Exploration of sequence space for protein engineering

A QM/MM study on the origin of retro-aldolase activity of a catalytic antibody

The retro-aldolase mechanism of methodol catalysed by the catalytic antibody 33F12 is described based on the exploration of the free energy landscape obtained with QM/MM methods. The amino acids involved in the reaction have been identified, as well as their specific role played in the active site and in the flexibility of the loops. Finally, the comparison with a de novo enzyme RA95.5-8F provides a deeper understanding of catalytic differences between such different protein scaffolds.

Novel enzymes through design and evolution

The generation of enzymes with new catalytic activities remains a major challenge. So far, several different strategies have been developed to tackle this problem, including site-directed mutagenesis, random mutagenesis (directed evolution), antibody catalysis, computational redesign, and de novo methods. Using these techniques, a broad array of novel enzymes has been created (aldolases, decarboxylases, dehydratases, isomerases, oxidases, reductases, and others), although their low efficiencies (10 to 100 M(-1) s(-l)) compared to those of the best natural enzymes (10(6) to 10(8) M(-1) s(-1)) remains a significant concern. Whereas rational design might be the most promising and versatile approach to generating new activities, directed evolution seems to be the best way to optimize the catalytic properties of novel enzymes. Indeed, impressive successes in enzyme engineering have resulted from a combination of rational and random design.

Laboratory-directed protein evolution

Systematic approaches to directed evolution of proteins have been documented since the 1970s. The ability to recruit new protein functions arises from the considerable substrate ambiguity of many proteins. The substrate ambiguity of a protein can be interpreted as the evolutionary potential that allows a protein to acquire new specificities through mutation or to regain function via mutations that differ from the original protein sequence. All organisms have evolutionarily exploited this substrate ambiguity. When exploited in a laboratory under controlled mutagenesis and selection, it enables a protein to "evolve" in desired directions. One of the most effective strategies in directed protein evolution is to gradually accumulate mutations, either sequentially or by recombination, while applying selective pressure. This is typically achieved by the generation of libraries of mutants followed by efficient screening of these libraries for targeted functions and subsequent repetition of the process using improved mutants from the previous screening. Here we review some of the successful strategies in creating protein diversity and the more recent progress in directed protein evolution in a wide range of scientific disciplines and its impacts in chemical, pharmaceutical, and agricultural sciences.

Directed molecular evolution by machine learning and the influence of nonlinear interactions

Alternative search strategies for the directed evolution of proteins are presented and compared with each other. In particular, two different machine learning strategies based on partial least-squares regression are developed: the first contains only linear terms that represent a given residue's independent contribution to fitness, the second contains additional nonlinear terms to account for potential epistatic coupling between residues. The nonlinear modeling strategy is further divided into two types, one that contains all possible nonlinear terms and another that makes use of a genetic algorithm to select a subset of important interaction terms. The performance of each modeling type as a function of training set size is analysed. Simulated molecular evolution on a synthetic protein landscape shows the use of machine learning techniques to guide library design can be a powerful addition to library generation methods such as DNA shuffling.

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.

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.

Randomization of the entire active-site helix alpha 1 of the thiol-disulfide oxidoreductase DsbA from Escherichia coli

DsbA from Escherichia coli is the most oxidizing member of the thiol-disulfide oxidoreductase family (E(o)' = -122 mV) and is required for efficient disulfide bond formation in the periplasm. The reactivity of the catalytic disulfide bond (Cys(30)-Pro(31)-His(32)-Cys(33)) is primarily due to an extremely low pK(a) value (3.4) of Cys(30), which is stabilized by the partial positive dipole charge of the active-site helix alpha1 (residues 30-37). We have randomized all non-cysteine residues of helix alpha1 (residues 31, 32, and 34-37) and found that two-thirds of the resulting variants complement DsbA deficiency in a dsbA deletion strain. Sequencing of 98 variants revealed a large number of non-conservative replacements in active variants, even at well conserved positions. This indicates that tertiary structure context strongly determines alpha-helical secondary structure formation of the randomized sequence. A subset of active and inactive variants was further characterized. All these variants were more reducing than wild type DsbA, but the redox potentials of active variants did not drop below -210 mV. All inactive variants had redox potentials lower than -210 mV, although some of the inactive proteins were still re-oxidized by DsbB. This demonstrates that efficient oxidation of substrate polypeptides is the crucial property of DsbA in vivo.