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An integrated workflow for charting the human interaction proteome: insights into the PP2A system.

ABSTRACT: Protein complexes represent major functional units for the execution of biological processes. Systematic affinity purification coupled with mass spectrometry (AP-MS) yielded a wealth of information on the compendium of protein complexes expressed in Saccharomyces cerevisiae. However, global AP-MS analysis of human protein complexes is hampered by the low throughput, sensitivity and data robustness of existing procedures, which limit its application for systems biology research. Here, we address these limitations by a novel integrated method, which we applied and benchmarked for the human protein phosphatase 2A system. We identified a total of 197 protein interactions with high reproducibility, showing the coexistence of distinct classes of phosphatase complexes that are linked to proteins implicated in mitosis, cell signalling, DNA damage control and more. These results show that the presented analytical process will substantially advance throughput and reproducibility in future systematic AP-MS studies on human protein complexes.

SUBMITTER: Glatter T

PROVIDER: S-EPMC2644174 | biostudies-other | 2009

REPOSITORIES: biostudies-other

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An integrated workflow for charting the human interaction proteome: insights into the PP2A system.

Glatter Timo T Wepf Alexander A Aebersold Ruedi R Gstaiger Matthias M

Molecular systems biology 20090120

Protein complexes represent major functional units for the execution of biological processes. Systematic affinity purification coupled with mass spectrometry (AP-MS) yielded a wealth of information on the compendium of protein complexes expressed in Saccharomyces cerevisiae. However, global AP-MS analysis of human protein complexes is hampered by the low throughput, sensitivity and data robustness of existing procedures, which limit its application for systems biology research. Here, we address ...[more]

PMID: 19156129

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Project description:BackgroundDeciphering protein-protein interaction (PPI) in domain level enriches valuable information about binding mechanism and functional role of interacting proteins. The 3D structures of complex proteins are reliable source of domain-domain interaction (DDI) but the number of proven structures is very limited. Several resources for the computationally predicted DDI have been generated but they are scattered in various places and their prediction show erratic performances. A well-organized PPI and DDI analysis system integrating these data with fair scoring system is necessary.MethodWe integrated three structure-based DDI datasets and twenty computationally predicted DDI datasets and constructed an interaction analysis system, named IDDI, which enables to browse protein and domain interactions with their relationships. To integrate heterogeneous DDI information, a novel scoring scheme is introduced to determine the reliability of DDI by considering the prediction scores of each DDI and the confidence levels of each prediction method in the datasets, and independencies between predicted datasets. In addition, we connected this DDI information to the comprehensive PPI information and developed a unified interface for the interaction analysis exploring interaction networks at both protein and domain level.ResultIDDI provides 204,705 DDIs among total 7,351 Pfam domains in the current version. The result presents that total number of DDIs is increased eight times more than that of previous studies. Due to the increment of data, 50.4% of PPIs could be correlated with DDIs which is more than twice of previous resources. Newly designed scoring scheme outperformed the previous system in its accuracy too. User interface of IDDI system provides interactive investigation of proteins and domains in interactions with interconnected way. A specific example is presented to show the efficiency of the systems to acquire the comprehensive information of target protein with PPI and DDI relationships. IDDI is freely available at http://pcode.kaist.ac.kr/iddi/.

Project description:IntroductionPosture extraction from videos is fundamental to many real-world applications, including health screenings. In this study, we extend the utility and specificity of a well-established protocol, the balance beam, for examining balance and active motor coordination in adult mice of both sexes.ObjectivesThe primary objective of this study is to design a workflow for analyzing the postures of mice walking on a balance beam.MethodsWe developed new tools and scripts based on the FluoRender architecture, which can interact with DeepLabCut (DLC) through Python code. Notably, twenty input videos were divided into four feature point groups (head, body, tail, and feet), based on camera positions relative to the balance beam (left and right), and viewing angles (90° and 45° from the beam). We determined key feature points on the mouse to track posture in a still video frame. We extracted a standard walk cycle (SWC) by focusing on foot movements, which were computed by a weighted average of the extracted walk cycles. The correlation of each walk cycle to the SWC was used as the weight.ResultsWe learned that positions of the camera angles significantly improved the performance of 2D pose estimation (90°) and 3D (45°). Comparing the SWCs from age-matched mice, we found a consistent pattern of supporting feet on the beam. Two feet were consistently on the beam followed by three feet and another three feet in a 2-3-3 pattern. However, this pattern can be mirrored among individual subjects. A subtle phase shift of foot movement was also observed from the SWCs. Furthermore, we compared the SWCs with speed values to reveal anomalies in mouse walk postures. Some anomalies can be explained as the start or finish of the traversal, while others may be correlated to the distractions of the test environment, which will need further investigation.ConclusionOur posture analysis workflow improves the classical behavioral testing and analysis, allowing the detection of subtle, but significant differences in vestibular function and motor coordination.

Dataset Information

An integrated workflow for charting the human interaction proteome: insights into the PP2A system.

Publications

An integrated workflow for charting the human interaction proteome: insights into the PP2A system.

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