Genetically programmed chiral organoborane synthesis

Recent advances in enzyme engineering and design have expanded nature’s catalytic repertoire to functions that are new to biology. However, only a subset of these engineered enzymes can function in living systems. Finding enzymatic pathways that form chemical bonds that are not found in biology is particularly difficult in the cellular environment, as this depends on the discovery not only of new enzyme activities, but also of reagents that are both sufficiently reactive for the desired transformation and stable in vivo. Here we report the discovery, evolution and generalization of a fully genetically encoded platform for producing chiral organoboranes in bacteria. Escherichia coli cells harbouring wild-type cytochrome c from Rhodothermus marinus8 (Rma cyt c) were found to form carbon–boron bonds in the presence of borane–Lewis base complexes, through carbene insertion into boron–hydrogen bonds. Directed evolution of Rma cyt c in the bacterial catalyst provided access to 16 novel chiral organoboranes. The catalyst is suitable for gram-scale biosynthesis, providing up to 15,300 turnovers, a turnover frequency of 6,100 h^(–1), a 99:1 enantiomeric ratio and 100% chemoselectivity. The enantiopreference of the biocatalyst could also be tuned to provide either enantiomer of the organoborane products. Evolved in the context of whole-cell catalysts, the proteins were more active in the whole-cell system than in purified forms. This study establishes a DNA-encoded and readily engineered bacterial platform for borylation; engineering can be accomplished at a pace that rivals the development of chemical synthetic methods, with the ability to achieve turnovers that are two orders of magnitude (over 400-fold) greater than those of known chiral catalysts for the same class of transformation. This tunable method for manipulating boron in cells could expand the scope of boron chemistry in living systems

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Exploration of evolutionary pathways Aspergillus niger\textit {Aspergillus niger} epoxide hydrolase

ISM (iterative Sättigungsmutagenese) ist vor kurzem als effiziente Methode der gerichteten Evolution vorgeschlagen. Hauptziel dieser Arbeit ist zu finden wie effizient ISM ist. Die Untersuchungen gliedern sich in zwei Bereiche: Die Erkundung der evolutionären Pfade einer Epoxydhydrolase aus $\textit {Aspergillus niger}$ (ANEH) zur Verbesserung ihrer Thermostabilität und zur Erhöhung ihrer Enantioselektivität. Die Ergebnisse aus der Untersuchungen der evolutionären Pfade zur Verbesserung der Thermostabilität weisen darauf hin, dass die beste Mutante in dem Fall erhalten wurde, wo die nur wenig verbesserte Mutante als elterliche Sequenz eingesetzt wurde, die unter normalen Umständen nicht gewählt worden wäre. 67% der theoretisch möglichen Pfade führen zur Verbesserung der Enantioselektivität, und insgesamt wurden 68 hoch enantioselektiven Mutanten identifiziert, dieses beweist die Effizienz von ISM

Exploring the past and the future of protein evolution with ancestral sequence reconstruction: the 'retro' approach to protein engineering

A central goal in molecular evolution is to understand the ways in which genes and proteins evolve in response to changing environments. In the absence of intact DNA from fossils, ancestral sequence reconstruction (ASR) can be used to infer the evolutionary precursors of extant proteins. To date, ancestral proteins belonging to eubacteria, archaea, yeast and vertebrates have been inferred that have been hypothesized to date from between several million to over 3 billion years ago. ASR has yielded insights into the early history of life on Earth and the evolution of proteins and macromolecular complexes. Recently, however, ASR has developed from a tool for testing hypotheses about protein evolution to a useful means for designing novel proteins. The strength of this approach lies in the ability to infer ancestral sequences encoding proteins that have desirable properties compared with contemporary forms, particularly thermostability and broad substrate range, making them good starting points for laboratory evolution. Developments in technologies for DNA sequencing and synthesis and computational phylogenetic analysis have led to an escalation in the number of ancient proteins resurrected in the last decade and greatly facilitated the use of ASR in the burgeoning field of synthetic biology. However, the primary challenge of ASR remains in accurately inferring ancestral states, despite the uncertainty arising from evolutionary models, incomplete sequences and limited phylogenetic trees. This review will focus, firstly, on the use of ASR to uncover links between sequence and phenotype and, secondly, on the practical application of ASR in protein engineering

In situ resource utilisation : The potential for space biomining

The world is entering a new era of exploring and exploiting outer space. The revolution in small, low-cost satellites, the recent initiatives from some countries to establish a legal framework, the increasing demand for technology metals and advances in space additive manufacturing have renewed the interest in space mining. Biomining, the use of microorganisms to extract and recover valuable metals from minerals and wastes, could be used as alternative ISRU technology for harnessing space resources. This paper reviews in situ resources available on the Moon, Mars, and Near-Earth Asteroids (NEAs) for implementing biomining processes in space, the effects of the space environment on biomining microbes, and space-based bioreactor designs that will enable leaching of metals from regoliths. A comparison between terrestrial and space biomining will also be presented, focusing on the differences in the composition of minerals on Earth and space, the types of microorganisms used for leaching, and the parameters that need to be optimised in the space biomining processes. Next steps to mature biomining approaches by combining knowledge from synthetic biology, systems biology, geomicrobiology and process engineering for space applications will also be explored. Through an integrative effort of these fields, biomining processes commonly employed on Earth can be harnessed for sustainable space exploration.</p

Learning epistatic interactions from sequence-activity data to predict enantioselectivity

Enzymes with a high selectivity are desirable for improving economics of chemical synthesis of enantiopure compounds. To improve enzyme selectivity mutations are often introduced near the catalytic active site. In this compact environment epistatic interactions between residues, where contributions to selectivity are non-additive, play a significant role in determining the degree of selectivity. Using support vector machine regression models we map mutations to the experimentally characterised enantioselectivities for a set of 136 variants of the epoxide hydrolase from the fungus Aspergillus niger (AnEH). We investigate whether the influence a mutation has on enzyme selectivity can be accurately predicted through linear models, and whether prediction accuracy can be improved using higher-order counterparts. Comparing linear and polynomial degree = 2 models, mean Pearson coefficients (r) from [Formula: see text]-fold cross-validation increase from 0.84 to 0.91 respectively. Equivalent models tested on interaction-minimised sequences achieve values of [Formula: see text] and [Formula: see text]. As expected, testing on a simulated control data set with no interactions results in no significant improvements from higher-order models. Additional experimentally derived AnEH mutants are tested with linear and polynomial degree = 2 models, with values increasing from [Formula: see text] to [Formula: see text] respectively. The study demonstrates that linear models perform well, however the representation of epistatic interactions in predictive models improves identification of selectivity-enhancing mutations. The improvement is attributed to higher-order kernel functions that represent epistatic interactions between residues

Computational tools for directed evolution: a comparison of prospective and retrospective strategies

Directed evolution methods have proved to be highly effective in the design of novel proteins and in the generation of large libraries of diverse sequences. However, searching through the vast number of mutants produced during such experiments in order to find the best represents a daunting and difficult task. In recent years, a number of computational tools have been developed to provide guidance during this exploratory process. It can, however, be unclear as to which tool or tools best complement the chosen library design strategy. In this review, we describe and critically evaluate some of the more notable tools in this area, discussing the rationale behind each, the requirements for their implementation, and potential issues faced when using them. Some examples of their application in an experimental setting are also provided. The tools have been classified based on contrasting strategies as to how they function: prospective tools SCHEMA and OPTCOMB use extant sequence and structural data to predict optimal locations for crossover sites, whereas retrospective tools ProSAR and ASRA use property data from the mutant library to predict beneficial mutations and features. From our evaluation, we suggest that each tool can play a role in the design process; however this is largely dictated by the data available and the desired experimental strategy for the project

Effect of Binding on Enantioselectivity of Epoxide Hydrolase

Molecular dynamics simulations and free energy calculations have been used to investigate the effect of ligand binding on the enantioselectivity of an epoxide hydrolase (EH) from Aspergillus Niger. Despite sharing a common mechanism, a wide range of alternative mechanisms have been proposed to explain the origin of enantiomeric selectivity in EHs. By comparing the interactions of (R)- and (S)-glycidyl phenyl ether (GPE) with both the wild type (WT, E = 3) and a mutant showing enhanced enantioselectivity to GPE (LW202, E = 193), we have examined whether enantioselectivity is due to differences in the binding pose, the affinity for the (R)- or (S)- enantiomers, or a kinetic effect. The two enantiomers were easily accommodated within the binding pockets of the WT enzyme and LW202. Free energy calculations suggested that neither enzyme had a preference for a given enantiomer. The two substrates sampled a wide variety of conformations in the simulations with the sterically hindered and unhindered carbon atoms of the GPE epoxide ring both coming in close proximity to the nucleophilic aspartic acid residue. This suggests that alternative pathways could lead to the formation of a (S)- and (R)-diol product. Together, the calculations suggest that the enantioselectivity is due to kinetic rather than thermodynamic effects and that the assumption that one substrate results in one product when interpreting the available experimental data and deriving E-values may be inappropriate in the case of EHs.</p