We have examined the effect of eight different protein classes (channels, GPCRs, kinases,
ligases, nuclear receptors, proteases, phosphatases, transporters) on the benchmarking performance of
the CANDO drug discovery and repurposing platform (http://protinfo.org/cando). The first version of
the CANDO platform utilizes a matrix of predicted interactions between 48278 proteins and 3733
human ingestible compounds (including FDA approved drugs and supplements) that map to 2030
indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol (> one
billion predicted interactions considered). The platform uses similarity of compound-proteome interaction signatures as
indicative of similar functional behavior and benchmarking accuracy is calculated across 1439 indications/diseases with
more than one approved drug. The CANDO platform yields a significant correlation (0.99, p-value < 0.0001) between the
number of proteins considered and benchmarking accuracy obtained indicating the importance of multitargeting for drug
discovery. Average benchmarking accuracies range from 6.2 % to 7.6 % for the eight classes when the top 10 ranked
compounds are considered, in contrast to a range of 5.5 % to 11.7 % obtained for the comparison/control sets consisting of
10, 100, 1000, and 10000 single best performing proteins. These results are generally two orders of magnitude better than
the average accuracy of 0.2% obtained when randomly generated (fully scrambled) matrices are used. Different
indications perform well when different classes are used but the best accuracies (up to 11.7% for the top 10 ranked
compounds) are achieved when a combination of classes are used containing the broadest distribution of protein folds.
Our results illustrate the utility of the CANDO approach and the consideration of different protein classes for devising
indication specific protocols for drug repurposing as well as drug discovery.
Keywords: Druggable proteins, protein drug interactions, protein folds, protein classes, proteome drug discovery, drug
discovery benchmark, multiscale modeling, polypharmacology.
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