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An optimized optogenetic clustering tool for probing protein interaction and function.

ABSTRACT: The Arabidopsis photoreceptor cryptochrome 2 (CRY2) was previously used as an optogenetic module, allowing spatiotemporal control of cellular processes with light. Here we report the development of a new CRY2-derived optogenetic module, 'CRY2olig', which induces rapid, robust, and reversible protein oligomerization in response to light. Using this module, we developed a novel protein interaction assay, Light-Induced Co-clustering, that can be used to interrogate protein interaction dynamics in live cells. In addition to use probing protein interactions, CRY2olig can also be used to induce and reversibly control diverse cellular processes with spatial and temporal resolution. Here we demonstrate disrupting clathrin-mediated endocytosis and promoting Arp2/3-mediated actin polymerization with light. These new CRY2-based approaches expand the growing arsenal of optogenetic strategies to probe cellular function.

SUBMITTER: Taslimi A

PROVIDER: S-EPMC4170572 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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An optimized optogenetic clustering tool for probing protein interaction and function.

Taslimi Amir A Vrana Justin D JD Chen Daniel D Borinskaya Sofya S Mayer Bruce J BJ Kennedy Matthew J MJ Tucker Chandra L CL

Nature communications 20140918

The Arabidopsis photoreceptor cryptochrome 2 (CRY2) was previously used as an optogenetic module, allowing spatiotemporal control of cellular processes with light. Here we report the development of a new CRY2-derived optogenetic module, 'CRY2olig', which induces rapid, robust, and reversible protein oligomerization in response to light. Using this module, we developed a novel protein interaction assay, Light-Induced Co-clustering, that can be used to interrogate protein interaction dynamics in l ...[more]

PMID: 25233328

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Project description:BackgroundProtein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies). High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE).ResultsA test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes.ConclusionThis analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions. The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks.

Dataset Information

An optimized optogenetic clustering tool for probing protein interaction and function.

Publications

An optimized optogenetic clustering tool for probing protein interaction and function.

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