Mutation Design of ceaS2 by using AEMD
Abstract
Engineering for the desired enzyme catalytic properties plays an important role in the biosynthesis of bulk chemicals and natural products. However, it is a time-consuming task to improve enzyme catalysis by traditional random mutagenesis. And the utility of rational design based on protein structure often was limited by the lack of protein structure for target enzymes and professional backgrounds of bioinformatics.
ceaS2 enzyme is the most important enzyme in our entire acrylic acid synthesis pathway, but the activity of wild type is not high. So it is exceedingly necessary to modify it on the basis of the "part" level to improve its catalytic reactivity. We used the AEMD platform to conduct the mutational design for ceaS2 enzyme in order to figure out a more accurate scheme of mutation, which can also exert great beneficial impact on the later experiments.
We have totally identified XX mutational sites, and its point mutation transformation. The experimental results show that there are XX sites, where the enzyme activity gets boosted, after the transformation. Compared to wild type ceaS2 enzyme, the highest activity has increased by XX times, whose effect is obviously noticeable. This also demonstrates the ability of this designing platform.
Introduction
Enzyme engineering has been extensively used to optimize biocatalysts in industrial biotechnology since most of enzymes in nature prefer to organisms adaptation but not industrial production (Alvizo, et al., 2014; Ma, et al., 2009; Savile, et al., 2010). Traditionally, optimized enzymes were obtained by random site-directed or saturated mutagenesis such as Error Prone PCR, DNA shuffling and so on (Kabumoto, et al., 2009; Qi, et al., 2009; Reetz and Carballeira, 2007; Yep, et al., 2008). Due to the immense possibility of sequence mutation at amino acids level, it is a time-consuming and low efficiency task to obtain a high efficient biocatalyst by random mutation.
With the availability of an increasing number of protein structural and biochemical data, rational design of enzymatic mutation has become more and more popular (Bloom, et al., 2005; Chica, et al., 2005; Kiss, et al., 2013; Li, et al., 2012; Steiner and Schwab, 2012). Many strategies have been used to obtain evolutionary information, catalytic sites and substrate channels by integrating sequence and structural features of enzymes. Previous studies have developed many effective computational tools for enzyme engineering, such as the enzyme design software Rosetta (Leaver-Fay, et al., 2011) and stability design software Foldx (Van, et al., 2011) and so on (Table S2). However, most of them only focus on one feature, like the thermo-stability based on the known PDB structure, and often request professional backgrounds in protein structure, biochemistry, bioinformatics and so on.
What is AEMD？
AEMD is a web-based pipeline, which integrates several approaches together for enzyme stability, selectivity and activity engineering. This pipeline can generate comprehensive reports, which include the recommended mutation for improving enzyme catalytic property. Specifically, users can get the recommended mutation only inputting sequence information of target enzymes, which is very useful in the situation without professional knowledge and the known protein structure, since AEMD contains a functional module that can automatically predict structure of the target enzyme based on the known structures in Protein Data Bank (PDB).
AEMD-Web provides a web interface, enabling users to conveniently predict mutants which could improve the stability, selectivity and activity of enzymes. Users can obtain the suggestion of mutations for almost all enzyme even without protein structure. In the future, we will construct a comprehensive enzymatic mutant database and integrate new computing technology, to improve the efficiency of enzyme engineering in industrial biotechnology.
Fig.1 Workflow of the Stability analysis (A), Selectivity analysis (B) and Activity analysis (C).The blue color rectangle blocks represent the inputs of sequence or PDB file, and the output of recommended mutation sites. The green and gray color rectangle blocks represent the evolution- and energy-based analysis process, respectively. The yellow color diamond blocks represent the use of other softwares and approaches. The processes were shown in Supplementary methods【click here】in more detail.
Process
This time we utilized AEMD's Stability mode (click here for AEMD user's guide) to screen for mutational sites that benefit the ceaS2 enzyme activity.
Because of the complexity of enzyme catalysis, it’s difficult to predict point mutation improving protein activity accurately. How AEMD work？
Firstly，the development team of AEMD recently described a method which is able to identify desired mutations by analyzing the coevolution information of protein sequences (Liu, et al., 2016). In the AEMD-web, some point mutations are suggested by this method. Besides, AEMD’s analysis generated some residues close to active center and transport tunnels which are recommended to saturated mutation to improve activity (Fig. 1C). For the input of target protein sequence, AEMD first obtain the PDB file using RosettaCM (Song, et al., 2013). Next, the substrate of template PDB was mapped into target PDB using the “struct_align” funciton of Schrodinger software (QikProp, 2015). The spatial location of substrate in target PDB can help to determine the ligand-binding pocket of target enzyme. If all potential template PDB had no substrate in the PDB file, AEMD predicted the ligand-binding pocket by a Rosetta script (gen_apo_grids.linuxgccrelease) (Zanghellini, et al., 2006). After the determination of ligand-binding pocket, AEMD generated the possible catalytic sites by search local Catalytic Site Atlas (Furnham, et al., 2014); the residues within 5Å distance from ligands by calculating the minimum distance between residue and substrate; and the residues located within 3 Å distance from transport tunnels by CAVER (Chovancova, et al., 2012).(see the Fig.1 (C))
We submitted the amino acid sequence and PDB file of ceaS2 online and got the prediction result in half an hour【结果文件】
【结果截图】
We first selected the program within the 5Å distance of active site, altogether 33 kinds, and then used point mutation to conduct molecular cloning operation. Next step was to synthesize the acrylic acid using the whole cell catalysis and determined the acrylic acid yield by HPLC. The results are as follows:
【33种突变点+wt产量图】
In these total 33 programs of mutational sites, there are XX programs with nearly XX% of acrylic acid yield higher than that of the wild type, which indicates a higher activity. The highest mutational site XXX presents a yield XX times the wild type. Therefore, it is valid and tangible for us to implement AEMD to design the mutational sites!
