It usually reveals larger than expected spectra of targets which are causing both therapeutic and adverse effects. Such unbiased target profiles are very valuable entry points to understand which regions of the cell machinery are perturbed Estrogen Receptor Pathway by a drug. It is hence desirable to develop new specific algorithms exploiting chemical proteomics profiles. Generally, it is natural that protein interaction networks are involved to characterize drug targets, action on diseases, and potential side effects. Existing methods are mainly based on the network topology and on an integration of gene expression data and phenotype similarities. Alternatively, precise modeling of perturbations which change the protein interaction network has the potential to predict new drug targets and to provide a detailed mechanism of action simultaneously.
Beside network approaches, classical gene ontology enrichment analyses of drug targets are commonly Belinostat used which result in no detailed mechanism but identify different processes and functions of direct involvement. However, one pivotal aspect is that drug targets can perturb protein interaction networks and biological processes without being directly part of the latter. Therefore, we present a new algorithm which combines direct and peripheral perturbations of functional sub networks and exploits chemical proteomics drug target profiles. The idea of functional sub networks is based on the finding that genes associated with the same disease often share protein protein interactions and gene ontology terms.
Our algorithm estimates the drug impact on biological processes and the detailed perturbation effects can be visualized as a network, which facilitates interpretation. Furthermore, we introduce an affinity score to weigh the drug target profile on the basis of interaction strengths. We applied our algorithm to the bafetinib target profile. Bafetinib is a small molecule tyrosine kinase inhibitor in development for chronic myeloid leukemia. It has been designed to potently and specifically inhibit BCRABL and the SRC family kinase LYN, but no other SFKs, with the purpose of displaying an improved safety profile over multi kinase and pan SFK inhibitors, such as dasatinib, while retaining the advantageous dual mechanism of action.
We have recently characterized the detailed target profile of bafetinib by chemical proteomics and to interpret the complex dataset obtained is challenging. One of the most popular methods for distinguishing specific drug targets from non specific background proteins is the competition of soluble drug molecules with the affinity matrix for drug binding proteins. Comparison of the protein eluates from a competed and a non competed drug pulldowns will highlight specific binders, while non specific binding proteins will not be affected. However, even after correct identification and potentially determination of quantitative interaction parameters for distinct drug protein pairs, a global or mechanistic understanding of drug effects is but a distant goal requiring some sophisticated experimental and/or theoretical follow up. Our theoretical effort advances significantly our mechanistic understanding of the effects of bafetinib and provides others w