High-quality and universal empirical atomic charges for chemoinformatics applications.

BackgroundPartial atomic charges describe the distribution of electron density in a molecule and therefore provide clues to the chemical behaviour of molecules. Recently, these charges have become popular in chemoinformatics, as they are informative descriptors that can be utilised in pharmacophore design, virtual screening, similarity searches etc. Especially conformationally-dependent charges perform very successfully. In particular, their fast and accurate calculation via the Electronegativity Equalization Method (EEM) seems very promising for chemoinformatics applications. Unfortunately, published EEM parameter sets include only parameters for basic atom types and they often miss parameters for halogens, phosphorus, sulphur, triple bonded carbon etc. Therefore their applicability for drug-like molecules is limited.ResultsWe have prepared six EEM parameter sets which enable the user to calculate EEM charges in a quality comparable to quantum mechanics (QM) charges based on the most common charge calculation schemes (i.e., MPA, NPA and AIM) and a robust QM approach (HF/6-311G, B3LYP/6-311G). The calculated EEM parameters exhibited very good quality on a training set ([Formula: see text]) and also on a test set ([Formula: see text]). They are applicable for at least 95 % of molecules in key drug databases (DrugBank, ChEMBL, Pubchem and ZINC) compared to less than 60 % of the molecules from these databases for which currently used EEM parameters are applicable.ConclusionsWe developed EEM parameters enabling the fast calculation of high-quality partial atomic charges for almost all drug-like molecules. In parallel, we provide a software solution for their easy computation (http://ncbr.muni.cz/eem_parameters). It enables the direct application of EEM in chemoinformatics

Simulasi Penambatan Molekuler Senyawa Kompleks Besi Terhadap Protein Hemofor sebagai Kandidat Fotosensitizer pada Terapi Fotodinamika

Resistensi antibiotika muncul sebagai polemik yang dapat mempengaruhi kesehatan manusia. Kemajuan teknologi membuka peluang dalam penemuan molekul senyawa baru yang mampu mencegah perkembangan mikroba patogen, seperti Pseudomonas aeruginosa yang resisten terhadap beberapa jenis antibiotika. Terapi fotodinamika dengan memanfaatkan penggunaan fotosensitizer yang berasal dari senyawa yang membentuk kompleks dengan besi merupakan salah satu pendekatan alternatif untuk mengatasi penyakit infeksi dengan risiko resistensi mikroba yang lebih rendah. Penelitian yang dilakukan secara in silico ini bertujuan untuk mengamati, mengeksplorasi, dan mengevaluasi mekanisme aksi berbasis struktural dari molekul senyawa yang membentuk kompleks dengan besi, yaitu besi-ftalosianina dan besi-salofen terhadap protein hemofor HasAp serta pengaruh molekularnya terhadap bagian situs aktif pengikatan dari protein hemofor HasR. Identifikasi interaksi molekuler dan afinitas antara molekul senyawa besi-ftalosianina dan besi-salofen terhadap protein hemofor HasAp dilakukan dengan simulasi ligan-protein docking mempergunakan software MGLTools 1.5.6 yang dilengkapi dengan AutoDock 4.2. Di samping itu, dilakukan juga simulasi protein-protein docking terhadap sistem kompleks ligan-protein untuk memastikan pengaruh molekularnya terhadap bagian situs aktif pengikatan dari protein hemofor HasR dengan mempergunakan software PatchDock. Berdasarkan simulasi ligan-protein docking diperoleh hasil bahwa senyawa besi-ftalosianina memiliki afinitas paling baik terhadap kedua protein hemofor HasAp, dengan nilai energi bebas pengikatan masing-masing sebesar −68,45 kJ/mol dan −65,23 kJ/mol. Menariknya, hasil simulasi protein-protein docking antara kompleks molekul senyawa besi-ftalosianina dan protein hemofor HasAp-besi-ftalosianina terhadap protein hemofor HasR memiliki nilai energi kontak atom yang positif sebesar 556,56 kJ/mol. Dari penelitian ini dapat diprediksikan bahwa perbedaan struktur molekul senyawa yang membentuk kompleks dengan besi mampu mempengaruhi mekanisme aksi berbasis structural terhadap protein hemofor target

Bisphosphonate inhibitors of squalene synthase protect cells against cholesterol‐dependent cytolysins

Certain species of pathogenic bacteria damage tissues by secreting cholesterol‐dependent cytolysins, which form pores in the plasma membranes of animal cells. However, reducing cholesterol protects cells against these cytolysins. As the first committed step of cholesterol biosynthesis is catalyzed by squalene synthase, we explored whether inhibiting this enzyme protected cells against cholesterol‐dependent cytolysins. We first synthesized 22 different nitrogen‐containing bisphosphonate molecules that were designed to inhibit squalene synthase. Squalene synthase inhibition was quantified using a cell‐free enzyme assay, and validated by computer modeling of bisphosphonate molecules binding to squalene synthase. The bisphosphonates were then screened for their ability to protect HeLa cells against the damage caused by the cholesterol‐dependent cytolysin, pyolysin. The most effective bisphosphonate reduced pyolysin‐induced leakage of lactate dehydrogenase into cell supernatants by >80%, and reduced pyolysin‐induced cytolysis from >75% to <25%. In addition, this bisphosphonate reduced pyolysin‐induced leakage of potassium from cells, limited changes in the cytoskeleton, prevented mitogen‐activated protein kinases cell stress responses, and reduced cellular cholesterol. The bisphosphonate also protected cells against another cholesterol‐dependent cytolysin, streptolysin O, and protected lung epithelial cells and primary dermal fibroblasts against cytolysis. Our findings imply that treatment with bisphosphonates that inhibit squalene synthase might help protect tissues against pathogenic bacteria that secrete cholesterol‐dependent cytolysins

In Vitro and In Silico Analyses of the Inhibition of Human Aldehyde Oxidase by Bazedoxifene, Lasofoxifene, and Structural Analogues

Aldehyde oxidase (AOX1) is a molybdo-flavoprotein and has emerged as a drug-metabolizing enzyme of potential therapeutic importance because drugs have been identified as AOX1 substrates. Selective oestrogen receptor modulators (SERM), which are drugs used to treat and prevent various conditions, differentially inhibit AOX1 catalytic activity. Tamoxifen, raloxifene, and nafoxidine are selective oestrogen receptor modulators (SERMs) reported to inhibit the catalytic activity of human aldehyde oxidase 1 (AOX1). How these drugs interact with AOX1 and whether other SERMs inhibit this drug-metabolizing enzyme are not known. Therefore, a detailed in vitro and in silico study involving parent drugs and their analogues was conducted to investigate the effect of specific SERMs, particularly acolbifene, bazedoxifene, and lasofoxifene on AOX1 catalytic activity, as assessed by carbazeran 4-oxidation, an AOX1-selective catalytic marker. The rank-order in the potency (based on IC50 values) of AOX1 inhibition by SERMs was raloxifene > bazedoxifene ~ lasofoxifene > tamoxifen > acolbifene. Inhibition of liver cytosolic AOX1 by bazedoxifene, lasofoxifene, and tamoxifen was competitive, whereas that by raloxifene was noncompetitive. Loss of 1-azepanylethyl group increased the inhibitory potency of bazedoxifene, whereas the N-oxide group decreased it. The 7-hydroxy group and the substituted pyrrolidine ring attached to the tetrahydronaphthalene structure contributed to AOX1 inhibition by lasofoxifene. These results are supported by molecular docking simulations in terms of predicted binding modes, encompassing binding orientation and efficiency, and analysis of key interactions, particularly hydrogen bonds. The extent of AOX1 inhibition by bazedoxifene was increased by estrone sulfate and estrone. In summary, SERMs differentially inhibited human AOX1 catalytic activity. Structural features of bazedoxifene and lasofoxifene contributed to AOX1 inhibition, whereas those of acolbifene rendered it considerably less susceptible to AOX1 inhibition. Overall, our novel biochemical findings and molecular docking analyses provide new insights into the interaction between SERMs and AOX1. Structural features of bazedoxifene and lasofoxifene contribute to AOX1 inhibition, whereas those of acolbifene render it considerably less susceptible to AOX1 inhibition. Our novel biochemical findings, together with molecular docking analyses, provide new insights into the differential inhibitory effect of SERMs on the catalytic activity of human AOX1, how SERMs bind to AOX1, and increase our understanding of the AOX1 pharmacophore in the inhibition of AOX1 by drugs and other chemicals

High-quality and universal empirical atomic charges for chemoinformatics applications.

BackgroundPartial atomic charges describe the distribution of electron density in a molecule and therefore provide clues to the chemical behaviour of molecules. Recently, these charges have become popular in chemoinformatics, as they are informative descriptors that can be utilised in pharmacophore design, virtual screening, similarity searches etc. Especially conformationally-dependent charges perform very successfully. In particular, their fast and accurate calculation via the Electronegativity Equalization Method (EEM) seems very promising for chemoinformatics applications. Unfortunately, published EEM parameter sets include only parameters for basic atom types and they often miss parameters for halogens, phosphorus, sulphur, triple bonded carbon etc. Therefore their applicability for drug-like molecules is limited.ResultsWe have prepared six EEM parameter sets which enable the user to calculate EEM charges in a quality comparable to quantum mechanics (QM) charges based on the most common charge calculation schemes (i.e., MPA, NPA and AIM) and a robust QM approach (HF/6-311G, B3LYP/6-311G). The calculated EEM parameters exhibited very good quality on a training set ([Formula: see text]) and also on a test set ([Formula: see text]). They are applicable for at least 95&nbsp;% of molecules in key drug databases (DrugBank, ChEMBL, Pubchem and ZINC) compared to less than 60&nbsp;% of the molecules from these databases for which currently used EEM parameters are applicable.ConclusionsWe developed EEM parameters enabling the fast calculation of high-quality partial atomic charges for almost all drug-like molecules. In parallel, we provide a software solution for their easy computation (http://ncbr.muni.cz/eem_parameters). It enables the direct application of EEM in chemoinformatics

Machine Learning Approaches for Improving Prediction Performance of Structure-Activity Relationship Models

In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to assess the activity and properties of small molecules. In silico methods such as Quantitative Structure-Activity/Property Relationship (QSAR) are used to correlate the structure of a molecule to its biological property in drug design and toxicological studies. In this body of work, I started with two in-depth reviews into the application of machine learning based approaches and feature reduction methods to QSAR, and then investigated solutions to three common challenges faced in machine learning based QSAR studies. First, to improve the prediction accuracy of learning from imbalanced data, Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms combined with bagging as an ensemble strategy was evaluated. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that this method significantly outperformed other conventional methods. SMOTEENN with bagging became less effective when IR exceeded a certain threshold (e.g., \u3e40). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p \u3c 0.001, ANOVA) by 22-27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Lastly, current features used for QSAR based machine learning are often very sparse and limited by the logic and mathematical processes used to compute them. Transformer embedding features (TEF) were developed as new continuous vector descriptors/features using the latent space embedding from a multi-head self-attention. The significance of TEF as new descriptors was evaluated by applying them to tasks such as predictive modeling, clustering, and similarity search. An accuracy of 84% on the Ames mutagenicity test indicates that these new features has a correlation to biological activity. Overall, the findings in this study can be applied to improve the performance of machine learning based Quantitative Structure-Activity/Property Relationship (QSAR) efforts for enhanced drug discovery and toxicology assessments

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Additional file 4: Table S2b. A list of molecules from the extended test set including their DrugBank IDs and summary formulas

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Additional file 2: EEM parameters. Values of EEM parameter sets for these six charge calculation approaches (i.e. B3LYP/6-311G/MPA,B3LYP/6-311G/NPA, B3LYP/6-311G/AIM, HF/6-311G/MPA, HF/6-311G/NPA, and HF/6-311G/AIM). These EEM parameter sets are in a format which can be used as an input file for EEM SOLVER

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Additional file 5: Table S3. RMSD and $${\overline{\Delta }}$$ Δ ¯ values of all tested EEM parameter sets on the test set