TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors:: Evaluation of different classification model combinations using bond-based linear indices

被引:83
作者
Casahola-Martin, Gerardo M.
Marrero-Ponce, Yovani [1 ]
Khan, Mahmud Tareq Hassan
Ather, Arjumand
Sultan, Sadia
Torrens, Francisco
Rotondo, Richard
机构
[1] Cent Univ Las Villas, Fac Chem Pharm, Chem Bioact Ctr,Unit Comp Aided Mol Biosilico Dis, CAMD,BIR Unit, Santa Clara 54830, Villa Clara, Cuba
[2] Cent Univ Las Villas, Chem Bioact Ctr, Dept Drug Design, Santa Clara 54830, Villa Clara, Cuba
[3] Univ Ciego Avila, Fac Agr Sci, Dept Biol Sci, Ciego De Avila 69450, Cuba
[4] Univ Valencia, Inst Univ Ciencia Mol, Edifici Inst Paterna, E-46071 Valencia, Spain
[5] Univ Sci & Technol, Fac Pharmaceut Sci, Pharmacol Res Lab, Chittagong, Bangladesh
[6] Univ Tromso, Med Biol Inst, Dept Pharmacol, N-9037 Tromso, Norway
[7] Univ Tromso, Norwegian Struct Biol Ctr, NorStruct, N-9037 Tromso, Norway
[8] Univ Karachi, HEJ Res Inst Chem, Karachi 32, Pakistan
[9] Mediscovery Inc, Minnetonka, MN 55305 USA
关键词
TOMOCOMD-CARDD software; non-stochastic and stochastic bond-based linear indices; LDA-based QSAR model; tyrosinase inhibitor; ethylsteroid compounds; Ligand-based virtual screening;
D O I
10.1016/j.bmc.2006.10.067
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
A new set of bond-level molecular descriptors (bond-based linear indices) are used here in QSAR (quantitative structure-activity relationship) studies of tyrosinase inhibitors, for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones. A database of 246 compounds was collected for this study; all organic chemicals were reported as tyrosinase inhibitors; they had great structural diversity. This dataset can be considered as a helpful tool, not only for theoretical chemists but also for other researchers in this area. The set used as inactive has 412 drugs with other clinical uses. Twelve LDA-based QSAR models were obtained, the first six using the non-stochastic total and local bond-based linear indices as well as the last six ones. the stochastic molecular descriptors. The best two discriminant models computed using the non-stochastic and stochastic molecular descriptors (Eqs. 7 and 13, respectively) had globally good classifications of 98.95% and 89.75% in the training set. with high Matthews correlation coefficients (C) of 0.98 and 0.78. The external prediction sets had accuracies of 98.89% and 89.44%. and (C) values of 0.98 and 0.78, for models 7 and 13, respectively. A virtual screening of compounds reported in the literature with such activity was carried out, to prove the ability of present models to search for tyrosinase inhibitors, not included in the training or test set. At the end, the fitted discriminant functions were used in the selection/identification of new ethylsteroids isolated from herbal plants, looking for tyrosinase inhibitory activity. A good behavior is shown between the theoretical and experimental results on mushroom tyrosinase enzyme. It might be highlighted that all the compounds showed values under 10 mu M and that ES2 (IC50 = 1.25 mu M) showed higher activity in the inhibition against the enzyme than reference compounds kojic acid (IC50 = 16.67 mu M) and L-mimosine (IC50 = 3.68 mu M). In addition, a comparison with other established methods was carried to prove the adequate discriminatory performance of the molecular descriptors used here. The present algorithm provided useful clues that can be used to speed up in the identification of new tyrosinase inhibitor compounds. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1483 / 1503
页数:21
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