In vitro scanning mutagenesis strategies are valuable tools to identify critical residues in proteins and to generate proteins with modified properties. We describe the fast and simple All-Codon Scanning (ACS) strategy that creates a defined gene library wherein each individual codon within a specific target region is changed into all possible codons with only a single codon change per mutagenesis product. ACS is based on a multiplexed overlapping mutagenesis primer design that saturates only the targeted gene region with single codon changes. We have used ACS to produce single amino-acid changes in small and large regions of the human tumor suppressor protein p53 to identify single amino-acid substitutions that can restore activity to inactive p53 found in human cancers. Single-tube reactions were used to saturate defined 30-nt regions with all possible codon changes. The same technique was used in 20 parallel reactions to scan the 600-bp fragment encoding the entire p53 core domain. Identification of several novel p53 cancer rescue mutations demonstrated the utility of the ACS approach. ACS is a fast, simple and versatile method, which is useful for protein structure–function analyses and protein design or evolution problems.

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p < 0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L.

A set of shuttle vectors was constructed to facilitate expression of genes for metabolic engineering in Saccharomyces cerevisiae. Selectable markers include the URA3, TRP1, MET15, LEU2-d8, HIS3 and CAN1 genes. Differential expression of genes can be achieved as each marker is available on both CEN/ARS- and 2 µ-containing plasmids. Unique restriction sites downstream of TEF1, PGK1 or HXT7-391 promoters and upstream of the CYC1 terminator allow insertion of open-reading frame cassettes for expression. Furthermore, a fragment appropriate for integration into the genome via homologous recombination can be readily generated in a polymerase chain reaction. Vector marker genes are flanked by loxP recognition sites for the CreA recombinase to allow efficient site-specific marker deletion and recycling. Expression and copy number were characterized for representative high- and low-copy vectors carrying the different marker and promoter sequences. Metabolic engineering typically requires the stable introduction of multiple genes and genomic integration is often preferred. This requires an expanded number of stable expression sites relative to standard gene expression studies. This study demonstrated the practicality of polymerase chain reaction amplification of an expression cassette and genetic marker, and subsequent replacement of endogenous retrotransposons by homologous recombination with flanking sequences. Such reporters were expressed comparably to those inserted at standard integration loci. This expands the number of available characterized integration sites and demonstrates that such sites provide a virtually inexhaustible pool of integration targets for stable expression of multiple genes. Together these vectors and expression loci will facilitate combinatorial gene expression for metabolic engineering.