Identification and Visualization of Kinase-Specific Subpockets

The identification and design of selective compounds is important for the reduction of unwanted side effects as well as for the development of tool compounds for target validation studies. This is, in particular, true for therapeutically important protein families that possess conserved folds and have numerous members such as kinases. To support the design of selective kinase inhibitors, we developed a novel approach that allows identification of specificity determining subpockets between closely related kinases solely based on their three-dimensional structures. To account for the intrinsic flexibility of the proteins, multiple X-ray structures of the target protein of interest as well as of unwanted off-target(s) are taken into account. The binding pockets of these protein structures are calculated and fused to a combined target and off-target pocket, respectively. Subsequently, shape differences between these two combined pockets are identified via fusion rules. The approach provides a user-friendly visualization of target-specific areas in a binding pocket which should be explored when designing selective compounds. Furthermore, the approach can be easily combined with in silico alanine mutation studies to identify selectivity determining residues. The potential impact of the approach is demonstrated in four retrospective experiments on closely related kinases, i.e., p38α vs Erk2, PAK1 vs PAK4, ITK vs AurA, and BRAF vs VEGFR2. Overall, the presented approach does not require any profiling data for training purposes, provides an intuitive visualization of a large number of protein structures at once, and could also be applied to other target classes

Additional file 1: of KinMap: a web-based tool for interactive navigation through human kinome data

(KinMap_Examples.zip) contains the input CSV files used to generate the annotated kinome trees in Fig. 1 (Example_1_Erlotinib_NSCLC.csv), Fig. 2a (Example_2_Sunitinib_Sorafenib_Cancer.csv), and Fig. 2b (Example_3_Kinase_Stats.csv). (ZIP 5 kb

Fast Protein Binding Site Comparison via an Index-Based Screening Technology

We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies