Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights of homologous sequences. However, deciphering the evolutionary variability and complex dependencies among residues presents substantial hurdles. Here, we present a new machine-learning method based on variational autoencoders and evolutionary sampling strategy to address those limitations. We customized our method to generate novel sequences of model enzymes, haloalkane dehalogenases. Three design-build-test cycles improved the solubility of variants from 11% to 75%. Thorough experimental validation including the microfluidic device MicroPEX resulted in 20 multiple-point variants. Nine of them, sharing as little as 67% sequence similarity with the template, showed a melting temperature increase of up to 9 °C and an average improvement of 3 °C. The most stable variant demonstrated a 3.5-fold increase in activity compared to the template. High-quality experimental data collected with 20 variants represent a valuable data set for the critical validation of novel protein design approaches. Python scripts, jupyter notebooks, and data sets are available on GitHub (https://github.com/loschmidt/vae-dehalogenases), and interactive calculations will be possible via https://loschmidt.chemi.muni.cz/fireprotasr/.

• Publikační typ
• časopisecké články MeSH

The limited number of well-characterised model bacteria cannot address all the challenges in a circular bioeconomy. Therefore, there is a growing demand for new production strains with enhanced resistance to extreme conditions, versatile metabolic capabilities and the ability to utilise cost-effective renewable resources while efficiently generating attractive biobased products. Particular thermophilic microorganisms fulfil these requirements. Non-virulent Gram-negative Caldimonas thermodepolymerans DSM15344 is one such attractive thermophile that efficiently converts a spectrum of plant biomass sugars into high quantities of polyhydroxyalkanoates (PHA)-a fully biodegradable substitutes for synthetic plastics. However, to enhance its biotechnological potential, the bacterium needs to be 'domesticated'. In this study, we established effective homologous recombination and transposon-based genome editing systems for C. thermodepolymerans. By optimising the electroporation protocol and refining counterselection methods, we achieved significant improvements in genetic manipulation and constructed the AI01 chassis strain with improved transformation efficiency and a ΔphaC mutant that will be used to study the importance of PHA synthesis in Caldimonas. The advances described herein highlight the need for tailored approaches when working with thermophilic bacteria and provide a springboard for further genetic and metabolic engineering of C. thermodepolymerans, which can be considered the first model of thermophilic PHA producer.

• Klíčová slova
• Caldimonas thermodepolymerans, gene deletion, genetic engineering, polyhydroxyalkanoates, thermophiles,
• MeSH
• editace genu * metody   MeSH
• elektroporace MeSH
• genom bakteriální MeSH
• homologní rekombinace MeSH
• metabolické inženýrství metody   MeSH
• polyhydroxyalkanoáty * metabolismus   biosyntéza   MeSH
• transpozibilní elementy DNA genetika   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• polyhydroxyalkanoáty * MeSH
• transpozibilní elementy DNA MeSH

Fluorinases, the only enzymes known to catalyze the transfer of fluorine to an organic molecule, are essential catalysts for the biological synthesis of valuable organofluorines. However, the few fluorinases identified so far have low turnover rates that hamper biotechnological applications. Here, we isolated and characterized putative fluorinases retrieved from systematic in silico mining and identified a nonconventional archaeal enzyme from Methanosaeta sp. that mediates the fastest SN2 fluorination rate reported to date. Furthermore, we demonstrate enhanced production of fluoronucleotides in vivo in a bacterial host engineered with this archaeal fluorinase, paving the way toward synthetic metabolism for efficient biohalogenation.

• Publikační typ
• časopisecké články MeSH

Polyethylene terephthalate (PET) is the most widespread synthetic polyester, having been utilized in textile fibers and packaging materials for beverages and food, contributing considerably to the global solid waste stream and environmental plastic pollution. While enzymatic PET recycling and upcycling have recently emerged as viable disposal methods for a circular plastic economy, only a handful of benchmark enzymes have been thoroughly described and subjected to protein engineering for improved properties over the last 16 years. By analyzing the specific material properties of PET and the reaction mechanisms in the context of interfacial biocatalysis, this Perspective identifies several limitations in current enzymatic PET degradation approaches. Unbalanced enzyme-substrate interactions, limited thermostability, and low catalytic efficiency at elevated reaction temperatures, and inhibition caused by oligomeric degradation intermediates still hamper industrial applications that require high catalytic efficiency. To overcome these limitations, successful protein engineering studies using innovative experimental and computational approaches have been published extensively in recent years in this thriving research field and are summarized and discussed in detail here. The acquired knowledge and experience will be applied in the near future to address plastic waste contributed by other mass-produced polymer types (e.g., polyamides and polyurethanes) that should also be properly disposed by biotechnological approaches.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

MOTIVATION: Poor protein solubility hinders the production of many therapeutic and industrially useful proteins. Experimental efforts to increase solubility are plagued by low success rates and often reduce biological activity. Computational prediction of protein expressibility and solubility in Escherichia coli using only sequence information could reduce the cost of experimental studies by enabling prioritization of highly soluble proteins. RESULTS: A new tool for sequence-based prediction of soluble protein expression in E.coli, SoluProt, was created using the gradient boosting machine technique with the TargetTrack database as a training set. When evaluated against a balanced independent test set derived from the NESG database, SoluProt's accuracy of 58.5% and AUC of 0.62 exceeded those of a suite of alternative solubility prediction tools. There is also evidence that it could significantly increase the success rate of experimental protein studies. SoluProt is freely available as a standalone program and a user-friendly webserver at https://loschmidt.chemi.muni.cz/soluprot/. AVAILABILITY AND IMPLEMENTATION: https://loschmidt.chemi.muni.cz/soluprot/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

• Publikační typ
• časopisecké články MeSH