Intramolecular Epistasis and the Evolution of a New Enzymatic Function

Atrazine chlorohydrolase (AtzA) and its close relative melamine deaminase (TriA) differ by just nine amino acid substitutions but have distinct catalytic activities. Together, they offer an informative model system to study the molecular processes that underpin the emergence of new enzymatic function. Here we have constructed the potential evolutionary trajectories between AtzA and TriA, and characterized the catalytic activities and biophysical properties of the intermediates along those trajectories. The order in which the nine amino acid substitutions that separate the enzymes could be introduced to either enzyme, while maintaining significant catalytic activity, was dictated by epistatic interactions, principally between three amino acids within the active site: namely, S331C, N328D and F84L. The mechanistic basis for the epistatic relationships is consistent with a model for the catalytic mechanisms in which protonation is required for hydrolysis of melamine, but not atrazine

Bootstrapping the energy flow in the beginning of life.

This paper suggests that the energy flow on which all living structures depend only started up slowly, the low-energy, initial phase starting up a second, slightly more energetic phase, and so on. In this way, the build up of the energy flow follows a bootstrapping process similar to that found in the development of computers, the first generation making possible the calculations necessary for constructing the second one, etc. In the biogenetic upstart of an energy flow, non-metals in the lower periods of the Periodic Table of Elements would have constituted the most primitive systems, their operation being enhanced and later supplanted by elements in the higher periods that demand more energy. This bootstrapping process would put the development of the metabolisms based on the second period elements carbon, nitrogen and oxygen at the end of the evolutionary process rather than at, or even before, the biogenetic even

Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential

Effect of carbon starvation on toluene degradation activity by toluene monooxygenase-expressing bacteria

Subsurface bacteria commonly exist in a starvation state with only periodic exposure to utilizable sources of carbon and energy. In this study, the effect of carbon starvation on aerobic toluene degradation was quantitatively evaluated with a selection of bacteria representing all the known toluene oxygenase enzyme pathways. For all the investigated strains, the rate of toluene biodegradation decreased exponentially with starvation time. First-order deactivation rate constants for TMO-expressing bacteria were approximately an order of magnitude greater than those for other oxygenase-expressing bacteria. When growth conditions (the type of growth substrate and the type and concentration of toluene oxygenase inducer) were varied in the cultures prior to the deactivation experiments, the rate of deactivation was not significantly affected, suggesting that the rate of deactivation is independent of previous substrate/inducer conditions. Because TMO-expressing bacteria are known to efficiently detoxify TCE in subsurface environments, these findings have significant implications for in situ TCE bioremediation, specifically for environments experiencing variable growth-substrate exposure conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45353/1/10532_2005_Article_9014.pd

Global analysis of adenylate-forming enzymes reveals beta-lactone biosynthesis pathway in pathogenic Nocardia

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and β-lactone formation. Despite their metabolic importance, the substrates and functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequences. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral phylogenetic reconstruction and sequence similarity networking of enzymes from these clusters suggested divergent evolution of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary CoA ligases toward more specialized functions including β-lactone synthetases. Our classifier predicted β-lactone synthetases in uncharacterized biosynthetic gene clusters conserved in >90 different strains of Nocardia. To test our prediction, we purified a candidate β-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the β-lactone natural product, nocardiolactone. We anticipate that our machine learning approach will aid in functional classification of enzymes and advance natural product discovery