Abstract:The influence of various factors on the accuracy of protein-ligand docking is examined. The factors investigated include the role of a grid representation of protein-ligand interactions, the initial ligand conformation and orientation, the sampling rate of the energy hyper-surface, and the final minimization. A representative docking method is used to study these factors, namely, CDOCKER, a molecular dynamics (MD) simulated-annealing-based algorithm. A major emphasis in these studies is to compare the relative performance and accuracy of various grid-based approximations to explicit all-atom force field calculations. In these docking studies, the protein is kept rigid while the ligands are treated as fully flexible and a final minimization step is used to refine the docked poses. A docking success rate of 74% is observed when an explicit all-atom representation of the protein (full force field) is used, while a lower accuracy of 66 -76% is observed for grid-based methods. All docking experiments considered a 41-member proteinligand validation set. A significant improvement in accuracy (76 vs. 66%) for the grid-based docking is achieved if the explicit all-atom force field is used in a final minimization step to refine the docking poses. Statistical analysis shows that even lower-accuracy grid-based energy representations can be effectively used when followed with full force field minimization. The results of these grid-based protocols are statistically indistinguishable from the detailed atomic dockings and provide up to a sixfold reduction in computation time. For the test case examined here, improving the docking accuracy did not necessarily enhance the ability to estimate binding affinities using the docked structures.

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The key to success for computational tools used in structure-based drug design is the ability to accurately place or "dock" a ligand in the binding pocket of the target of interest. In this report we examine the effect of several factors on docking accuracy, including ligand and protein flexibility. To examine ligand flexibility in an unbiased fashion, a test set of 41 ligand-protein cocomplex X-ray structures were assembled that represent a diversity of size, flexibility, and polarity with respect to the ligands. Four docking algorithms, DOCK, FlexX, GOLD, and CDOCKER, were applied to the test set, and the results were examined in terms of the ability to reproduce X-ray ligand positions within 2.0Å heavy atom root-mean-square deviation. Overall, each method performed well (>50% accuracy) but for all methods it was found that docking accuracy decreased substantially for ligands with eight or more rotatable bonds. Only CDOCKER was able to accurately dock most of those ligands with eight or more rotatable bonds (71% accuracy rate). A second test set of structures was gathered to examine how protein flexibility influences docking accuracy. CDOCKER was applied to X-ray structures of trypsin, thrombin, and HIV-1-protease, using protein structures bound to several ligands and also the unbound (apo) form. Docking experiments of each ligand to one "average" structure and to the apo form were carried out, and the results were compared to docking each ligand back to its originating structure. The results show that docking accuracy falls off dramatically if one uses an average or apo structure. In fact, it is shown that the drop in docking accuracy mirrors the degree to which the protein moves upon ligand binding.

An increasingly competitive pharmaceutical market demands improvement in the efficiency and probability of drug candidate discovery. Usually these new drug candidates are targeted for oral administration, so a detailed understanding of the molecular-level properties that relate to optimal pharmacokinetics is a critical step toward improving the probability of selecting successful clinical candidates. Although the characteristics of druglike molecules have been previously discussed in the literature, the importance of this topic sustains a continued interest for additional perspective and further detailed statistical analyses. In this contribution, we approach the analysis from the perspective of profiling distinguishing features of orally administered drugs. We have compiled both structural and route-administration information for a total of 1729 marketed drugs to provide a solid basis for developing a new perspective on the characteristics of over 1000 orally administered drugs. The molecular properties and most commonly occurring structural elements are statistically analyzed to capture the differences between routes of administration, as well as between marketed drugs and SAR or clinical compounds. We find that, with respect to other routes of administration, oral drugs tend to be lighter and have fewer H-bond donors, acceptors, and rotatable bonds than drugs with other routes of administration. These differences are particularly pronounced when comparing the mean values for oral vs injectable drugs. We also demonstrate that the mean property values for oral drugs do not vary substantially with respect to launch date, suggesting that the range of acceptable oral properties is independent of synthetic complexity or targeted receptor. Finally, we note that, while these properties are descriptive of each class, they are not necessarily predictive of what class any particular drug will reside in, since there is significant overlap in the acceptable ranges found for each drug class.