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Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

TLDR
It is found that 21% of the proteins in the PPI network are indispensable, Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states.
Abstract
The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

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Citations
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Structure-based control of complex networks with nonlinear dynamics

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Identifying drug–target interactions based on graph convolutional network and deep neural network

TL;DR: A novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification that first uses a graph convolved network to learn the features for each DPP, and uses a deep neural network to predict the final label.
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