A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology

Jacob D Durrant,Frank Wunder,Michael D Urbaniak,Michael A J Ferguson,Anne Marie Di Guilmi,Philip E Bourne,Zhijun Chen,J Andrew Mccammon,Lei Xie,Rommie E Amaro,Antti Haapalainen

doi:10.1371/journal.pcbi.1000648

Abstract

Conventional drug design embraces the “one gene, one drug, one disease” philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.

Highlights

Researchers have traditionally focused in silico efforts on designing inhibitors of specific protein targets, giving less attention to the computational identification of unpredicted secondary targets
To demonstrate the utility of the strategy, we identify several human and pathogen secondary targets of compound 1 (NSC-45208), 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a recently discovered micromolar inhibitor of T. brucei RNA editing ligase 1 (TbREL1) [20]
The remaining PDB30 proteins and the clusters they represented were merged into a single list containing 2,897 chains, a list enriched with possible secondary targets (Figure 1E)

Summary

Introduction

Researchers have traditionally focused in silico efforts on designing inhibitors of specific protein targets, giving less attention to the computational identification of unpredicted secondary targets. This tendency is surprising given the frequency with which secondary receptors are responsible for both detrimental and beneficial pharmacological effects. Millions of dollars are typically invested to advance a compound through clinical trials, but one third of these compounds fail or are later removed from the market due to unacceptable, medically harmful side effects [2] often caused by binding to off-target receptors. Compound binding to multiple therapeutic targets (polypharmacology) is beneficial.

Author Summary

Results

Discussion

Materials and Methods

Homology Clustering

SOIPPA