We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available.

Abstract. Public biological databases contain vast amounts of rich data that can also be used to create and evaluate new biological hypothesis. We propose a method for link discovery in biological databases, i.e., for prediction and evaluation of implicit or previously unknown connections between biological entities and concepts. In our framework, information extracted from available databases is represented as a graph, where vertices correspond to entities and concepts, and edges represent known, annotated relationships between vertices. A link, an (implicit and possibly unknown) relation between two entities is manifested as a path or a subgraph connecting the corresponding vertices. We propose measures for link goodness that are based on three factors: edge reliability, relevance, and rarity. We handle these factors with a proper probabilistic interpretation. We give practical methods for finding and evaluating links in large graphs and report experimental results with Alzheimer genes and protein interactions.

Hyperactivated RAS drives progression of many human malignancies. However, oncogenic activity of RAS is dependent on simultaneous inactivation of protein phosphatase 2A (PP2A) activity. Although PP2A is known to regulate some of the RAS effector pathways, it has not been systematically assessed how these proteins functionally interact. Here we have analyzed phosphoproteomes regulated by either RAS or PP2A, by phosphopeptide enrichment followed by mass-spectrometry-based label-free quantification. To allow data normalization in situations where depletion of RAS or PP2A inhibitor CIP2A causes a large uni-directional change in the phosphopeptide abundance, we developed a novel normalization strategy, named pairwise normalization. This normalization is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of the pairwise normalization was verified by various independent methods. Additionally, we demonstrate how the selected normalization method influences the downstream analyses and interpretation of pathway activities. Consequently, bioinformatics analysis of RAS and CIP2A regulated phosphoproteomes revealed a significant overlap in their functional pathways. This is most likely biologically meaningful as we observed a synergistic survival effect between CIP2A and RAS expression as well as KRAS activating mutations in TCGA pan-cancer data set, and synergistic relationship between CIP2A and KRAS depletion in colony growth assays.

Influenza A viruses cause infections in the human respiratory tract and give rise to annual seasonal outbreaks, as well as more rarely dreaded pandemics. Influenza A viruses become quickly resistant to the virus-directed antiviral treatments, which are the current main treatment options. A promising alternative approach is to target host cell factors that are exploited by influenza viruses. To this end, we characterized the phosphoproteome of influenza A virus infected primary human macrophages to elucidate the intracellular signaling pathways and critical host factors activated upon influenza infection. We identified 1675 phosphoproteins, 4004 phosphopeptides and 4146 nonredundant phosphosites. The phosphorylation of 1113 proteins (66%) was regulated upon infection, highlighting the importance of such global phosphoproteomic profiling in primary cells. Notably, 285 of the identified phosphorylation sites have not been previously described in publicly available phosphorylation databases, despite many published large-scale phosphoproteome studies using human and mouse cell lines. Systematic bioinformatics analysis of the phosphoproteome data indicated that the phosphorylation of proteins involved in the ubiquitin/proteasome pathway (such as TRIM22 and TRIM25) and antiviral responses (such as MAVS) changed in infected macrophages. Proteins known to play roles in small GTPase-, mitogen-activated protein kinase-, and cyclin-dependent kinase- signaling were also regulated by phosphorylation upon infection. In particular, the influenza infection had a major influence on the phosphorylation profiles of a large number of cyclin-dependent kinase substrates. Functional studies using cyclin-dependent kinase inhibitors showed that the cyclin-dependent kinase activity is required for efficient viral replication and for activation of the host antiviral responses. In addition, we show that cyclin-dependent kinase inhibitors protect IAV-infected mice from death. In conclusion, we provide the first comprehensive phosphoproteome characterization of influenza A virus infection in primary human macrophages, and provide evidence that cyclin-dependent kinases represent potential therapeutic targets for more effective treatment of influenza infections.