Distribution of nitrogen fixation and nitrogenase-like sequences amongst microbial genomes

Abstract Background The metabolic capacity for nitrogen fixation is known to be present in several prokaryotic species scattered across taxonomic groups. Experimental detection of nitrogen fixation in microbes requires species-specific conditions, making it difficult to obtain a comprehensive census of this trait. The recent and rapid increase in the availability of microbial genome sequences affords novel opportunities to re-examine the occurrence and distribution of nitrogen fixation genes. The current practice for computational prediction of nitrogen fixation is to use the presence of the nifH and/or nifD genes. Results Based on a careful comparison of the repertoire of nitrogen fixation genes in known diazotroph species we propose a new criterion for computational prediction of nitrogen fixation: the presence of a minimum set of six genes coding for structural and biosynthetic components, namely NifHDK and NifENB. Using this criterion, we conducted a comprehensive search in fully sequenced genomes and identified 149 diazotrophic species, including 82 known diazotrophs and 67 species not known to fix nitrogen. The taxonomic distribution of nitrogen fixation in Archaea was limited to the Euryarchaeota phylum; within the Bacteria domain we predict that nitrogen fixation occurs in 13 different phyla. Of these, seven phyla had not hitherto been known to contain species capable of nitrogen fixation. Our analyses also identified protein sequences that are similar to nitrogenase in organisms that do not meet the minimum-gene-set criteria. The existence of nitrogenase-like proteins lacking conserved co-factor ligands in both diazotrophs and non-diazotrophs suggests their potential for performing other, as yet unidentified, metabolic functions. Conclusions Our predictions expand the known phylogenetic diversity of nitrogen fixation, and suggest that this trait may be much more common in nature than it is currently thought. The diverse phylogenetic distribution of nitrogenase-like proteins indicates potential new roles for anciently duplicated and divergent members of this group of enzymes.

Analysis of energy dissipation in stirred suspension polymerisation reactors using computational fluid dynamics

This work evaluates the spatial distribution of normalised rates of droplet breakage and droplet coalescence in liquidliquid dispersions maintained in agitated tanks at operation conditions normally used to perform suspension polymerisation reactions. Particularly, simulations are performed with multiphase computational fluid dynamics (CFD) models to represent the flow field in liquidliquid styrene suspension polymerisation reactors for the first time. CFD tools are used first to compute the spatial distribution of the turbulent energy dissipation rates (e) inside the reaction vessel; afterwards, normalised rates of droplet breakage and particle coalescence are computed as functions of e. Surprisingly, multiphase simulations showed that the rates of energy dissipation can be very high near the free vortex surfaces, which has been completely neglected in previous works. The obtained results indicate the existence of extremely large energy dissipation gradients inside the vessel, so that particle breakage occurs primarily in very small regions that surround the impeller and the free vortex surface, while particle coalescence takes place in the liquid bulk. As a consequence, particle breakage should be regarded as an independent source term or a boundary phenomenon. Based on the obtained results, it can be very difficult to justify the use of isotropic assumptions to formulate particle population balances in similar systems, even when multiple compartment models are used to describe the fluid dynamic behaviour of the agitated vessel. (C) 2011 Canadian Society for Chemical Engineering

Relationship between global structural parameters and Enzyme Commission hierarchy: Implications for function prediction

In protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them. (C) 2012 Elsevier Ltd. All rights reserved.