Enzymes are able to catalyze many specific reactions and are widely used in practical application. Previously, the enzyme functions were limited around their natural use; nowadays, the enzymes could be developed by engineering their activity and selectivity for meeting human demands (Bornscheuer et al., 2012; Damborsky and Brezovsky, 2014). Two common engineering strategies are the directed evolution based on Darwinian theory and rational design based on the structure-function relationship (Bornscheuer and Pohl, 2001). Rational design often focused on the substrate binding pocket which directly affluence the enzymatic process (Bornscheuer and Pohl, 2001). However, the experiences of directed evolution told us the residues outside the active site also influence the enzyme properties (Kress et al., 2018). Researching on those “non-hotspot” rather than typical active site may be beneficial for expanding the understanding of proteins.
The urgent problem now confronting us is how to obtain the important “non-hotspot” domains beyond the enzyme active site. Tunnel engineering may be one of the answers. Enzymes spanning all of the six classes are found the exist of the tunnels (Kingsley and Lill, 2015). It was reported that more than 64% enzymes annotated in Catalytic Site Atlas library have the buried active site with the tunnels connecting the enzyme binding pocket and the environment (Pravda et al., 2014). The tunnels could support the transport of solvent, product and solvent between the enzyme active site and bulk solvent, which play important role in enzymatic reaction (Kokkonen et al., 2019; Zhou and McCammon, 2010). The behavior of substrate on the tunnels to the active site could affect the activity, stability and substrate selectivity (Kingsley and Lill, 2015; Lu et al., 2019; Yu et al., 2013). A typical example might be that the R47 and Y51 residues, two polar amino acids located at the end of access tunnel of P450 BM3, could regulate the entry of substrate, water and co-solvents (Whitehouse et al., 2012). Cheng et al. found that a single position located on the access tunnel of nitrile hydratase could invert the regio-selectivity towards aliphatic α,ω-dinitriles (Cheng et al., 2016). Tunnel engineering is becoming a promising strategy to optimize the enzyme property. Several scientists have developed many algorithms for determination of enzymatic tunnels, such as CAVER (Kozlikova et al., 2014), MOLE (Sehnal et al., 2013), AQUA-DUCT (Magdziarz et al., 2017) and CCCPP (Benkaidali et al., 2014). However, compared with the prediction of tunnels, only a few experimental tunnel engineering examples were reported (Kress et al., 2018). Here, we aim to develop the application of tunnel engineering for modulating the substrate preference.
α-alkenes are multifunctional compounds that perform an important industrial value and an extraordinary economic importance due to their flexible and active chemical performance. Particularly, long-chain α-alkenes can be used in the synthesis of high-value biofuel, lubrication and surfactant (Lee et al., 2008). However, α-alkenes mostly come from non-renewable petroleum cracking (Dutta et al., 2014). Energy-intensive process and harsh reaction conditions prompted researchers to focus on enzymatic synthetic strategy of α-alkenes (Schirmer et al., 2010). To date, three biotransformation strategies that convert fatty acids or its derivative to alkene were reported (Yi et al., 2014). A three-gene cluster from Micrococcus luteus represented a production of long-chain alkenes from a head-to-head condensation of fatty acids (Beller et al., 2010); a type I polyketide synthases from Synechococcus sp. was involved in a production of medium-chain α-alkenes via an elongation decarboxylation mechanism (Mendez-Perez et al., 2011); and some members from cytochrome P450 family are able to decarboxylate fatty acids and produce α-alkenes directly (Grant et al., 2015; Hsieh and Makris, 2016). Among those pathways, the decarboxylic reaction catalyzed by P450 is the simplest strategy. Free fatty acids could be used as substrate directly, which represent potential of producing α-alkenes on a large-scale based in engineered cell factory.
Representative P450 decarboxylases belong to CYP152 subfamily, such as P450Bsβ (CYP152A1) from Bacillus subtilis (Matsunaga et al., 1999), P450Spα (CYP152B1) from Sphingomonas paucimobilis (Matsunaga et al., 2000), and P450oleT (CYP152L1) from Jeotgalicoccus sp. (Yi et al., 2014). As the first member of CYP152 subfamily, P450Bsβ was the research hotspot since it was discovered by Isamu et al. in 1999. However, wild-type P450Bsβ showed higher hydroxylation activity rather than decarboxylation activity (Matsunaga, et al., 1999). In contrast, P450oleT presented prominent decarboxylation property, which is able to convert medium- or long-chain fatty acids to corresponding carboxylic acids (Rude et al., 2011). Interestingly, some P450Bsβ variants also exhibited satisfactory decarboxylation ability. Xu et al. reported a P450BsβHI variant (Q85H/V170I) which displayed enhanced decarboxylation activity towards medium- or long-chain fatty acids (Xu et al., 2017). However, the yield of α-alkenes drops sharply as the length of the carbon chain of the substrate increases, which is the main limitation of the use of P450BsβHI for long chain α-alkenes synthesis.
In the present work, we systematically analyzed the access tunnels in decarbonylase P450BsβHI. In order to improve the substrate preference of P450BsβHI to long chain fatty acids, two residues related to the access tunnels diameter were identified and mutated. In addition, the substrate selection mechanism controlled by tunnels of P450BsβHI was briefly discussed.