Ram Samudrala
State University of New York, USA
Title: Multiscale modelling of relationships between protein classes and drug behavior across all diseases using the cando platform
Biography
Biography: Ram Samudrala
Abstract
We have examined the effect of eight different protein classes (channels, GPCRs, kinases, ligases, nuclear receptors, proteases, phosphatases and transporters) on the benchmarking performance of the CANDO drug discovery and repurposing platform. The first version of the CANDO platform utilizes a matrix of predicted interactions between 48,278 proteins and 3733 human use compounds that map to 2030 indications/diseases using a hierarchical chem and bio-informatic fragment based docking with dynamics protocol. 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 numbers of proteins considered and benchmarking accuracy obtained indicating the importance of multi-targeting 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 the range from 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 two orders of magnitude better than the average accuracy of 0.2% obtained when using randomly generated matrices. Different indications perform well when different classes are used but the best accuracies (11.7%) are achieved with a combination of classes consisting of 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.