FireProtASR: A Web Server for Fully Automated Ancestral Sequence Reconstruction

Characterization, structural, and evolutionary analysis of an extremophilic GH5 endoglucanase from Bacillus sp. G131: Insights from ancestral sequence reconstruction

Nature has developed extremozymes that catalyze complex reaction processes in extreme environmental conditions. Accordingly, a combined approach consisting of extremozyme screening, ancestral sequence resurrection (ASR), and molecular dynamic simulation was utilized to construct a developed endoglucanase. The primary experimental and in-silico data led to the prediction of a hypothetical sequence of endoglucanase (EG5-G131) using Bacillus sp. G131 confirmed by amplification and sequencing. EG5-G131 exhibited noticeable stability in a broad-pH range, several detergents, organic solvents, and temperatures up to 80 °C. The molecular weight, Vmax, and Km of the purified endoglucanase were estimated to be 36 kDa, 4.32 μmol/min, and 23.62 mg/ml, respectively. The calculated thermodynamic parameters for EG5-G131 confirmed its intrinsic thermostability. Computational analysis revealed Glu142 and Glu230 as active-site residues of the enzyme. Furthermore, the enzyme remained bound to cellotetraose at 298 K, 333 K, 343 K, and 353 K for 300 ns, consistent with our experimental data. ASR of EG5-G131 led to the introduction of ancestral ANC204 and ANC205, which show similar thermodynamic characteristics with the last Firmicute common ancestor. Finally, truncating loops from the N-terminal of two sequences created two variants with desirable thermal stability, suggesting the evolutionary deciphering of the functional domain of the GH5 family in Bacillus sp. G131.

Hook loop dynamics engineering transcended the barrier of activity-stability trade-off and boosted the thermostability of enzymes

The improvement of enzyme thermostability often accompanies the decreased activity due to the loss of the key regions' flexibility. As a representative structure, unlocking the potential of loop dynamics will not only provide new ideas for stabilization strategies, but also help to deepen the understanding of the relationship between enzyme structural dynamics and function. In this study, a creative "hook loop dynamics engineering" (HLoD) strategy was successfully proposed for simultaneously improving the thermostability and maintaining activity of the model enzyme, Candida Antarctica lipase B. A small and smart mutant library involving five key residues located at the "hook loop" was meticulously identified and systematically investigated and thus yielded a five-point multiple mutant M1 (L147S/T244P/S250P/T256D/N292D), demonstrating a remarkable 7.0-fold increase in thermostability at 60 °C compared to the wild-type (WT). Furthermore, the activity of M1 remained comparable to that of WT, effectively transcending the barrier of activity-stability trade-off. Molecular dynamics simulations revealed that the precise regulation of hook loop dynamics via intermolecular interactions, such as salt bridges and hydrogen bonding, curbed the excessive flexibility of the pivotal regions α5 and α10 at high temperatures, thus driving the substantial enhancement of the thermostability of M1. Refining the dynamics of the flexible region via HLoD, which transcended the barrier of activity-stability trade-off, exhibited to be a robust and potentially universal strategy for designing enzymes with outstanding thermostability and activity.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

Combining Flexible Region Design and Automatic Design to Enhance the Thermal Stability and Catalytic Efficiency of Leucine Dehydrogenase

Leucine dehydrogenase (LeuDH, EC 1.4.1.9) can reversibly catalyze the oxidative deamination of l-leucine and some other specific α-amino acids to form the corresponding α-ketoacids. This reaction has great significance in the field of food additives and the pharmaceutical industry. The LeuDH from Exiguobacterium sibiricum (EsLeuDH) has high catalytic efficiency but limited thermal stability, hindering its widespread industrial application. In this study, a mutant N5F/I12L/A352Y of EsLeuDH (referred to as M2) was developed with enhanced thermal stability and catalytic activity through rational modification. The M2 mutant exhibits a half-life at 60 °C (t1/2(60 °C)) of 975.7 min and a specific activity of 69.6 U mg-1, which is 5.4 and 2.1 times higher than those of EsLeuDH, respectively. This research may facilitate the utilization of EsLeuDH at elevated temperatures, enhancing its potential for industrial applications. The findings offer a practical and efficient approach for optimizing LeuDH and other industrial enzymes.

Evolutionary divergence of TLR9 through ancestral sequence reconstruction

The transmembrane pattern recognition receptor, Toll-like receptor (TLR), are best known for their roles in innate immunity via recognition of pathogen and initiation of signaling response. Mammalian TLRs recognize molecular patterns associated with pathogens and initiate innate immune response. We have studied the evolutionary diversity of mammalian TLR genes for differences in immunological response. Reconstruction of ancestral sequences is a key aspect of the molecular evolution of TLR to track changes across the TLR genes. The comprehensive analysis of mammalian TLRs revealed a distinct pattern of evolution of TLR9. Various sequence-based features such as amino acid usage, hydrophobicity, GC content, and evolutionary constraints are found to influence the divergence of TLR9 from other TLRs. Ancestral sequence reconstruction analysis also revealed that the gradual evolution of TLR genes in several ancestral lineages leads to the distinct pattern of TLR9. It demonstrates evolutionary divergence with the progressive accumulation of mutations results in the distinct pattern of TLR9.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

Correlation Analysis of Key Residue Sites between Computational-Aided Design Thermostability d-Amino Acid Oxidase and Ancestral Enzymes

The d-amino acid oxidase (DAAO) from Rhodotorula taiwanensis has proven to have great potential for applications due to its excellent catalytic kinetic parameters. However, its poor thermal stability has limited its performance in biocatalysis. Herein, starting from the variant SHVG of RtwDAAO, this study employed a comprehensive computational design approach for protein stability engineering, resulting in positive substitutions at specific sites (A43S, T45M, C234L, E195Y). The generated variant combination, SHVG/SMLY, exhibited a significant synergistic effect, leading to an extension of the half-life and Tmapp. The ancestral sequence reconstruction revealed the conservation of the variant sites. The association of the variant sites with the highly stable ancestral enzyme was further explored. After determining the contribution of the variant sites to thermal stability, it was applied to other homologous sequences and validated. Molecular dynamics simulations indicated that the increased hydrophobicity of the variant SHVG/SMLY was a key factor for the increased stability, with strengthened intersubunit interactions playing an important role. In addition, the physical properties of the amino acids themselves were identified as crucial factors for thermal stability generality in homologous enzymes, which is important for the rapid acquisition of a series of stable enzymes.

Analysis of a Novel Peptide That Is Capable of Inhibiting the Enzymatic Activity of the Protein Kinase A Catalytic Subunit-Like Protein from Trypanosoma equiperdum

A 26-residue peptide possessing the αN-helix motif of the protein kinase A (PKA) regulatory subunit-like proteins from the Trypanozoom subgenera (VAP26, sequence = VAPYFEKSEDETALILKLLTYNVLFS), was shown to inhibit the enzymatic activity of the Trypanosoma equiperdum PKA catalytic subunit-like protein, in a similar manner that the mammalian heat-stable soluble PKA inhibitor known as PKI. However, VAP26 does not contain the PKI inhibitory sequence. Bioinformatics analyzes of the αN-helix motif from various Trypanozoon PKA regulatory subunit-like proteins suggested that the sequence could form favorable peptide-protein interactions of hydrophobic nature with the PKA catalytic subunit-like protein, which possibly may represent an alternative PKA inhibitory mechanism. The sequence of the αN-helix motif of the Trypanozoon proteins was shown to be highly homologous but significantly divergent from the corresponding αN-helix motifs of their Leishmania and mammalian counterparts. This sequence divergence contrasted with the proposed secondary structure of the αN-helix motif, which appeared conserved in every analyzed regulatory subunit-like protein. In silico mutation experiments at positions I234, L238 and F244 of the αN-helix motif from the Trypanozoon proteins destabilized both the specific motif and the protein. On the contrary, mutations at positions T239 and Y240 stabilized the motif and the protein. These results suggested that the αN-helix motif from the Trypanozoon proteins probably possessed a different evolutionary path than their Leishmania and mammalian counterparts. Moreover, finding stabilizing mutations indicated that new inhibitory peptides may be designed based on the αN-helix motif from the Trypanozoon PKA regulatory subunit-like proteins.

Incongruence in the phylogenomics era

Genome-scale data and the development of novel statistical phylogenetic approaches have greatly aided the reconstruction of a broad sketch of the tree of life and resolved many of its branches. However, incongruence - the inference of conflicting evolutionary histories - remains pervasive in phylogenomic data, hampering our ability to reconstruct and interpret the tree of life. Biological factors, such as incomplete lineage sorting, horizontal gene transfer, hybridization, introgression, recombination and convergent molecular evolution, can lead to gene phylogenies that differ from the species tree. In addition, analytical factors, including stochastic, systematic and treatment errors, can drive incongruence. Here, we review these factors, discuss methodological advances to identify and handle incongruence, and highlight avenues for future research.

Recent advances in structure-based enzyme engineering for functional reconstruction

Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.

Enzyme function and evolution through the lens of bioinformatics

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

A comparative analysis of fruit fly and human glutamate dehydrogenases in Drosophila melanogaster sperm development

Glutamate dehydrogenases are enzymes that take part in both amino acid and energy metabolism. Their role is clear in many biological processes, from neuronal function to cancer development. The putative testis-specific Drosophila glutamate dehydrogenase, Bb8, is required for male fertility and the development of mitochondrial derivatives in spermatids. Testis-specific genes are less conserved and could gain new functions, thus raising a question whether Bb8 has retained its original enzymatic activity. We show that while Bb8 displays glutamate dehydrogenase activity, there are significant functional differences between the housekeeping Gdh and the testis-specific Bb8. Both human GLUD1 and GLUD2 can rescue the bb8 ms mutant phenotype, with superior performance by GLUD2. We also tested the role of three conserved amino acids observed in both Bb8 and GLUD2 in Gdh mutants, which showed their importance in the glutamate dehydrogenase function. The findings of our study indicate that Drosophila Bb8 and human GLUD2 could be novel examples of convergent molecular evolution. Furthermore, we investigated the importance of glutamate levels in mitochondrial homeostasis during spermatogenesis by ectopic expression of the mitochondrial glutamate transporter Aralar1, which caused mitochondrial abnormalities in fly spermatids. The data presented in our study offer evidence supporting the significant involvement of glutamate metabolism in sperm development.

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.

LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.

AsiteDesign: a Semirational Algorithm for an Automated Enzyme Design

With advances in protein structure predictions, the number of available high-quality structures has increased dramatically. In light of these advances, structure-based enzyme engineering is expected to become increasingly important for optimizing biocatalysts for industrial processes. Here, we present AsiteDesign, a Monte Carlo-based protocol for structure-based engineering of active sites. AsiteDesign provides a framework for introducing new catalytic residues in a given binding pocket to either create a new catalytic activity or alter the existing one. AsiteDesign is implemented using pyRosetta and incorporates enhanced sampling techniques to efficiently explore the search space. The protocol was tested by designing an alternative catalytic triad in the active site of Pseudomonas fluorescens esterase (PFE). The designed variant was experimentally verified to be active, demonstrating that AsiteDesign can find alternative catalytic triads. Additionally, the AsiteDesign protocol was employed to enhance the hydrolysis of a bulky chiral substrate (1-phenyl-2-pentyl acetate) by PFE. The experimental verification of the designed variants demonstrated that F158L/F198A and F125A/F158L mutations increased the hydrolysis of 1-phenyl-2-pentyl acetate from 8.9 to 66.7 and 23.4%, respectively, and reversed the enantioselectivity of the enzyme from (R) to (S)-enantiopreference, with 32 and 55% enantiomeric excess (ee), respectively.

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design

The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.

Engineering the Thermostability of a d-Carbamoylase Based on Ancestral Sequence Reconstruction for the Efficient Synthesis of d-Tryptophan

Employing ancestral sequence reconstruction and consensus sequence analysis, the thermostability of a novel d-carbamoylase derived from Nitratireductor indicus (NiHyuC) was engineered through greedy-oriented iterative combinatorial mutagenesis. A mutant S202P/E208D/R277L (M4Th3) was obtained with significantly elevated thermostability. M4Th3 has a half-life of 36.5 h at 40 °C, about 28.5 times of 1.3 h of its parent M4. For the reaction at 40 °C, M4Th3 can catalyze 10 mM N-carbamoyl-d-tryptophan to produce d-tryptophan with a conversion ratio of 96.4% after 12 h, which is significantly higher than 64.1% of M4. MD simulation reveals that new hydrogen bonds emerging from E208D on the surface can increase the hydrophobicity of the protein, leading to improved stability. More importantly, R277L could contribute to enhanced interface stability of homodimeric M4. This study provides a thermostable d-carbamoylase for the "hydantoinase process", which has potential in the industrial synthesis of optically pure natural and non-natural amino acids.

Molecular advances to study the function, evolution and spectral tuning of arthropod visual opsins

Visual opsins of vertebrates and invertebrates diversified independently and converged to detect ultraviolet to long wavelengths (LW) of green or red light. In both groups, colour vision largely derives from opsin number, expression patterns and changes in amino acids interacting with the chromophore. Functional insights regarding invertebrate opsin evolution have lagged behind those for vertebrates because of the disparity in genomic resources and the lack of robust in vitro systems to characterize spectral sensitivities. Here, we review bioinformatic approaches to identify and model functional variation in opsins as well as recently developed assays to measure spectral phenotypes. In particular, we discuss how transgenic lines, cAMP-spectroscopy and sensitive heterologous expression platforms are starting to decouple genotype-phenotype relationships of LW opsins to complement the classical physiological-behavioural-phylogenetic toolbox of invertebrate visual sensory studies. We illustrate the use of one heterologous method by characterizing novel LW Gq opsins from 10 species, including diurnal and nocturnal Lepidoptera, a terrestrial dragonfly and an aquatic crustacean, expressing them in HEK293T cells, and showing that their maximum absorbance spectra (λmax) range from 518 to 611 nm. We discuss the advantages of molecular approaches for arthropods with complications such as restricted availability, lateral filters, specialized photochemistry and/or electrophysiological constraints. This article is part of the theme issue 'Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods'.

Ancestral sequence reconstruction as a tool to study the evolution of wood decaying fungi

The study of evolution is limited by the techniques available to do so. Aside from the use of the fossil record, molecular phylogenetics can provide a detailed characterization of evolutionary histories using genes, genomes and proteins. However, these tools provide scarce biochemical information of the organisms and systems of interest and are therefore very limited when they come to explain protein evolution. In the past decade, this limitation has been overcome by the development of ancestral sequence reconstruction (ASR) methods. ASR allows the subsequent resurrection in the laboratory of inferred proteins from now extinct organisms, becoming an outstanding tool to study enzyme evolution. Here we review the recent advances in ASR methods and their application to study fungal evolution, with special focus on wood-decay fungi as essential organisms in the global carbon cycling.

Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

Making Enzymes Suitable for Organic Chemistry by Rational Protein Design

This review outlines recent developments in protein engineering of stereo- and regioselective enzymes, which are of prime interest in organic and pharmaceutical chemistry as well as biotechnology. The widespread application of enzymes was hampered for decades due to limited enantio-, diastereo- and regioselectivity, which was the reason why most organic chemists were not interested in biocatalysis. This attitude began to change with the advent of semi-rational directed evolution methods based on focused saturation mutagenesis at sites lining the binding pocket. Screening constitutes the labor-intensive step (bottleneck), which is the reason why various research groups are continuing to develop techniques for the generation of small and smart mutant libraries. Rational enzyme design, traditionally an alternative to directed evolution, provides small collections of mutants which require minimal screening. This approach first focused on thermostabilization, and did not enter the field of stereoselectivity until later. Computational guides such as the Rosetta algorithms, HotSpot Wizard metric, and machine learning (ML) contribute significantly to decision making. The newest advancements show that semi-rational directed evolution such as CAST/ISM and rational enzyme design no longer develop on separate tracks, instead, they have started to merge. Indeed, researchers utilizing the two approaches have learned from each other. Today, the toolbox of organic chemists includes enzymes, primarily because the possibility of controlling stereoselectivity by protein engineering has ensured reliability when facing synthetic challenges. This review was also written with the hope that undergraduate and graduate education will include enzymes more so than in the past.

Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling

HaloTag labeling technology has introduced unrivaled potential in protein chemistry and molecular and cellular biology. A wide variety of ligands have been developed to meet the specific needs of diverse applications, but only a single protein tag, DhaAHT, is routinely used for their incorporation. Following a systematic kinetic and computational analysis of different reporters, a tetramethylrhodamine- and three 4-stilbazolium-based fluorescent ligands, we showed that the mechanism of incorporating different ligands depends both on the binding step and the efficiency of the chemical reaction. By studying the different haloalkane dehalogenases DhaA, LinB, and DmmA, we found that the architecture of the access tunnels is critical for the kinetics of both steps and the ligand specificity. We showed that highly efficient labeling with specific ligands is achievable with natural dehalogenases. We propose a simple protocol for selecting the optimal protein tag for a specific ligand from the wide pool of available enzymes with diverse access tunnel architectures. The application of this protocol eliminates the need for expensive and laborious protein engineering.

Insertions and deletions in protein evolution and engineering

Protein evolution or engineering studies are traditionally focused on amino acid substitutions and the way these contribute to fitness. Meanwhile, the insertion and deletion of amino acids is often overlooked, despite being one of the most common sources of genetic variation. Recent methodological advances and successful engineering stories have demonstrated that the time is ripe for greater emphasis on these mutations and their understudied effects. This review highlights the evolutionary importance and biotechnological relevance of insertions and deletions (indels). We provide a comprehensive overview of approaches that can be employed to include indels in random, (semi)-rational or computational protein engineering pipelines. Furthermore, we discuss the tolerance to indels at the structural level, address how domain indels can link the function of unrelated proteins, and feature studies that illustrate the surprising and intriguing potential of frameshift mutations.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

Mechanism-Based Design of Efficient PET Hydrolases

Polyethylene terephthalate (PET) is the most widespread synthetic polyester, having been utilized in textile fibers and packaging materials for beverages and food, contributing considerably to the global solid waste stream and environmental plastic pollution. While enzymatic PET recycling and upcycling have recently emerged as viable disposal methods for a circular plastic economy, only a handful of benchmark enzymes have been thoroughly described and subjected to protein engineering for improved properties over the last 16 years. By analyzing the specific material properties of PET and the reaction mechanisms in the context of interfacial biocatalysis, this Perspective identifies several limitations in current enzymatic PET degradation approaches. Unbalanced enzyme-substrate interactions, limited thermostability, and low catalytic efficiency at elevated reaction temperatures, and inhibition caused by oligomeric degradation intermediates still hamper industrial applications that require high catalytic efficiency. To overcome these limitations, successful protein engineering studies using innovative experimental and computational approaches have been published extensively in recent years in this thriving research field and are summarized and discussed in detail here. The acquired knowledge and experience will be applied in the near future to address plastic waste contributed by other mass-produced polymer types (e.g., polyamides and polyurethanes) that should also be properly disposed by biotechnological approaches.

Deep Insights into the Specific Evolution of Fungal Hybrid B Heme Peroxidases

Simple Summary

Fungi are well equipped to cope with oxidative stress and the reactive oxygen species that are, in the case of phytopathogens, produced mainly by the plant host for defence purposes. Peroxidases represent the major line of evolution for rapid decomposition of harmful peroxides in all aerobically metabolising organisms. In all the sequenced fungal genomes, many divergent genes coding for various peroxidases have been discovered, and Hybrid B heme peroxidases represent a distinctive mode of fungal-gene evolution within a large peroxidase–catalase superfamily that ranges from bacteria to plants.

Abstract

In this study, we focus on a detailed bioinformatics analysis of hyBpox genes, mainly within the genomes of Sclerotiniaceae (Ascomycota, Leotiomycetes), which is a specifically evolved fungal family of necrotrophic host generalists and saprophytic or biotrophic host specialists. Members of the genus Sclerotium produce only sclerotia and no fruiting bodies or spores. Thus, their physiological role for peroxidases remains open. A representative species, S. cepivorum, is a dangerous plant pathogen causing white rot in Allium species, particularly in onions, leeks, and garlic. On a worldwide basis, the white rot caused by this soil-borne fungus is apparently the most serious threat to Allium-crop production. We have also found very similar peroxidase sequences in the related fungus S. sclerotiorum, although with minor yet important modifications in the architecture of its active centre. The presence of ScephyBpox1-specific mRNA was confirmed by transcriptomic analysis. The presence of Hybrid B peroxidase at the protein level as the sole extracellular peroxidase of this fungus was confirmed in the secretome of S. cepivorum through detailed proteomic analyses. This prompted us to systematically search for all available genes coding for Hybrid B heme peroxidases in the whole fungal family of Sclerotiniaceae. We present here a reconstruction of their molecular phylogeny and analyse the unique aspects of their conserved-sequence features and structural folds in corresponding ancestral sequences.

Using the Evolutionary History of Proteins to Engineer Insertion-Deletion Mutants from Robust, Ancestral Templates Using Graphical Representation of Ancestral Sequence Predictions (GRASP)

Analyzing the natural evolution of proteins by ancestral sequence reconstruction (ASR) can provide valuable information about the changes in sequence and structure that drive the development of novel protein functions. However, ASR has also been used as a protein engineering tool, as it often generates thermostable proteins which can serve as robust and evolvable templates for enzyme engineering. Importantly, ASR has the potential to provide an insight into the history of insertions and deletions that have occurred in the evolution of a protein family. Indels are strongly associated with functional change during enzyme evolution and represent a largely unexplored source of genetic diversity for designing proteins with novel or improved properties. Current ASR methods differ in the way they handle indels; inclusion or exclusion of indels is often managed subjectively, based on assumptions the user makes about the likelihood of each recombination event, yet most currently available ASR tools provide limited, if any, opportunities for evaluating indel placement in a reconstructed sequence. Graphical Representation of Ancestral Sequence Predictions (GRASP) is an ASR tool that maps indel evolution throughout a reconstruction and enables the evaluation of indel variants. This chapter provides a general protocol for performing a reconstruction using GRASP and using the results to create indel variants. The method addresses protein template selection, sequence curation, alignment refinement, tree building, ancestor reconstruction, evaluation of indel variants and approaches to library development.

Ancestral Sequence Reconstruction for Exploring Alkaloid Evolution

The complex and bioactive monoterpene indole alkaloids (MIAs) found in Catharanthus roseus and related species are the products of many millions of years of evolution through mutation and natural selection. Ancestral sequence reconstruction (ASR) is a method that combines phylogenetic analysis and experimental biochemistry to infer details about past events in protein evolution. Here, I propose that ASR could be leveraged to understand how enzymes catalyzing the formation of complex alkaloids arose over evolutionary time. I discuss the steps of ASR, including sequence selection, multiple sequence alignment, tree inference, and the generation and characterization of inferred ancestral enzymes.

Web-based tools for computational enzyme design

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.