Molecular Formal Concept Analysis for Compound Selectivity Profiling in Biologically Annotated Databases

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r²) of the regression models was 0.664 (for test sets). The 2B-3 (A_2Bvs A₃) model performed best with q² = 0.769 for training sets (10-fold cross-validation), and r² = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A_2Avs A₃) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

Molecular test systems for computational selectivity studies and systematic analysis of compound selectivity profiles

For chemical genetics and chemical biology, an important task is the identification of small molecules that are selective against individual targets and can be used as molecular probes for specific biological functions. To aid in the development of computational methods for selectivity analysis, molecular benchmark systems have been developed that capture compound selectivity data for pairs of targets. These molecular test systems are utilized for "selectivity searching" and the analysis of structure-selectivity relationships. Going beyond binary selectivity sets focusing on target pairs, a methodological framework, Molecular Formal Concept Analysis (MolFCA), is described for the definition and systematic mining of compound selectivity profiles.

Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs

The similarity ensemble approach (SEA) relates proteins based on the set-wise chemical similarity among their ligands. It can be used to rapidly search large compound databases and to build cross-target similarity maps. The emerging maps relate targets in ways that reveal relationships one might not recognize based on sequence or structural similarities alone. SEA has previously revealed cross talk between drugs acting primarily on G-protein coupled receptors (GPCRs). Here we used SEA to look for potential off-target inhibition of the enzyme protein farnesyltransferase (PFTase) by commercially available drugs. The inhibition of PFTase has profound consequences for oncogenesis, as well as a number of other diseases. In the present study, two commercial drugs, Loratadine and Miconazole, were identified as potential ligands for PFTase and subsequently confirmed as such experimentally. These results point toward the applicability of SEA for the prediction of not only GPCR-GPCR drug cross talk but also GPCR-enzyme and enzyme-enzyme drug cross talk.