Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.

Engineering proteins to enhance thermal stability is a widely utilized approach for creating industrially relevant biocatalysts. The development of new experimental datasets and computational tools to guide these engineering efforts remains an active area of research. Thus, to complement the previously reported measures of T 50 and kinetic constants, we are reporting an expansion of our previously published dataset of mutants for ß-glucosidase to include both measures of T M and ΔΔG. For a set of 51 mutants, we found that T 50 and T M are moderately correlated, with a Pearson correlation coefficient and Spearman's rank coefficient of 0.58 and 0.47, respectively, indicating that the two methods capture different physical features. The performance of predicted stability using nine computational tools was also evaluated on the dataset of 51 mutants, none of which are found to be strong predictors of the observed changes in T 50, T M, or ΔΔG. Furthermore, the ability of the nine algorithms to predict the production of isolatable soluble protein was examined, which revealed that Rosetta ΔΔG, FoldX, DeepDDG, PoPMuSiC, and SDM were capable of predicting if a mutant could be produced and isolated as a soluble protein. These results further highlight the need for new algorithms for predicting modest, yet important, changes in thermal stability as well as a new utility for current algorithms for prescreening designs for the production of mutants that maintain fold and soluble production properties.

Phototrophic Co-cultures From Extreme Environments: Community Structure and Potential Value for Fundamental and Applied Research.

Cyanobacteria are found in most illuminated environments and are key players in global carbon and nitrogen cycling. Although significant efforts have been made to advance our understanding of this important phylum, still little is known about how members of the cyanobacteria affect and respond to changes in complex biological systems. This lack of knowledge is in part due to our dependence on pure cultures when determining the metabolism and function of a microorganism. We took advantage of the Culture Collection of Microorganisms from Extreme Environments (CCMEE), a collection of more than 1,000 publicly available photosynthetic co-cultures maintained at the Pacific Northwest National Laboratory, and assessed via 16S rRNA amplicon sequencing if samples readily available from public culture collection could be used in the future to generate new insights into the role of microbial communities in global and local carbon and nitrogen cycling. Results from this work support the existing notion that culture depositories in general hold the potential to advance fundamental and applied research. Although it remains to be seen if co-cultures can be used at large scale to infer roles of individual organisms, samples that are publicly available from existing co-cultures depositories, such as the CCMEE, might be an economical starting point for such studies. Access to archived biological samples, without the need for costly field work, might in some circumstances be one of the few remaining ways to advance the field and to generate new insights into the biology of ecosystems that are not easily accessible. The current COVID-19 pandemic, which makes sampling expeditions almost impossible without putting the health of the participating scientists on the line, is a very timely example.

Defining the Metabolic Functions and Roles in Virulence of the rpoN1 and rpoN2 Genes in Ralstonia solanacearum GMI1000.

The alternative sigma factor RpoN is a unique regulator found among bacteria. It controls numerous processes that range from basic metabolism to more complex functions such as motility and nitrogen fixation. Our current understanding of RpoN function is largely derived from studies on prototypical bacteria such as Escherichia coli. Bacillus subtilis and Pseudomonas putida. Although the extent and necessity of RpoN-dependent functions differ radically between these model organisms, each bacterium depends on a single chromosomal rpoN gene to meet the cellular demands of RpoN regulation. The bacterium Ralstonia solanacearum is often recognized for being the causative agent of wilt disease in crops, including banana, peanut and potato. However, this plant pathogen is also one of the few bacterial species whose genome possesses dual rpoN genes. To determine if the rpoN genes in this bacterium are genetically redundant and interchangeable, we constructed and characterized ΔrpoN1, ΔrpoN2 and ΔrpoN1 ΔrpoN2 mutants of R. solanacearum GMI1000. It was found that growth on a small range of metabolites, including dicarboxylates, ethanol, nitrate, ornithine, proline and xanthine, were dependent on only the rpoN1 gene. Furthermore, the rpoN1 gene was required for wilt disease on tomato whereas rpoN2 had no observable role in virulence or metabolism in R. solanacearum GMI1000. Interestingly, plasmid-based expression of rpoN2 did not fully rescue the metabolic deficiencies of the ΔrpoN1 mutants; full recovery was specific to rpoN1. In comparison, only rpoN2 was able to genetically complement a ΔrpoN E. coli mutant. These results demonstrate that the RpoN1 and RpoN2 proteins are not functionally equivalent or interchangeable in R. solanacearum GMI1000.