A Signalling Logic Underlying Polypharmacology: Abstract The promiscuity of several?if not most?drugs have irked medicinal chemists. The off-target effects are undesired although a lack of specificity is not always detrimental, rather an improved therapeutic response is sometimes observed. The targets involved are often unrelated by sequence and by family, but can be unified by ligand similarity. The Similarity Ensemble Approach (SEA) has been effective in discovering the ligand-based protein- protein relationships. Moreover, the findings have successfully rationalized unexplained side effects and prospectively been used to re-appropriate drugs. Here SEA will be used to identify targets that share similar ligands and share an endogenous signalling molecule with the goal of providing a guide for polypharmacology. The motivating idea behind this proposal is that receptors have evolved around a glossary of hormones and neurotransmitters (estrogen, serotonin, dopamine, etc.). Organic signalling molecules quickly become fixed due to their numerous constraints and functional dependencies, whereas genetically encoded proteins evolve around them, develop recognition sites for them, and emerge as targets of the drugs that mimic them. This study seeks to demonstrate that molecular evolution underlies polypharmacology?an idea from which new signalling molecules can be deduced and effective multi-target drugs can be designed. This proposal claims that 1. there exists a metabolic code that inverts our thinking of biological organization?suggesting that proteins have evolved around organic signalling molecules. Consequently, 2. proteins can be arranged according to ligand similarity. The new organization can be used to 3. predict unknown signalling molecules, 4. associate sequence-unrelated proteins by their shared signalling molecule, and 5. identify synthetic molecules that can modulate these target-pairs to produce synergistic cellular effects. The assertions will be tested with a combination of chemoinformatics and experimental techniques. SEA will predict targets for neurotransmitters and hormones and predict target pairs related by similar ligand sets. Dendrograms?akin to kinome trees?will be built for each protein family, ordered by ligand similarity. With the new target-target associations, synthetic co-modulators will be tested for polypharmacology on target pairs that share an endogenous signalling molecule.
This project proposes to explain polypharmacology as a feature of biological signaling networks that have become fixed through evolution. Should it be successful, it would provide new targets for synergistic polypharmacology and, perhaps most useful, a method to find them.
