Exploiting enzyme evolution for computational protein design

We can learn from nature's tricks by reconstructing evolutionary trajectories to design improved enzymes.Ancestral sequence reconstruction (ASR) provides a compelling tool to obtain enzymes with customized catalytic properties.Conformational dynamics in enzyme design is crucial in increasing the sampling of states with new catalytic functions as well as reducing the sampling of non-productive conformations.A catalog of fragments characterized by specific biophysical features may provide an invaluable resource for the design of custom-made enzymes. Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies. Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies. Roughly three decades have passed since the first attempts to design new enzymes using computational approaches [1.Hellinga H.W. Richards F.M. Construction of new ligand binding sites in proteins of known structure: I. Computer-aided modeling of sites with pre-defined geometry.J. Mol. Biol. 1991; 222: 763-785Google Scholar,2.Dahiyat B.I. Mayo S.L. Protein design automation.Protein Sci. 1996; 5: 895-903Google Scholar], and the field has matured considerably since then. While the earliest attempts at computational enzyme design focused primarily on side-chain positioning [1.Hellinga H.W. Richards F.M. Construction of new ligand binding sites in proteins of known structure: I. Computer-aided modeling of sites with pre-defined geometry.J. Mol. Biol. 1991; 222: 763-785Google Scholar, 2.Dahiyat B.I. Mayo S.L. Protein design automation.Protein Sci. 1996; 5: 895-903Google Scholar, 3.Voigt C.A. et al.Computational method to reduce the search space for directed protein evolution.Proc. Natl. Acad. Sci. U. S. 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Expanding the enzyme universe with genetically encoded unnatural amino acids.Nat. Catal. 2020; 3: 193-202Google Scholar]. In addition, computational design approaches are becoming ever-more streamlined, such that there now exists a range of powerful web servers that can assist in the design process [9.Marques S.M. et al.Web-based tools for computational enzyme design.Curr. Opin. Struct. Biol. 2021; 69: 19-34Google Scholar]. In principle, computational design approaches can take two very loosely defined directions: structure-based design approaches that require some level of knowledge of the system of interest, including information about the chemical mechanisms, transition states, and key catalytic residues involved; and sequence-based design approaches that can, for example, draw on evolutionary information to predict potential hotspots for protein engineering as well as new variants with desired physicochemical properties, something that is in particular increasingly being achieved using machine-learning approaches [10.Xu Y. et al.Deep dive into machine learning models for protein engineering.J. Chem. Inf. Model. 2020; 60: 2773-2790Google Scholar]. Computational approaches that require minimal knowledge of the molecular details of the chemical processes involved are attractive for their speed and efficiency, as exploring the underlying mechanisms and transition states typically requires significant experimental and/or computational effort. However, much like their experimental counterparts, such approaches are likely to hit optimization plateaus [11.Chou H.-H. et al.Diminishing returns epistasis among beneficial mutations decelerates adaptation.Science. 2011; 332: 1190-1192Google Scholar,12.Tokuriki N. et al.Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme.Nat. Commun. 2012; 3: 1257Google Scholar] where further improvement in activity becomes extremely challenging, and without knowledge of the underlying chemistry it can be difficult-to-impossible to overcome such plateaus. Therefore, rather than competing with each other, sequence- and structure-based approaches are highly complementary as each provides different types of insights into how to improve a given system. In addition, nature has already provided a blueprint for how new enzymes evolve, and by reconstructing evolutionary trajectories and probing the natural evolution of enzymes, we can learn from nature's tricks to improve enzymes both in vitro and in silico. Experimental 'protein engineers turned evolutionists' [13.Trudeau D.L. Tawfik D.S. Protein engineers turned evolutionists – the quest for the optimal starting point.Curr. Opin. Biotechnol. 2019; 60: 46-52Google Scholar] have had substantial progress in enzyme design using insights from natural evolution. However, there has also been significant progress in computational design studies based on evolutionary information, in particular due to increasing awareness of the role of conformational dynamics in the natural evolution of enzymes [12.Tokuriki N. et al.Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme.Nat. Commun. 2012; 3: 1257Google Scholar,14.James L.C. Tawfik D.S. Conformational diversity and protein evolution – a 60-year-old hypothesis revisited.Trends Biochem. Sci. 2003; 28: 361-368Google Scholar, 15.Maria-Solano M.A. et al.Role of conformational dynamics in the evolution of novel enzyme function.Chem. Commun. 2018; 54: 6622-6634Google Scholar, 16.Campbell E.C. et al.Laboratory evolution of protein conformational dynamics.Curr. Opin. Struct. Biol. 2018; 50: 49-57Google Scholar, 17.Crean R.M. et al.Harnessing conformational plasticity to generate designer enzymes.J. Am. Chem. 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In this review, we discuss three directions where evolutionary information is being used to guide the computational design process, specifically: (i) repurposing of protein scaffolds identified through ASR as potential starting points for the generation of new enzyme activities; (ii) harnessing conformational dynamics, a key driver of natural enzyme evolution, in computational enzyme design; and (iii) modular design processes based on identifying evolutionarily important subdomain segments that can be rearranged to create new enzymes. These are just some of the current directions where evolutionary information can be used to drive the design process, but they showcase the potential of this field. While there are various ways evolutionary information can be used in enzyme design, one of the most obvious is the use of ASR to identify potential starting points for subsequent experimental or computational design effort. Enzymes obtained from ASR make attractive starting points for enzyme design as they tend to be highly thermostable, conformationally flexible, and evolvable [19.Romero-Romero M.L. et al.Engineering ancestral protein hyperstability.Biochem. J. 2016; 473: 3611-3620Google Scholar, 20.Zou T. et al.Evolution of conformational dynamics determines the conversion of a promiscuous generalist into a specialist enzyme.Mol. Biol. Evol. 2015; 32: 132-143Scopus (92) Google Scholar, 21.Trudeau D.L. et al.On the potential origins of the high stability of reconstructed ancestral proteins.Mol. Biol. Evol. 2016; 33: 2633-2641Google Scholar]. While many different computational algorithms exist with which to perform ASR (Table 1), the basic principle of all of these is to use the sequences of known, extant proteins to reconstruct phylogenetic trees containing sequences of putative ancestors, based on the probability of finding a given amino acid substitution at the given point in amino acid sequence [22.Spence M.A. et al.Ancestral sequence reconstruction for protein engineers.Curr. Opin. Struct. Biol. 2021; 69: 131-141Google Scholar,23.Selberg A.G.A. et al.Ancestral sequence reconstruction: from chemical paleogenetics to maximum likelihood algorithms and beyond.J. Mol. Evol. 2021; 89: 157-164Google Scholar]. Such ancestral inference yields a 'cloud' of sequences that relate to putative historical ancestors [24.Randall R.N. et al.An experimental phylogeny to benchmark ancestral sequence reconstruction.Nat. 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Evol. 2017; 34: 247-261Google Scholar], although typically only the most probabilistic of these sequences is subject to further experimental or computational characterization of the evolutionary trajectory.Table 1Overview of computational resources of relevance to enzyme designaNote that this list is based on a constantly expanding toolkit, and therefore it is impossible to be exhaustive.CategoryNameDescriptionURLFeatures and limitationsASRBali-phyA standalone tool for the estimation of multiple sequence alignments and evolutionary treeshttp://www.bali-phy.orgFeatures: Excellent performance on tested protein data setsLimitations: Tends to systematically under-align on the biological sequence dataIQ-TREEStandalone software and web tool for the inference of phylogenetic trees using the maximum-likelihood approachhttp://www.iqtree.orgFeatures: It is an integral component of many biomedical research open-source applications such as Galaxy, Nextstrain, and QIIME 2Limitations: Both IQ-Tree- and RaxML-NG-inferred maximum-likelihood gene trees have been suggested to have reproducibility issues (https://doi.org/10.1038/s41467-020-20005-6)Molecular Evolutionary Genetics Analysis (MEGA X)User-friendly software for molecular evolution analysis and construction of phylogenetic treeshttps://www.megasoftware.netFeatures: Easy-to-use graphical user interface (GUI) with good documentation available; works across all platformsLimitations: Works like a black box for some parameters, with no user controlMrBayesA program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary modelshttp://nbisweden.github.io/MrBayesFeatures: GPU acceleration availableLimitations: Requires quite complex input data, which can hinder non-experts from performing ASR successfullyPhylogenetic Analysis by Maximum Likelihood (PAML)Standalone software and web tool to perform phylogenetic analysis using the maximum likelihood approachhttp://abacus.gene.ucl.ac.uk/software/paml.htmlFeatures: Has a GUI version; very customizableLimitations: Steep learning curve for novice usersRandomized Axelerated Maximum Likelihood (RAxML)A standalone tool for ASR using the maximum-likelihood approach; a GUI is being developedhttps://cme.h-its.org/exelixis/web/software/raxmlFeatures: Has a GUI (under development but already available for use)Limitations: Both IQ-Tree- and RaxML-NG-inferred maximum-likelihood gene trees have been suggested to have reproducibility issues (https://doi.org/10.1038/s41467-020-20005-6)The FastML Server (FastML)A web tool for ASR using the maximum-likelihood approachhttp://fastml.tau.ac.ilFeatures: Easy-to-use web tool; can take unaligned sequences as inputLimitations: Accepts only the FASTA file format as inputAllosteryDFIA protocol developed to identify per-residue contributions to a protein's overall dynamical profilehttps://github.com/avishekrk/DFIFeatures: Code easily available and ready to useLimitations: Requires atomic coordinatesOhmWeb tool that predicts allosteric sites and inter-residue correlation and identifies the allosteric pathways between themhttps://dokhlab.med.psu.edu/ohm/#Features: Easy-to-use web tool requiring only the insertion of a 3D structureLimitations: Structure-based predictions do not take dynamics into accountSPMTool developed for the identification of distal mutations affecting function, based on the shortest-path-map algorithmhttps://silviaosuna.wordpress.com/toolsFeatures: Uses long dynamic simulations to predict allosteric sitesLimitations: Poor availability of the codeDatabases (assorted)BRENDAElectronic repository containing molecular and biochemical information on enzymeshttps://www.brenda-enzymes.orgFeatures: Several different queries are available as examples; integrates CATH and SCOPeLimitations: Requires login and there is a 'professional' versionPDBA protein databank containing more than 179 000 macromolecular structureshttps://www.rcsb.orgFeatures: General database for protein structures obtained through NMR, X-ray crystallography, and cryoelectron microscopyLimitations: Contains redundant data and the search function could be betterUniProtA central repository of protein data created by combining the Swiss-Prot, TrEMBL, and PIR-PSD databaseshttps://www.uniprot.orgFeatures: De facto go-to database for general protein informationLimitations: Overwhelming amount of data for newcomersDatabases (family information)CATH Protein Structure Classification databaseA database containing information about the evolutionary relationships of protein domainshttp://cathdb.infoFeatures: Huge open-source database helps with automated implementation in one's own workflowsLimitations: Drug compound information is still being developedFuzzleA database containing evolutionary information about protein fragmentshttps://fuzzle.uni-bayreuth.deFeatures: Evolutionary information on fragments as opposed to full proteins onlyLimitations: Hierarchical databases can lead to misclassification when slight differences are present in the sequencesPfamA database with information about protein families, represented by multiple sequence alignments generated using Hidden Markov modelshttp://pfam.xfam.orgFeatures: Constant development and integration with other European Bioinformatics Institute (EBI) toolsLimitations: There is a high-quality database and a low-quality database that can lead to errorsSCOPA database of proteins classified based on structural relatedness, such as superfamilies, families, and foldshttps://scop.mrc-lmb.cam.ac.ukFeatures: Uses data deposited in the PDB to group proteins; curated to be non-redundantLimitations: New version is not totally backwards compatibleModelingAlphaFoldProtein prediction tool using neural networks; achieved a score twice as good as the second-best protein predictor in CASP14https://github.com/deepmind/alphafoldFeatures: New gold standard for protein structure prediction; makes use of a newly developed neural networkLimitations: It is better where others were good, but still lacks good loop region predictions.iTasserBoth a web tool and a standalone tool, it predicts protein structure using a hierarchical approach; runs iteratively until the lowest-energy structures are achieved and then uses publicly available function information to identify closely related templates with the same functionhttps://zhanglab.dcmb.med.umich.edu/I-TASSERFeatures: Good and easy-to-use web tool and a powerful standalone toolLimitations: Lacks accuracy when few templates are available and is slower than similar options availableModellerProtein structure modeling tool that predicts structures by the satisfaction of spatial restraints obtained from a sequence alignment and shown as a probability-density functionhttps://salilab.org/modellerFeatures: Can be installed on any platform and is very fast in creating a new modelLimitations: Speed comes at the cost of accuracy for models where slower tools yield better resultsMultiple sequence alignment (MSA)Clustal OmegaMultiple sequence alignment web toolhttps://www.ebi.ac.uk/Tools/msa/clustaloFeatures: Has been constantly developed since the 1980s for MSALimitations: Does not yield good results when a large amount of sequences is provided as inputMultiple Alignment Using Fast Fourier Transform (MAFFT)Multiple sequence alignment web tool; can also be used locally as a standalone toolhttps://mafft.cbrc.jp/alignment/softwareFeatures: Users can choose between various multiple alignment methodsLimitations: It requires more memory to runMultiple Sequence Comparison by Log-Expectation (MUSCLE)Multiple sequence alignment web tool; as with Clustal Omega, it is integrated in the EBI ecosystemhttps://www.ebi.ac.uk/Tools/msa/muscleFeatures: Can achieve better average accuracy and speed than ClustalW2 or T-CoffeeLimitations: The Kimura distance used at the second stage, although fast, does not consider which changes of amino acids occur between sequencesProtein designAbDesignAn algorithm for backbone design using structure- and sequence-based informationhttps://github.com/Fleishman-Lab/AbDesign_for_enzymesFeatures: Stepwise workflow for the design of antibodies that focuses on stability and binding affinityLimitations: Requires sufficient sequence data on homologs as well as atomic coordinatesFuncLibWeb tool to design and rank multiple point mutations based on evolutionary information and protein-folding stability calculationshttps://funclib.weizmann.ac.ilFeatures: Multipoint variant design tool with an easy-to-use web serverLimitations: Works better with a pre-stabilized protein scaffold; poor knowledge of one's system may lead to poor resultsLoop GrafterA web tool with a workflow to compare loop dynamics between proteins and transplant loops from one protein to anotherhttps://loschmidt.chemi.muni.cz/loopgrafterFeatures: Automated way to transfer loops from one protein to the otherLimitations: Works only with the input of both the template and the scaffold proteinsPROSSA user-friendly web tool to predict protein amino acid substitutions that yield higher-stability variantshttps://pross.weizmann.ac.il/step/pross-termsFeatures: Automated way to stabilize proteins by inserting mutations in the original protein; the method is reliable enough that only a more limited number of designs as output is sufficientLimitations: Requires a structure (which may not always be available) for stability calculationsProtLegoA Python library for chimera design and analysishttps://hoecker-lab.github.io/protlegoFeatures: Automatic construction and ranking of chimerasLimitations: The correlation between structural features and experimental success is not yet clearRosettaA comprehensive software suite with several algorithms that can be used for the modeling and analysis of proteinshttps://www.rosettacommons.orgFeatures: Encompasses many modules under the same umbrella nameLimitations: Not unified and developed by many people through the years; can be hard to implement and use different modulesSEWINGA protocol to design new tertiary protein structures by 'sewing' together secondary-structure building blockshttps://klab.web.unc.edu/sewing-new-proteinsFeatures: Continuous and discontinuous SEWING can be merged to create additional diversityLimitations: At present, it appears to have been applied only to the construction of all-α-helix chimerasTunnels and cavitiesCAVERSoftware tool for the analysis and visualization of tunnels and channels in protein structurehttps://www.caver.czFeatures: Integration with other CaverSuite tools allows deeper analysisLimitations: Still lacks the possibility of calculating poresPOVMEStandalone tool for ligand-binding pocket calculationshttps://github.com/POVME/POVMEFeatures: Calculates ligand-binding pockets using MD snapshotsLimitations: The lack of a GUI makes it less accessible to non-bioinformaticiansSimilarity searchBLASTA tool to calculate statistical significance between biological sequenceshttps://blast.ncbi.nlm.nih.gov/Blast.cgiFeatures: One of the most-used tools for local alignment search; fast and easy to useLimitations: Developed in 1990 and has not changed substantially since thenFASTAA web tool to provide a heuristic local search with a protein queryhttps://www.ebi.ac.uk/Tools/sss/fasta/Features: First tool in the field; as with any other tool from the EBI, it is integrated with many other tools and analyses; newer tools exist that have evolved from thisLimitations: As opposed to BLAST, it does not remove low-complexity regionsStructure and trajectory dataBio3DR package for the analysis of protein structure and trajectory data; provides a variety of approaches for conformational analysis of a proteinhttp://thegrantlab.org/bio3dFeatures: Comprehensive tool with many tutorials and easy installationLimitations: Lack of GUI and a webserver makes for a steep learning curve for non-bioinformaticiansa Note that this list is based on a constantly expanding toolkit, and therefore it is impossible to be exhaustive. Open table in a new tab There is, of course, doubt about how realistic the sequence predictions from ASR are: as pointed out by Copley [27.Copley S.D. Setting the stage for evolution of a new enzyme.Curr. Opin. Struct. Biol. 2021; 69: 41-49Google Scholar], due to ambiguities in the reconstruction, the probability of even the most probabilistic sequence being the real sequence is very low, as, in practice, many positions are predicted with a probability of <50%, particularly if the data set of extant proteins used for the reconstruction is highly diverged. In addition, particular challenges are posed by gaps in alignments (i.e., insertion/deletion evolutionary events), since different ways of dealing with these can lead to different sequences and phenotypes [28.Thomas A. et al.Highly thermostable carboxylic acid reductases generated by ancestral sequence reconstruction.Commun. Biol. 2019; 2: 429Google Scholar]. Furthermore, while many biochemical studies of reconstructed ancestral proteins suggest enhanced thermostability of the putative ancestors, it has been argued that there is a risk that this enhanced thermostability is a result of reconstruction bias rather than a true property of the putative ancestors (e.g., see the detailed discussion in [29.Wheeler L.C. et al.The thermostability and specificity of ancient proteins.Curr. Opin. Struct. Biol. 2016; 38: 37-43Google Scholar]). However, a recent study has used experimental phylogenetics [30.Hillis D.M. et al.Experimental phylogenetics: generation of a known phylogeny.Science. 1992; 255: 589-592Google Scholar] to reproduce in the laboratory an evolutionary trajectory of an RFP [24.Randall R.N. et al.An experimental phylogeny to benchmark ancestral sequence reconstruction.Nat. Commun. 2016; 7: 12847Google Scholar]. The advantage of this approach is that the sequences of the true ancestral nodes are actually known and can be phenotypically characterized and compared with the sequences predicted by ASR. This study demonstrated that although there exists some level of mistakes in the reconstructed sequences (which are exacerbated the more ancient the node), the phenotypes of the actual and reconstructed ancestors are similar. In addition, one can reconstruct multiple putative ancestors and compare their properties to increase the likelihood that one is observing the phenotypic properties of the 'true' ancestor [27.Copley S.D. Setting the stage for evolution of a new enzyme.Curr. Opin. Struct. Biol. 2021; 69: 41-49Google Scholar]. Clearly, however, while there are challenges in inferring actual evolutionary information through the use of ASR, what is obvious is that protein scaffolds obtained through ASR are excellent starting points for subsequent protein design effort due to their high thermostability and evolvability [31.Gardner J.M. et al.Manipulating conformational dynamics to repurpose ancient proteins for modern catalytic functions.ACS Catal. 2020; 10: 4863-4870Google Scholar]. There have been a great number of experimental studies using ancestrally reconstructed proteins for enzyme design due to their greater thermostability; more recently, interest has also shifted to using ASR to obtain scaffolds that can be used as starting points for the engineering of catalytic properties [22.Spence M.A. et al.Ancestral sequence reconstruction for protein engineers.Curr. Opin. Struct. Biol. 2021; 69: 131-141Google Scholar,23.Selberg A.G.A. et al.Ancestral sequence reconstruction: from chemical paleogenetics to maximum likelihood algorithms and beyond.J. Mol. Evol. 2021; 89: 157-164Google Scholar,27.Copley S.D. Setting the stage for evolution of a new enzyme.Curr. Opin. Struct. Biol. 2021; 69: 41-49Google Scholar,31.Gardner J.M. et al.Manipulating conformational dynamics to repurpose ancient proteins for modern catalytic functions.ACS Catal. 2020; 10: 4863-4870Google Scholar]. Several such studies have focused specifically on using ASR to obtain flexible scaffolds that can be manipulated for engineering purposes in terms of their conformational properties. These are discussed in more detail in the subsequent section. However, as some (of many) other examples of recent success stories, ASR has been used to understand allosteric communication in a multienzyme complex of a key metabolic enzyme, tryptophan synthase [32.Schupfner M. et al.Analysis of allosteric communication in a multienzyme complex by ancestral sequence reconstruction.Proc. Natl. Acad. Sci. U. S. A. 2020; 117: 346-354Google Scholar,33.Maria-Solano M.A. et al.Rational prediction of distal activity enhancing mutations in tryptophan synthase.ChemRxiv. 2021; (Published online March 4, 2021)https://doi.org/10.26434/chemrxiv.14151989.v1Google Scholar], to obtain a high-redox-potential laccase [34.Gomez-Fernandez B.J. et al.Consensus design of an evolved high-redox potential laccase.Front. Bioeng. Biotechnol. 2020; 8: 354Google Scholar], to modulate the catalytic adaptability of an extremophile kinase [35.Zamora R.A. et al.Tuning of conformational dynamics through evolution-based design modulates the catalytic adaptability of an extremophile kinase.ACS Catal. 2020; 10: 10847-10857Google Scholar], and to identify novel heme binding that modulates the allosteric regulation of an ancestral glycosidase (Figure 1) [36.Gamiz-Arco G. et al.Heme-binding enables allosteric modulation in an ancient TIM-barrel glycosidase.Nat. Commun. 2021; 12: 380Google Scholar]. This latter study is notable as heme binding was not observed in any of ~5500 crystal structures of ~1400 modern glycosidases. Experimental characterization of a number of modern glycosidases showed appreciable levels of heme binding but significantly lower than that of the ancestral glycosidase, indicating that the ability to efficiently bind heme to allosterically regulate catalysis is a specific feature of the ancestral enzyme [36.Gamiz-Arco G. et al.Heme-binding enables allosteric modulation in an ancient TIM-barrel glycosidase.Nat. Commun. 2021; 12: 380Google Scholar]. Overall, the use of evolutionary information obtained from ASR is a powerful tool for enzyme engineering, and with growing interest in the use of ASR to obtain enzymes with tailored catalytic properties [22.Spence M.A. et al.Ancestral sequence reconstruction for protein engineers.Curr. Opin. Struct. Biol. 2021; 69: 131-141Google Scholar,31.Gardner J.M. et al.Manipulating conformational dynamics to repurpose ancient proteins for modern catalytic functions.ACS Catal. 2020; 10: 4863-4870Google Scholar], it is likely that we will observe much greater usage of this technique in protein design in the coming years. Recent years have seen increasing awa