A QSAR study on influenza neuraminidase inhibitors

Exploring flexibility, intermolecular interactions and ADMET profiles of anti-influenza agent isorhapontigenin: A quantum chemical and molecular docking study

Isorhapontigenin (IRPG) drug emerges as promising efficient inhibitor for H1N1 and H3N2 subtypes which belong to influenza A virus; reported with IC₅₀ value of 35.62 and 63.50 μM respectively. When experimental data are compared to the predicted geometrical parameters and vibrational assignments (FT-IR and FT-Raman), the findings indicated a strong correlation. The absorption bands of π→π∗ transitions are revealed through UV-Vis electronic properties; this confirms that the IRPG molecule shows strong bands. Through NBO and HOMO-LUMO analysis, the kinetic stability and chemical reactivity of the IRPG molecule were investigated. By using an MEP map, the IRPG's electrophilic and nucleophilic site selectivity was assessed. In a molecular docking investigation, the IRPG molecule shows a stronger inhibition constant and binding affinity for the H1N1 and H3N2 influenza virus. The IRPG molecule thus reveals good biological actions in nature and can be used as a potential therapeutic drug candidate for H1N1 and H3N2 virus A influenza.

Machine Learning for the Prediction of Antiviral Compounds Targeting Avian Influenza A/H9N2 Viral Proteins

Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model’s quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings.

TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features

BACKGROUND

The increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.

RESULTS

Using the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.

CONCLUSION

By representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment.

Combining crystallographic information and an aspherical-atom data bank in the evaluation of the electrostatic interaction energy in an enzyme-substrate complex: influenza neuraminidase inhibition

Although electrostatic interactions contribute only a part of the interaction energies between macromolecules, unlike dispersion forces they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In the reported study, a wide range of complexes of influenza neuraminidases with inhibitor molecules (sialic acid derivatives and others) have been analyzed using charge densities from a transferable aspherical-atom data bank. The strongest interactions of the residues are with the acidic group at the C2 position of the inhibitor ( approximately -300 kJ mol(-1) for -COO(-) in non-aromatic inhibitors, approximately -120-210 kJ mol(-1) for -COO(-) in aromatic inhibitors and approximately -450 kJ mol(-1) for -PO(3)(2-)) and with the amino and guanidine groups at C4 ( approximately -250 kJ mol(-1)). Other groups contribute less than approximately 100 kJ mol(-1). Residues Glu119, Asp151, Glu227, Glu276 and Arg371 show the largest variation in electrostatic energies of interaction with different groups of inhibitors, which points to their important role in the inhibitor recognition. The Arg292-->Lys mutation reduces the electrostatic interactions of the enzyme with the acidic group at C2 for all inhibitors that have been studied (SIA, DAN, 4AM, ZMR, G20, G28, G39 and BCZ), but enhances the interactions with the glycerol group at C6 for inhibitors that contain it. This is in agreement with the lower level of resistance of the mutated virus to glycerol-containing inhibitors compared with the more hydrophobic derivatives.

Quantitative structure activity relationship studies on thiourea analogues as influenza virus neuraminidase inhibitors

Influenza virus is a major global threat that impacts the world in one form or another as flu infections. Neuraminidase, one of the targets for these viruses, has recently been exploited in the treatment of these infections. Quantitative structure activity relationship studies were performed on thiourea analogues using spatial, topological, electronic, thermodynamic and E-state indices. Genetic algorithm based genetic function approximation method of variable selection was used to generate the model. Highly statistically significant model was obtained when number of descriptors in the equation was set to 5. The atom type log P and shadow indices descriptors showed enormous contributions to neuraminidase inhibition. The validation of the model was done by cross validation, randomization and external test set prediction. The model gives insight on structural requirements for designing more potent analogues against influenza virus neuraminidase.

Synthesis and anti-influenza activities of carboxyl alkoxyalkyl esters of 4-guanidino-Neu5Ac2en (zanamivir)

Three alkoxyalkyl 2-carboxylate ester derivatives related to zanamivir were synthesized. All of the analogs of zanamivir modified at carboxylic moiety with alkoxyalkyl esters 1a-c showed higher activities than ribavirin on influenza A and B virus in the MDCK cells. Oral treatment or intraperitoneal administration of compound 1c showed significantly protective effects in mice infected with influenza A virus with low toxicities.

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.

Matrix metalloproteinases (MMPs): chemical-biological functions and (Q)SARs

Matrix metalloproteinases (MMPs) are a large family of calcium-dependent zinc-containing endopeptidases, which are responsible for the tissue remodeling and degradation of the extracellular matrix (ECM), including collagens, elastins, gelatin, matrix glycoproteins, and proteoglycan. They are regulated by hormones, growth factors, and cytokines, and are involved in ovarian functions. MMPs are excreted by a variety of connective tissue and pro-inflammatory cells including fibroblasts, osteoblasts, endothelial cells, macrophages, neutrophils, and lymphocytes. These enzymes are expressed as zymogens, which are subsequently processed by other proteolytic enzymes (such as serine proteases, furin, plasmin, and others) to generate the active forms. Matrix metalloproteinases are considered as promising targets for the treatment of cancer due to their strong involvement in malignant pathologies. Clinical/preclinical studies on MMP inhibition in tumor models brought positive results raising the idea that the development of strategies to inhibit MMPs may be proved to be a powerful tool to fight against cancer. However, the presence of an inherent flexibility in the MMP active-site limits dramatically the accurate modeling of MMP-inhibitor complexes. The interest in the application of quantitative structure-activity relationships (QSARs) has steadily increased in recent decades and we hope it may be useful in elucidating the mechanisms of chemical-biological interactions for this enzyme. In the present review, an attempt has been made to explore the in-depth knowledge from the classification of this enzyme to the clinical trials of their inhibitors. A total number of 92 QSAR models (44 published and 48 new formulated QSAR models) have also been presented to understand the chemical-biological interactions. QSAR results on the inhibition of various compound series against MMP-1, -2, -3, -7, -8, -9, -12, -13, and -14 reveal a number of interesting points. The most important of these are hydrophobicity and molar refractivity, which are the most important determinants of the activity.

QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

The recent wide spreading of the H5N1 avian influenza virus (AIV) in Asia, Europe and Africa and its ability to cause fatal infections in human has raised serious concerns about a pending global flu pandemic. Neuraminidase (NA) inhibitors are currently the only option for treatment or prophylaxis in humans infected with this strain. However, drugs currently on the market often meet with rapidly emerging resistant mutants and only have limited application as inadequate supply of synthetic material. To dig out helpful information for designing potent inhibitors with novel structures against the NA, we used automated docking, CoMFA, CoMSIA, and HQSAR methods to investigate the quantitative structure-activity relationship for 126 NA inhibitors (NIs) with great structural diversities and wide range of bioactivities against influenza A virus. Based on the binding conformations discovered via molecular docking into the crystal structure of NA, CoMFA and CoMSIA models were successfully built with the cross-validated q (2) of 0.813 and 0.771, respectively. HQSAR was also carried out as a complementary study in that HQSAR technique does not require 3D information of these compounds and could provide a detailed molecular fragment contribution to the inhibitory activity. These models also show clearly how steric, electrostatic, hydrophobicity, and individual fragments affect the potency of NA inhibitors. In addition, CoMFA and CoMSIA field distributions are found to be in well agreement with the structural characteristics of the corresponding binding sites. Therefore, the final 3D-QSAR models and the information of the inhibitor-enzyme interaction should be useful in developing novel potent NA inhibitors.