5,924 research outputs found
Evolutionary and molecular foundations of multiple contemporary functions of the nitroreductase superfamily.
Insight regarding how diverse enzymatic functions and reactions have evolved from ancestral scaffolds is fundamental to understanding chemical and evolutionary biology, and for the exploitation of enzymes for biotechnology. We undertook an extensive computational analysis using a unique and comprehensive combination of tools that include large-scale phylogenetic reconstruction to determine the sequence, structural, and functional relationships of the functionally diverse flavin mononucleotide-dependent nitroreductase (NTR) superfamily (>24,000 sequences from all domains of life, 54 structures, and >10 enzymatic functions). Our results suggest an evolutionary model in which contemporary subgroups of the superfamily have diverged in a radial manner from a minimal flavin-binding scaffold. We identified the structural design principle for this divergence: Insertions at key positions in the minimal scaffold that, combined with the fixation of key residues, have led to functional specialization. These results will aid future efforts to delineate the emergence of functional diversity in enzyme superfamilies, provide clues for functional inference for superfamily members of unknown function, and facilitate rational redesign of the NTR scaffold
The Future of Systematics: Tree-Thinking Without the Tree
Phylogenetic trees are meant to represent the genealogical history of life and apparently derive their justification from the existence of the tree of life and the fact that evolutionary processes are tree-like. However, there are a number of problems for these assumptions. Here it is argued that once we understand the important role that phylogenetic trees play as models which contain idealizations, we can accept these criticisms and deny the reality of the tree while justifying the continued use of trees in phylogenetic theory and preserving nearly all of what defenders of trees have called āthe importance of tree-thinking.
Probabilistic methods in the analysis of protein interaction networks
Imperial Users onl
A practical approximation algorithm for solving massive instances of hybridization number for binary and nonbinary trees
Reticulate events play an important role in determining evolutionary
relationships. The problem of computing the minimum number of such events to
explain discordance between two phylogenetic trees is a hard computational
problem. Even for binary trees, exact solvers struggle to solve instances with
reticulation number larger than 40-50. Here we present CycleKiller and
NonbinaryCycleKiller, the first methods to produce solutions verifiably close
to optimality for instances with hundreds or even thousands of reticulations.
Using simulations, we demonstrate that these algorithms run quickly for large
and difficult instances, producing solutions that are very close to optimality.
As a spin-off from our simulations we also present TerminusEst, which is the
fastest exact method currently available that can handle nonbinary trees: this
is used to measure the accuracy of the NonbinaryCycleKiller algorithm. All
three methods are based on extensions of previous theoretical work and are
publicly available. We also apply our methods to real data
An exploration of alternative visualisations of the basic helix-loop-helix protein interaction network
Background: Alternative representations of biochemical networks emphasise different aspects of the data and contribute to the understanding of complex biological systems. In this study we present a variety of automated methods for visualisation of a protein-protein interaction network, using the basic helix-loop-helix ( bHLH) family of transcription factors as an example.
Results: Network representations that arrange nodes ( proteins) according to either continuous or discrete information are investigated, revealing the existence of protein sub-families and the retention of interactions following gene duplication events. Methods of network visualisation in conjunction with a phylogenetic tree are presented, highlighting the evolutionary relationships between proteins, and clarifying the context of network hubs and interaction clusters. Finally, an optimisation technique is used to create a three-dimensional layout of the phylogenetic tree upon which the protein-protein interactions may be projected.
Conclusion: We show that by incorporating secondary genomic, functional or phylogenetic information into network visualisation, it is possible to move beyond simple layout algorithms based on network topology towards more biologically meaningful representations. These new visualisations can give structure to complex networks and will greatly help in interpreting their evolutionary origins and functional implications. Three open source software packages (InterView, TVi and OptiMage) implementing our methods are available
Characterization and gene expression analysis of the cir multi-gene family of plasmodium chabaudi chabaudi (AS)
Background:
The pir genes comprise the largest multi-gene family in Plasmodium, with members found in P. vivax, P. knowlesi and the rodent malaria species. Despite comprising up to 5% of the genome, little is known about the functions of the proteins encoded by pir genes. P. chabaudi causes chronic infection in mice, which may be due to antigenic variation. In this model, pir genes are called cir s and may be involved in this mechanism, allowing evasion of host immune responses. In order to fully understand the role(s) of CIR proteins during P. chabaudi infection, a detailed characterization of the cir gene family was required.
Results: The cir repertoire was annotated and a detailed bioinformatic characterization of the encoded CIR proteins was performed. Two major sub-families were identified, which have been named A and B. Members of each sub-family displayed different amino acid motifs, and were thus predicted to have undergone functional divergence. In addition, the expression of the entire cir repertoire was analyzed via RNA sequencing and microarray. Up to 40% of the cir gene repertoire was expressed in the parasite population during infection, and dominant cir transcripts could be identified. In addition, some differences were observed in the pattern of expression between the cir subgroups at the peak of P. chabaudi infection. Finally, specific cir genes were expressed at different time points during asexual blood stages.
Conclusions: In conclusion, the large number of cir genes and their expression throughout the intraerythrocytic cycle of development indicates that CIR proteins are likely to be important for parasite survival. In particular, the detection of dominant cir transcripts at the peak of P. chabaudi infection supports the idea that CIR proteins are expressed, and could perform important functions in the biology of this parasite. Further application of the methodologies described here may allow the elucidation of CIR sub-family A and B protein functions, including their contribution to antigenic variation and immune evasion
Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model
Food webs, networks of feeding relationships among organisms, provide
fundamental insights into mechanisms that determine ecosystem stability and
persistence. Despite long-standing interest in the compartmental structure of
food webs, past network analyses of food webs have been constrained by a
standard definition of compartments, or modules, that requires many links
within compartments and few links between them. Empirical analyses have been
further limited by low-resolution data for primary producers. In this paper, we
present a Bayesian computational method for identifying group structure in food
webs using a flexible definition of a group that can describe both functional
roles and standard compartments. The Serengeti ecosystem provides an
opportunity to examine structure in a newly compiled food web that includes
species-level resolution among plants, allowing us to address whether groups in
the food web correspond to tightly-connected compartments or functional groups,
and whether network structure reflects spatial or trophic organization, or a
combination of the two. We have compiled the major mammalian and plant
components of the Serengeti food web from published literature, and we infer
its group structure using our method. We find that network structure
corresponds to spatially distinct plant groups coupled at higher trophic levels
by groups of herbivores, which are in turn coupled by carnivore groups. Thus
the group structure of the Serengeti web represents a mixture of trophic guild
structure and spatial patterns, in contrast to the standard compartments
typically identified in ecological networks. From data consisting only of nodes
and links, the group structure that emerges supports recent ideas on spatial
coupling and energy channels in ecosystems that have been proposed as important
for persistence.Comment: 28 pages, 6 figures (+ 3 supporting), 2 tables (+ 4 supporting
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