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Invited review: Small GTPases and their GAPs.

ABSTRACT: Widespread utilization of small GTPases as major regulatory hubs in many different biological systems derives from a conserved conformational switch mechanism that facilitates cycling between GTP-bound active and GDP-bound inactive states under control of guanine nucleotide exchange factors (GEFs) and GTPase activating proteins (GAPs), which accelerate slow intrinsic rates of activation by nucleotide exchange and deactivation by GTP hydrolysis, respectively. Here we review developments leading to current understanding of intrinsic and GAP catalyzed GTP hydrolytic reactions in small GTPases from structural, molecular and chemical mechanistic perspectives. Despite the apparent simplicity of the GTPase cycle, the structural bases underlying the hallmark hydrolytic reaction and catalytic acceleration by GAPs are considerably more diverse than originally anticipated. Even the most fundamental aspects of the reaction mechanism have been challenging to decipher. Through a combination of experimental and in silico approaches, the outlines of a consensus view have begun to emerge for the best studied paradigms. Nevertheless, recent observations indicate that there is still much to be learned. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 431-448, 2016.

SUBMITTER: Mishra AK

PROVIDER: S-EPMC5439442 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Invited review: Small GTPases and their GAPs.

Mishra Ashwini K AK Lambright David G DG

Biopolymers 20160801 8

Widespread utilization of small GTPases as major regulatory hubs in many different biological systems derives from a conserved conformational switch mechanism that facilitates cycling between GTP-bound active and GDP-bound inactive states under control of guanine nucleotide exchange factors (GEFs) and GTPase activating proteins (GAPs), which accelerate slow intrinsic rates of activation by nucleotide exchange and deactivation by GTP hydrolysis, respectively. Here we review developments leading t ...[more]

PMID: 26972107

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Project description:Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V's) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V's) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.

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Invited review: Small GTPases and their GAPs.

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Invited review: Small GTPases and their GAPs.

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