A computational strategy applicable to different  drug profilesAuthor Summary Protein interaction data are accumulating rapidly and, although imperfect and incomplete, they provide a valuable global description of the complex interplay of proteins in a human cell. In parallel, modern proteomics technologies ETA-receptor make it possible to measure in an unbiased manner the protein targets of a drug. Such data reveal multiple targets in a view that contrasts with a previously prevalent paradigm that drugs had single or a very limited number of targets. In this context of newly available systems level data and more precise and complete information about drug interactions, it is natural to try to determine the global perturbation exerted by a drug on a human cell to identify potential side effects and additional indications.
We present a computational method that aims at making such predictions and apply it to bafetinib, a recently developed leukemia drug. We show that meaningful predictions of additional applications to other cancers BSI-201 or resistant cases and likely side effects are obtained that are not straightforward to determine with existing algorithms. Our method has a strong potential to be applicable to other drugs. Materials and Methods Our computational approach to predict the impact of bafetinib on a functional network is based on the human protein protein interaction network, on the annotation of its nodes and on a drug target profile associated with an affinity measure. Human protein interaction network The network is constructed from protein protein interactions found in the public interaction databases HPRD, MINT, Intact, DIP and BioGRID.
Furthermore, it is supplemented with published interactions of the BCR ABL core complex which is the primary target of bafetinib in chronic myeloid leukemia. The resulting undirected network contains 11505 proteins and 80363 interactions. Uniform functional sub network The human network of all known protein protein interactions is associated with its biological processes of gene ontology derived from UniProtKB and Entrez Gene. All ancestors of the GO tree are assigned in addition to achieve a complete and consistent annotation. In total, the human interaction network consists of 6390 different BP terms. 8939 nodes of the human interactome are at least associated with one biological process.
A uniform functional sub network is a connected fraction of the interactome, in which all the proteins share the same function, i.e, one unique GO term. The interactome can contain multiple disjoint functional sub networks for the same annotation. Drug target profile The recently published drug target profile of the kinase inhibitor bafetinib measured in the cell line K562 is used. Rix et al took three quality criteria into account: The drug target profile is devoid of proteins in the K562 core proteome. No frequent hitters are included. The proteins must be seen in replicates. In addition, splice variants and protein fragments are excluded. The 33 proteins are listed in Table S1. Perturbation of function Bafetinib can impact the uniform functional sub networks in two ways via its targets : The drug inhibits directly a node of the uniform functional sub network. The drug target interacts with the uniform functional subn