Details

Autor(en) / Beteiligte

• Vasina, Michal
• Vanacek, Pavel
• Hon, Jiri
• Kovar, David
• Faldynova, Hana
• Kunka, Antonin
• Buryska, Tomas
• Badenhorst, Christoffel P.S.
• Mazurenko, Stanislav
• Bednar, David
• Stavrakis, Stavros
• Bornscheuer, Uwe T.
• deMello, Andrew
• Damborsky, Jiri
• Prokop, Zbynek

Titel

Advanced database mining of efficient haloalkane dehalogenases by sequence and structure bioinformatics and microfluidics

Ist Teil von

• Chem catalysis, 2022-10, Vol.2 (10), p.2704-2725

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Next-generation sequencing doubles genomic databases every 2.5 years. The accumulation of sequence data provides a unique opportunity to identify interesting biocatalysts directly in the databases without tedious and time-consuming engineering. Herein, we present a pipeline integrating sequence and structural bioinformatics with microfluidic enzymology for bioprospecting of efficient and robust haloalkane dehalogenases. The bioinformatic part identified 2,905 putative dehalogenases and prioritized a “small-but-smart” set of 45 genes, yielding 40 active enzymes, 24 of which were biochemically characterized by microfluidic enzymology techniques. Combining microfluidics with modern global data analysis provided precious mechanistic insights related to the high catalytic efficiency of selected enzymes. Overall, we have doubled the dehalogenation “toolbox” characterized over three decades, yielding biocatalysts that surpass the efficiency of currently available wild-type and engineered enzymes. This pipeline is generally applicable to other enzyme families and can accelerate the identification of efficient biocatalysts for industrial use. [Display omitted] •A pipeline integrating bioinformatics and microfluidic enzymology is introduced•A small-but-smart set of selected genes yielded a 90% success rate of active enzymes•Microfluidics and global data analysis provided mechanistic insight into biocatalysis•The obtained dehalogenases outperform previously discovered or engineered variants For decades, scientists have asked themselves how to obtain better enzymes: should they discover new enzymes from nature or improve known enzymes by protein engineering? The success of many protein engineering studies might lead to underestimating the potential of natural diversity represented by genomic databases. We present a pipeline integrating sequence and structural bioinformatics with microfluidic enzymology to discover efficient and robust biocatalysts. Bioinformatic analysis prioritizes promising candidates, while microfluidic enzymology facilitates efficient characterization of these enzymes, leading to mechanistic insights. The obtained enzymes catalytically outperformed previously known variants, independently of whether these had been newly discovered or engineered. This study represents an interesting conceptual view of current approaches used in biocatalyst development, which should explore the great potential of structural and functional diversity found in nature. We present a pipeline integrating sequence and structural bioinformatics with microfluidic enzymology to discover efficient and robust haloalkane dehalogenases. Our smart bioinformatic identification of promising candidates in genomic databases is followed by efficient microfluidic characterization, in terms of activity, specificity, stability, and mechanistic insights. The obtained biocatalysts outperform the previously known wild-type and engineered dehalogenases. This strategy is applicable to other enzyme families, paving the way toward accelerating the identification of novel biocatalysts for industrial applications.