Abstract
In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.
Highlights
Multi-target strategies are a natural approach to tackling complex diseases
The few examples of intended polypharmacology are usually within protein families [4], and, traditionally, the discovery of alternative targets has been applied to drug repositioning instead of holistic therapies [55]
Structural biology offers a systematic means to explore the space of protein cross-pharmacology by detecting and comparing putative binding sites [16]
Summary
Multi-target strategies are a natural approach to tackling complex diseases. A fine way to achieve a multi-target effect is through drug polypharmacology, i.e. the simultaneous modulation of several targets by means of one single agent [1, 2], which poses pharmacokinetic advantages over drug combinations [3]. Challenging to achieve rationally, polypharmacology is a recognized feature of many approved drugs [10], and even those molecules praised to be highly specific, like imatinib, end up eliciting a quite rich interaction profile [11] This unavoidable promiscuity has long been regarded as detrimental due to adverse off-target reactions [12, 13], but at the same time it paves the way to a reverse drug design strategy, where one would first massively look for proteins that are likely to bind the same ligand, and only do network analysis to identify the small fraction of putative target combinations that are of therapeutic interest
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