Statistical analysis of the in silico binding affinity of P-glycoprotein and its substrates with their experimentally known parameters to demonstrate a cost-effective approach for screening, ranking and possible prediction of potential substrates
by A. Suneetha Susan Cleave; P.K. Suresh
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 15, No. 4, 2019

Abstract: Over-expression of P-glycoprotein (P-gp) has been reported as a cause of multi-drug resistance in cancers and other diseases. Transport assays, which are generally used to find out the specificity of a compound to be effluxed, have always been time consuming, resource-intensive and expensive and thus, have inherent limitations to easily predict a compound's specificity. Hence, there is a clear-cut, unmet need to develop cost-effective methods for screening, identification and ranking of P-gp substrates. All compounds (23 substrates and 3 non-substrates) were docked to two homology modelled human P-gp conformations. The in silico binding affinities, obtained for all substrates, were checked for correlation with their experimentally determined efflux ratios, LogP values and number of hydrogen bond acceptors they possess. Docking results showed that all compounds demonstrated differences in relative binding affinity. Experimentally-derived efflux ratio obtained for 19 substrates from literature, for the first time showed a significant, Spearman correlation with binding energies to outward-facing conformation. Thus, it can be said that binding energies obtained from docking studies can possibly have significant potential in identifying the specificity and ranking P-gp substrates. This approach provides a sound foundation to strengthen the relationship of in silico binding energies with other experimentally defined physico-chemical parameters and can also be part of an iterative process to identify and develop a potential, validatable solution.

Online publication date: Fri, 29-Nov-2019

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