An optimized optogenetic clustering tool for probing protein interaction and function.
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ABSTRACT:
SUBMITTER: Taslimi A
PROVIDER: S-EPMC4170572 | biostudies-literature | 2014 Sep
REPOSITORIES: biostudies-literature
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Project description:Optogenetic protein dimerization systems are powerful tools to investigate the biochemical networks that cells use to make decisions and coordinate their activities. These tools, including the improved Light-Inducible Dimer (iLID) system, offer the ability to selectively recruit components to subcellular locations, such as micron-scale regions of the plasma membrane. In this way, the role of individual proteins within signaling networks can be examined with high spatiotemporal resolution. Currently, consistent recruitment is limited by heterogeneous optogenetic component expression, and spatial precision is diminished by protein diffusion, especially over long time scales. Here, we address these challenges within the iLID system with alternative membrane anchoring domains and fusion configurations. Using live cell imaging and mathematical modeling, we demonstrate that the anchoring strategy affects both component expression and diffusion, which in turn impact recruitment strength, kinetics, and spatial dynamics. Compared to the commonly used C-terminal iLID fusion, fusion proteins with large N-terminal anchors show stronger local recruitment, slower diffusion of recruited components, efficient recruitment over wider gene expression ranges, and improved spatial control over signaling outputs. We also define guidelines for component expression regimes for optimal recruitment for both cell-wide and subcellular recruitment strategies. Our findings highlight key sources of imprecision within light-inducible dimer systems and provide tools that allow greater control of subcellular protein localization across diverse cell biological applications.
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Project description:Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.
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Project description:Protein homo-oligomerization is an important molecular mechanism in many biological processes. Therefore, the ability to control protein homo-oligomerization allows the manipulation and interrogation of numerous cellular events. To achieve this, cryptochrome 2 (CRY2) from Arabidopsis thaliana has been recently utilized for blue light-dependent spatiotemporal control of protein homo-oligomerization. However, limited knowledge on molecular characteristics of CRY2 obscures its widespread applications. Here, we identify important determinants for efficient cryptochrome 2 clustering and introduce a new CRY2 module, named ''CRY2clust'', to induce rapid and efficient homo-oligomerization of target proteins by employing diverse fluorescent proteins and an extremely short peptide. Furthermore, we demonstrate advancement and versatility of CRY2clust by comparing against previously reported optogenetic tools. Our work not only expands the optogenetic clustering toolbox but also provides a guideline for designing CRY2-based new optogenetic modules.Cryptochrome 2 (CRY2) from A. thaliana can be used to control light-dependent protein homo-oligomerization, but the molecular mechanism of CRY2 clustering is not known, limiting its application. Here the authors identify determinants of CRY2 clustering and engineer fusion partners to modulate clustering efficiency.
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Project description:BackgroundThis paper describes an automated method for finding clusters of interconnected proteins in protein interaction networks and retrieving protein annotations associated with these clusters.ResultsProtein interaction graphs were separated into subgraphs of interconnected proteins, using the JUNG implementation of Girvan and Newman's Edge-Betweenness algorithm. Functions were sought for these subgraphs by detecting significant correlations with the distribution of Gene Ontology terms which had been used to annotate the proteins within each cluster. The method was implemented using freely available software (JUNG and the R statistical package). Protein clusters with significant correlations to functional annotations could be identified and included groups of proteins know to cooperate in cell metabolism. The method appears to be resilient against the presence of false positive interactions.ConclusionThis method provides a useful tool for rapid screening of small to medium size protein interaction datasets.
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Project description:BackgroundDuring the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings.MethodsIn this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes.ResultsThe efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results.ConclusionGIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods.
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Project description:BackgroundProtein-protein interactions can be considered the basic skeleton for living organism self-organization and homeostasis. Impressive quantities of experimental data are being obtained and computational tools are essential to integrate and to organize this information. This paper presents Protopia, a biological tool that offers a way of searching for proteins and their interactions in different Protein Interaction Web Databases, as a part of a multidisciplinary initiative of our institution for the integration of biological data http://asp.uma.es.ResultsThe tool accesses the different Databases (at present, the free version of Transfac, DIP, Hprd, Int-Act and iHop), and results are expressed with biological protein names or databases codes and can be depicted as a vector or a matrix. They can be represented and handled interactively as an organic graph. Comparison among databases is carried out using the Uniprot codes annotated for each protein.ConclusionThe tool locates and integrates the current information stored in the aforementioned databases, and redundancies among them are detected. Results are compatible with the most important network analysers, so that they can be compared and analysed by other world-wide known tools and platforms. The visualization possibilities help to attain this goal and they are especially interesting for handling multiple-step or complex networks.
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Project description:The hallmarks of neurodegenerative diseases, including neural fibrils, reactive oxygen species, and cofilin-actin rods, present numerous challenges in the development of in vivo diagnostic tools. Biomarkers such as β-amyloid (Aβ) fibrils and Tau tangles in Alzheimer's disease are accessible only via invasive cerebrospinal fluid assays, and reactive oxygen species can be fleeting and challenging to monitor in vivo Although remaining a challenge for in vivo detection, the protein-protein interactions underlying these disease-specific biomarkers present opportunities for the engineering of in vitro pathology-sensitive biosensors. These tools can be useful for investigating early stage events in neurodegenerative diseases in both cellular and animal models and may lead to clinically useful reagents. Here, we report a light- and cellular stress-gated protein switch based on cofilin-actin rod formation, occurring in stressed neurons in the Alzheimer's disease brain and following ischemia. By coupling the stress-sensitive cofilin-actin interaction with the light-responsive Cry2-CIB blue-light switch, referred to hereafter as the CofActor, we accomplished both light- and energetic/oxidative stress-gated control of this interaction. Site-directed mutagenesis of both cofilin and actin revealed residues critical for sustaining or abrogating the light- and stress-gated response. Of note, the switch response varied depending on whether cellular stress was generated via glycolytic inhibition or by both glycolytic inhibition and azide-induced ATP depletion. We also demonstrate light- and cellular stress-gated switch function in cultured hippocampal neurons. CofActor holds promise for the tracking of early stage events in neurodegeneration and for investigating actin's interactions with other proteins during cellular stress.
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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.
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