Pathway-based Screening Strategy for Multitarget Inhibitors of Diverse Proteins in Metabolic Pathways

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Abstract

Many virtual screening methods have been developed for identifying single-target inhibitors based on the strategy of "one-disease, one-target, one-drug". The hit rates of these methods are often low because they cannot capture the features that play key roles in the biological functions of the target protein. Furthermore, single-target inhibitors are often susceptible to drug resistance and are ineffective for complex diseases such as cancers. Therefore, a new strategy is required for enriching the hit rate and identifying multitarget inhibitors. To address these issues, we propose the pathway-based screening strategy (called PathSiMMap) to derive binding mechanisms for increasing the hit rate and discovering multitarget inhibitors using site-moiety maps. This strategy simultaneously screens multiple target proteins in the same pathway; these proteins bind intermediates with common substructures. These proteins possess similar conserved binding environments (pathway anchors) when the product of one protein is the substrate of the next protein in the pathway despite their low sequence identity and structure similarity. We successfully discovered two multitarget inhibitors with IC50 of <10 μM for shikimate dehydrogenase and shikimate kinase in the shikimate pathway of Helicobacter pylori. Furthermore, we found two selective inhibitors (IC50 of <10 μM) for shikimate dehydrogenase using the specific anchors derived by our method. Our experimental results reveal that this strategy can enhance the hit rates and the pathway anchors are highly conserved and important for biological functions. We believe that our strategy provides a great value for elucidating protein binding mechanisms and discovering multitarget inhibitors. © 2013 Hsu et al.

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Hsu, K. C., Cheng, W. C., Chen, Y. F., Wang, W. C., & Yang, J. M. (2013). Pathway-based Screening Strategy for Multitarget Inhibitors of Diverse Proteins in Metabolic Pathways. PLoS Computational Biology, 9(7). https://doi.org/10.1371/journal.pcbi.1003127

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