Engineering functional thermostable proteins using ancestral sequence reconstruction

Natural proteins are often only slightly more stable in the native state than the denatured state, and an increase in environmental temperature can easily shift the balance toward unfolding. Therefore, the engineering of proteins to improve protein stability is an area of intensive research. Thermostable proteins are required to withstand industrial process conditions, for increased shelf-life of protein therapeutics, for developing robust 'biobricks' for synthetic biology applications, and for research purposes (e.g., structure determination). In addition, thermostability buffers the often destabilizing effects of mutations introduced to improve other properties. Rational design approaches to engineering thermostability require structural information, but even with advanced computational methods, it is challenging to predict or parameterize all the relevant structural factors with sufficient precision to anticipate the results of a given mutation. Directed evolution is an alternative when structures are unavailable but requires extensive screening of mutant libraries. Recently, however, bioinspired approaches based on phylogenetic analyses have shown great promise. Leveraging the rapid expansion in sequence data and bioinformatic tools, ancestral sequence reconstruction can generate highly stable folds for novel applications in industrial chemistry, medicine, and synthetic biology. This review provides an overview of the factors important for successful inference of thermostable proteins by ancestral sequence reconstruction and what it can reveal about the determinants of stability in proteins. Natural proteins are often only slightly more stable in the native state than the denatured state, and an increase in environmental temperature can easily shift the balance toward unfolding. Therefore, the engineering of proteins to improve protein stability is an area of intensive research. Thermostable proteins are required to withstand industrial process conditions, for increased shelf-life of protein therapeutics, for developing robust 'biobricks' for synthetic biology applications, and for research purposes (e.g., structure determination). In addition, thermostability buffers the often destabilizing effects of mutations introduced to improve other properties. Rational design approaches to engineering thermostability require structural information, but even with advanced computational methods, it is challenging to predict or parameterize all the relevant structural factors with sufficient precision to anticipate the results of a given mutation. Directed evolution is an alternative when structures are unavailable but requires extensive screening of mutant libraries. Recently, however, bioinspired approaches based on phylogenetic analyses have shown great promise. Leveraging the rapid expansion in sequence data and bioinformatic tools, ancestral sequence reconstruction can generate highly stable folds for novel applications in industrial chemistry, medicine, and synthetic biology. This review provides an overview of the factors important for successful inference of thermostable proteins by ancestral sequence reconstruction and what it can reveal about the determinants of stability in proteins. Native protein structures are complex three-dimensional arrangements of functional groups, which have evolved to carry out discrete biological functions that almost always depend on the maintenance of specific spatial relationships. However, native protein structures typically represent a metastable balance between conformational flexibility and stability that can be disturbed by environmental factors such as heat, organic solvents, chaotropic agents, and pH (1Pace C.N. Conformational stability of globular proteins.Trends Biochem. Sci. 1990; 15: 14-17Abstract Full Text PDF PubMed Scopus (387) Google Scholar). Both enthalpic and entropic factors determine how a linear polymer of amino acid residues folds reproducibly into a specific structure, including intramolecular interactions between different structural elements and the degree of solvation of polar and hydrophobic regions of the structure (2Baker D. What has de novo protein design taught us about protein folding and biophysics?.Protein Sci. 2019; 28: 678-683Crossref PubMed Scopus (85) Google Scholar). Any change to the sequence of a protein can affect these factors and therefore alter the ability of a polypeptide chain to fold into a functional structure. Nature has explored only a small proportion of the available sequence space, so there is much scope to engineer novel proteins with useful properties. However, to be useful for industrial applications, most novel proteins must fold easily into stable domains (3Bommarius A.S. Paye M.F. Stabilizing biocatalysts.Chem. Soc. Rev. 2013; 42: 6534-6565Crossref PubMed Scopus (320) Google Scholar, 4Burton S.G. Cowan D.A. Woodley J.M. The search for the ideal biocatalyst.Nat. Biotech. 2002; 20: 37-45Crossref PubMed Scopus (0) Google Scholar) (an exception being intrinsically disordered proteins), and so, an understanding of factors that underpin stable structures is essential for effective protein design. Studies have shown that more robust protein scaffolds are better able to accept potentially destabilizing mutations that confer novel activities or properties (5Tokuriki N. Tawfik D.S. Stability effects of mutations and protein evolvability.Curr. Opin. Struct. Biol. 2009; 19: 596-604Crossref PubMed Scopus (494) Google Scholar, 6Socha R.D. Tokuriki N. Modulating protein stability - directed evolution strategies for improved protein function.FEBS J. 2013; 280: 5582-5595Crossref PubMed Scopus (67) Google Scholar). Indeed the robustness of different folds is a key factor behind the power law describing the extent to which different folds have been exploited in evolution: inherently stable folds are observed more commonly (7Magner A. Szpankowski W. Kihara D. On the origin of protein superfamilies and superfolds.Sci. Rep. 2015; 5: 8166Crossref PubMed Scopus (14) Google Scholar). Enzymes represent a particular case where evolution has produced versatile and specific catalysts that can lower the activation energy of chemical reactions. Just as in nature, in industry, enzymes have the potential to improve the efficiency and sustainability of many chemical processes. Increasing the operational temperature of chemical reactions improves yield and reduces waste by enhancing reaction rates, improving reagent solubility and reducing microbial contamination; however, most native enzymes have limited stability even under their normal physiological conditions and are rapidly denatured at elevated temperatures. Since the biocatalyst (i.e., the enzyme or a cell containing it) is often the most expensive part of a biocatalytic process, to be commercially competitive against chemocatalysis, the enzymes used need to have long operational lifetimes (3Bommarius A.S. Paye M.F. Stabilizing biocatalysts.Chem. Soc. Rev. 2013; 42: 6534-6565Crossref PubMed Scopus (320) Google Scholar). While enzymes from thermophilic organisms are one option, it is rarely possible to find an enzyme in a thermophile with the catalytic profile of interest. Therefore, the operational stability of 'mesophilic' enzymes usually needs to be extended, and intensive efforts over the last ∼40 years have been put toward engineering enzymes to be more thermostable. Industrial biocatalysis is not the only motivation for stabilizing proteins however. Thermostable enzymes have also found wide application in basic research, for example, the PCR is only possible due to the use of thermostable polymerases, originally sourced from thermophiles, which enable the iterative replication and amplification of specific DNA templates. The development of numerous protein therapeutics has provided added impetus for engineering other types of protein for thermostability. Thermostable proteins have a longer shelf life and can be used in a wider range of therapeutic contexts than less stable proteins. More recently, the emergence of synthetic biology has expanded the use of independently folding and stable protein domains as biobricks in bioinspired devices. Yet another motivation for stabilizing proteins by engineering is that more stable homologs of proteins are often needed for structural and mechanistic studies, since they are typically more easily expressed and purified, and stand up better to biophysical characterization. While analysis of proteins from thermophiles has provided valuable information on factors that can stabilize particular protein folds, not all proteins of interest have thermophilic homologs and it has become clear that the success of stabilization strategies is often dependent on the structural context. Good structural data are important for most rational and computational approaches to enhancing protein thermostability. However, structures are not always available, and the alternative, 'blind' approach of directed evolution usually requires intensive characterization of large libraries of mutants. Fortuitously, another source of inspiration from nature has emerged in recent years, namely the resurrection of thermostable ancestral enzymes, which alongside consensus approaches, leverages the huge expansion in available sequence from genome sequencing projects. This review will briefly summarize traditional approaches to engineering proteins for thermostability, then explore the use of ancestral sequence reconstruction (ASR) as an alternative strategy for engineering and elucidation of the determinants of thermostability. The free energy difference between the folded and unfolded states of a protein is only ∼5 to 15 kcal/mol (1Pace C.N. Conformational stability of globular proteins.Trends Biochem. Sci. 1990; 15: 14-17Abstract Full Text PDF PubMed Scopus (387) Google Scholar) and often only a few interactions are needed to stabilize a protein. However, determining the appropriate changes to make, without unwanted effects on protein function, has been an ongoing challenge. Figure 1 compares the alternative approaches to engineering thermostability in terms of information required, typical screening effort required, and the extent of sequence space that can be sampled. Rational design methods have been used most commonly and have involved designing in improved hydrophobic core packing, salt bridges, and disulfide bonds. Alternatively constraining the most flexible regions of proteins by shortening loops, replacing glycine, and introducing proline residues has been useful. Critically, all of these approaches rely on having structural information of the protein of interest and involve some hypothesis as to the basis to the putative stabilization effect. The success of different rational approaches will vary with the structural context presented by an individual protein and be affected by the complex landscape of epistatic interactions. Recently, rational design has been facilitated by numerous computational tools (reviewed by (6Socha R.D. Tokuriki N. 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However, in such cases, the quality of the data available determines the accuracy of such tools and the available data are biased toward particular types of mutation (10Modarres H.P. Mofrad M.R. Sanati-Nezhad A. Protein thermostability engineering.RSC Adv. 2016; 6: 115252-115270Crossref Google Scholar). Current computational tools have difficulty modeling small but often critical alterations in stability (2Baker D. What has de novo protein design taught us about protein folding and biophysics?.Protein Sci. 2019; 28: 678-683Crossref PubMed Scopus (85) Google Scholar, 20Huang P. Chu S.K.S. Frizzo H.N. Connolly M.P. Caster R.W. Siegel J.B. Evaluating protein engineering thermostability prediction tools using an independently generated dataset.ACS Omega. 2020; 5: 6487-6493Crossref PubMed Scopus (22) Google Scholar). Expansion and standardization of the information available from databases, plus high throughput approaches that can afford comprehensive data obtained under comparable conditions, such as deep mutational scanning and analysis of combinatorial data (21Nisthal A. Wang C.Y. Ary M.L. Mayo S.L. Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis.Proc. Natl. Acad. Sci. U. S. A. 2019; 116: 16367-16377Crossref PubMed Scopus (59) Google Scholar), may facilitate better predictions by augmenting training data. Artificial intelligence approaches, such as AlphaFold (22Jumper J. Evans R. Pritzel A. Green T. Figurnov M. Ronneberger O. et al.Highly accurate protein structure prediction with AlphaFold.Nature. 2021; 596: 583-589Crossref PubMed Scopus (5177) Google Scholar), should also make the prediction and design of protein stability more robust and are likely to lead to another step change. Importantly, AlphaFold predicts structures that can then be used as inputs for other methods that require them, such as PROSS (23Goldenzweig A. Goldsmith M. Hill S.E. Gertman O. Laurino P. Ashani Y. et al.Automated structure- and sequence-based design of proteins for high bacterial expression and stability.Mol. Cell. 2016; 63: 337-346Abstract Full Text Full Text PDF PubMed Scopus (223) Google Scholar). Directed evolution emerged in the 1990s as a useful 'blind' or 'brute force' technique for stabilization of proteins that was independent of any prior hypothesis concerning the mechanism of stabilization. It mimics the process of natural selection by using iterative rounds of genetic diversification (such as random mutagenesis or recombination of related sequences) combined with phenotypic screening and selection for high thermal stability and other required properties. In the absence of structural information on which to base hypotheses, random mutagenesis can be used to find residues that determine stability, which can then be targeted by saturation mutagenesis. Directed evolution approaches employing entirely random methods for sequence diversification require large screening efforts to detect useful mutants (Fig. 1). While it is possible to, for example, assess activity at a stringent temperature in high throughput fashion, more detailed analysis of melting temperatures (Tms) or temperatures at which half the population of proteins remains intact or active (T50 values) is resource intensive. Therefore, strategies that focus directed evolution efforts on smaller, more fertile areas of sequence space have been sought. The focus has been on identifying flexible regions to target (e.g., by iterative saturation mutagenesis combined with B-factor analysis (24Reetz M.T. Carballeira J.D. Vogel A. 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However, regions of homologous proteins that have diverged in different evolutionary branches and acquired different epistatic relationships with other structural elements in a protein fold, can be incompatible when fragments of homologs are recombined, leading to loss of stabilizing interactions or introduction of steric clashes or electrostatic repulsion. Computational approaches have been applied to improve directed evolution strategies, just as for rational design. In particular, structure-based approaches have been used to increase the average structural integrity of mutant libraries created by recombinatorial evolution. Chief amongst these approaches is SCHEMA, which uses the sequences of homologous proteins and a representative structure to estimate optimal positions for recombination to minimize the disruption of interactions that stabilize the protein fold (28Voigt C.A. Martinez C. Wang Z.G. Mayo S.L. Arnold F.H. Protein building blocks preserved by recombination.Nat. Struct. 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Importantly, mutants identified by these 'augmented' recombination approaches differ from the starting points in dozens to hundreds of positions, meaning they would not be readily identified by conventional rational or random point mutagenesis methods. In that respect, they are analogous to extensive fold optimization approaches enabled by Rosetta and other recent computational approaches to protein (re)design. The difference is that mutation and selection is used as the 'algorithm', leveraging evolutionarily proven folds found in nature as templates. Over the last ∼25 years, alternative approaches to leveraging the information implicit in natural evolutionary pathways for engineering thermostability have emerged, namely the consensus approach and ASR. Assuming the function of a protein confers a growth advantage on the organism, natural selection will tend to select for stabilizing residues and against residues that destabilize the structure. Thus, consensus residues are at least unlikely to be frankly destabilizing, unless they confer a selection advantage that is independent of, and greater than, the destabilizing effect. Therefore, consensus residues that are at least marginally stabilizing would tend to dominate a position over long-term evolution (30Steipe B. Schiller B. Plückthun A. Steinbacher S. Sequence statistics reliably predict stabilizing mutations in a protein domain.J. Mol. Biol. 1994; 240: 188-192Crossref PubMed Scopus (273) Google Scholar, 31Bershtein S. Goldin K. Tawfik D.S. Intense neutral drifts yield robust and evolvable consensus proteins.J. Mol. Biol. 2008; 379: 1029-1044Crossref PubMed Scopus (194) Google Scholar). Many studies have taken advantage of this approach to improve stability by introducing 'consensus' residues at one or more positions in a protein of interest (e.g., (30Steipe B. Schiller B. Plückthun A. Steinbacher S. 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However, the inference of which residues represent the 'consensus' can be heavily biased by imbalances in the amount of sequence information available for certain organisms relative to others. Consequently, it can be hard to dissociate stochastic or historical effects from the true consensus residues at a given position. ASR has frequently yielded ancestor proteins that are more thermostable than their extant counterparts, providing some support for the hypothesis that primordial organisms were thermophilic. The earliest example was the inference of an ancestral sequence of 3-isopropylmalate dehydrogenase (IPMDH) from the last universal common ancestor (39Miyazaki J. Nakaya S. Suzuki T. Tamakoshi M. Oshima T. Yamagishi A. Ancestral residues stabilizing 3-isopropylmalate dehydrogenase of an extreme thermophile: experimental evidence supporting the thermophilic common ancestor hypothesis.J. Biochem. 2001; 129: 777-782Crossref PubMed Google Scholar). 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The most basal ancestor of all lineages showed a comparable optimal temperature for substrate binding (∼65 °C) to extant forms from thermophiles. Analysis of seven intermediate ancestors revealed a trend of progressively increased thermostability going back in time from 0.5 to 3.5 billion years ago (Ga); the Tm values of the youngest ancestors were ∼44 to 48 °C compared to ∼65 to 74 °C for the oldest ancestors (41Gaucher E.A. Ganesh O.K. Govindarajan S. Palaeotemperature trend for Precambrian life inferred from resurrected proteins.Nature. 2008; 451: 704-707Crossref PubMed Scopus (264) Google Scholar). This foundational work was followed by similar studies in which sets of ancestral proteins of various evolutionary ages were resurrected and assessed for their thermostability as a means of assessing the experimental support for the existence of a thermophilic universal common ancestor (42Akanuma S. Nakajima Y. Yokobori S. Kimura M. Nemoto N. 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These studies have covered a broad range of protein families, phylogenetic taxa (bacteria and eukarya; including plants, animals, and fungi), and evolutionary ages (from a few hundred thousand years up to four billion years old). The prevailing observation has been that the mesostable proteins in existence today evolved from more thermostable forms. Enhancements in stability of ancestral proteins over directly related extant forms have ranged from a few degrees to more than 40 °C (Fig. 2; Table 1). However, it is also clear from re