develop novel drugs and treatment protocols capable of reducing the emergence of resistance and combating it when it does occur. In this Review, we present an overview of the status and underlying molecular mechanisms of drug resistance in these three diseases. We also discuss current strategies to address resistance during the research and development of next-generation therapies. These strategies vary depending on the infectious agent and the array of resistance mechanisms involved. Furthermore, we explore the potential for cross-fertilization of knowledge and technology among these diseases to create innovative approaches for minimizing drug resistance and advancing the discovery and development of new anti-infective treatments. In conclusion, we advocate for the implementation of well-defined strategies to effectively mitigate and manage resistance in all interventions against infectious diseases.

Medicinal chemistry perspectives on the development of piperazine-containing HIV-1 inhibitors

The Human immunodeficiency virus (HIV) is the causative agent of acquired immunodeficiency syndrome (AIDS), one of the most perilous diseases known to humankind. A 2023 estimate put the number of people living with HIV around 40 million worldwide, with the majority benefiting from various antiretroviral therapies. Consequently, the urgent need for the development of effective drugs to combat this virus cannot be overstated. In the realm of medicinal and organic chemistry, the synthesis and identification of novel compounds capable of inhibiting HIV enzymes at different stages of their life cycle are of paramount importance. Notably, the spotlight is on the progress made in enhancing the potency of HIV inhibitors through the use of piperazine-based compounds. Multiple studies have revealed that the incorporation of a piperazine moiety results in a noteworthy enhancement of anti-HIV activity. The piperazine ring assumes a pivotal role in shaping the pharmacophore responsible for inhibiting HIV-1 at critical stage, including attachment, reverse transcription, integration, and protease activity. This review also sheds light on the various opportunities that can be exploited to develop effective antiretroviral targets and eliminate latent HIV reservoirs. The advancement of highly potent analogues in HIV inhibitor research has been greatly facilitated by contemporary medicinal strategies, including molecular/fragment hybridization, structure-based drug design, and bioisosterism. These techniques have opened up new avenues for the development of compounds with enhanced efficacy in combating the virus.

Unveiling tetrahydroquinolines as promising BVDV entry inhibitors: Targeting the envelope protein

Bovine viral diarrhea virus (BVDV) is known to cause financial losses and decreased productivity in the cattle industry worldwide. Currently, there are no available antiviral treatments for effectively controlling BVDV infections in laboratories or farms. The BVDV envelope protein (E2) mediates receptor recognition on the cell surface and is required for fusion of virus and cell membranes after the endocytic uptake of the virus during the entry process. Therefore, E2 is an attractive target for the development of antiviral strategies. To identify BVDV antivirals targeting E2 function, we defined a binding site in silico located in domain IIIc at the interface between monomers in the disulfide linked dimer of E2. Employing a de novo design methodology to identify compounds with the potential to inhibit the E2 function, compound 9 emerged as a promising candidate with remarkable antiviral activity and minimal toxicity. In line with targeting of E2 function, compound 9 was found to block the virus entry into host cells. Furthermore, we demonstrated that compound 9 selectively binds to recombinant E2 in vitro. Molecular dynamics simulations (MD) allowed describing a possible interaction pattern between compound 9 and E2 and indicated that the S enantiomer of compound 9 may be responsible for the antiviral activity. Future research endeavors will focus on synthesizing enantiomerically pure compounds to further support these findings. These results highlight the usefulness of de novo design strategies to identify a novel class of BVDV inhibitors that block E2 function inhibiting virus entry into the host cell.

Ensemble Geometric Deep Learning of Aqueous Solubility

Geometric deep learning is one of the main workhorses for harnessing the power of big data to predict molecular properties such as aqueous solubility, which is key to the pharmacokinetic improvement of drug candidates. Two ensembles of graph neural network architectures were built, one based on spectral convolution and the other on spatial convolution. The pretrained models, denoted respectively as SolNet-GCN and SolNet-GAT, significantly outperformed the existing neural networks benchmarked on a validation set of 207 molecules. The SolNet-GCN model demonstrated the best performance on both the training and validation sets, with RMSE values of 0.53 and 0.72 log molar unit and Pearson r2 values of 0.95 and 0.75, respectively. Further, the ranking power of the SolNet models agreed well with a QM-based thermodynamic cycle approach at the PBE-vdW level of theory on a series of benzophenylurea derivatives and a series of benzodiazepine derivatives. Nevertheless, testing the resultant models on a set of inhibitors of the macrophage migration inhibitory factor (MIF) illustrated that the inclusion of atomic attributes to discriminate atoms with a higher tendency to form intermolecular hydrogen bonds in the crystalline state and to identify planar or nonplanar substructures can be beneficial for the prediction of aqueous solubility.

Key essential oil components delocalize Candida albicans Kar3p and impact microtubule structure

BACKGROUND

Treatment of Candida albicans associated infections is often ineffective in the light of resistance, with an urgent need to discover novel antimicrobials. Fungicides require high specificity and can contribute to antifungal resistance, so inhibition of fungal virulence factors is a good strategy for developing new antifungals.

OBJECTIVES

Examine the impact of four plant-derived essential oil components (1,8-cineole, α-pinene, eugenol, and citral) on C. albicans microtubules, kinesin motor protein Kar3 and morphology.

METHODS

Microdilution assays were used to determine minimal inhibitory concentrations, microbiological assays assessed germ tube, hyphal and biofilm formation, confocal microscopy probed morphological changes and localization of tubulin and Kar3p, and computational modelling was used to examine the theoretical binding of essential oil components to tubulin and Kar3p.

RESULTS

We show for the first time that essential oil components delocalize the Kar3p, ablate microtubules, and induce psuedohyphal formation with reduced biofilm formation. Single and double deletion mutants of kar3 were resistant to 1,8-cineole, sensitive to α-pinene and eugenol, but unimpacted by citral. Strains with homozygous and heterozygous Kar3p disruption had a gene-dosage effect for all essential oil components, resulting in enhanced resistance or susceptibility patterns that were identical to that of cik1 mutants. The link between microtubule (αβ-tubulin) and Kar3p defects was further supported by computational modeling, showing preferential binding to αβ-tubulin and Kar3p adjacent to their Mg²⁺-binding sites.

CONCLUSION

This study highlights how essential oil components interfere with the localization of the kinesin motor protein complex Kar3/Cik1 and disrupt microtubules, leading to their destabilization which results in hyphal and biofilm defects.

Design, synthesis, and biological testing of biphenylmethyloxazole inhibitors targeting HIV-1 reverse transcriptase

We report non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) using a biphenylmethyloxazole pharmacophore. A crystal structure of benzyloxazole 1 was obtained and suggested the potential viability of biphenyl analogues. In particular, 6a, 6b, and 7 turned out to be potent NNRTIs with low-nanomolar activity in enzyme inhibition and infected T-cell assays, and with low cytotoxicity. Though modeling further suggested that analogues with fluorosulfate and epoxide warheads might provide covalent modification of Tyr188, synthesis and testing did not find evidence for this outcome.

In silico design of a novel nucleotide antiviral agent by free energy perturbation

Nucleoside analogs are the backbone of antiviral therapies. Drugs from this class undergo processing by host or viral kinases to form the active nucleoside triphosphate species that selectively inhibits the viral polymerase. It is the central hypothesis that the nucleoside triphosphate analog must be a favorable substrate for the viral polymerase and the nucleoside precursor must be a satisfactory substrate for the host kinases to inhibit viral replication. Herein, free energy perturbation (FEP) was used to predict substrate affinity for both host and viral enzymes. Several uridine 5'-monophosphate prodrug analogs known to inhibit hepatitis C virus (HCV) were utilized in this study to validate the use of FEP. Binding free energies to the host monophosphate kinase and viral RNA-dependent RNA polymerase (RdRp) were calculated for methyl-substituted uridine analogs. The 2'-C-methyl-uridine and 4'-C-methyl-uridine scaffolds delivered favorable substrate binding to the host kinase and HCV RdRp that were consistent with results from cellular antiviral activity in support of our new approach. In a prospective evaluation, FEP results suggest that 2'-C-dimethyl-uridine scaffold delivered favorable monophosphate and triphosphate substrates for both host kinase and HCV RdRp, respectively. Novel 2'-C-dimethyl-uridine monophosphate prodrug was synthesized and exhibited sub-micromolar inhibition of HCV replication. Using this novel approach, we demonstrated for the first time that nucleoside analogs can be rationally designed that meet the multi-target requirements for antiviral activity.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

Optimization of Triarylpyridinone Inhibitors of the Main Protease of SARS-CoV-2 to Low-Nanomolar Antiviral Potency

Non-covalent inhibitors of the main protease (Mpro) of SARS-CoV-2 having a pyridinone core were previously reported with IC50 values as low as 0.018 μM for inhibition of enzymatic activity and EC50 values as low as 0.8 μM for inhibition of viral replication in Vero E6 cells. The series has now been further advanced by consideration of placement of substituted five-membered-ring heterocycles in the S4 pocket of Mpro and N-methylation of a uracil ring. Free energy perturbation calculations provided guidance on the choice of the heterocycles, and protein crystallography confirmed the desired S4 placement. Here we report inhibitors with EC50 values as low as 0.080 μM, while remdesivir yields values of 0.5-2 μM in side-by-side testing with infectious SARS-CoV-2. A key factor in the improvement is enhanced cell permeability, as reflected in PAMPA measurements. Compounds 19 and 21 are particularly promising as potential therapies for COVID-19, featuring IC50 values of 0.044-0.061 μM, EC50 values of ca. 0.1 μM, good aqueous solubility, and no cytotoxicity.

A Reduced Generalized Force Field for Biological Halogen Bonds

The halogen bond (or X-bond) is a noncovalent interaction that is increasingly recognized as an important design tool for engineering protein-ligand interactions and controlling the structures of proteins and nucleic acids. In the past decade, there have been significant efforts to characterize the structure-energy relationships of this interaction in macromolecules. Progress in the computational modeling of X-bonds in biological molecules, however, has lagged behind these experimental studies, with most molecular mechanics/dynamics-based simulation methods not properly treating the properties of the X-bond. We had previously derived a force field for biological X-bonds (ffBXB) based on a set of potential energy functions that describe the anisotropic electrostatic and shape properties of halogens participating in X-bonds. Although fairly accurate for reproducing the energies within biomolecular systems, including X-bonds engineered into a DNA junction, the ffBXB with its seven variable parameters was considered to be too unwieldy for general applications. In the current study, we have generalized the ffBXB by reducing the number of variables to just one for each halogen type and show that this remaining electrostatic variable can be estimated for any new halogenated molecule through a standard restricted electrostatic potential calculation of atomic charges. In addition, we have generalized the ffBXB for both nucleic acids and proteins. As a proof of principle, we have parameterized this reduced and more general ffBXB against the AMBER force field. The resulting parameter set was shown to accurately recapitulate the quantum mechanical landscape and experimental interaction energies of X-bonds incorporated into DNA junction and T4 lysozyme model systems. Thus, this reduced and generalized ffBXB is more readily adaptable for incorporation into classical molecular mechanics/dynamics algorithms, including those commonly used to design inhibitors against therapeutic targets in medicinal chemistry and materials in biomolecular engineering.

Performance of Relative Binding Free Energy Calculations on an Automatically Generated Dataset of Halogen-Deshalogen Matched Molecular Pairs

In this study, we generated a matched molecular pair dataset of halogen/deshalogen compounds with reliable binding affinity data and structural binding mode information from public databases. The workflow includes automated system preparation and setup of free energy perturbation relative binding free energy calculations. We demonstrate the suitability of these datasets to investigate the performance of molecular mechanics force fields and molecular simulation algorithms for the purpose of in silico affinity predictions in lead optimization. Our datasets of a total of 115 matched molecular pairs show highly accurate binding free energy predictions with an average error of <1 kcal/mol despite the semi-automated calculation scheme. We quantify the accuracy of the optimized potential for liquid simulations (OPLS) force field to predict the effect of halogen addition to compounds, a commonly employed chemical modification in the design of drug-like molecules.

Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands

In this work, the discrete λ variant of the Gibbs sampler-based λ-dynamics (d-GSλD) method is developed to enable multiple functional group perturbations to be investigated at one or more sites of substitution off a common ligand core. The theoretical framework and special considerations for constructing discrete λ states for multisite d-GSλD are presented. The precision and accuracy of the d-GSλD method is evaluated with three test cases of increasing complexity. Specifically, methyl → methyl symmetric perturbations in water, 1,4-benzene hydration free energies and protein-ligand binding affinities for an example HIV-1 reverse transcriptase inhibitor series are computed with d-GSλD. Complementary MSλD calculations were also performed to compare with d-GSλD's performance. Excellent agreement between d-GSλD and MSλD is observed, with mean unsigned errors of 0.12 and 0.22 kcal/mol for computed hydration and binding free energy test cases, respectively. Good agreement with experiment is also observed, with errors of 0.5-0.7 kcal/mol. These findings support the applicability of the d-GSλD free energy method for a variety of molecular design problems, including structure-based drug design. Finally, a discussion of d-GSλD versus MSλD approaches is presented to compare and contrast features of both methods.

Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations

The quantum mechanical bespoke (QUBE) force-field approach has been developed to facilitate the automated derivation of potential energy function parameters for modeling protein-ligand binding. To date, the approach has been validated in the context of Monte Carlo simulations of protein-ligand complexes. We describe here the implementation of the QUBE force field in the alchemical free-energy calculation molecular dynamics simulation package SOMD. The implementation is validated by demonstrating the reproducibility of absolute hydration free energies computed with the QUBE force field across the SOMD and GROMACS software packages. We further demonstrate, by way of a case study involving two series of non-nucleoside inhibitors of HIV-1 reverse transcriptase, that the availability of QUBE in a modern simulation package that makes efficient use of graphics processing unit acceleration will facilitate high-throughput alchemical free-energy calculations.

Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations

Starting from our previous finding of 14 known drugs as inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19, we have redesigned the weak hit perampanel to yield multiple noncovalent, nonpeptidic inhibitors with ca. 20 nM IC50 values in a kinetic assay. Free-energy perturbation (FEP) calculations for Mpro-ligand complexes provided valuable guidance on beneficial modifications that rapidly delivered the potent analogues. The design efforts were confirmed and augmented by determination of high-resolution X-ray crystal structures for five analogues bound to Mpro. Results of cell-based antiviral assays further demonstrated the potential of the compounds for treatment of COVID-19. In addition to the possible therapeutic significance, the work clearly demonstrates the power of computational chemistry for drug discovery, especially FEP-guided lead optimization.

Covalent Inhibition of Wild-Type HIV-1 Reverse Transcriptase Using a Fluorosulfate Warhead

Covalent inhibitors of wild-type HIV-1 reverse transcriptase (CRTIs) are reported. Three compounds derived from catechol diether non-nucleoside inhibitors (NNRTIs) with addition of a fluorosulfate warhead are demonstrated to covalently modify Tyr181 of HIV-RT. X-ray crystal structures for complexes of the CRTIs with the enzyme are provided, which fully demonstrate the covalent attachment, and confirmation is provided by appropriate mass shifts in ESI-TOF mass spectra. The three CRTIs and six noncovalent analogues are found to be potent inhibitors with both IC₅₀ values for in vitro inhibition of WT RT and EC₅₀ values for cytopathic protection of HIV-1-infected human T-cells in the 5-320 nM range.

Is Structure-Based Drug Design Ready for Selectivity Optimization?

Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.

Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs

Drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedentedly high hit rate, leading to successful identification of 15 potent inhibitors of SARS-CoV-2 main protease (M^pro) from 25 computationally selected drugs under a threshold of K_i = 4 μM. The outcomes of this study are valuable for not only drug repurposing to treat COVID-19 but also demonstrating the promising potential of the FEP-ABFE prediction-based virtual screening approach.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (M^pro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE−based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 M^pro. The most potent one is dipyridamole (inhibitory constant K_i = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (K_i = 0.36 µM) and chloroquine (K_i = 0.56 µM) were also found to potently inhibit SARS-CoV-2 M^pro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

Structural investigation of 2-naphthyl phenyl ether inhibitors bound to WT and Y181C reverse transcriptase highlights key features of the NNRTI binding site

Human immunodeficiency virus (HIV)-1 remains as a global health issue that is primarily treated with highly active antiretroviral therapy, a combination of drugs that target the viral life cycle. One class of these drugs are non-nucleoside reverse transcriptase inhibitors (NNRTIs) that target the viral reverse transcriptase (RT). First generation NNRTIs were troubled with poor pharmacological properties and drug resistance, incentivizing the development of improved compounds. One class of developed compounds are the 2-naphthyl phenyl ethers, showing promising efficacy against the Y181C RT mutation. Further biochemical and structural work demonstrated differences in potency against the Y181C mutation and binding mode of the compounds. This work aims to understand the relationship between the binding mode and ability to overcome drug resistance using macromolecular x-ray crystallography. Comparison of 2-naphthyl phenyl ethers bound to Y181C RT reveal that compounds that interact with the invariant W229 are more capable of retaining efficacy against the resistance mutation. Additional modifications to these compounds at the 4-position, computationally designed to compensate for the Y181C mutation, do not demonstrate improved potency. Ultimately, we highlight important considerations for the development of future HIV-1 drugs that are able to combat drug resistance.

Experimentally-driven protein structure modeling

Revolutions in natural and exact sciences started at the dawn of last century have led to the explosion of theoretical, experimental, and computational approaches to determine structures of molecules, complexes, as well as their rich conformational dynamics. Since different experimental methods produce information that is attributed to specific time and length scales, corresponding computational methods have to be tailored to these scales and experiments. These methods can be then combined and integrated in scales, hence producing a fuller picture of molecular structure and motion from the "puzzle pieces" offered by various experiments. Here, we describe a number of computational approaches to utilize experimental data to glance into structure of proteins and understand their dynamics. We will also discuss the limitations and the resolution of the constraints-based modeling approaches. SIGNIFICANCE: Experimentally-driven computational structure modeling and determination is a rapidly evolving alternative to traditional approaches for molecular structure determination. These new hybrid experimental-computational approaches are proving to be a powerful microscope to glance into the structural features of intrinsically or partially disordered proteins, dynamics of molecules and complexes. In this review, we describe various approaches in the field of experimentally-driven computational structure modeling.

In silico studies of the inhibition mechanism of dengue with papain

Dengue virus is becoming a major global disease; the envelope protein is the major target for vaccine development against Dengue. Nowadays, the attention has focused on developing inhibitors based on Papain is a promising target for treating Dengue. In the present work, the theoretical studies of E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and His159) complexes are analysed by Density Functional Theory (M06-2X/cc-pVDZ) method. Among the E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and Hys159) complexes, E-protein(Glu76)…Papain(Cys25) complex has the highest interaction value of -352.22 kcal/mol. Moreover, the natural bond orbital analysis also supports the above results. The 100 ns Molecular Dynamics simulation reveals that, E-protein(Ala54-Ile129)…Papain(Cys25) complex had the lowest root mean square deviation value of 1 Å compared to the E-protein(Ala54-Ile129)… Papain(Asn175 & His159) complexes. The salt bridge formation between the Asp103 and Lys110 residues are the important stabilizing factor in E-protein(Ala54-Ile129)…Papain(Cys25) complex. This result can extend our knowledge of the functional behaviour of Papain and provides structural insight to target Envelope protein as forthcoming drug targets in Dengue.

Design, synthesis and biological evaluation of quinoxaline compounds as anti-HIV agents targeting reverse transcriptase enzyme

Infection by human immunodeficiency virus still represents a continuous serious concern and a global threat to human health. Due to appearance of multi-resistant virus strains and the serious adverse side effects of the antiretroviral therapy administered, there is an urgent need for the development of new treatment agents, more active, less toxic and with increased tolerability to mutations. Quinoxaline derivatives are an emergent class of heterocyclic compounds with a wide spectrum of biological activities and therapeutic applications. These types of compounds have also shown high potency in the inhibition of HIV reverse transcriptase and HIV replication in cell culture. For these reasons we propose, in this work, the design, synthesis and biological evaluation of quinoxaline derivatives targeting HIV reverse transcriptase enzyme. For this, we first carried out a structure-based development of target-specific compound virtual chemical library of quinoxaline derivatives. The rational construction of the virtual chemical library was based on previously assigned pharmacophore features. This library was processed by a virtual screening protocol employing molecular docking and 3D-QSAR. Twenty-five quinoxaline compounds were selected for synthesis in the basis of their docking and 3D-QSAR scores and chemical synthetic simplicity. They were evaluated as inhibitors of the recombinant wild-type reverse transcriptase enzyme. Finally, the anti-HIV activity and cytotoxicity of the synthesized quinoxaline compounds with highest reverse transcriptase inhibitory capabilities was evaluated. This simple screening strategy led to the discovery of two selective and potent quinoxaline reverse transcriptase inhibitors with high selectivity index.

Absolute Free Energy of Binding Calculations for Macrophage Migration Inhibitory Factor in Complex with a Druglike Inhibitor

Calculation of the absolute free energy of binding (ΔG_bind) for a complex in solution is challenging owing to the need for adequate configurational sampling and an accurate energetic description, typically with a force field (FF). In this study, Monte Carlo (MC) simulations with improved side-chain and backbone sampling are used to assess ΔG_bind for the complex of a druglike inhibitor (MIF180) with the protein macrophage migration inhibitory factor (MIF) using free energy perturbation (FEP) calculations. For comparison, molecular dynamics (MD) simulations were employed as an alternative sampling method for the same system. With the OPLS-AA/M FF and CM5 atomic charges for the inhibitor, the ΔG_bind results from the MC/FEP and MD/FEP simulations, -8.80 ± 0.74 and -8.46 ± 0.85 kcal/mol, agree well with each other and with the experimental value of -8.98 ± 0.28 kcal/mol. The convergence of the results and analysis of the trajectories indicate that sufficient sampling was achieved for both approaches. Repeating the MD/FEP calculations using current versions of the CHARMM and AMBER FFs led to a 6 kcal/mol range of computed ΔG_bind. These results show that calculation of accurate ΔG_bind for large ligands is both feasible and numerically equivalent, within error limits, using either methodology.

Development of a Robust Indirect Approach for MM → QM Free Energy Calculations That Combines Force-Matched Reference Potential and Bennett's Acceptance Ratio Methods

We use the PBE0/6-31G* density functional method to perform ab initio quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) simulations under periodic boundary conditions with rigorous electrostatics using the ambient potential composite Ewald method in order to test the convergence of MM → QM/MM free energy corrections for the prediction of 17 small-molecule solvation free energies and eight ligand binding free energies to T4 lysozyme. The "indirect" thermodynamic cycle for calculating free energies is used to explore whether a series of reference potentials improve the statistical quality of the predictions. Specifically, we construct a series of reference potentials that optimize a molecular mechanical (MM) force field's parameters to reproduce the ab initio QM/MM forces from a QM/MM simulation. The optimizations form a systematic progression of successively expanded parameters that include bond, angle, dihedral, and charge parameters. For each reference potential, we calculate benchmark quality reference values for the MM → QM/MM correction by performing the mixed MM and QM/MM Hamiltonians at 11 intermediate states, each for 200 ps. We then compare forward and reverse application of Zwanzig's relation, thermodynamic integration (TI), and Bennett's acceptance ratio (BAR) methods as a function of reference potential, simulation time, and the number of simulated intermediate states. We find that Zwanzig's equation is inadequate unless a large number of intermediate states are explicitly simulated. The TI and BAR mean signed errors are very small even when only the end-state simulations are considered, and the standard deviations of the TI and BAR errors are decreased by choosing a reference potential that optimizes the bond and angle parameters. We find a robust approach for the data sets of fairly rigid molecules considered here is to use bond + angle reference potential together with the end-state-only BAR analysis. This requires QM/MM simulations to be performed in order to generate reference data to parametrize the bond + angle reference potential, and then this same simulation serves a dual purpose as the full QM/MM end state. The convergence of the results with respect to time suggests that computational resources may be used more efficiently by running multiple simulations for no more than 50 ps, rather than running one long simulation.

Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications

A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure-activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.

Overcoming Challenging Substituent Perturbations with Multisite λ-Dynamics: A Case Study Targeting β-Secretase 1

Alchemical free energy calculations have made a dramatic impact upon the field of structure-based drug design by allowing functional group modifications to be explored computationally prior to experimental synthesis and assay evaluation, thereby informing and directing synthetic strategies. In furthering the advancement of this area, a series of 21 β-secretase 1 (BACE1) inhibitors developed by Janssen Pharmaceuticals were examined to evaluate the ability to explore large substituent perturbations, some of which contain scaffold modifications, with multisite λ-dynamics (MSλD), an innovative alchemical free energy framework. Our findings indicate that MSλD is able to efficiently explore all structurally diverse ligand end-states simultaneously within a single MD simulation with a high degree of precision and with reduced computational costs compared to the widely used approach TI/MBAR. Furthermore, computational predictions were shown to be accurate to within 0.5-0.8 kcal/mol when CM1A partial atomic charges were combined with CHARMM or OPLS-AA-based force fields, demonstrating that MSλD is force field independent and a viable alternative to FEP or TI approaches for drug design.

Oxadiazole-An Important Bioactive Scaffold for Drug Discovery and Development Process Against HIV and Cancer- A Review

Background:

Acquired immunodeficiency syndrome (AIDS) and cancer treatment have been a major task for research scientists and pharmaceutical industry for the last many years. Seeking to the development, many promising chemical entities especially five-membered heterocyclic rings like oxadiazole have revealed good anticancer and anti HIV activities. The current review enlists some recently developed anti-HIV and anti-cancer oxadiazole moieties.

Methods:

on the basis of structural modification for the syntheses of new oxadiazole analogs, the new anti-HIV and anti-cancer agents have been summarized, which can improve treatment of AIDs and cancer.

Results:

The oxadiazole ring is more potent in comparison to some other heterocyclic rings (five and six membered) towards anti-HIV and anti-cancer activities. The important mechanisms involved for anti HIV and anticancer activity are mainly inhibition of enzymes like protease, HIV-integrase, telomerase, histone deacetylase, methionine amino peptidase, thymidylate synthase and focal adhesion kinase and inhibition of some growth factors.

Conclusion:

By reviving the past literature about 50 most potent oxadiazole derivatives, depending upon activity and structural modifications, have been selected as potent anti-HIV, and anti-cancer agents. Thus, oxadiazole seems to be a ‘privileged structure’ for further screening and syntheses of the new drug analogs against life threatening HIV and cancer like diseases.

An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of drugs approved were nucleoside reverse transcriptase inhibitors. A newer generation of reverse transcriptase inhibitors have become susceptible to drug resistant strains of HIV, and hence, alternatives are urgently needed. We have recently pioneered the use of Bayesian machine learning to generate models with public data to identify new compounds for testing against different disease targets. The current study has used the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database for machine learning studies. We curated and cleaned data from HIV-1 wild-type cell-based and reverse transcriptase (RT) DNA polymerase inhibition assays. Compounds from this database with ≤1 μM HIV-1 RT DNA polymerase activity inhibition and cell-based HIV-1 inhibition are correlated (Pearson r = 0.44, n = 1137, p < 0.0001). Models were trained using multiple machine learning approaches (Bernoulli Naive Bayes, AdaBoost Decision Tree, Random Forest, support vector classification, k-Nearest Neighbors, and deep neural networks as well as consensus approaches) and then their predictive abilities were compared. Our comparison of different machine learning methods demonstrated that support vector classification, deep learning, and a consensus were generally comparable and not significantly different from each other using 5-fold cross validation and using 24 training and test set combinations. This study demonstrates findings in line with our previous studies for various targets that training and testing with multiple data sets does not demonstrate a significant difference between support vector machine and deep neural networks.

Absolute Binding Free Energy Calculation and Design of a Subnanomolar Inhibitor of Phosphodiesterase-10

Accurate prediction of absolute protein-ligand binding free energy could considerably enhance the success rate of structure-based drug design but is extremely challenging and time-consuming. Free energy perturbation (FEP) has been proven reliable but is limited to prediction of relative binding free energies of similar ligands (with only minor structural differences) in binding with a same drug target in practical drug design applications. Herein, a Gaussian algorithm-enhanced FEP (GA-FEP) protocol has been developed to enhance the FEP simulation performance, enabling to efficiently carry out the FEP simulations on vanishing the whole ligand and, thus, predict the absolute binding free energies (ABFEs). Using the GA-FEP protocol, the FEP simulations for the ABFE calculation (denoted as GA-FEP/ABFE) can achieve a satisfactory accuracy for both structurally similar and diverse ligands in a dataset of more than 100 receptor-ligand systems. Further, our GA-FEP/ABFE-guided lead optimization against phosphodiesterase-10 led to the discovery of a subnanomolar inhibitor (IC₅₀ = 0.87 nM, ∼2000-fold improvement in potency) with cocrystal confirmation.

Unbinding Dynamics of Non-Nucleoside Inhibitors from HIV-1 Reverse Transcriptase

Non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs), which bind to an allosteric site 10-15 Å from the polymerase active site, play a central role in anti-HIV chemotherapy. Though NNRTIs have been known for 30 years, the pathways by which they bind and unbind from HIV-RT have not been characterized. In crystal structures for complexes, three channels are found to extend from the NNRTI binding site to the exterior of the protein, while added mystery comes from the fact that the binding site is collapsed in the unliganded protein. To address this issue, metadynamics simulations have been performed to elucidate the unbinding of four NNRTIs from HIV-RT. A general and transferable collective variable defined by the distance between the center-of-mass (COM) of the binding pocket and COM of the ligand is used to follow the dynamics while minimizing the bias. The metadynamics also allows computation of the barriers to unbinding, which are compared with the observed potencies of the compounds in an antiviral assay.

Modeling the value of predictive affinity scoring in preclinical drug discovery

Drug discovery is widely recognized to be a difficult and costly activity in large part due to the challenge of identifying chemical matter which simultaneously optimizes multiple properties, one of which is affinity for the primary biological target. Further, many of these properties are difficult to predict ahead of expensive and time-consuming compound synthesis and experimental testing. Here we highlight recent work to develop compound affinity prediction models, and extensively investigate the value such models may provide to preclinical drug discovery. We demonstrate that the ability of these models to improve the overall probability of success is crucially dependent on the shape of the error distribution, not just the root-mean-square error. In particular, while scoring more molecule ideas generally improves the probability of project success when the error distribution is Gaussian, fat-tail distributions such as a Cauchy distribution, can lead to a situation where scoring more ideas actually decreases the overall probability of success.

Advances in diarylpyrimidines and related analogues as HIV-1 nonnucleoside reverse transcriptase inhibitors

HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs) have been playing an important role in the fight against acquired immunodeficiency syndrome (AIDS). Diarylpyrimidines (DAPYs) as the second generation NNRTIs, represented by etravirine (TMC125) and rilpivirine (TMC278), have attracted extensive attention due to their extraordinary potency, high specificity and low toxicity. However, the rapid emergence of drug-resistant virus strains and dissatisfactory pharmacokinetics of DAPYs present new challenges. In the past two decades, an increasing number of novel DAPY derivatives have emerged, which significantly enriched the structure-activity relationship of DAPYs. Studies of crystallography and molecular modeling have afforded a lot of useful information on structural requirements of NNRTIs, which contributes greatly to the improvement of their resistance profiles. In this review, we reviewed the discovery history and their evolution of DAPYs including their structural modification, derivatization and scaffold hopping in continuous pursuit of excellent anti-HIV drugs. And also, we discussed the prospect of DAPYs and the directions of future efforts.

Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics

In this study, we demonstrate the extensive scalability of the biasing potential replica exchange multisite λ dynamics (BP-REX MSλD) free energy method by calculating binding affinities for 512 inhibitors to HIV Reverse Transcriptase (HIV-RT). This is the largest exploration of chemical space using free energy methods known to date, requires only a few simulations, and identifies 55 new inhibitor designs against HIV-RT predicted to be at least as potent as a tight binding reference compound (i.e., as potent as 56 nM). We highlight that BP-REX MSλD requires an order of magnitude less computational resources than conventional free energy methods while maintaining a similar level of precision, overcomes the inherent poor scalability of conventional free energy methods, and enables the exploration of combinatorially large chemical spaces in the context of in silico drug discovery.

Optimization of Pyrazoles as Phenol Surrogates to Yield Potent Inhibitors of Macrophage Migration Inhibitory Factor

Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that is implicated in the regulation of inflammation, cell proliferation, and neurological disorders. MIF is also an enzyme that functions as a keto-enol tautomerase. Most potent MIF tautomerase inhibitors incorporate a phenol, which hydrogen bonds to Asn97 in the active site. Starting from a 113-μm docking hit, we report results of structure-based and computer-aided design that have provided substituted pyrazoles as phenol alternatives with potencies of 60-70 nm. Crystal structures of complexes of MIF with the pyrazoles highlight the contributions of hydrogen bonding with Lys32 and Asn97, and aryl-aryl interactions with Tyr36, Tyr95, and Phe113 to the binding.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and [Formula: see text]-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 [Formula: see text]. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 [Formula: see text] for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For [Formula: see text]-dynamics techniques, including multisite [Formula: see text]-dynamics (MS[Formula: see text]D), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their associated benefits, and proposed procedures for their use in future applications are discussed.

Systematic Study of Effects of Structural Modifications on the Aqueous Solubility of Drug-like Molecules

Aqueous solubilities and activities have been measured for 17 members of the quinolinyltriazole series of inhibitors of human macrophage migration inhibitory factor (MIF). Systematic variation of a solvent-exposed substituent provided increases in solubility from 2 μg/mL for the parent compound 3a up to 867 μg/mL. The low solubility of 3a results from its near-planar structure and an intermolecular hydrogen bond, as revealed in a small-molecule X-ray structure. Removal of the hydrogen bond yields a 3-fold increase in solubility, but a 7-fold drop in activity. 5b emerges as the most potent MIF inhibitor with a K_i of 14 nM and good solubility, 47 μg/mL, while 4e has both high potency and solubility.

Design, Conformation, and Crystallography of 2-Naphthyl Phenyl Ethers as Potent Anti-HIV Agents

Catechol diethers that incorporate a 7-cyano-2-naphthyl substituent are reported as non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs). Many of the compounds have 1-10 nM potencies toward wild-type HIV-1. An interesting conformational effect allows two unique conformers for the naphthyl group in complexes with HIV-RT. X-ray crystal structures for 4a and 4f illustrate the alternatives.