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Secretion of proteins into the extracellular space has great advantages for the production of recombinant proteins. Type 1 secretion systems (T1SS) are attractive candidates to be optimized for biotechnological applications, as they have a relatively simple architecture compared to other classes of secretion systems. A paradigm of T1SS is the hemolysin A type 1 secretion system (HlyA T1SS) from Escherichia coli harboring only three membrane proteins, which makes the plasmid-based expression of the system easy. Although for decades the HlyA T1SS has been successfully applied for secretion of a long list of heterologous proteins from different origins as well as peptides, but its utility at commercial scales is still limited mainly due to low secretion titers of the system. To address this drawback, we engineered the inner membrane complex of the system, consisting of HlyB and HlyD proteins, following KnowVolution strategy. The applied KnowVolution campaign in this study provided a novel HlyB variant containing four substitutions (T36L/F216W/S290C/V421I) with up to 2.5-fold improved secretion for two hydrolases, a lipase and a cutinase. Key points • An improvement in protein secretion via the use of T1SS • Reaching almost 400 mg/L of soluble lipase into the supernatant • A step forward to making E. coli cells more competitive for applying as a secretion host Graphical Abstract
Publications
Cytochrome P450s are versatile catalysts for biosynthesis applications. In the P450 catalytic cycle, two electrons are required to reduce the heme iron and activate the subsequent reductions through proposed electron transfer pathways (eTPs), which often represent the rate-limiting step in reactions. Herein, the P450 BM3 from Bacillus megaterium was engineered for improved catalytic performance by redesigning proposed eTPs. By introducing aromatic amino acids on eTPs of P450 BM3, the “best” variant P2H02 (A399Y/Q403F) showed 13.9-fold improved catalytic efficiency (kcat/KM = 913.5 L mol−1 s−1) compared with P450 BM3 WT (kcat/KM = 65.8 L mol−1 s−1). Molecular dynamics simulations and electron hopping pathways analysis revealed that aromatic amino acid substitutions bridging the cofactor flavin mononucleotide and heme iron could increase electron transfer rates and improve catalytic performance. Moreover, the introduction of tyrosines showed positive effects on catalytic efficiency by potentially protecting P450 from oxidative damage. In essence, engineering of eTPs by aromatic amino acid substitutions represents a powerful approach to design catalytically efficient P450s (such as CYP116B3) and could be expanded to other oxidoreductases relying on long-range electron transfer pathways.
Publications
The combinatorial complexity of the protein sequence space presents a significant challenge for recombination experiments targeting beneficial positions. To overcome these difficulties, a machine learning (ML) approach was employed, which was trained on a limited literature dataset and combined with iterative generation and experimental data implementation. The PyPEF method was utilized to identify existing variants and predict recombinant variants targeting the substrate channel of P450 CYP116B3. Through molecular dynamics simulations, eight multiple-substituted improved variants were successfully validated. Specifically, the RMSF of variant A86T/T91H/M108S/A109M/T111P was decreased from 3.06 Å (wild type) to 1.07 Å. Additionally, the average RMSF of the variant A86T/T91P/M108V/A109M/T111P decreased to 1.41 Å, compared to the wild type’s 1.53 Å. Of particular significance was the prediction that the variant A86T/T91H/M108G/A109M/T111P exhibited an activity approximately 15 times higher than that of the wild type. Furthermore, during the selection of the regression model, PLS and MLP regressions were compared. The effect of data size and data relevance on the two regression approaches has been summarized. The aforementioned conclusions provide evidence for the feasibility of the strategy that combines ML with experimental approaches. This integrated strategy proves effective in exploring potential variations within the protein sequence space. Furthermore, this method facilitates a deeper understanding of the substrate channel in P450 CYP116B3.
Publications
Naturally occurring and engineered flavin-binding, blue-light-sensing, light, oxygen, voltage (LOV) photoreceptor domains have been used widely to design fluorescent reporters, optogenetic tools, and photosensitizers for the visualization and control of biological processes. In addition, natural LOV photoreceptors with engineered properties were recently employed for optimizing plant biomass production in the framework of a plant-based bioeconomy. Here, the understanding and fine-tuning of LOV photoreceptor (kinetic) properties is instrumental for application. In response to blue-light illumination, LOV domains undergo a cascade of photophysical and photochemical events that yield a transient covalent FMN-cysteine adduct, allowing for signaling. The rate-limiting step of the LOV photocycle is the darkrecovery process, which involves adduct scission and can take between seconds and days. Rational engineering of LOV domains with fine-tuned dark recovery has been challenging due to the lack of a mechanistic model, the long time scale of the process, which hampers atomistic simulations, and a gigantic protein sequence space covering known mutations (combinatorial challenge). To address these issues, we used machine learning (ML) trained on scarce literature data and iteratively generated and implemented experimental data to design LOV variants with faster and slower dark recovery. Over the three prediction−validation cycles, LOV domain variants were successfully predicted, whose adduct-state lifetimes spanned 7 orders of magnitude, yielding optimized tools for synthetic (opto)biology. In summary, our results demonstrate ML as a viable method to guide the design of proteins even with limited experimental data and when no mechanistic model of the underlying physical principles is available.