Efficient sampling of SCHEMA chimera families to identify useful sequence elements

Thermostable Cellulases / Xylanases From Thermophilic and Hyperthermophilic Microorganisms: Current Perspective

The bioconversion of lignocellulose into monosaccharides is critical for ensuring the continual manufacturing of biofuels and value-added bioproducts. Enzymatic degradation, which has a high yield, low energy consumption, and enhanced selectivity, could be the most efficient and environmentally friendly technique for converting complex lignocellulose polymers to fermentable monosaccharides, and it is expected to make cellulases and xylanases the most demanded industrial enzymes. The widespread nature of thermophilic microorganisms allows them to proliferate on a variety of substrates and release substantial quantities of cellulases and xylanases, which makes them a great source of thermostable enzymes. The most significant breakthrough of lignocellulolytic enzymes lies in lignocellulose-deconstruction by enzymatic depolymerization of holocellulose into simple monosaccharides. However, commercially valuable thermostable cellulases and xylanases are challenging to produce in high enough quantities. Thus, the present review aims at giving an overview of the most recent thermostable cellulases and xylanases isolated from thermophilic and hyperthermophilic microbes. The emphasis is on recent advancements in manufacturing these enzymes in other mesophilic host and enhancement of catalytic activity as well as thermostability of thermophilic cellulases and xylanases, using genetic engineering as a promising and efficient technology for its economic production. Additionally, the biotechnological applications of thermostable cellulases and xylanases of thermophiles were also discussed.

Recombination of 2Fe-2S Ferredoxins Reveals Differences in the Inheritance of Thermostability and Midpoint Potential

Recombination can be used in the laboratory to overcome component limitations in synthetic biology by creating enzymes that exhibit distinct activities and stabilities from native proteins. To investigate how recombination affects the properties of an oxidoreductase that transfers electrons in cells, we created ferredoxin (Fd) chimeras by recombining distantly related cyanobacterial and cyanomyophage Fds (53% identity) that present similar midpoint potentials but distinct thermostabilities. Fd chimeras having a wide range of amino acid substitutions retained the ability to coordinate an iron-sulfur cluster, although their thermostabilities varied with the fraction of residues inherited from each parent. The midpoint potentials of chimeric Fds also varied. However, all of the synthetic Fds exhibited midpoint potentials outside of the parental protein range. Each of the chimeric Fds could also support electron transfer between Fd-NADP reductase and sulfite reductase in Escherichia coli, although the chimeric Fds varied in the expression required for similar levels of cellular electron transfer. These results show how Fds can be diversified through recombination and reveal differences in the inheritance of thermostability and electrochemical properties. Furthermore, they illustrate how electron transfer efficiencies of chimeric Fds can be rapidly evaluated using a synthetic metabolic pathway.

A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures

Current state-of-the-art approaches to computational protein design (CPD) aim to capture the determinants of structure from physical principles. While this has led to many successful designs, it does have strong limitations associated with inaccuracies in physical modeling, such that a reliable general solution to CPD has yet to be found. Here, we propose a design framework-one based on identifying and applying patterns of sequence-structure compatibility found in known proteins, rather than approximating them from models of interatomic interactions. We carry out extensive computational analyses and an experimental validation for our method. Our results strongly argue that the Protein Data Bank is now sufficiently large to enable proteins to be designed by using only examples of structural motifs from unrelated proteins. Because our method is likely to have orthogonal strengths relative to existing techniques, it could represent an important step toward removing remaining barriers to robust CPD.

Generation of Stable Influenza Virus Hemagglutinin through Structure-Guided Recombination

Hemagglutinin (HA) is the major surface antigen of influenza virus and the most promising influenza vaccine immunogen. In 2013, the devastating H7N9 influenza virus was identified in China, which induced high mortality. The HA of this virus (H7) is relatively unstable, making it challenging to produce an effective vaccine. To improve the stability of HA protein from H7N9 influenza virus for better vaccine antigens without impairing immunogenicity, we recombined the HA from H7N9 (H7) with a more stable HA from H3N2 (H3) by structure-guided recombination, resulting in six chimeric HAs, FrA-FrF. Two of these chimeric HAs, FrB and FrC, exhibited proper hemagglutination activity and presented improved thermal stability compared to the original H7. Mice immunized with FrB and FrC elicited H7-specific antibodies comparable to those induced by parental H7, and the antisera collected from these immunized mice successfully inhibited H7N9 infection in a microneutralization assay. These results suggest that our structural-recombination approach can create stabilizing chimeric antigens while maintaining proper immunogenicity, which may not only benefit the construction of more stable HA vaccines to fight against H7N9 infection, but also facilitate effective vaccine improvements for other influenza viruses or infectious pathogens. In addition, this study also demonstrates the potential for better engineering of multimeric protein complexes like HA to achieve improved function, which are often immunologically or pharmaceutically important but difficult to modify.

In Vivo Selection of a Computationally Designed SCHEMA AAV Library Yields a Novel Variant for Infection of Adult Neural Stem Cells in the SVZ

Directed evolution continues to expand the capabilities of complex biomolecules for a range of applications, such as adeno-associated virus vectors for gene therapy; however, advances in library design and selection strategies are key to develop variants that overcome barriers to clinical translation. To address this need, we applied structure-guided SCHEMA recombination of the multimeric adeno-associated virus (AAV) capsid to generate a highly diversified chimeric library with minimal structural disruption. A stringent in vivo Cre-dependent selection strategy was implemented to identify variants that transduce adult neural stem cells (NSCs) in the subventricular zone. A novel variant, SCH9, infected 60% of NSCs and mediated 24-fold higher GFP expression and a 12-fold greater transduction volume than AAV9. SCH9 utilizes both galactose and heparan sulfate as cell surface receptors and exhibits increased resistance to neutralizing antibodies. These results establish the SCHEMA library as a valuable tool for directed evolution and SCH9 as an effective gene delivery vector to investigate subventricular NSCs.

Exploring the Mechanism Responsible for Cellulase Thermostability by Structure-Guided Recombination

Cellulases from Bacillus and Geobacillus bacteria are potentially useful in the biofuel and animal feed industries. One of the unique characteristics of these enzymes is that they are usually quite thermostable. We previously identified a cellulase, GsCelA, from thermophilic Geobacillus sp. 70PC53, which is much more thermostable than its Bacillus homolog, BsCel5A. Thus, these two cellulases provide a pair of structures ideal for investigating the mechanism regarding how these cellulases can retain activity at high temperature. In the present study, we applied the SCHEMA non-contiguous recombination algorithm as a novel tool, which assigns protein sequences into blocks for domain swapping in a way that lessens structural disruption, to generate a set of chimeric proteins derived from the recombination of GsCelA and BsCel5A. Analyzing the activity and thermostability of this designed library set, which requires only a limited number of chimeras by SCHEMA calculations, revealed that one of the blocks may contribute to the higher thermostability of GsCelA. When tested against swollen Avicel, the highly thermostable chimeric cellulase C10 containing this block showed significantly higher activity (22%-43%) and higher thermostability compared to the parental enzymes. With further structural determinations and mutagenesis analyses, a 3₁₀ helix was identified as being responsible for the improved thermostability of this block. Furthermore, in the presence of ionic calcium and crown ether (CR), the chimeric C10 was found to retain 40% residual activity even after heat treatment at 90°C. Combining crystal structure determinations and structure-guided SCHEMA recombination, we have determined the mechanism responsible for the high thermostability of GsCelA, and generated a novel recombinant enzyme with significantly higher activity.

Why reinvent the wheel? Building new proteins based on ready-made parts

We protein engineers are ambivalent about evolution: on the one hand, evolution inspires us with myriad examples of biomolecular binders, sensors, and catalysts; on the other hand, these examples are seldom well-adapted to the engineering tasks we have in mind. Protein engineers have therefore modified natural proteins by point substitutions and fragment exchanges in an effort to generate new functions. A counterpoint to such design efforts, which is being pursued now with greater success, is to completely eschew the starting materials provided by nature and to design new protein functions from scratch by using de novo molecular modeling and design. While important progress has been made in both directions, some areas of protein design are still beyond reach. To this end, we advocate a synthesis of these two strategies: by using design calculations to both recombine and optimize fragments from natural proteins, we can build stable and as of yet un-sampled structures, thereby granting access to an expanded repertoire of conformations and desired functions. We propose that future methods that combine phylogenetic analysis, structure and sequence bioinformatics, and atomistic modeling may well succeed where any one of these approaches has failed on its own.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Design of proteins from smaller fragments-learning from evolution

Nature has generated an impressive set of proteins with diverse folds and functions. It has been able to do so using mechanisms such as duplication and fusion as well as recombination of smaller protein fragments that serve as building blocks. These evolutionary mechanisms provide a template for the rational design of new proteins from fragments of existing proteins. Design by duplication and fusion has been explored for a number of symmetric protein folds, while design by rational recombination has just emerged. First experiments in recombining fragments from the same and different folds are proving successful in building new proteins that harbor easily evolvable properties originating from the parents. Overall, duplication and recombination of smaller fragments shows much potential for future applications in the design of proteins.

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.