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

System engineering toolbox for design-oriented engineers

This system engineering toolbox is designed to provide tools and methodologies to the design-oriented systems engineer. A tool is defined as a set of procedures to accomplish a specific function. A methodology is defined as a collection of tools, rules, and postulates to accomplish a purpose. For each concept addressed in the toolbox, the following information is provided: (1) description, (2) application, (3) procedures, (4) examples, if practical, (5) advantages, (6) limitations, and (7) bibliography and/or references. The scope of the document includes concept development tools, system safety and reliability tools, design-related analytical tools, graphical data interpretation tools, a brief description of common statistical tools and methodologies, so-called total quality management tools, and trend analysis tools. Both relationship to project phase and primary functional usage of the tools are also delineated. The toolbox also includes a case study for illustrative purposes. Fifty-five tools are delineated in the text

A pentapeptide as minimal antigenic determinant for MHC class I-restricted T lymphocytes

Peptides that are antigenic for T lymphocytes are ligands for two receptors, the class I or II glycoproteins that are encoded by genes in the major histocompatibility complex, and the idiotypic / chain T-cell antigen receptor1–9. That a peptide must bind to an MHC molecule to interact with a T-cell antigen receptor is the molecular basis of the MHC restriction of antigen-recognition by T lymphocytes10,11. In such a trimolecular interaction the amino-acid sequence of the peptide must specify the contact with both receptors: agretope residues bind to the MHC receptor and epitope residues bind to the T-cell antigen receptor12,13. From a compilation of known antigenic peptides, two algorithms have been proposed to predict antigenic sites in proteins. One algorithm uses linear motifs in the sequence14, whereas the other considers peptide conformation and predicts antigenicity for amphipathic -helices15,16. We report here that a systematic delimitation of an antigenic site precisely identifies a predicted pentapeptide motif as the minimal antigenic determinant presented by a class I MHC molecule and recognized by a cytolytic T lymphocyte clone

Evolutionarily Conserved Substrate Substructures for Automated Annotation of Enzyme Superfamilies

The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies

How accurate is the phenotype? – An analysis of developmental noise in a cotton aphid clone

<p>Abstract</p> <p>Background</p> <p>The accuracy by which phenotype can be reproduced by genotype potentially is important in determining the stability, environmental sensitivity, and evolvability of morphology and other phenotypic traits. Because two sides of an individual represent independent development of the phenotype under identical genetic and environmental conditions, average body asymmetry (or "fluctuating asymmetry") can estimate the developmental instability of the population. The component of developmental instability not explained by intrapopulational differences in gene or environment (or their interaction) can be further defined as internal developmental noise. Surprisingly, developmental noise remains largely unexplored despite its potential influence on our interpretations of developmental stability, canalization, and evolvability. Proponents of fluctuating asymmetry as a bioindicator of environmental or genetic stress, often make the assumption that developmental noise is minimal and, therefore, that phenotype can respond sensitively to the environment. However, biologists still have not measured whether developmental noise actually comprises a significant fraction of the overall environmental response of fluctuating asymmetry observed within a population.</p> <p>Results</p> <p>In a morphometric study designed to partition developmental noise from fluctuating asymmetry in the wing morphology of a monoclonal culture of cotton aphid, <it>Aphis gossipyii</it>, it was discovered that fluctuating asymmetry in the aphid wing was nearly four times higher than in other insect species. Also, developmental noise comprised a surprisingly large fraction (≈ 50%) of the overall response of fluctuating asymmetry to a controlled graded temperature environment. Fluctuating asymmetry also correlated negatively with temperature, indicating that environmentally-stimulated changes in developmental instability are mediated mostly by changes in the development time of individuals.</p> <p>Conclusion</p> <p>The amount of developmental noise revealed in this trait potentially does interfere with a substantial amount of the sensitivity of fluctuating asymmetry to change in temperature. Assuming that some genetic-based variation in individual buffering of developmental instability exists in natural aphid populations, the amount of internal developmental noise determined in this study could also substantially reduce evolvability of the aphid wing. The overall findings here suggest that individual response to the seemingly high cost of stabilizing some aspects of the phenotype may account for the frequent observation of trait and species specificity in levels of fluctuating asymmetry.</p

Using Sequence Similarity Networks for Visualization of Relationships Across Diverse Protein Superfamilies

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships

Is EC class predictable from reaction mechanism?

We thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.Publisher PDFPeer reviewe