Predictive Models for the Free Energy of Hydrogen Bonded Complexes with Single and Cooperative Hydrogen Bonds

© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimIn this work, we report QSPR modeling of the free energy ΔG of 1 : 1 hydrogen bond complexes of different H-bond acceptors and donors. The modeling was performed on a large and structurally diverse set of 3373 complexes featuring a single hydrogen bond, for which ΔG was measured at 298 K in CCl4. The models were prepared using Support Vector Machine and Multiple Linear Regression, with ISIDA fragment descriptors. The marked atoms strategy was applied at fragmentation stage, in order to capture the location of H-bond donor and acceptor centers. Different strategies of model validation have been suggested, including the targeted omission of individual H-bond acceptors and donors from the training set, in order to check whether the predictive ability of the model is not limited to the interpolation of H-bond strength between two already encountered partners. Successfully cross-validating individual models were combined into a consensus model, and challenged to predict external test sets of 629 and 12 complexes, in which donor and acceptor formed single and cooperative H-bonds, respectively. In all cases, SVM models outperform MLR. The SVM consensus model performs well both in 3-fold cross-validation (RMSE=1.50 kJ/mol), and on the external test sets containing complexes with single (RMSE=3.20 kJ/mol) and cooperative H-bonds (RMSE=1.63 kJ/mol)

Expert system for predicting reaction conditions: The Michael reaction case

© 2015 American Chemical Society. A generic chemical transformation may often be achieved under various synthetic conditions. However, for any specific reagents, only one or a few among the reported synthetic protocols may be successful. For example, Michael β-addition reactions may proceed under different choices of solvent (e.g., hydrophobic, aprotic polar, protic) and catalyst (e.g., Brønsted acid, Lewis acid, Lewis base, etc.). Chemoinformatics methods could be efficiently used to establish a relationship between the reagent structures and the required reaction conditions, which would allow synthetic chemists to waste less time and resources in trying out various protocols in search for the appropriate one. In order to address this problem, a number of 2-classes classification models have been built on a set of 198 Michael reactions retrieved from literature. Trained models discriminate between processes that are compatible and respectively processes not feasible under a specific reaction condition option (feasible or not with a Lewis acid catalyst, feasible or not in hydrophobic solvent, etc.). Eight distinct models were built to decide the compatibility of a Michael addition process with each considered reaction condition option, while a ninth model was aimed to predict whether the assumed Michael addition is feasible at all. Different machine-learning methods (Support Vector Machine, Naive Bayes, and Random Forest) in combination with different types of descriptors (ISIDA fragments issued from Condensed Graphs of Reactions, MOLMAP, Electronic Effect Descriptors, and Chemistry Development Kit computed descriptors) have been used. Models have good predictive performance in 3-fold cross-validation done three times: balanced accuracy varies from 0.7 to 1. Developed models are available for the users at http://infochim.u-strasbg.fr/webserv/VSEngine.html. Eventually, these were challenged to predict feasibility conditions for ∼50 novel Michael reactions from the eNovalys database (originally from patent literature)

Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules

© 2016 Wiley-VCH Verlag GmbH & Co. KGaA.Halogen bonding (XB) strength assesses the ability of an electron-enriched group to be involved in complexes with polarizable electrophilic halogenated or diatomic halogen molecules. Here, we report QSPR models of XB of particular relevance for an efficient screening of large sets of compounds. The basicity is described by pKBI2, the decimal logarithm of the experimental 1 : 1 (B :I2) complexation constant K of organic compounds (B) with diiodine (I2) as a reference halogen-bond donor in alkanes at 298K. Modeling involved ISIDA fragment descriptors, using SVM and MLR methods on a set of 598 organic compounds. Developed models were then challenged to make predictions for an external test set of 11 polyfunctional compounds for which unambiguous assignment of the measured effective complexation constant to specific groups out of the putative acceptor sites is not granted. At this stage, developed models were used to predict pKBI2 of all putative acceptor sites, followed by an estimation of the predicted effective complexation constant using the ChemEqui program. The best consensus models perform well both in cross-validation (root mean squared error RMSE=0.39-0.47logKBI2 units) and external predictions (RMSE=0.49). The SVM models are implemented on our website (http://infochim.u-strasbg.fr/webserv/VSEngine.html) together with the estimation of their applicability domain and an automatic detection of potential halogen-bond acceptor atoms

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu

Virtual screening, synthesis and biological evaluation of DNA intercalating antiviral agents

© 2017 Elsevier Ltd This paper describes computer-aided design of new anti-viral agents against Vaccinia virus (VACV) potentially acting as nucleic acid intercalators. Earlier obtained experimental data for DNA intercalation affinities and activities against Vesicular stomatitis virus (VSV) have been used to build, respectively, pharmacophore and QSAR models. These models were used for virtual screening of a database of 245 molecules generated around typical scaffolds of known DNA intercalators. This resulted in 12 hits which then were synthesized and tested for antiviral activity against VaV together with 43 compounds earlier studied against VSV. Two compounds displaying high antiviral activity against VaV and low cytotoxicity were selected for further antiviral activity investigations

An investigation of breast cancer risk factors in Cyprus: a case control study

Background: Breast cancer is the most common form of malignancy affecting women worldwide. It is also the leading cancer in females in Cyprus, with approximately 400 new cases diagnosed annually. It is well recognized that genetic variation as well as environmental factors modulate breast cancer risk. The main aim of this study was to assess the strength of associations between recognized risk factors and breast cancer among Cypriot women. This is the first epidemiological investigation on risk factors of breast cancer among the Cypriot female population.Methods: We carried out a case-control study, involving 1,109 breast cancer patients and a group of 1,177 controls who were recruited while participating in the National screening programme for breast cancer. Information on demographic characteristics and potential risk factors were collected from both groups during a standardized interview. Logistic regression analysis was used to assess the strength of the association between each risk factor and breast cancer risk, before and after adjusting for the possible confounding effect of other factors.Results: In multivariable models, family history of breast cancer (OR 1.64, 95% CI 1.23, 2.19) was the strongest predictor of breast cancer risk in the Cypriot population. Late menarche (OR 0.64, 95% CI 0.45, 0.92 among women reaching menarche after the age of 15 vs. before the age of 12) and breastfeeding (OR 0.74, 95% CI 0.59, 0.92) exhibited a strong protective effect. In the case of breastfeeding, the observed effect appeared stronger than the effect of pregnancy alone. Surprisingly, we also observed an inverse association between hormone replacement therapy (HRT) although this may be a product of the retrospective nature of this study.Conclusion: Overall the findings of our study corroborate with the results of previous investigations on descriptive epidemiology of risk factors for breast cancer. This investigation provides important background information for designing detailed studies that aim to improve our understanding of the epidemiology of breast cancer in the Cypriot population, including the study of gene-environment interactions. Furthermore, our study provides the first scientific evidence for formulating targeted campaigns for prevention and early diagnosis of breast cancer in Cyprus

Not saying, not doing: Convergences, contingencies and causal mechanisms of state reform and decentralisation in Hollande’s France

Are States in contemporary Europe subject to new forms of convergence under the impact of economic crisis, enhanced European steering and international monitoring? Or is the evolution of governance (national and sub-national) driven fundamentally by diverging, mainly domestic pressures? Drawing on extensive new data, the article combines analysis of the State Modernisation and Decentralisation reform programmes of the Hollande–Ayrault administration, drawing comparisons where appropriate with the previous Sarkozy regime. The limits of President Hollande’s anti-Sarkozy method were demonstrated in the first 2 years; framing state reform and decentralisation in negative terms prevented the emergence of a coherent legitimising discourse. The empirical data is interpreted with reference to a comparative ‘States of Convergence’ framework, which is conceptualised as a heuristic device for analysing variation between places, countries and policy fields. The article concludes that the forces of hard convergence are gaining ground, as economic, epistemic and European pressures continually challenge the forces of institutional inertia

Systematic Deletion of Homeobox Genes in Podospora anserina Uncovers Their Roles in Shaping the Fruiting Body

Higher fungi, which comprise ascomycetes and basidiomycetes, play major roles in the biosphere. Their evolutionary success may be due to the extended dikaryotic stage of their life cycle, which is the basis for their scientific name: the Dikarya. Dikaryosis is maintained by similar structures, the clamp in basidiomycetes and the crozier in ascomycetes. Homeodomain transcription factors are required for clamp formation in all basidiomycetes studied. We identified all the homeobox genes in the filamentous ascomycete fungus Podospora anserina and constructed deletion mutants for each of these genes and for a number of gene combinations. Croziers developed normally in these mutants, including those with up to six deleted homeogenes. However, some mutants had defects in maturation of the fruiting body, an effect that could be rescued by providing wild-type maternal hyphae. Analysis of mutants deficient in multiple homeogenes revealed interactions between the genes, suggesting that they operate as a complex network. Similar to their role in animals and plants, homeodomain transcription factors in ascomycetes are involved in shaping multicellular structures

Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

This is the original submitted version, before peer review. The final peer-reviewed version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/ci500721x.Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis. A specific way to include this information is using protein-ligand interaction fingerprints (IFPs). In this paper we report the activity landscape modeling of 507 ligand-kinase complexes (from the KLIFS database) including IFP, which facilitates the analysis and interpretation of activity cliffs. Here we introduce the structure-activity-interaction similarity (SAIS) maps that incorporate information on ligand-target contact similarity. We also introduce the concept of interaction cliffs defined as ligand-target complexes with high structural and interaction similarity but have a large potency difference of the ligands. Moreover, the information retrieved regarding the specific interaction allowed the identification of activity cliff hot spots, which help to rationalize activity cliffs from the target point of view. In general, the information provided by IFPs provides a structure-based understanding of some activity landscape features. This paper shows examples of analyses that can be carried out when IFPs are added to the activity landscape model.M-L is very grateful to CONACyT (No. 217442/312933) and the Cambridge Overseas Trust for funding. AB thanks Unilever for funding and the European Research Council for a Starting Grant (ERC-2013- StG-336159 MIXTURE). J.L.M-F. is grateful to the School of Chemistry, Department of Pharmacy of the National Autonomous University of Mexico (UNAM) for support. This work was supported by a scholarship from the Secretariat of Public Education and the Mexican government