2School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
*L.T. and H.G. contributed equally to this work
Site-saturation mutagenesis is a powerful tool for protein optimization due to its efficiency and simplicity. A degenerate codon NNN or NNS (K) is often used to encode the 20 standard amino acids, but this will produce redundant codons and cause uneven distribution of amino acids in the constructed library. Here we present a novel “small-intelligent” strategy to construct mutagenesis libraries that have a minimal gene library size without inherent amino acid biases, stop codons, or rare codons of Escherichia coli by coupling well-designed combinatorial degenerate primers with suitable PCR-based mutagenesis methods. The designed primer mixture contains exactly one codon per amino acid and thus allows the construction of small-intelligent mutagenesis libraries with one gene per protein. In addition, the software tool DC-Analyzer was developed to assist in primer design according to the user-defined randomization scheme for library construction. This small-intelligent strategy was successfully applied to the randomization of halohydrin dehalogenases with one or two randomized sites. With the help of DC-Analyzer, the strategy was proven to be as simple as NNS randomization and could serve as a general tool to efficiently randomize target genes at positions of interest.
In the past few decades, directed evolution has emerged as a powerful approach for creating proteins with desired properties (1,2). Harnessing Darwinian evolution in vitro, this evolution strategy comprises the seamless combination of gene diversification, expression of mutant enzymes, and high-throughput screening or selection. The advantage of the directed evolution approach is that there is no prerequisite for the mechanisms of the desired activity or the structure of enzymes. Most early directed evolution studies followed a “nonrational” route and applied random mutagenesis methods, such as error-prone PCR or DNA shuffling, to generate large and diverse libraries (3,4). Although a plethora of successful examples utilizing such strategies have been demonstrated in numerous scientific and patent publications, these approaches rely on the availability of high-throughput screening methods. It is apparent that high-quality enzyme libraries could reduce the screening effort required and make directed evolution experiments more efficient.
Currently, enzyme optimization has moved from pure “blind” to “semi-rational” approaches in order to accommodate the demands of rapid creation of novel industrial biocatalysts (5-8). Mutagenesis “hot spots” and regions are generally identified based on mechanistic and structural information or by applying computationally guided mutagenesis strategies (9,10). Over the years, many computational methods, such as B-FITTER, PatchFinder, and Rate4Site, have been introduced, and the option of generating small focused mutant libraries at the defined positions has become preferred (11-13). Although the conventional degenerate codon NNN or NNK (S) covers all 20 amino acids and is often used to randomize those identified hot spots, the resulting focused libraries would contain redundant codons that encode amino acids unevenly. Moreover, codon redundancy and amino acid biases will increase exponentially when the number of mutation sites is increased, which undoubtedly decreases the quality of the constructed library. Consequently, it is difficult to screen low frequency positive colonies from a large-size library. However, in most cases utilizing only one degenerate codon may not be ideal.
To circumvent this limitation, several chemical and biochemical strategies have been developed, which include the use of specialized dinucleotide and trinucleotide phosphoramidites during the synthesis of randomized oligonucleotides (14-16). Although these systems could efficiently eliminate codon redundancy and introduce any subset of codons at target positions, their costs are high, and their application is limited to laboratories with expertise in specialized DNA synthesis (14). In 2003, Hine and coworkers presented their MAX randomization methodology for constructing maximally diverse libraries with a minimized gene library size (17). The essence of the procedure is to synthesize sets of 9-mer primers for each codon to be randomized, which are subsequently used to hybridize to a synthesized template strand with NNN randomization at each target site. The hybridized and ligated selection strands are amplified by a subsequent PCR step. In this way, the resulting randomized DNA cassette contains no redundant codons and no inherent amino acid biases. Obviously, the advantage of the MAX system over the aforementioned phosphoramidate oligonucleotide strategy is that the MAX technology needs only simple primers. However, the MAX system is more complex and expensive than NNN/NNK randomization.
Here, we provide an alternative way to construct small-intelligent mutagenesis libraries without inherent amino acid biases by using well-designed combinatorial degenerate primers. Ideally, the constructed library contains colonies with just one gene for one protein as MAX does. Moreover, the software tool DC-Analyzer was developed to assist in primer design for our small-intelligent mutagenesis library construction. With the assistance of DC-Analyzer, our small-intelligent randomization strategy could serve as a good substitute for NNS randomization.Materials and methods Mutagenesis
Site-directed saturation mutagenesis experiments were carried out using Phusion high-fidelity DNA polymerase PCR kit (New England Biolabs, Ipswich, MA, USA) with the recombinant expression vector pBADHheA containing the wild-type hheA gene (accession no: AF397297) as a template. For full randomization of one site using the small-intelligent method, a mixture of four pairs of well-designed complementary primers at a ratio of 12:6:1:1 were used (forward primers: 5′-TCTTCAACXXXCCGACATA-3′, where XXX represents NDT, VMA, ATG, and TGG, respectively), while for NNS randomization, one pair of complementary primers was used (forward primer: 5′-TTCAACNNSCCGACATACTT-3′). All primers were supplied by Invitrogen (Shanghai, China). The two randomization reactions were performed in 50-µL reactions under the same conditions. The reactions contain 1 × HF buffer, 200 µM each dNTP, 1 mM Mg2+, 100 ng template, 2 µM each mixed primer, and 0.01 U/µL Phusion polymerase. The temperature program used was 98°C for 3 min followed by 30 cycles of 10 s at 98°C, 45 s at 50°C, and 2 min at 72°C, and finished with a final incubation at 72°C for 10 min.