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Prediction of Tyrosinase Inhibition Activity Using Atom-Based Bilinear Indices

Authors

  • Yovani Marrero-Ponce Prof. Dr.,

    1. Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, Poligon la Coma s/n (detras de Canal Nou) P.O. Box 22085, 46071 Valencia, Spain, Fax: (+53) 42-281130 or 42-281455, Cuba, and Fax: (+96) 354-3156, Spain
    2. Unit of Computer-Aided Molecular “BioSilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemistry–Pharmacy and Department of Drug Design, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba
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  • Mahmud Tareq Hassan Khan Prof. Dr.,

    1. Pharmacology Research Laboratory, Faculty of Pharmaceutical Sciences, University of Science and Technology, Chittagong, Bangladesh
    2. Department of Pharmacology, Institute of Medical Biology, University of Tromsø, 9037 Tromsø, Norway
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  • Gerardo M. Casañola Martín Dr.,

    1. Unit of Computer-Aided Molecular “BioSilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemistry–Pharmacy and Department of Drug Design, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba
    2. Department of Biological Sciences, Faculty of Agricultural Sciences, University of Ciego de Avila, 69450 Ciego de Avila, Cuba
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  • Arjumand Ather Prof. Dr.,

    1. The Norwegian Structural Biology Centre (NorStruct), University of Tromsø, 9037 Tromsø, Norway
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  • Mukhlis N. Sultankhodzhaev,

    1. S. Yunusov Institute of Chemistry of Plant Substances, Academy of Sciences, Uzbekistan (Tashkent)
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  • Francisco Torrens Prof. Dr.,

    1. Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, Poligon la Coma s/n (detras de Canal Nou) P.O. Box 22085, 46071 Valencia, Spain, Fax: (+53) 42-281130 or 42-281455, Cuba, and Fax: (+96) 354-3156, Spain
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  • Richard Rotondo

    1. Advanced Medisyns, Inc. 601 Carlson Parkway, Suite 1050, Minnetonka, MN 55305, USA
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Abstract

A set of novel atom-based molecular fingerprints is proposed based on a bilinear map similar to that defined in linear algebra. These molecular descriptors (MDs) are proposed as a new means of molecular parametrization easily calculated from 2D molecular information. The nonstochastic and stochastic molecular indices match molecular structure provided by molecular topology by using the kth nonstochastic and stochastic graph-theoretical electronic-density matrices, Mk and Sk, respectively. Thus, the kth nonstochastic and stochastic bilinear indices are calculated using Mk and Sk as matrix operators of bilinear transformations. Chemical information is coded by using different pair combinations of atomic weightings (mass, polarizability, vdW volume, and electronegativity). The results of QSAR studies of tyrosinase inhibitors using the new MDs and linear discriminant analysis (LDA) demonstrate the ability of the bilinear indices in testing biological properties. A database of 246 structurally diverse tyrosinase inhibitors was assembled. An inactive set of 412 drugs with other clinical uses was used; both active and inactive sets were processed by hierarchical and partitional cluster analyses to design training and predicting sets. Twelve LDA-based QSAR models were obtained, the first six using the nonstochastic total and local bilinear indices and the last six with the stochastic MDs. The discriminant models were applied; globally good classifications of 99.58 and 89.96 % were observed for the best nonstochastic and stochastic bilinear indices models in the training set along with high Matthews correlation coefficients (C) of 0.99 and 0.79, respectively, in the learning set. External prediction sets used to validate the models obtained were correctly classified, with accuracies of 100 and 87.78 %, respectively, yielding C values of 1.00 and 0.73. This subset contains 180 active and inactive compounds not considered to fit the models. A simulated virtual screen demonstrated this approach in searching tyrosinase inhibitors from compounds never considered in either training or predicting series. These fitted models permitted the selection of new cycloartane compounds isolated from herbal plants as new tyrosinase inhibitors. A good correspondence between theoretical and experimental inhibitory effects on tyrosinase was observed; compound CA6 (IC50=1.32 μM) showed higher activity than the reference compounds kojic acid (IC50=16.67 μM) and L-mimosine (IC50=3.68 μM).

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