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      Exploring the Potentiality of Natural Products from Essential Oils as Inhibitors of Odorant-Binding Proteins: A Structure- and Ligand-Based Virtual Screening Approach To Find Novel Mosquito Repellents

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          Abstract

          Odorant-binding proteins (OBPs) are the main olfactory proteins of mosquitoes, and their structures have been widely explored to develop new repellents. In the present study, we combined ligand- and structure-based virtual screening approaches using as a starting point 1633 compounds from 71 botanical families obtained from the Essential Oil Database (EssOilDB). Using as reference the crystallographic structure of N, N-diethyl- meta-toluamide interacting with the OBP1 homodimer of Anopheles gambiae ( AgamOBP1), we performed a structural and pharmacophoric similarity search to select potential natural products from the library . Thymol acetate, 4-(4-methyl phenyl)-pentanal, thymyl isovalerate, and p-cymen-8-yl demonstrated a favorable chemical correlation with DEET and also had high-affinity interactions with the OBP binding pocket that molecular dynamics simulations showed to be stable. To the best of our knowledge, this is the first study to evaluate on a large scale the potentiality of NPs from essential oils as inhibitors of the mosquito OBP1 using in silico approaches. Our results could facilitate the design of novel repellents with improved selectivity and affinity to the protein binding pocket and can shed light on the mechanism of action of these compounds against insect olfactory recognition.

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          Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?

          Background Cheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/distance metrics are compared on a large dataset of molecular fingerprints with sum of ranking differences (SRD) and ANOVA analysis. The effects of molecular size, selection methods and data pretreatment methods on the outcome of the comparison are also assessed. Results A supplier database (https://mcule.com/) was used as the source of compounds for the similarity calculations in this study. A large number of datasets, each consisting of one hundred compounds, were compiled, molecular fingerprints were generated and similarity values between a randomly chosen reference compound and the rest were calculated for each dataset. Similarity metrics were compared based on their ranking of the compounds within one experiment (one dataset) using sum of ranking differences (SRD), while the results of the entire set of experiments were summarized on box and whisker plots. Finally, the effects of various factors (data pretreatment, molecule size, selection method) were evaluated with analysis of variance (ANOVA). Conclusions This study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics. Graphical Abstract A visual summary of the comparison of similarity metrics with sum of ranking differences (SRD). Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0069-3) contains supplementary material, which is available to authorized users.
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            A fast SHAKE algorithm to solve distance constraint equations for small molecules in molecular dynamics simulations

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              Pharmit: interactive exploration of chemical space

              Pharmit (http://pharmit.csb.pitt.edu) provides an online, interactive environment for the virtual screening of large compound databases using pharmacophores, molecular shape and energy minimization. Users can import, create and edit virtual screening queries in an interactive browser-based interface. Queries are specified in terms of a pharmacophore, a spatial arrangement of the essential features of an interaction, and molecular shape. Search results can be further ranked and filtered using energy minimization. In addition to a number of pre-built databases of popular compound libraries, users may submit their own compound libraries for screening. Pharmit uses state-of-the-art sub-linear algorithms to provide interactive screening of millions of compounds. Queries typically take a few seconds to a few minutes depending on their complexity. This allows users to iteratively refine their search during a single session. The easy access to large chemical datasets provided by Pharmit simplifies and accelerates structure-based drug design. Pharmit is available under a dual BSD/GPL open-source license.
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                Author and article information

                Journal
                ACS Omega
                ACS Omega
                ao
                acsodf
                ACS Omega
                American Chemical Society
                2470-1343
                17 December 2019
                31 December 2019
                : 4
                : 27
                : 22475-22486
                Affiliations
                []Institute of Biodiversity, Federal University of Western Pará , 68035-110 Santarém, Pará, Brazil
                []Department of Pharmaceutical Sciences, Federal University of Pará , 66060-902 Belém, Pará, Brazil
                [§ ]Museu Paraense Emilio Goeldi, Botany Coordination, Adolpho Ducke Laboratory , 66040-170 Belém, Pará Brazil
                [4] Institute of Exact and Natural Sciences and Institute of Biological Sciences, Federal University of Pará , 66075-110 Belém, Pará, Brazil
                Author notes
                [* ]E-mail: kaue.costa@ 123456ufopa.edu.br . Phone: +55 93 2101-6771 (K.S.D.C).
                [* ]E-mail: lameira@ 123456ufpa.br (J.L.).
                Article
                10.1021/acsomega.9b03157
                6941369
                31909330
                327bd159-7661-4701-a345-7261bfaa344a
                Copyright © 2019 American Chemical Society

                This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.

                History
                : 25 September 2019
                : 29 November 2019
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                ao9b03157

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