Recent advances in computer-aided drug design as applied to anti-influenza drug discovery

Unveiling MurE ligase potential inhibitors for treating multi-drug resistant Acinetobacter baumannii

Acinetobacter baumannii is an opportunistic pathogen with ability to cause serious infection such as bacteremia, ventilator associated pneumonia, and wound infections. As strains of A. baumannii are resistant to almost all clinically used antibiotics and with the emergence of carbapenems resistant phenotypes warrants the search for novel antibiotics. Considering this, herein, a series of computer aided drug designing approach was utilized to search novel chemical scaffolds that bind stronger to MurE ligase enzyme of A. baumannii, which is involved peptidoglycan synthesis. The work identified LAS_22461675, LAS_34000090 and LAS_51177972 compounds as promising binding molecules with MurE enzyme having binding energy score of -10.5 kcal/mol, -9.3 kcal/mol and -8.6 kcal/mol, respectively. The compounds were found to achieve docked inside the MurE substrate binding pocket and established close distance chemical interactions. The interaction energies were dominated by van der Waals and less contributions were seen from hydrogen bonding energy. The dynamic simulation assay predicted the complexes stable with no major global and local changes noticed. The docked stability was also validated by MM/PBSA and MM/GBSA binding free energy methods. The net MM/GBSA binding free energy of LAS_22461675 complex, LAS_34000090 complex and LAS_51177972 complex is -26.25 kcal/mol, -27.23 kcal/mol and -29.64 kcal/mol, respectively. Similarly in case of MM-PBSA, the net energy value was in following order; LAS_22461675 complex (-27.67 kcal/mol), LAS_34000090 complex (-29.94 kcal/mol) and LAS_51177972 complex (-27.32 kcal/mol). The AMBER entropy and WaterSwap methods also confirmed stable complexes formation. Further, molecular features of the compounds were determined that predicted compounds to have good druglike properties and pharmacokinetic favorable. The study concluded the compounds to good candidates to be tested by in vivo and in vitro experimental assays.Communicated by Ramaswamy H. Sarma.

Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician

BACKGROUND

Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection.

OBJECTIVES

We summarise recent and potential future applications of AI and its relevance to clinical infection practice.

METHODS

1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions.

RESULTS

There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias.

CONCLUSIONS

Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.

Identification and dynamics of novel scaffolds against Enterococcus faecium serine hydroxymethyltransferase enzyme: a potential target for antibiotics development

Serine hydroxymethyltransferase enzyme is a significant player in purine, thymidylate, and L-serine biosynthesis and has been tagged as a potential target for cancer, viruses, and parasites. However, this enzyme as an anti-bacterial druggable target has not been explored much. Herein, in this work, different computational chemistry and biophysics techniques were applied to identify potential computational predicted inhibitory molecules against Enterococcus faecium serine hydroxymethyltransferase enzyme. By structure based virtual screening process of ASINEX antibacterial library against the enzyme two main compounds: Top-1_BDC_21204033 and Top-2_BDC_20700155 were reported as best binding molecules. The Top-1_BDC_21204033 and Top-2_BDC_20700155 binding energy value is -9.3 and -8.9 kcal/mol, respectively. The control molecule binding energy score is -6.55 kcal/mol. The mean RMSD of Top-1-BDC_21204033, Top-2-BDC_20700155 and control is 3.7 Å (maximum 5.03 Å), 1.7 Å (maximum 3.05 Å), and 3.84 Å (maximum of 6.7 Å), respectively. During the simulation time, the intermolecular docked conformation and interactions were seen stable despite of few small jumps by the compounds/control, responsible for high RMSD in some frames. The MM/GBSA and MM/PBSA binding free energy of lead Top-2-BDC_20700155 complex is -79.52 and -82.63 kcal/mol, respectively. This complex was seen as the most stable compared to the control. Furthermore, the lead molecules and control showed good druglikeness and pharmacokinetics profile. The lead molecules were non-toxic and non-mutagenic. In short, the compounds are promising in terms of binding to the serine hydroxymethyltransferase enzyme and need to be subjected to experimental studies.Communicated by Ramaswamy H. Sarma.

A cheminformatics-biophysics correlate to identify promising lead molecules against matrix metalloproteinase-2 (MMP-2) enzyme: A promising anti-cancer target

Matrix metalloproteinase-2 (MMP-2) is an endopeptidase enzyme that is devoted to extracellular matrix proteins degradation. The enzyme is warranted as promising drugs target for different light threating diseases such as arthritis, cancer and fibrosis. Herein, in this study, three drug molecules: CMNPD8322, CMNPD8320, and CMNPD8318 were filtered as high affinity binding compounds with binding energy score of -9.75 kcal/mol, -9.11 kcal/mol, -9.05 kcal/mol, respectively. The control binding energy score was -9.01 kcal/mol. The compounds docked deeply inside the pocket interacting with S1 pocket residues. The docked complexes dynamics in real time at cellular environment was then done to decipher the stable binding conformation and intermolecular interactions network. The compounds complexes achieved very stable dynamics with root mean square deviation (RMSD) with mean value of around 2-3 Å compared to control complex that showed higher fluctuations of 5 Å. The simulation trajectories frames based binding free energy demonstrated all the compounds-MMP-2 complexes reported highly stable energy, particularly the van der Waals energy dominate the overall net energy. Similarly, the complexes revalidation of WaterSwap based energies also disclosed the complexes highly stable in term docked conformation. Also, the compounds illustrated the compounds favorable pharmacokinetics and were non-toxic and non-mutagenic. Thus, the compounds might be used thorough experimental assays to confirm compounds selective biological potency against MMP-2 enzyme.

Open access databases available for the pesticide lead discovery

Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.

Role of Probiotics in the Management of COVID-19: A Computational Perspective

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.

Epicatechin is a promising novel inhibitor of SARS-CoV-2 entry by disrupting interactions between angiotensin-converting enzyme type 2 and the viral receptor binding domain: A computational/simulation study

Angiotensin-converting enzyme 2 (ACE2) is the first target of SARS-CoV-2 and a key functional host receptor through which this virus hooks into and infects human cells. The necessity to block this receptor is one of the essential means to prevent the outbreak of COVID-19. This study was conducted to determine the most eligible natural compound to suppress ACE2 to counterfeit its interaction with the viral infection. To do this, the most known compounds of sixty-six Iraqi medicinal plants were generated and retrieved from PubChem database. After preparing a library for Iraqi medicinal plants, 3663 unique ligands’ conformers were docked to ACE2 using the GLIDE tool. Results found that twenty-three compounds exhibited the highest binding affinity with ACE2. The druglikeness and toxicity potentials of these compounds were evaluated using SwissADME and Protox servers respectively. Out of these virtually screened twenty-three compounds, epicatechin and kempferol were predicted to exert the highest druglikeness and lowest toxicity potentials. Extended Molecular dynamics (MD) simulations showed that ACE2-epicatechin complex exhibited a slightly higher binding stability than ACE2-kempferol complex. In addition to the well-known ACE2 inhibitors that were identified in previous studies, this study revealed for the first time that epicatechin from Hypericum perforatum provided a better static and dynamic inhibition for ACE2 with highly favourable pharmacokinetic properties than the other known ACE2 inhibiting compounds. This study entailed the ability of epicatechin to be used as a potent natural inhibitor that can be used to block or at least weaken the SARS-CoV-2 entry and its subsequent invasion. In vitro experiments are required to validate epicatechin effectiveness against the activity of the human ACE2 receptor.

Structural Insights into Novel 15-Prostaglandin Dehydrogenase Inhibitors

We discovered SW033291 in a high throughput chemical screen aimed at identifying 15-prostaglandin dehydrogenase (15-PGDH) modulators. The compound exhibited inhibitory activity in in vitro biochemical and cell-based assays of 15-PGDH activity. We subsequently demonstrated that this compound, and several analogs thereof, are effective in in vivo mouse models of bone marrow transplant, colitis, and liver regeneration, where increased levels of PGE2 positively potentiate tissue regeneration. To better understand the binding of SW033291, we carried out docking studies for both the substrate, PGE2, and an inhibitor, SW033291, to 15-PGDH. Our models suggest similarities in the ways that PGE2 and SW033291 interact with key residues in the 15-PGDH-NAD+ complex. We carried out molecular dynamics simulations (MD) of SW033291 bound to this complex, in order to understand the dynamics of the binding interactions for this compound. The butyl side chain (including the sulfoxide) of SW033291 participates in crucial binding interactions that are similar to those observed for the C15-OH and the C16-C20 alkyl chain of PGE2. In addition, interactions with residues Ser138, Tyr151, and Gln148 play key roles in orienting and stabilizing SW033291 in the binding site and lead to enantioselectivity for the R-enantiomer. Finally, we compare the binding mode of (R)-S(O)-SW033291 with the binding interactions of published 15-PGDH inhibitors.

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed. To do this, the following studies were carried out: (i) an autoencoder architecture was constructed; (ii) a virtual compound library of potential anti-HIV-1 agents for training the neural network was formed by the concept of click chemistry allowing one to generate a large number of drug candidates by their assembly from small modular units; (iii) molecular docking of all compounds from this library with gp120 was made and calculations of the values of binding free energy were performed; (iv) molecular fingerprints of chemical compounds from the training dataset were generated; (v) training of the developed autoencoder was implemented followed by the validation of this neural network using more than 21 million molecules from the ZINC15 database. As a result, three small drug-like compounds that exhibited the high-affinity binding to gp120 were identified. According to the data from molecular docking, machine learning, quantum chemical calculations, and molecular dynamics simulations, these compounds show the low values of binding free energy in the complexes with gp120 similar to those calculated using the same computational protocols for the HIV-1 entry inhibitors NBD-11021 and NBD-14010, highly potent and broad anti-HIV-1 agents presenting a new generation of the viral CD4 antagonists. The identified CD4-mimetic candidates are suggested to present good scaffolds for the design of novel antiviral drugs inhibiting the early stages of HIV-1 infection.Communicated by Ramaswamy H. Sarma.

Unlocking COVID therapeutic targets: A structure-based rationale against SARS-CoV-2, SARS-CoV and MERS-CoV Spike

There are no approved target therapeutics against SARS-CoV-2 or other beta-CoVs. The beta-CoV Spike protein is a promising target considering the critical role in viral infection and pathogenesis and its surface exposed features. We performed a structure-based strategy targeting highly conserved druggable regions resulting from a comprehensive large-scale sequence analysis and structural characterization of Spike domains across SARSr- and MERSr-CoVs. We have disclosed 28 main consensus druggable pockets within the Spike. The RBD and SD1 (S1 subunit); and the CR, HR1 and CH (S2 subunit) represent the most promising conserved druggable regions. Additionally, we have identified 181 new potential hot spot residues for the hSARSr-CoVs and 72 new hot spot residues for the SARSr- and MERSr-CoVs, which have not been described before in the literature. These sites/residues exhibit advantageous structural features for targeted molecular and pharmacological modulation. This study establishes the Spike as a promising anti-CoV target using an approach with a potential higher resilience to resistance development and directed to a broad spectrum of Beta-CoVs, including the new SARS-CoV-2 responsible for COVID-19. This research also provides a structure-based rationale for the design and discovery of chemical inhibitors, antibodies or other therapeutic modalities successfully targeting the Beta-CoV Spike protein.

In Silico Identification of Novel Aromatic Compounds as Potential HIV-1 Entry Inhibitors Mimicking Cellular Receptor CD4

Despite recent progress in the development of novel potent HIV-1 entry/fusion inhibitors, there are currently no licensed antiviral drugs based on inhibiting the critical interactions of the HIV-1 envelope gp120 protein with cellular receptor CD4. In this connection, studies on the design of new small-molecule compounds able to block the gp120-CD4 binding are still of great value. In this work, in silico design of drug-like compounds containing the moieties that make the ligand active towards gp120 was performed within the concept of click chemistry. Complexes of the designed molecules bound to gp120 were then generated by molecular docking and optimized using semiempirical quantum chemical method PM7. Finally, the binding affinity analysis of these ligand/gp120 complexes was performed by molecular dynamic simulations and binding free energy calculations. As a result, five top-ranking compounds that mimic the key interactions of CD4 with gp120 and show the high binding affinity were identified as the most promising CD4-mimemic candidates. Taken together, the data obtained suggest that these compounds may serve as promising scaffolds for the development of novel, highly potent and broad anti-HIV-1 therapeutics.

Size and Flexibility Define the Inhibition of the H3N2 Influenza Endonuclease Enzyme by Calix[n]arenes

Inhibition of H3N2 influenza PA endonuclease activity by a panel of anionic calix[n]arenes and β-cyclodextrin sulfate has been studied. The joint experimental and theoretical results reveal that the larger, more flexible and highly water-soluble sulfonato-calix[n]arenes have high inhibitory activity, with para-sulfonato-calix[8]arene, SC8, having an IC₅₀ value of 6.4 μM. Molecular docking calculations show the SC8 can interact at both the polyanion binding site and also the catalytic site of H3N2 influenza PA endonuclease.

To hit or not to hit: Large-scale sequence analysis and structure characterization of influenza A NS1 unlocks new antiviral target potential

Influenza NS1 protein is among the most promising novel druggable anti-influenza target, based on its structure; multiple interactions; and global function in influenza replication and pathogenesis. Notwithstanding, drug development guidance based on NS1 structural biology is lacking. Here, we design a promising strategy directed to highly conserved druggable regions as a result of an exhaustive large-scale sequence analysis and structure characterization of NS1 protein across human-infecting influenza A subtypes, over the past 100 years. We have identified 3 druggable pockets and 8 new potential hot spot residues in the NS1 protein, not described before, additionally to other 16 sites previously identified, which represent attractive targets for pharmacological modulation. This study provides a rationale towards structure-function studies of NS1 druggable sites, which have the potential to accelerate the NS1 target validation. This research also contributes to a deeper comprehension and insight into the evolutionary dynamics of influenza A NS1 protein.

Docking with SwissDock

Protein-ligand docking simulation is central in drug design and development. Therefore, the development of web servers intended to docking simulations is of pivotal importance. SwissDock is a web server dedicated to carrying out protein-ligand docking simulation intuitively and elegantly. SwissDock is based on the protein-ligand docking program EADock DSS and has a simple and integrated interface. The SwissDock allows the user to upload structure files for a protein and a ligand, and returns the results by e-mail. To facilitate the upload of the protein and ligand files, we can prepare these input files using the program UCSF Chimera. In this chapter, we describe how to use UCSF Chimera and SwissDock to perform protein-ligand docking simulations. To illustrate the process, we describe the molecular docking of the competitive inhibitor roscovitine against the structure of human cyclin-dependent kinase 2.

Synergistic AML Cell Death Induction by Marine Cytotoxin (+)-1(R), 6(S), 1'(R), 6'(S), 11(R), 17(S)-Fistularin-3 and Bcl-2 Inhibitor Venetoclax

Treatment of acute myeloid leukemia (AML) patients is still hindered by resistance and relapse, resulting in an overall poor survival rate. Recently, combining specific B-cell lymphoma (Bcl)-2 inhibitors with compounds downregulating myeloid cell leukemia (Mcl)-1 has been proposed as a new effective strategy to eradicate resistant AML cells. We show here that 1(R), 6(S), 1’(R), 6’(S), 11(R), 17(S)-fistularin-3, a bromotyrosine compound of the fistularin family, isolated from the marine sponge Suberea clavata, synergizes with Bcl-2 inhibitor ABT-199 to efficiently kill Mcl-1/Bcl-2-positive AML cell lines, associated with Mcl-1 downregulation and endoplasmic reticulum stress induction. The absolute configuration of carbons 11 and 17 of the fistularin-3 stereoisomer was fully resolved in this study for the first time, showing that the fistularin we isolated from the marine sponge Subarea clavata is in fact the (+)-11(R), 17(S)-fistularin-3 stereoisomer keeping the known configuration 1(R), 6(S), 1’(R), and 6’(S) for the verongidoic acid part. Docking studies and in vitro assays confirm the potential of this family of molecules to inhibit DNA methyltransferase 1 activity.

Inhibition of sialidase activity as a therapeutic approach

The demand for novel anti-influenza drugs persists, which is highlighted by the recent pandemics of influenza affecting thousands of people across the globe. One of the approaches to block the virus spreading is inhibiting viral sialidase (neuraminidase). This enzyme cleaves the sialic acid link between the newly formed virions and the host cell surface liberating the virions from the cell and maintaining the cycle of infection. Viral neuraminidases appear therefore as attractive therapeutic targets for preventing further spread of influenza infection. Compared to ion channel blockers that were the first approved anti-influenza drugs, neuraminidase inhibitors are well tolerated and target both influenza A and B viruses. Moreover, neuraminidase/sialidase inhibitors may be useful for managing some other human pathologies, such as cancer. In this review, we discuss the available knowledge on neuraminidase or sialidase inhibitors, their design, clinical application, and the current challenges.

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.

Protein-Structure Assisted Optimization of 4,5-Dihydroxypyrimidine-6-Carboxamide Inhibitors of Influenza Virus Endonuclease

Influenza is a serious hazard to human health that causes hundreds of thousands of deaths annually. Though vaccines and current therapeutics can blunt some of the perilous impact of this viral infection, new treatments are needed due to the constantly evolving nature of this virus. Recently, our growing understanding of an essential influenza viral protein, PA, has led to the development of focused libraries of new small molecules that specifically target the active site of the PA influenza endonuclease, which we report here. Our overarching approach has been to proactively develop lead inhibitors that are less likely to rapidly develop clinical resistance by optimizing inhibitors that retain activity against induced resistant mutants. Here, we report details behind the discovery of new potent inhibitors of wild type and resistant mutant endonucleases along with their high-resolution co-crystal structure-activity relationships. These results add to our understanding of nuclease protein targets and potentially serve as starting points for a new therapeutic approach to the treatment of influenza.

Design, Synthesis, and in vitro and in vivo Evaluations of (Z)-3,4,5-Trimethoxystyrylbenzenesulfonamides/sulfonates as Highly Potent Tubulin Polymerization Inhibitors

Newer therapeutics can be developed in drug discovery by adopting the strategy of scaffold hopping of the privileged scaffolds from known bioactive compounds. This strategy has been widely employed in drug-discovery processes. Structure-based docking studies illustrate the basic underlying concepts and reveal that interactions of the sulfonamide group and hydrophobic interactions are crucial. On the basis of this strategy, over 60 synthetic analogues were synthesized and evaluated for their cytotoxicity against the NCI panel of 60 human cancer cell lines; the majority of these compounds exhibited promising cytotoxicity with GI₅₀ values ranging between 18 and 50 nm. Among these compounds, (Z)-N-[2,3-dimethoxy-5-(3,4,5-trimethoxystyryl)phenyl]-4-methoxybenzenesulfonamide (7 a) and (Z)-N-[2-hydroxy-3-methoxy-6-(3,4,5-trimethoxystyryl)phenyl]-4-methoxybenzenesulfonamide (9 a) were found to be potent. Similar results were obtained against three human cancer cell lines with IC₅₀ values ranging between 0.04 and 3.0 μm. Studies aimed at elucidating the mechanism of action of these new analogues revealed that they inhibited the in vitro polymerization of tubulin and disorganized the assembly of microtubules in HeLa and MCF-7cancer cells. Lead compounds 7 a and 9 a displayed notable in vivo antitumor activity in a HeLa tumor xenograft model. Our studies have resulted in the identification of a scaffold that can target tubulin polymerization, which should have significant potential toward the development of new antitumor drugs.

Methods for Docking and Drug Designing

Molecular docking is the prediction of conformational complementarity between ligand and receptor molecule. The process of docking integrates two schematic approaches namely sampling of ligand conformations and ranking of selected conformations based on scoring functions. The authors have discussed established methodologies for molecular docking and well-known tools implementing these methods. A brief account of different classes of scoring functions such as force field based, empirical, knowledge based, and descriptor based scoring functions is given along with the exemplary implementations of these scoring functions. By replacing test and trial based ligand screening with structure based virtual screening, molecular docking has helped in shortening the duration of novel drug discovery up to some extent. However, the developments made in the field of drug discovery are assisted by the advances in the techniques of molecular docking, but there is strong need of enrichment in the techniques, especially in scoring functions, to tackle the inbound problems of de novo drug discovery.

A Novel Endonuclease Inhibitor Exhibits Broad-Spectrum Anti-Influenza Virus Activity In Vitro

Antiviral drugs are important in preventing and controlling influenza, particularly when vaccines are ineffective or unavailable. A single class of antiviral drugs, the neuraminidase inhibitors (NAIs), is recommended for treating influenza. The limited therapeutic options and the potential risk of antiviral resistance are driving the search for additional small-molecule inhibitors that act on influenza virus proteins. The acid polymerase (PA) of influenza viruses is a promising target for new antivirals because of its essential role in initiating virus transcription. Here, we characterized a novel compound, RO-7, identified as a putative PA endonuclease inhibitor. RO-7 was effective when added before the cessation of genome replication, reduced polymerase activity in cell-free systems, and decreased relative amounts of viral mRNA and genomic RNA during influenza virus infection. RO-7 specifically inhibited the ability of the PA endonuclease domain to cleave a nucleic acid substrate. RO-7 also inhibited influenza A viruses (seasonal and 2009 pandemic H1N1 and seasonal H3N2) and B viruses (Yamagata and Victoria lineages), zoonotic viruses (H5N1, H7N9, and H9N2), and NAI-resistant variants in plaque reduction, yield reduction, and cell viability assays in Madin-Darby canine kidney (MDCK) cells with nanomolar to submicromolar 50% effective concentrations (EC50s), low toxicity, and favorable selective indices. RO-7 also inhibited influenza virus replication in primary normal human bronchial epithelial cells. Overall, RO-7 exhibits broad-spectrum activity against influenza A and B viruses in multiple in vitro assays, supporting its further characterization and development as a potential antiviral agent for treating influenza.

Exploring the Inhibitory Mechanism of Approved Selective Norepinephrine Reuptake Inhibitors and Reboxetine Enantiomers by Molecular Dynamics Study

Selective norepinephrine reuptake inhibitors (sNRIs) provide an effective class of approved antipsychotics, whose inhibitory mechanism could facilitate the discovery of privileged scaffolds with enhanced drug efficacy. However, the crystal structure of human norepinephrine transporter (hNET) has not been determined yet and the inhibitory mechanism of sNRIs remains elusive. In this work, multiple computational methods were integrated to explore the inhibitory mechanism of approved sNRIs (atomoxetine, maprotiline, reboxetine and viloxazine), and 3 lines of evidences were provided to verify the calculation results. Consequently, a binding mode defined by interactions between three chemical moieties in sNRIs and eleven residues in hNET was identified as shared by approved sNRIs. In the meantime, binding modes of reboxetine's enantiomers with hNET were compared. 6 key residues favoring the binding of (S, S)-reboxetine over that of (R, R)-reboxetine were discovered. This is the first study reporting that those 11 residues are the common determinants for the binding of approved sNRIs. The identified binding mode shed light on the inhibitory mechanism of approved sNRIs, which could help identify novel scaffolds with improved drug efficacy.

Per-residue energy decomposition pharmacophore model to enhance virtual screening in drug discovery: a study for identification of reverse transcriptase inhibitors as potential anti-HIV agents

A novel virtual screening approach is implemented herein, which is a further improvement of our previously published "target-bound pharmacophore modeling approach". The generated pharmacophore library is based only on highly contributing amino acid residues, instead of arbitrary pharmacophores, which are most commonly used in the conventional approaches in literature. Highly contributing amino acid residues were distinguished based on free binding energy contributions obtained from calculation from molecular dynamic (MD) simulations. To the best of our knowledge; this is the first attempt in the literature using such an approach; previous approaches have relied on the docking score to generate energy-based pharmacophore models. However, docking scores are reportedly unreliable. Thus, we present a model for a per-residue energy decomposition, constructed from MD simulation ensembles generating a more trustworthy pharmacophore model, which can be applied in drug discovery workflow. This work is aimed at introducing a more rational approach to the field of drug design, rather than comparing the validity of this approach against those previously reported. We recommend additional computational and experimental work to further validate this approach. This approach was used to screen for potential reverse transcriptase inhibitors using the pharmacophoric features of compound GSK952. The complex was subjected to docking, thereafter, MD simulation confirmed the stability of the system. Experimentally determined inhibitors with known HIV-reverse transcriptase inhibitory activity were used to validate the protocol. Two potential hits (ZINC46849657 and ZINC54359621) showed a significant potential with regard to free binding energy. Reported results obtained from this work confirm that this new approach is favorable in the future of the drug design industry.

Theory and applications of covalent docking in drug discovery: merits and pitfalls

The present art of drug discovery and design of new drugs is based on suicidal irreversible inhibitors. Covalent inhibition is the strategy that is used to achieve irreversible inhibition. Irreversible inhibitors interact with their targets in a time-dependent fashion, and the reaction proceeds to completion rather than to equilibrium. Covalent inhibitors possessed some significant advantages over non-covalent inhibitors such as covalent warheads can target rare, non-conserved residue of a particular target protein and thus led to development of highly selective inhibitors, covalent inhibitors can be effective in targeting proteins with shallow binding cleavage which will led to development of novel inhibitors with increased potency than non-covalent inhibitors. Several computational approaches have been developed to simulate covalent interactions; however, this is still a challenging area to explore. Covalent molecular docking has been recently implemented in the computer-aided drug design workflows to describe covalent interactions between inhibitors and biological targets. In this review we highlight: (i) covalent interactions in biomolecular systems; (ii) the mathematical framework of covalent molecular docking; (iii) implementation of covalent docking protocol in drug design workflows; (iv) applications covalent docking: case studies and (v) shortcomings and future perspectives of covalent docking. To the best of our knowledge; this review is the first account that highlights different aspects of covalent docking with its merits and pitfalls. We believe that the method and applications highlighted in this study will help future efforts towards the design of irreversible inhibitors.