A highly efficient conformer search algorithm based on a divide-and-conquer and recursive conformer build-up approach is presented in this paper. This approach is combined with consideration of local rotational symmetry so that conformer duplicates due to topological symmetry in the systematic search can be efficiently eliminated. This new algorithm, termed CAESAR (Conformer Algorithm based on Energy Screening and Recursive Buildup), has been implemented in Discovery Studio 1.7 as part of the Catalyst Component Collection. CAESAR has been validated by comparing the conformer models generated by the new method and Catalyst/FAST. CAESAR is consistently 5-20 times faster than Catalyst/FAST for all data sets investigated. The speedup is even more dramatic for molecules with high topological symmetry or for molecules that require a large number of conformers to be sampled. The quality of the conformer models generated by CAESAR has been validated by assessing the ability to reproduce the receptor-bound X-ray conformation of ligands extracted for the Protein Data Bank (PDB) and assessing the ability to adequately cover the pharmacophore space. It is shown that CAESAR is able to reproduce the receptor-bound conformation slightly better than the Catalyst/FAST method for a data set of 918 ligands retrieved from the PDB. In addition, it is shown that CEASAR covers the pharmacophore space as well or better than Catalyst/FAST.
"In the 3D pharmacophore modeling approach using Discovery Studio (Accelrys version 2.5.5. San Diego, CA, described previously ), ten hypotheses were generated using hydrophobic, HBA, HBD, and the positive and negative ionizable features, and the CAESAR algorithm  was applied to the molecular data set (maximum of 255 conformations per molecule and maximum energy of 20 kcal/mol) to generate conformers. The pharmcophore hypothesis with the lowest energy cost was selected for further analysis as this model possessed features representative of all the hypotheses. "
[Show abstract][Hide abstract] ABSTRACT: Dispensing and dilution processes may profoundly influence estimates of biological activity of compounds. Published data show Ephrin type-B receptor 4 IC50 values obtained via tip-based serial dilution and dispensing versus acoustic dispensing with direct dilution differ by orders of magnitude with no correlation or ranking of datasets. We generated computational 3D pharmacophores based on data derived by both acoustic and tip-based transfer. The computed pharmacophores differ significantly depending upon dispensing and dilution methods. The acoustic dispensing-derived pharmacophore correctly identified active compounds in a subsequent test set where the tip-based method failed. Data from acoustic dispensing generates a pharmacophore containing two hydrophobic features, one hydrogen bond donor and one hydrogen bond acceptor. This is consistent with X-ray crystallography studies of ligand-protein interactions and automatically generated pharmacophores derived from this structural data. In contrast, the tip-based data suggest a pharmacophore with two hydrogen bond acceptors, one hydrogen bond donor and no hydrophobic features. This pharmacophore is inconsistent with the X-ray crystallographic studies and automatically generated pharmacophores. In short, traditional dispensing processes are another important source of error in high-throughput screening that impacts computational and statistical analyses. These findings have far-reaching implications in biological research.
PLoS ONE 05/2013; 8(5):e62325. DOI:10.1371/journal.pone.0062325 · 3.23 Impact Factor
"Template molecule structures were downloaded from ChemSpider (www.chemspider.com), and conformer generation was carried out by using the CAESAR algorithm (Conformer Algorithm based on Energy Screening and Recursive Buildup; Li et al., 2007) applied to the selected template molecules (maximum of 255 conformations per molecule and maximum energy of 20 kcal/mol). 3D-Quantitative Structure/Activity Relationship development used the Hypogen method in Discovery Studio. "
[Show abstract][Hide abstract] ABSTRACT: The present study compared the selectivity of two homologous transport proteins, multidrug and toxin extruders 1 and 2-K (MATE1 and MATE2-K), and developed three-dimensional pharmacophores for inhibitory ligand interaction with human MATE1 (hMATE1). The human orthologs of MATE1 and MATE2-K were stably expressed in Chinese hamster ovary cells, and transport function was determined by measuring uptake of the prototypic organic cation (OC) substrate 1-methyl-4-phenylpyridinium (MPP). Both MATEs had similar apparent affinities for MPP, with K(tapp) values of 4.4 and 3.7 μM for MATE1 and MATE2-K, respectively. Selectivity was assessed for both transporters from IC(50) values for 59 structurally diverse compounds. Whereas the two transporters discriminated markedly between a few of the test compounds, the IC(50) values for MATE1 and MATE2-K were within a factor of 3 for most of them. For hMATE1 there was little or no correlation between IC(50) values and the individual molecular descriptors LogP, total polar surface area, or pK(a). The IC(50) values were used to generate a common-features pharmacophore, quantitative pharmacophores for hMATE1, and a bayesian model suggesting molecular features favoring and not favoring the interaction of ligands with hMATE1. The models identified hydrophobic regions, hydrogen bond donor and hydrogen bond acceptor sites, and an ionizable (cationic) feature as key determinants for ligand binding to MATE1. In summary, using a combined in vitro and computational approach, MATE1 and MATE2-K were found to have markedly overlapping selectivities for a broad range of cationic compounds, including representatives from seven novel drug classes of Food and Drug Administration-approved drugs.
Journal of Pharmacology and Experimental Therapeutics 03/2012; 341(3):743-55. DOI:10.1124/jpet.112.191577 · 3.97 Impact Factor
"Generally, these methods can be classified into two main categories: systematic methods and stochastic methods. Systematic methods exhaustively enumerate all possible torsions at certain discrete intervals, therefore such approach is usually limited to small molecules and becomes inapplicable for very flexible molecules due to the combinatorial explosion[15,18,19]. To overcome the combinatorial difficulty of systematic search algorithms, many programs, such as CAESAR, OMEGA and CONAN have adopted the divide-and-conquer strategy to divide the molecule into small pieces and then assemble conformations of the whole molecules from small fragments. "
[Show abstract][Hide abstract] ABSTRACT: Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA), we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM) and multiple empirical criteria based method (MECBM) hybrided with different force fields.
Our analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD) of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%). On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost.
By incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational ensemble with high accuracy but approximately the same computational cost in comparison with FFBM method. Our analysis also reveals that the performance of conformational generation is irrelevant to the types of force field adopted in characterization of conformational accessibility. Moreover, post energy minimization is not necessary and may even undermine the diversity of conformational ensemble. All the results guide us to explore more empirical criteria like geometric restraints during the conformational process, which may improve the performance of conformational generation in combination with energetic accessibility, regardless of force field types adopted.
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