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

Pasculescu, Adrian; Schoof, Erwin M.; Creixell, Pau; Zheng, Yong; Olhovsky, Marina; Tian, Ruijun; So, Jonathan; Vanderlaan, Rachel D.; Pawson, Tony; Linding, Rune; Colwill, Karen

doi:10.1016/j.jprot.2014.01.023

Cited by 11 publications

(10 citation statements)

References 20 publications

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“…Only peptides with a MaxQuant posterior error probability [PEP] of Յ0.01 were retained for further analysis. Corrections for labeling inefficiencies and proline-to-arginine conversions were performed as previously described (29). For each peptide, three pairwise intensity ratios from the L, M, and H channels were obtained, reflecting the relative ratio in the amount of the peptide from the three treatment conditions.…”

Section: Msmentioning

confidence: 99%

“…Only proteins with at least 2 distinct peptides with posterior error probabilities of Յ0.01 were retained. Corrections were made for nonincorporated label or arginine-to-proline conversions (29), nonreproducible outliers were removed, and the values from two independent experiments were combined (Pearson correlation coefficient [R 2 ] ϭ 0.54). We quantified 232 proteins, of which 77 were protein kinases.…”

Section: Proteomic Profiling Of Kinase Response To Hsp90 Inhibitionmentioning

confidence: 99%

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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

Jin

Tian

Pasculescu

et al. 2016

Molecular and Cellular Biology

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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 3␤ (GSK3␤) forms. Using model cell lines stably expressing these two GSK3␤ forms, we observed no interaction between WT GSK3␤ and HSP90, in stark contrast to KD GSK3␤ 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. As protein kinases regulate a multitude of cellular functions, it is imperative that their activity be highly regulated. This meticulous control is achieved through a variety of mechanisms, including phosphorylation, proteolytic processing, and direct engagement with other molecules, including chaperones. The heat shock protein 90 (HSP90) chaperone, in particular, is recruited to kinase clients to assist in their nascent folding and, depending on the kinase, to further contribute to its stability and function (1). A recent kinome-wide study of pairwise HSP90-kinase interactions observed various strengths of binding affinities between protein kinases and HSP90 (2), consistent with HSP90 assuming different roles depending on the client. HSP90 typically requires its cochaperone cell division cycle 37 (CDC37) to engage a kinase client, but the mechanism for kinase selection is not fully understood. There is no particular kinase class that forms stronger interactions with HSP90 (2), and there are even distinct binding specificities for closely related kinases. For example, epidermal growth factor receptor (EGFR) does not associate with HSP90, but close family member Erb-b2 receptor tyrosine kinase 2 (ERBB2) forms strong interactions with HSP90, and inhibition of HSP90 by geldanamycin induces rapid proteasome-mediated degradation of ERBB2 (3-5). Thus, amino acid sequences or structural features conserved in close family members are unlikely to be primary determinants for chaperone recognition, which is not surprising given that protein kinases represent a fraction of HSP90 clients (6). Instead, it appears that HSP90 recognizes kinases in a par...

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Section: Msmentioning

confidence: 99%

Section: Proteomic Profiling Of Kinase Response To Hsp90 Inhibitionmentioning

confidence: 99%

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

Jin

Tian

Pasculescu

et al. 2016

Molecular and Cellular Biology

Self Cite

View full text Add to dashboard Cite

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“…We deployed a MySQL database to store these model definitions and implement the model base. To improve the ability to replicate model-based research, we implemented our framework using LAMP architecture to build the modelling management features, including parameterizations and runtime control [24,58,59]. LAMP consists of several open source projects that provide a complete and robust solution for the operating system, the web server, the database, and a scripting language.…”

Section: Model Base Design and Implementationmentioning

confidence: 99%

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

Zhang

Zheng

et al. 2017

IJGI

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Abstract: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.

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“…Data output from the Analyst software was uploaded into CoreFlow, a workflow management system (100). Fluorescence polarization (FP) was derived from the parallel (I || ) and perpendicular (I ⊥ ) intensities using the following equation:…”

Section: Rnai Screenmentioning

confidence: 99%

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

Pasculescu

Dai

et al. 2015

Sci. Signal.

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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.

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

Cited by 11 publications

References 20 publications

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

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

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

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

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