Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.7.11.8 (FAST)
 758 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We examined the quality of Catalyst's conformational model generation algorithm via a large scale study based on the crystal structures of a sample of 510 pharmaceutically relevant protein-ligand complexes extracted from the Protein Data Bank (PDB). Our results show that the tested algorithms implemented within Catalyst are able to produce high quality conformers, which in most of the cases are well suited for in silico drug research. Catalyst-specific settings were analyzed, such as the method used for the conformational model generation (FAST vs BEST) and the maximum number of generated conformers. By setting these options for higher fitting quality, the average RMS values describing the similarity of experimental and simulated conformers were improved from an RMS of 1.06 with max. 50 FAST generated conformers to an RMS of 0.93 with max. 255 BEST generated conformers, which represents an improvement by 12%. Each method provides best fitting conformers with an RMS value<1.50 in more than 80% of all cases. We analyzed the computing time/quality ratio of various conformational model generation settings and examined ligands in high energy conformations. Furthermore, properties of the same ligands in various proteins were investigated, and the fitting qualities of experimental conformations from the PDB and the Cambridge Structural Database (CSD) were compared. One of the most important conclusions of former studies, the fact that bioactive conformers often have energy high above that of global minima, was confirmed.
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PMID:Comparative analysis of protein-bound ligand conformations with respect to catalyst's conformational space subsampling algorithms. 1580 8

In continuation of our recent studies on the quality of conformational models generated with CATALYST and OMEGA we present a large-scale survey focusing on the impact of conformational model quality and several screening parameters on pharmacophore-based and shape-based virtual high throughput screening (vHTS). Therefore, we collected known active compounds of CDK2, p38 MAPK, PPAR-gamma, and factor Xa and built a set of druglike decoys using ilib:diverse. Subsequently, we generated 3D structures using CORINA and also calculated conformational models for all compounds using CAESAR, CATALYST FAST, and OMEGA. A widespread set of 103 structure-based pharmacophore models was developed with LigandScout for virtual screening with CATALYST. The performance of both database search modes (FAST and BEST flexible database search) as well as the fit value calculation procedures (FAST and BEST fit) available in CATALYST were analyzed in terms of their ability to discriminate between active and inactive compounds and in terms of efficiency. Moreover, these results are put in direct comparison to the performance of the shape-based virtual screening platform ROCS. Our results prove that high enrichment rates are not necessarily in conflict with efficient vHTS settings: In most of the experiments, we obtained the highest yield of actives in the hit list when parameter sets for the fastest search algorithm were used.
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PMID:Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shape-based approaches. 1792 99

Predictive pharmacophore models have been developed for a series of arylamino-substituted benzo[b]thiophenes exhibiting free radical scavenging activity. 3D pharmacophore models were generated using a set of 20 training set compounds and subsequently validated by mapping 6 test set compounds using Discovery Studio 2.1 software. Further model validation was performed by randomizing the data using Fischer's validation technique at the 95% confidence level. The most predictive pharmacophore model developed using the conformers obtained from the BEST method showed a correlation coefficient (r) of 0.942 and consisted of three features: hydrogen bond donor, hydrogen bond acceptor and aromatic ring. Acceptable values of external validation parameters, like R2pred (0.853) and r2m(test) (0.844), also implied that the external predictivity of the model was significant. The development of further pharmacophore models using conformers obtained from the FAST method yielded a few models with good predictivity, with the best one (r=0.904) consisting of two features: hydrogen bond donor and hydrogen bond acceptor. Significant values of external validation parameters, R2pred (0.913) and r2m(test) (0.821), also reflect the high predictive ability of the model. Again, Fischer validation results implied that the models developed were robust enough and their good results were not based on mere chance. These validation approaches indicate the reliability of the predictive abilities of the 3D pharmacophore models developed here, which may thus be further utilized as a 3D query tool in the virtual screening of new chemical entities with potent antioxidant activities.
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PMID:Pharmacophore mapping of arylamino-substituted benzo[b]thiophenes as free radical scavengers. 2019 68