History of protein–protein interactions: From egg‐white to complex networks

Braun, Pascal; Gingras, Anne‐Claude

doi:10.1002/pmic.201100563

Cited by 248 publications

(168 citation statements)

References 276 publications

(285 reference statements)

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“…Thus, identification of PPIs is critical to understanding the protein functions. Over the past few decades, many experimental techniques, such as yeast two-hybrid systems (Y2H) [4], mass spectrometry (MS) [5], tandem affinity purification (TAP) [6], and protein chip [7], have been developed to detect PPIs. These experimental approaches have provided an enormous amount of PPI data, which have facilitated the development of PPI databases such as IntAct [8], BioGRID [9], and HPRD [10].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2017

RJLBPCS

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Protein-protein interactions (PPIs) play an essential role in various biological processes. A range of computational methods have been proposed to predict PPIs from protein sequences. Among these, homology-based methods and machine-learning methods have been widely used. However, to the best of our knowledge, these two methods have not been compared using the same dataset. Thus in this study, we have developed both homology-based and machine-learning methods to predict PPIs from amino-acid sequences and compared the prediction results. In the homology-based method, BLASTP search was used to identify sequence homology. Regarding the machine-learning methods, two popular methods, support vector machine and random forest, as well as six different protein features, were employed to build classifiers. We collected the PPI pairs with high-confidence scores from HitPredict4 to build the positive dataset and we built the negative dataset from the Negatome 2.0 database, in which non-interacting pairs were verified by experiments and 3D structure analysis. Our results show that machine-learning methods achieved better performance than homology-based method but there are many PPIs that are predicted only by the homology-based method. The integration of the two methods is expected to enhance the performance.KEYWORDS: protein-protein interactions, protein-protein interaction site prediction, support vector machine, machine learning, homology search.

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Section: Introductionmentioning

confidence: 99%

Untitled

2017

RJLBPCS

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“…Finally it is worth mentioning that complex networks emerge not only in power grids and other human-made systems-including Internet [129,130], the topology of web pages (where the nodes are individual webs and edges are hyperlinks) [57,62], airline routes [131], electronic circuits [132] or socioeconomic systems [133]-but also in systems stemming from Nature, e.g., evolution [134], metabolic networks [135], protein interactions [136] and food webs [137]. For more details regarding the description and bibliographic references of these complex networks, which are outside the scope of this paper, we refer the interested reader to the recent books [62,90].…”

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confidence: 99%

A Critical Review of Robustness in Power Grids Using Complex Networks Concepts

et al. 2015

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This paper reviews the most relevant works that have investigated robustness in power grids using Complex Networks (CN) concepts. In this broad field there are two different approaches. The first one is based solely on topological concepts, and uses metrics such as mean path length, clustering coefficient, efficiency and betweenness centrality, among many others. The second, hybrid approach consists of introducing (into the CN framework) some concepts from Electrical Engineering (EE) in the effort of enhancing the topological approach, and uses novel, more efficient electrical metrics such as electrical betweenness, net-ability, and others. There is however a controversy about whether these approaches are able to provide insights into all aspects of real power grids. The CN community argues that the topological approach does not aim to focus on the detailed operation, but to discover the unexpected emergence of collective behavior, while part of the EE community asserts that this leads to an excessive simplification. Beyond this open debate it seems to be no predominant structure (scale-free, small-world) in high-voltage transmission power grids, the vast majority of power grids studied so far. Most of them have in common that they are vulnerable to targeted attacks on the most connected nodes and robust to random failure. In this respect there are only a few works that propose strategies to improve robustness such as intentional islanding, restricted link addition, microgrids and Energies 2015, 8 9212 smart grids, for which novel studies suggest that small-world networks seem to be the best topology.

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“…Protein-protein interactions (PPIs) are fundamental to biology 1 and are tightly regulated via spatiotemporal mechanisms across many time and length scales. Studies on cell signaling on the cell membrane, for example, have revealed dynamic, nanoscale spatial compartments that facilitate specific PPIs and cellular processes 2 .…”

Section: Introductionmentioning

confidence: 99%

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Nickerson

Huang

Lin

et al. 2015

JoVE

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Protein-protein interactions (PPIs) are key molecular events to biology. However, it remains a challenge to visualize PPIs with sufficient resolution and sensitivity in cells because the resolution of conventional light microscopy is diffraction-limited to ~250 nm. By combining bimolecular fluorescence complementation (BiFC) with photoactivated localization microscopy (PALM), PPIs can be visualized in cells with single molecule sensitivity and nanometer spatial resolution. BiFC is a commonly used technique for visualizing PPIs with fluorescence contrast, which involves splitting of a fluorescent protein into two non-fluorescent fragments. PALM is a recent superresolution microscopy technique for imaging biological samples at the nanometer and single molecule scales, which uses phototransformable fluorescent probes such as photoactivatable fluorescent proteins (PA-FPs). BiFC-PALM was demonstrated by splitting PAmCherry1, a PA-FP compatible with PALM, for its monomeric nature, good single molecule brightness, high contrast ratio, and utility for stoichiometry measurements. When split between amino acids 159 and 160, PAmCherry1 can be made into a BiFC probe that reconstitutes efficiently at 37 °C with high specificity to PPIs and low non-specific reconstitution. Ras-Raf interaction is used as an example to show how BiFC-PALM helps to probe interactions at the nanometer scale and with single molecule resolution. Their diffusion can also be tracked in live cells using single molecule tracking (smt-) PALM. In this protocol, factors to consider when designing the fusion proteins for BiFC-PALM are discussed, sample preparation, image acquisition, and data analysis steps are explained, and a few exemplary results are showcased. Providing high spatial resolution, specificity, and sensitivity, BiFC-PALM is a useful tool for studying PPIs in intact biological samples. Video LinkThe video component of this article can be found at

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History of protein–protein interactions: From egg‐white to complex networks

Cited by 248 publications

References 276 publications

Untitled

Untitled

A Critical Review of Robustness in Power Grids Using Complex Networks Concepts

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

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