Engineering genes for predictable protein expression.

The DNA sequence used to encode a polypeptide can have dramatic effects on its expression. Lack of readily available tools has until recently inhibited meaningful experimental investigation of this phenomenon. Advances in synthetic biology and the application of modern engineering approaches now provide the tools for systematic analysis of the sequence variables affecting heterologous expression of recombinant proteins. We here discuss how these new tools are being applied and how they circumvent the constraints of previous approaches, highlighting some of the surprising and promising results emerging from the developing field of gene engineering.

A family of thermostable fungal cellulases created by structure-guided recombination.

SCHEMA structure-guided recombination of 3 fungal class II cellobiohydrolases (CBH II cellulases) has yielded a collection of highly thermostable CBH II chimeras. Twenty-three of 48 genes sampled from the 6,561 possible chimeric sequences were secreted by the Saccharomyces cerevisiae heterologous host in catalytically active form. Five of these chimeras have half-lives of thermal inactivation at 63 degrees C that are greater than the most stable parent, CBH II enzyme from the thermophilic fungus Humicola insolens, which suggests that this chimera collection contains hundreds of highly stable cellulases. Twenty-five new sequences were designed based on mathematical modeling of the thermostabilities for the first set of chimeras. Ten of these sequences were expressed in active form; all 10 retained more activity than H. insolens CBH II after incubation at 63 degrees C. The total of 15 validated thermostable CBH II enzymes have high sequence diversity, differing from their closest natural homologs at up to 63 amino acid positions. Selected purified thermostable chimeras hydrolyzed phosphoric acid swollen cellulose at temperatures 7 to 15 degrees C higher than the parent enzymes. These chimeras also hydrolyzed as much or more cellulose than the parent CBH II enzymes in long-time cellulose hydrolysis assays and had pH/activity profiles as broad, or broader than, the parent enzymes. Generating this group of diverse, thermostable fungal CBH II chimeras is the first step in building an inventory of stable cellulases from which optimized enzyme mixtures for biomass conversion can be formulated.

SCHEMA recombination of a fungal cellulase uncovers a single mutation that contributes markedly to stability.

A quantitative linear model accurately (R(2) = 0.88) describes the thermostabilities of 54 characterized members of a family of fungal cellobiohydrolase class II (CBH II) cellulase chimeras made by SCHEMA recombination of three fungal enzymes, demonstrating that the contributions of SCHEMA sequence blocks to stability are predominantly additive. Thirty-one of 31 predicted thermostable CBH II chimeras have thermal inactivation temperatures higher than the most thermostable parent CBH II, from Humicola insolens, and the model predicts that hundreds more CBH II chimeras share this superior thermostability. Eight of eight thermostable chimeras assayed hydrolyze the solid cellulosic substrate Avicel at temperatures at least 5 degrees C above the most stable parent, and seven of these showed superior activity in 16-h Avicel hydrolysis assays. The sequence-stability model identified a single block of sequence that adds 8.5 degrees C to chimera thermostability. Mutating individual residues in this block identified the C313S substitution as responsible for the entire thermostabilizing effect. Introducing this mutation into the two recombination parent CBH IIs not featuring it (Hypocrea jecorina and H. insolens) decreased inactivation, increased maximum Avicel hydrolysis temperature, and improved long time hydrolysis performance. This mutation also stabilized and improved Avicel hydrolysis by Phanerochaete chrysosporium CBH II, which is only 55-56% identical to recombination parent CBH IIs. Furthermore, the C313S mutation increased total H. jecorina CBH II activity secreted by the Saccharomyces cerevisiae expression host more than 10-fold. Our results show that SCHEMA structure-guided recombination enables quantitative prediction of cellulase chimera thermostability and efficient identification of stabilizing mutations.

Gene Designer: a synthetic biology tool for constructing artificial DNA segments.

BACKGROUND: Direct synthesis of genes is rapidly becoming the most efficient way to make functional genetic constructs and enables applications such as codon optimization, RNAi resistant genes and protein engineering. Here we introduce a software tool that drastically facilitates the design of synthetic genes. RESULTS: Gene Designer is a stand-alone software for fast and easy design of synthetic DNA segments. Users can easily add, edit and combine genetic elements such as promoters, open reading frames and tags through an intuitive drag-and-drop graphic interface and a hierarchical DNA/Protein object map. Using advanced optimization algorithms, open reading frames within the DNA construct can readily be codon optimized for protein expression in any host organism. Gene Designer also includes features such as a real-time sliding calculator of oligonucleotide annealing temperatures, sequencing primer generator, tools for avoidance or inclusion of restriction sites, and options to maximize or minimize sequence identity to a reference. CONCLUSION: Gene Designer is an expandable Synthetic Biology workbench suitable for molecular biologists interested in the de novo creation of genetic constructs.

The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology.

The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-driven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.

Redesigning and characterizing the substrate specificity and activity of Vibrio fluvialis aminotransferase for the synthesis of imagabalin.

Several protein engineering approaches were combined to optimize the selectivity and activity of Vibrio fluvialis aminotransferase (Vfat) for the synthesis of (3S,5R)-ethyl 3-amino-5-methyloctanoate; a key intermediate in the synthesis of imagabalin, an advanced candidate for the treatment of generalized anxiety disorder. Starting from wild-type Vfat, which had extremely low activity catalyzing the desired reaction, we engineered an improved enzyme with a 60-fold increase in initial reaction velocity for transamination of (R)-ethyl 5-methyl 3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate. To achieve this, <450 variants were screened, which allowed accurate assessment of enzyme performance using a low-throughput ultra performance liquid chromatography assay. During the course of this work, crystal structures of Vfat wild type and an improved variant (Vfat variant r414) were solved and they are reported here for the first time. This work also provides insight into the critical residues for substrate specificity for the transamination of (R)-ethyl 5-methyl 3-oxooctanoate and structurally related ß-ketoesters.

In silico design of functional DNA constructs.

The promise of synthetic biology lies in the creation of novel function from the proper combination of genetic elements. De novo gene synthesis has become a cost-effective method for building virtually any conceptualized genetic construct, removing the constraints of extant sequences, and greatly facilitating study of the relationships between gene sequence and function. With the rapid increase in the number and variety of characterized and cataloged genetic elements, tools that facilitate assembly of such parts into functional constructs (genes, vectors, circuits, etc.) are essential. The Gene Designer software allows scientists and engineers to readily manage and recombine genetic elements into novel assemblies. It also provides tools for the simulation of molecular cloning schemes as well as the engineering and optimization of protein-coding sequences. Together, the functions in Gene Designer provide a complete capability to design functional genetic constructs.

Designing genes for successful protein expression.

DNA sequences are now far more readily available in silico than as physical DNA. De novo gene synthesis is an increasingly cost-effective method for building genetic constructs, and effectively removes the constraint of basing constructs on extant sequences. This allows scientists and engineers to experimentally test their hypotheses relating sequence to function. Molecular biologists, and now synthetic biologists, are characterizing and cataloging genetic elements with specific functions, aiming to combine them to perform complex functions. However, the most common purpose of synthetic genes is for the expression of an encoded protein. The huge number of different proteins makes it impossible to characterize and catalog each functional gene. Instead, it is necessary to abstract design principles from experimental data: data that can be generated by making predictions followed by synthesizing sequences to test those predictions. Because of the degeneracy of the genetic code, design of gene sequences to encode proteins is a high-dimensional problem, so there is no single simple formula to guarantee success. Nevertheless, there are several straightforward steps that can be taken to greatly increase the probability that a designed sequence will result in expression of the encoded protein. In this chapter, we discuss gene sequence parameters that are important for protein expression. We also describe algorithms for optimizing these parameters, and troubleshooting procedures that can be helpful when initial attempts fail. Finally, we show how many of these methods can be accomplished using the synthetic biology software tool Gene Designer.

You're one in a googol: optimizing genes for protein expression.

A vast number of different nucleic acid sequences can all be translated by the genetic code into the same amino acid sequence. These sequences are not all equally useful however; the exact sequence chosen can have profound effects on the expression of the encoded protein. Despite the importance of protein-coding sequences, there has been little systematic study to identify parameters that affect expression. This is probably because protein expression has largely been tackled on an ad hoc basis in many independent projects: once a sequence has been obtained that yields adequate expression for that project, there is little incentive to continue work on the problem. Synthetic biology may now provide the impetus to transform protein expression folklore into design principles, so that DNA sequences may easily be designed to express any protein in any system. In this review, we offer a brief survey of the literature, outline the major challenges in interpreting existing data and constructing robust design algorithms, and propose a way to proceed towards the goal of rational sequence engineering.

Design parameters to control synthetic gene expression in Escherichia coli.

BACKGROUND: Production of proteins as therapeutic agents, research reagents and molecular tools frequently depends on expression in heterologous hosts. Synthetic genes are increasingly used for protein production because sequence information is easier to obtain than the corresponding physical DNA. Protein-coding sequences are commonly re-designed to enhance expression, but there are no experimentally supported design principles. PRINCIPAL FINDINGS: To identify sequence features that affect protein expression we synthesized and expressed in E. coli two sets of 40 genes encoding two commercially valuable proteins, a DNA polymerase and a single chain antibody. Genes differing only in synonymous codon usage expressed protein at levels ranging from undetectable to 30% of cellular protein. Using partial least squares regression we tested the correlation of protein production levels with parameters that have been reported to affect expression. We found that the amount of protein produced in E. coli was strongly dependent on the codons used to encode a subset of amino acids. Favorable codons were predominantly those read by tRNAs that are most highly charged during amino acid starvation, not codons that are most abundant in highly expressed E. coli proteins. Finally we confirmed the validity of our models by designing, synthesizing and testing new genes using codon biases predicted to perform well. CONCLUSION: The systematic analysis of gene design parameters shown in this study has allowed us to identify codon usage within a gene as a critical determinant of achievable protein expression levels in E. coli. We propose a biochemical basis for this, as well as design algorithms to ensure high protein production from synthetic genes. Replication of this methodology should allow similar design algorithms to be empirically derived for any expression system.