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Review
. 2011 Apr;21(2):189-99.
doi: 10.1016/j.sbi.2011.01.004. Epub 2011 Feb 1.

Structure-based systems biology for analyzing off-target binding

Lei Xie  1 , Li Xie, Philip E Bourne
Affiliations
Review

Structure-based systems biology for analyzing off-target binding

Lei Xie et al. Curr Opin Struct Biol. 2011 Apr.

Abstract

Here off-target binding implies the binding of a small molecule of therapeutic interest to a protein target other than the primary target for which it was intended. Increasingly such off-targeting appears to be the norm rather than the exception, rational drug design notwithstanding, and can lead to detrimental side-effects, or opportunities to reposition a therapeutic agent to treat a different condition. Not surprisingly, there is significant interest in determining a priori what off-targets exist on a proteome-wide scale. Beyond determining putative off-targets is the need to understand the impact of such binding on the complete biological system, with the ultimate goal of being able to predict the phenotypic outcome. While a very ambitious goal, some progress is being made.

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Figures

Figure 1
Figure 1
Proteome coverage of drug targets, PDB structures and homology models in human, virus, M. tuberculosis, and T. brucei.
Figure 2
Figure 2. Distribution of 2D molecular fingerprint similarities between 6,127 drugs and PDB ligands
Around 15% and 60% of drugs have similar molecular structures to PDB ligands with fingerprint similarity scores larger than 90 and 70, respectively.
Figure 3
Figure 3. Off-target binding depends on both the ligand binding propensity of receptors and the chemical nature of ligands
For two receptors A and B that share a similar ligand binding pocket, only ligand L2 of receptor A and ligand L3 of receptor B demonstrate cross-reactivity between receptors A and B. L2 and L3 may bind receptor C in a totally different pose than when binding receptors A and B. Existing structural bioinformatics methods may not detect that receptor C is an off-target of receptors A and B.
Figure 4
Figure 4. An example of a knowledge graph that links a drug-target network to clinical endpoints through biological pathways
The graph shows the effects of CETP inhibitors Torcetrapib, Anacetrapib and JTT705 on hypertension, inflammation and cancer through the combinational control of nuclear hormone receptors and fatty acid binding protein (FABP). The red, purple, and blue lines between inhibitors and off-targets indicate strong, relatively strong, and weak binding affinities, respectively. The brown and black lines between off-targets and pathways or clinical indications represent positive and negative regulation, respectively.
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