Hepatotoxicity, drug-induced liver injury, and competitive Cytochrome P-450 (CYP) isozyme binding are serious problems associated with drug use. It would be favorable to avoid or to understand potential CYP inhibition at the developmental stages. However, current in silico CYP prediction models or available public prediction servers can provide only yes/no classification results for just one or a few CYP enzymes. In this study, we utilized a rule-based C5.0 algorithm with different descriptors, including PaDEL, Mold(2), and PubChem fingerprints, to construct rule-based inhibition prediction models for five major CYP enzymes-CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4-that account for 90% of drug oxidation or hydrolysis. We also developed a rational sampling algorithm for the selection of compounds in the training data set, to enhance the performance of these CYP prediction models. The optimized models include several improved features. First, the final models significantly outperformed all of the currently available models. Second, the final models can also be used for rapid virtual screening of a large set of compounds due to their ruleset-based nature. Moreover, such rule-based prediction models can provide rulesets for structural features related to the five major CYP enzymes. The five most significant rules for CYP inhibition were identified for each CYP enzymes and discussed. An example was chosen for each of the five CYP enzymes to demonstrate how rule-based models can be used to gain insights into structural features that correspond with CYP inhibitions. A newer version of the freely accessible CYP prediction server, CypRules, is presented here as a result of the aforementioned improvements.
CypRules is freely accessible at http://cyprules.cmdm.tw/ and models, descriptor and program files for all compounds are publically available at http://cyprules.cmdm.tw/sources/sources.rar.
Objective-Currently prescribed antiplatelet drugs have 1 common side effect-an increased risk of hemorrhage and thrombocytopenia. On the contrary, bleeding defects associated with glycoprotein VI (GPVI) expression deficiency are usually slightly prolonged bleeding times. However, GPVI antagonists are lacking in clinic. Approach and Results-Using reverse-phase high-performance liquid chromatography and sequencing, we revealed the partial sequence of trowaglerix α subunit, a potent specific GPVI-targeting snaclec (snake venom C-type lectin protein). Hexapeptide (Troα6 [trowaglerix a chain hexapeptide, CKWMNV]) and decapeptide (Troα10) derived from trowaglerix specifically inhibited collagen-induced platelet aggregation through blocking platelet GPVI receptor. Computational peptide design helped to design a series of Troα6/Troα10 peptides. Protein docking studies on these decapeptides and GPVI suggest that Troα10 was bound at the lower surface of D1 domain and outer surface of D2 domain, which was at the different place of the collagen-binding site and the scFv (single-chain variable fragment) D2-binding site. The newly discovered site was confirmed by inhibitory effects of polyclonal antibodies on collagen-induced platelet aggregation. This indicates that D2 domain of GPVI is a novel and important binding epitope on GPVI-mediated platelet aggregation. Troα6/Troα10 displayed prominent inhibitory effect of thrombus formation in fluorescein sodium-induced platelet thrombus formation of mesenteric venules and ferric chloride-induced carotid artery injury thrombosis model without prolonging the in vivo bleeding time. Conclusions-We develop a novel antithrombotic peptides derived from trowaglerix that acts through GPVI antagonism with greater safety-no severe bleeding. The binding epitope of polypeptides on GPVI is novel and important. These hexa/ decapeptides have therapeutic potential for developing ideal small-mass GPVI antagonists for arterial thrombogenic diseases. Visual Overview-An online visual overview is available for this article. GPVI and GPIb. 10,11 The structure of GPVI together with its ligand has been partially determined, 12 casting some light on the potential binding sites at molecular level. Because GPVI ligand complex structure was not fully evaluated, the detailed GPVI-ligandbinding domain and the dissection of critical sequence responsible for design of target inhibition still need further investigation.Previous literature has shown that GPVI recognizes glycine-proline-hydroxyproline repeat motifs in the triple helical structure of collagen.13 GPVI has 2 extracellular C2-type immunoglobulin-like domains (D1 and D2). The residues of GPVI extracellular domain, involved in the GPVI-collagen interaction, are majorly located on the surface of D1 domain.14,15 It also was suggested that CVX exists in solution as a dimer of α4β4 rings and contains 8 distinct GPVI-binding sites and binds GPVI with high-binding affinities.16 Also, presence of 2 distinct GPVI-binding surfaces on the (α4β4)2 CVX dimers al...
The inclusion and accessibility of different methodologies to explore chemical data sets has been beneficial to the field of predictive modeling, specifically in the chemical sciences in the field of Quantitative Structure-Activity Relationship (QSAR) modeling. This study discusses using contemporary protocols and QSAR modeling methods to properly model two biomolecular systems that have historically not performed well using traditional and three-dimensional QSAR methodologies. Herein, we explore, analyze, and discuss the creation of a classification human Ether-a-go-go Related Gene (hERG) potassium channel model and a continuous Tetrahymena pyriformis (T. pyriformis) model using Support Vector Machine (SVM) and Support Vector Regression (SVR), respectively. The models are constructed with three types of molecular descriptors that capture the gross physicochemical features of the compounds: (i) 2D, 2 1/2D, and 3D physical features, (ii) VolSurf-like molecular interaction fields, and (iii) 4D-Fingerprints. The best hERG SVM model achieved 89% accuracy and the three-best SVM models were able to screen a Pubchem data set with an accuracy of 97%. The best T. pyriformis model had an R(2) value of 0.924 for the training set and was able to predict the continuous end points for two test sets with R(2) values of 0.832 and 0.620, respectively. The studies presented within demonstrate the predictive ability (classification and continuous end points) of QSAR models constructed from curated data sets, biologically relevant molecular descriptors, and Support Vector Machines and Support Vector Regression. The ability of these protocols and methodologies to accommodate large data sets (several thousands compounds) that are chemically diverse - and in the case of classification modeling unbalanced (one experimental outcome dominates the data set) - allows scientists to further explore a remarkable amount of biological and chemical information.
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