We have developed a statistical method named MAP (mutagenesis assistant program) to equip protein engineers with a tool to develop promising directed evolution strategies by comparing 19 mutagenesis methods. Instead of conventional transition/transversion bias indicators as benchmarks for comparison, we propose to use three indicators based on the subset of amino acid substitutions generated on the protein level: (1) protein structure indicator; (2) amino acid diversity indicator with a codon diversity coefficient; and (3) chemical diversity indicator. A MAP analysis for a single nucleotide substitution was performed for four genes: (1) heme domain of cytochrome P450 BM-3 from Bacillus megaterium (EC 1.14.14.1); (2) glucose oxidase from Aspergillus niger (EC 1.1.3.4); (3) arylesterase from Pseudomonas fluorescens (EC 3.1.1.2); and (4) alcohol dehydrogenase from Saccharomyces cerevisiae (EC 1.1.1.1). Based on the MAP analysis of these four genes, 19 mutagenesis methods have been evaluated and criteria for an ideal mutagenesis method have been proposed. The statistical analysis showed that existing gene mutagenesis methods are limited and highly biased. An average amino acid substitution per residue of only 3.15-7.4 can be achieved with current random mutagenesis methods. For the four investigated gene sequences, an average fraction of amino acid substitutions of 0.5-7% results in stop codons and 4.5-23.9% in glycine or proline residues. An average fraction of 16.2-44.2% of the amino acid substitutions are preserved, and 45.6% (epPCR method) are chemically different. The diversity remains low even when applying a non-biased method: an average of seven amino acid substitutions per residue, 2.9-4.7% stop codons, 11.1-16% glycine/proline residues, 21-25.8% preserved amino acids, and 55.5% are amino acids with chemically different side-chains. Statistical information for each mutagenesis method can further be used to investigate the mutational spectra in protein regions regarded as important for the property of interest.

• PMID: 16325201
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Journal Article | Research Support, Non-U.S. Gov't

Authors

Wong TS, Roccatano D, Zacharias M, Schwaneberg U

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