CoreFlow: a computational platform for integration, analysis and modeling of complex biological data

Inferring Protein-Protein Interaction Networks From Mass Spectrometry-Based Proteomic Approaches: A Mini-Review

Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.

Quantitative Proteomic Analysis in Candida albicans Using SILAC-Based Mass Spectrometry

Stable isotope labelling by amino acids in cell culture (SILAC) in conjunction with MS analysis is a sensitive and reliable technique for quantifying relative differences in protein abundance and posttranslational modifications between cell populations. We develop and utilise SILAC-MS workflows for quantitative proteomics in the fungal pathogen Candida albicans. Arginine metabolism provides important cues for escaping host defences during pathogenesis, which limits the use of auxotrophs in Candida research. Our strategy eliminates the need for engineering arginine auxotrophs for SILAC experiments and allows the use of ARG4 as selectable marker during strain construction. Cells that are auxotrophic for lysine are successfully labelled with both lysine and arginine stable isotopes. We find that prototrophic C. albicans preferentially uses exogenous arginine and down-regulates internal production, which allow it to achieve high incorporation rates. However, similar to other yeast, C. albicans is able to metabolise heavy arginine to heavy proline, which compromised the accuracy of protein quantification. A computational method is developed to correct for the incorporation of heavy proline. In addition, we utilise the developed SILAC labelling in C. albicans for the global quantitative proteomic analysis of a strain expressing a phosphatase-dead mutant Cdc14^PD .

Design of a Model Base Framework for Model Environment Construction in a Virtual Geographic Environment (VGE)

The model environment is a key component that enables a virtual geographic environment (VGE) to meet the scientific requirements for simulating dynamic phenomena and performing analyses. Considering the comprehensiveness of geographic processes and the requirements for the replication of model-based research, this paper proposes a model base framework for a model environment of a VGE that supports both model construction and modelling management, resulting in improved reproducibility. In this framework, model management includes model metadata, creation, deposition, encapsulation, integration, and adaptation; while modelling management focuses on invoking the model, model computation, and runtime control of the model. Based on this framework, to consider the problem of ever-worsening air quality, we applied the Linux-Apache-MySQL-Perl stack plus Supervisor to implement the model base to support a VGE prototype using professional meteorological and air quality models. Using this VGE prototype, we simulated a typical air pollution case for January 2010. The prototype not only illustrates how a VGE application can be built on the proposed model base, but also facilitates air quality simulations and emergency management.

Mutational Analysis of Glycogen Synthase Kinase 3β Protein Kinase Together with Kinome-Wide Binding and Stability Studies Suggests Context-Dependent Recognition of Kinases by the Chaperone Heat Shock Protein 90

The heat shock protein 90 (HSP90) and cell division cycle 37 (CDC37) chaperones are key regulators of protein kinase folding and maturation. Recent evidence suggests that thermodynamic properties of kinases, rather than primary sequences, are recognized by the chaperones. In concordance, we observed a striking difference in HSP90 binding between wild-type (WT) and kinase-dead (KD) glycogen synthase kinase 3β (GSK3β) forms. Using model cell lines stably expressing these two GSK3β forms, we observed no interaction between WT GSK3β and HSP90, in stark contrast to KD GSK3β forming a stable complex with HSP90 at a 1:1 ratio. In a survey of 91 ectopically expressed kinases in DLD-1 cells, we compared two parameters to measure HSP90 dependency: static binding and kinase stability following HSP90 inhibition. We observed no correlation between HSP90 binding and reduced stability of a kinase after pharmacological inhibition of HSP90. We expanded our stability study to >50 endogenous kinases across four cell lines and demonstrated that HSP90 dependency is context dependent. These observations suggest that HSP90 binds to its kinase client in a particular conformation that we hypothesize to be associated with the nucleotide-processing cycle. Lastly, we performed proteomics profiling of kinases and phosphopeptides in DLD-1 cells to globally define the impact of HSP90 inhibition on the kinome.

Integrative analysis of kinase networks in TRAIL-induced apoptosis provides a source of potential targets for combination therapy

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is an endogenous secreted peptide and, in preclinical studies, preferentially induces apoptosis in tumor cells rather than in normal cells. The acquisition of resistance in cells exposed to TRAIL or its mimics limits their clinical efficacy. Because kinases are intimately involved in the regulation of apoptosis, we systematically characterized kinases involved in TRAIL signaling. Using RNA interference (RNAi) loss-of-function and cDNA overexpression screens, we identified 169 protein kinases that influenced the dynamics of TRAIL-induced apoptosis in the colon adenocarcinoma cell line DLD-1. We classified the kinases as sensitizers or resistors or modulators, depending on the effect that knockdown and overexpression had on TRAIL-induced apoptosis. Two of these kinases that were classified as resistors were PX domain-containing serine/threonine kinase (PXK) and AP2-associated kinase 1 (AAK1), which promote receptor endocytosis and may enable cells to resist TRAIL-induced apoptosis by enhancing endocytosis of the TRAIL receptors. We assembled protein interaction maps using mass spectrometry-based protein interaction analysis and quantitative phosphoproteomics. With these protein interaction maps, we modeled information flow through the networks and identified apoptosis-modifying kinases that are highly connected to regulated substrates downstream of TRAIL. The results of this analysis provide a resource of potential targets for the development of TRAIL combination therapies to selectively kill cancer cells.