The FGGY carbohydrate kinase family : insights into the evolution of functional specificities

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in PLoS Computational Biology 7 (2011): e1002318, doi:10.1371/journal.pcbi.1002318.Function diversification in large protein families is a major mechanism driving expansion of cellular networks, providing organisms with new metabolic capabilities and thus adding to their evolutionary success. However, our understanding of the evolutionary mechanisms of functional diversity in such families is very limited, which, among many other reasons, is due to the lack of functionally well-characterized sets of proteins. Here, using the FGGY carbohydrate kinase family as an example, we built a confidently annotated reference set (CARS) of proteins by propagating experimentally verified functional assignments to a limited number of homologous proteins that are supported by their genomic and functional contexts. Then, we analyzed, on both the phylogenetic and the molecular levels, the evolution of different functional specificities in this family. The results show that the different functions (substrate specificities) encoded by FGGY kinases have emerged only once in the evolutionary history following an apparently simple divergent evolutionary model. At the same time, on the molecular level, one isofunctional group (L-ribulokinase, AraB) evolved at least two independent solutions that employed distinct specificity-determining residues for the recognition of a same substrate (L-ribulose). Our analysis provides a detailed model of the evolution of the FGGY kinase family. It also shows that only combined molecular and phylogenetic approaches can help reconstruct a full picture of functional diversifications in such diverse families.This study was funded by NIH and DOE grants

Sustainability, epistemology, ecocentric business and marketing strategy:ideology, reality and vision

This conceptual article examines the relationship between marketing and sustainability through the dual lenses of anthropocentric and ecocentric epistemology. Using the current anthropocentric epistemology and its associated dominant social paradigm, corporate ecological sustainability in commercial practice and business school research and teaching is difficult to achieve. However, adopting an ecocentric epistemology enables the development of an alternative business and marketing approach that places equal importance on nature, the planet, and ecological sustainability as the source of human and other species' well-being, as well as the source of all products and services. This article examines ecocentric, transformational business, and marketing strategies epistemologically, conceptually and practically and thereby proposes six ecocentric, transformational, strategic marketing universal premises as part of a vision of and solution to current global un-sustainability. Finally, this article outlines several opportunities for management practice and further research

The practical politics of sharing personal data

The focus of this paper is upon how people handle the sharing of personal data as an interactional concern. A number of ethnographic studies of domestic environments are drawn upon in order to articulate a range of circumstances under which data may be shared. In particular a distinction is made between the in situ sharing of data with others around you and the sharing of data with remote parties online. A distinction is also drawn between circumstances of purposefully sharing data in some way and circumstances where the sharing of data is incidental or even unwitting. On the basis of these studies a number of the organisational features of how people seek to manage the ways in which their data is shared are teased out. The paper then reflects upon how data sharing practices have evolved to handle the increasing presence of digital systems in people’s environments and how these relate to the ways in which people traditionally orient to the sharing of information. In conclusion a number of ways are pointed out in which the sharing of data remains problematic and there is a discussion of how systems may need to adapt to better support people’s data sharing practices in the future

Sympathetic cooling of positrons to cryogenic temperatures for antihydrogen production

The positron, the antiparticle of the electron, predicted by Dirac in 1931 and discovered by Anderson in 1933, plays a key role in many scientific and everyday endeavours. Notably, the positron is a constituent of antihydrogen, the only long-lived neutral antimatter bound state that can currently be synthesized at low energy, presenting a prominent system for testing fundamental symmetries with high precision. Here, we report on the use of laser cooled Be+ ions to sympathetically cool a large and dense plasma of positrons to directly measured temperatures below 7 K in a Penning trap for antihydrogen synthesis. This will likely herald a significant increase in the amount of antihydrogen available for experimentation, thus facilitating further improvements in studies of fundamental symmetries

Investigation of the fine structure of antihydrogen

At the historic Shelter Island Conference on the Foundations of Quantum Mechanics in 1947, Willis Lamb reported an unexpected feature in the fne structure of atomic hydrogen: a separation of the 2S$_{1/2}$ and 2P$_{1/2}$ states1. The observation of this separation, now known as the Lamb shift, marked an important event in the evolution of modern physics, inspiring others to develop the theory of quantum electrodynamics2–5. Quantum electrodynamics also describes antimatter, but it has only recently become possible to synthesize and trap atomic antimatter to probe its structure. Mirroring the historical development of quantum atomic physics in the twentieth century, modern measurements on anti-atoms represent a unique approach for testing quantum electrodynamics and the foundational symmetries of the standard model. Here we report measurements of the fne structure in the $n=$ 2 states of antihydrogen, the antimatter counterpart of the hydrogen atom. Using optical excitation of the 1S–2P Lyman-α transitions in antihydrogen6 , we determine their frequencies in a magnetic feld of 1 tesla to a precision of 16 parts per billion. Assuming the standard Zeeman and hyperfne interactions, we infer the zero-feld fne-structure splitting (2P$_{1/2}$–2P$_{3/2}$) in antihydrogen. The resulting value is consistent with the predictions of quantum electrodynamics to a precision of 2 per cent. Using our previously measured value of the 1S–2S transition frequency6,7, we fnd that the classic Lamb shift in antihydrogen (2S$_{1/2}$–2P$_{1/2}$ splitting at zero feld) is consistent with theory at a level of 11 per cent. Our observations represent an important step towards precision measurements of the fne structure and the Lamb shift in the antihydrogen spectrum as tests of the charge– parity–time symmetry8 and towards the determination of other fundamental quantities, such as the antiproton charge radius9,10, in this antimatter system

H2r: Identification of evolutionary important residues by means of an entropy based analysis of multiple sequence alignments

BACKGROUND: A multiple sequence alignment (MSA) generated for a protein can be used to characterise residues by means of a statistical analysis of single columns. In addition to the examination of individual positions, the investigation of co-variation of amino acid frequencies offers insights into function and evolution of the protein and residues. RESULTS: We introduce conn(k), a novel parameter for the characterisation of individual residues. For each residue k, conn(k) is the number of most extreme signals of co-evolution. These signals were deduced from a normalised mutual information (MI) value U(k, l) computed for all pairs of residues k, l. We demonstrate that conn(k) is a more robust indicator than an individual MI-value for the prediction of residues most plausibly important for the evolution of a protein. This proposition was inferred by means of statistical methods. It was further confirmed by the analysis of several proteins. A server, which computes conn(k)-values is available at http://www-bioinf.uni-regensburg.de. CONCLUSION: The algorithms H2r, which analyses MSAs and computes conn(k)-values, characterises a specific class of residues. In contrast to strictly conserved ones, these residues possess some flexibility in the composition of side chains. However, their allocation is sensibly balanced with several other positions, as indicated by conn(k)

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors

Accurate Protein Structure Annotation through Competitive Diffusion of Enzymatic Functions over a Network of Local Evolutionary Similarities

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks

Integration of Evolutionary Features for the Identification of Functionally Important Residues in Major Facilitator Superfamily Transporters

The identification of functionally important residues is an important challenge for understanding the molecular mechanisms of proteins. Membrane protein transporters operate two-state allosteric conformational changes using functionally important cooperative residues that mediate long-range communication from the substrate binding site to the translocation pathway. In this study, we identified functionally important cooperative residues of membrane protein transporters by integrating sequence conservation and co-evolutionary information. A newly derived evolutionary feature, the co-evolutionary coupling number, was introduced to measure the connectivity of co-evolving residue pairs and was integrated with the sequence conservation score. We tested this method on three Major Facilitator Superfamily (MFS) transporters, LacY, GlpT, and EmrD. MFS transporters are an important family of membrane protein transporters, which utilize diverse substrates, catalyze different modes of transport using unique combinations of functional residues, and have enough characterized functional residues to validate the performance of our method. We found that the conserved cores of evolutionarily coupled residues are involved in specific substrate recognition and translocation of MFS transporters. Furthermore, a subset of the residues forms an interaction network connecting functional sites in the protein structure. We also confirmed that our method is effective on other membrane protein transporters. Our results provide insight into the location of functional residues important for the molecular mechanisms of membrane protein transporters