CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

ABSTRACT: A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R 2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R 2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured t

Improving Protocols for Protein Mapping through Proper Comparison to Crystallography Data

Computational approaches to fragment-based drug design (FBDD) can complement experiments and facilitate the identification of potential hot spots along the protein surface. However, the evaluation of computational methods for mapping binding sites frequently focuses upon the ability to reproduce crystallographic coordinates to within a low RMSD threshold. This dependency on the deposited coordinate data overlooks the original electron density from the experiment, thus techniques may be developed based upon subjectiveor even erroneousatomic coordinates. This can become a significant drawback in applications to systems where the location of hot spots is unknown. On the basis of comparison to crystallographic density, we previously showed that mixed-solvent molecular dynamics (MixMD) accurately identifies the active site for HEWL, with acetonitrile as an organic solvent. Here, we concentrated on the influence of protic solvent on simulation and refined the optimal MixMD approach for extrapolation of the method to systems without established sites. Our results establish an accurate approach for comparing simulations to experiment. We have outlined the most efficient strategy for MixMD, based on simulation length and number of runs. The development outlined here makes MixMD a robust method which should prove useful across a broad range of target structures. Lastly, our results with MixMD match experimental data so well that consistency between simulations and density may be a useful way to aid the identification of probes vs waters during the refinement of future multiple solvent crystallographic structures

Predictive ability of each docking method as determined by AUC.

<p>Predictive ability of each docking method as determined by AUC.</p

Impact of different approaches for docking the strict set to HSA.

<p>The ROC curves for the strict set of HSA binders that result from different approaches to prediction of binding affinity and pose: A) use of the calculated descriptor <i>QPlogP_o/w</i>, B) best XP score from rigid docking with Glide to 20 sites versus 70 sites, C) best IFD score from docking to the 2 site model with a FA, and D) combined score based on <i>QPlogP_o/w</i> and the best IFD score from the 2-site FA model.</p

Computational workflow for docking compounds to HSA.

<p>Schematic illustrates the approach used for preparing the protein and ligand structures, docking, and analyzing the results.</p

Distinguishing the impact of small structural changes on HSA binding.

<p>Predicted ranking of %HSA binding for congeneric series of indole-3-acetic acid analogues based on A) use of the calculated descriptor <i>QPlogP_o/w</i>, B) best XP score from rigid docking with Glide to all structures, C) best IFD score from docking to the 2 site model with a FA, D) combined score based on <i>QPlogP_o/w</i> and the best IFD score, and E) use of the QSAR-based descriptor <i>QPlogK_hsa</i>.</p

Discriminating between Site II and Site I binders.

<p>Receiver-operating characteristic curve for predicting site I binders vs. site II binders (dashed blue line).</p