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Claes Gustafsson

Publications and source records attributed to Claes Gustafsson.

5 recordsLinked to original sources
PubMed2003

Systematic variation of amino acid substitutions for stringent assessment of pairwise covariation.

During protein evolution, amino acids change due to a combination of functional constraints and genetic drift. Proteins frequently contain pairs of amino acids that appear to change together (covariation). Analysis of covariation from naturally occurring sets of orthologs cannot distinguish between residue pairs retained by functional requirements of the protein and those pairs existing due to changes along a common evolutionary path. Here, we have separated the two types of covariation by independently recombining every naturally occurring amino acid variant within a set of 15 subtilisin orthologs. Our analysis shows that in this family of subtilisin orthologs, almost all possible pairwise combinations of amino acids can coexist. This suggests that amino acid covariation found in the subtilisin orthologs is almost entirely due to common ancestral origin of the changes rather than functional constraints. We conclude that naturally occurring sequence diversity can be used to identify positions that can vary independently without destroying protein function.

Amino Acid Sequence↗
PubMed2003

Engineering the aveC gene to enhance the ratio of doramectin to its CHC-B2 analogue produced in Streptomyces avermitilis.

Avermectin and its analogues are produced by the actinomycete Streptomyces avermitilis and are major commercial products for parasite control in the fields of animal health, agriculture, and human infections. Historically, the avermectin analogue doramectin (CHC-B1), which is sold commercially as Dectomax is co-produced during fermentation with the undesired analogue CHC-B2 at a CHC-B2:CHC-B1 ratio of 1.6:1. Although the identification of the avermectin gene cluster has allowed for characterization of most of the biosynthetic pathway, the mechanism for determining the avermectin B2:B1 ratio remains unclear. The aveC gene, which has an essential role in avermectin biosynthesis, was inactivated by insertional inactivation and mutated by site-specific mutagenesis and error-prone PCR. Several unrelated mutations were identified that resulted in improved ratios of the desirable avermectin analogue CHC-B1, produced relative to the undesired CHC-B2 fermentation component. High-throughput (HTP) screening of cultures grown on solid-phase fermentation plates and analysis using electrospray mass spectrometry was implemented to significantly increase screening capability. An aveC gene with mutations that result in a 4-fold improvement in the ratio of doramectin to CHC-B2 was identified. Subsequent integration of the enhanced aveC gene into the chromosome of the S. avermitilis production strain demonstrates the successful engineering of a specific biosynthetic pathway gene to significantly improve fermentation productivity of a commercially important product.

Base Sequence↗
PubMed2003

Putting engineering back into protein engineering: bioinformatic approaches to catalyst design.

Complex multivariate engineering problems are commonplace and not unique to protein engineering. Mathematical and data-mining tools developed in other fields of engineering have now been applied to analyze sequence-activity relationships of peptides and proteins and to assist in the design of proteins and peptides with specified properties. Decreasing costs of DNA sequencing in conjunction with methods to quickly synthesize statistically representative sets of proteins allow modern heuristic statistics to be applied to protein engineering. This provides an alternative approach to expensive assays or unreliable high-throughput surrogate screens.

Algorithms↗
PubMed2003

Optimizing the search algorithm for protein engineering by directed evolution.

An in silico protein model based on the Kauffman NK-landscape, where N is the number of variable positions in a protein and K is the degree of coupling between variable positions, was used to compare alternative search strategies for directed evolution. A simple genetic algorithm (GA) was used to model the performance of a standard DNA shuffling protocol. The search effectiveness of the GA was compared to that of a statistical approach called the protein sequence activity relationship (ProSAR) algorithm, which consists of two steps: model building and library design. A number of parameters were investigated and found to be important for the comparison, including the value of K, the screening size, the system noise and the number of replicates. The statistical model was found to accurately predict the measured activities for small values of the coupling between amino acids, K <or= 1. The ProSAR strategy was found to perform well for small to moderate values of coupling, 0 <or= K <or= 3, and was found to be robust to noise. Some implications for protein engineering are discussed.

Algorithms↗