Abstract
The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.
Highlights
Identifying novel drug targets and prioritizing proteins for target validation and therapeutic development are essential activities in modern mechanism-driven drug discovery, and are key if we are to benefit from large-scale genomic initiatives [1]
The need for well-validated targets for drug discovery is more pressing than ever, especially in cancer in view of resistance to current therapeutics coupled with late stage drug failures
We identify clear distinctions between the network environment of cancer-drug targets and targets from other therapeutics areas. We use these distinguishing properties to build a predictive methodology to prioritize potential drug targets based on network parameters alone and PLOS Computational Biology | DOI:10.1371/journal.pcbi
Summary
Identifying novel drug targets and prioritizing proteins for target validation and therapeutic development are essential activities in modern mechanism-driven drug discovery, and are key if we are to benefit from large-scale genomic initiatives [1]. While many genes have been identified as disease-causing (see for example reports on cancer [10,11] and cardiovascular disease [12]), the products of relatively few of these have become targets for approved therapeutics. The challenges facing researchers attempting to target a gene and its product proteins for clinical application lie both in validating their pathogenic role and in their technical ‘doability’. As well as possessing a pocket or interface suitable for drug binding, a potential drug target must exert an appropriate influence on the system, enabling a drug to have a selective and enduring therapeutic effect. It is essential that the network environment of a potential drug target should be incorporated into target selection rationale
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