Combinatorial recombination of gene fragments to construct a library of chimeras

The Generation of Thermostable Fungal Laccase Chimeras by SCHEMA-RASPP Structure-Guided Recombination in Vivo

Fungal laccases are biotechnologically relevant enzymes that are capable of oxidizing a wide array of compounds, using oxygen from the air and releasing water as the only byproduct. The laccase structure is comprised of three cupredoxin domains sheltering two copper centers-the T1Cu site and the T2/T3 trinuclear Cu cluster-connected to each other through a highly conserved internal electron transfer pathway. As such, the generation of laccase chimeras with high sequence diversity from different orthologs is difficult to achieve without compromising protein functionality. Here, we have obtained a diverse family of functional chimeras showing increased thermostability from three fungal laccase orthologs with ∼70% protein sequence identity. Assisted by the high frequency of homologous DNA recombination in Saccharomyces cerevisiae, computationally selected SCHEMA-RASPP blocks were spliced and cloned in a one-pot transformation. As a result of this in vivo assembly, an enriched library of laccase chimeras was rapidly generated, with multiple recombination events simultaneously occurring between and within the SCHEMA blocks. The resulting library was screened at high temperature, identifying a collection of thermostable chimeras with considerable sequence diversity, which varied from their closest parent homologue by 46 amino acids on average. The most thermostable variant increased its half-life of thermal inactivation at 70 °C 5-fold (up to 108 min), whereas several chimeras also displayed improved stability at acidic pH. The two catalytic copper sites spanned different SCHEMA blocks, shedding light on the recognition of specific residues involved in substrate oxidation. In summary, this case-study, through comparison with previous laccase engineering studies, highlights the benefits of bringing together computationally guided recombination and in vivo shuffling as an invaluable strategy for laccase evolution, which can be translated to other enzyme systems.

Combinatorial Library Based on Restriction Enzyme-mediated Modular Assembly

Combinatorial approaches in directed evolution were proven to be more efficient for exploring sequence space and innovating function of protein. Here, we presented the modular assembly of secondary structures (MASS) for constructing a combinatorial library. In this approach, secondary structure elements were extracted from natural existing protein. The common linkers were flanking secondary structure elements, and then secondary structure elements were digested by Hinf I restriction endonuclease that was used in the construction of combinatorial library for the first time. The digested DNA fragments were randomly ligated in the sense orientation, then in sequence to be amplified by PCR and transformation. This approach showed that different DNA fragments without homologous sequences could be randomly assembled to create significant sequence space. With the structure analysis of recombinants, it would be beneficial to the rational design, even to the design of protein de novo, and to evolve any genetic part or circuit.

Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration

Genetic designs can consist of dozens of genes and hundreds of genetic parts. After evaluating a design, it is desirable to implement changes without the cost and burden of starting the construction process from scratch. Here, we report a two-step process where a large design space is divided into deep pools of composite parts, from which individuals are retrieved and assembled to build a final construct. The pools are built via multiplexed assembly and sequenced using next-generation sequencing. Each pool consists of ∼20 Mb of up to 5000 unique and sequence-verified composite parts that are barcoded for retrieval by PCR. This approach is applied to a 16-gene nitrogen fixation pathway, which is broken into pools containing a total of 55 848 composite parts (71.0 Mb). The pools encompass an enormous design space (1043 possible 23 kb constructs), from which an algorithm-guided 192-member 4.5 Mb library is built. Next, all 1030 possible genetic circuits based on 10 repressors (NOR/NOT gates) are encoded in pools where each repressor is fused to all permutations of input promoters. These demonstrate that multiplexing can be applied to encompass entire design spaces from which individuals can be accessed and evaluated.

Library generation by gene shuffling

This unit describes the process of gene shuffling, also known as sexual PCR. Gene shuffling is a facile method for the generation of sequence libraries containing the information from a family of related genes. Essentially, related genes are fragmented by DNase I digestion and reassembled by primer-less PCR. The resulting chimeric genes can then be screened or selected for a desired function.

Designing libraries of chimeric proteins using SCHEMA recombination and RASPP

SCHEMA is a method for designing libraries of novel proteins by recombination of homologous sequences. The goal is to maximize the number of folded proteins while simultaneously generating significant sequence diversity. Here, we use the RASPP algorithm to identify optimal SCHEMA designs for shuffling contiguous elements of sequence. To exemplify the method, SCHEMA is used to recombine five fungal cellobiohydrolases (CBH1s) to produce a library of more than 390,000 novel CBH1 sequences.

Random field model reveals structure of the protein recombinational landscape

We are interested in how intragenic recombination contributes to the evolution of proteins and how this mechanism complements and enhances the diversity generated by random mutation. Experiments have revealed that proteins are highly tolerant to recombination with homologous sequences (mutation by recombination is conservative); more surprisingly, they have also shown that homologous sequence fragments make largely additive contributions to biophysical properties such as stability. Here, we develop a random field model to describe the statistical features of the subset of protein space accessible by recombination, which we refer to as the recombinational landscape. This model shows quantitative agreement with experimental results compiled from eight libraries of proteins that were generated by recombining gene fragments from homologous proteins. The model reveals a recombinational landscape that is highly enriched in functional sequences, with properties dominated by a large-scale additive structure. It also quantifies the relative contributions of parent sequence identity, crossover locations, and protein fold to the tolerance of proteins to recombination. Intragenic recombination explores a unique subset of sequence space that promotes rapid molecular diversification and functional adaptation.

Mutation and recombination are the primary sources of genetic variation in evolving populations. The relative benefit of these two diversification mechanisms and how they complement each other has been a long-standing question in evolutionary biology. While it is clear what types of genetic diversity these two mechanisms can create, a significant challenge is relating these sequence changes to changes in fitness. The fitness landscape, which describes this mapping from genotype to phenotype, is extraordinarily complex and defined over an incomprehensibly large space of sequences. Here, we develop a model of the landscape that relies not on the details of this mapping, but rather on the statistical relationships between sequences. By studying the expected values of landscape properties, we can gain insights into the structure of the landscape that are independent of the details of how genotype dictates phenotype. We use this random field model to understand how recombination explores a functionally enriched and diverse subset of protein sequence space.

Computational tools for metabolic engineering

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area.