Automated Design of Efficient and Functionally Diverse Enzyme Repertoires

Fungal cellulases: protein engineering and post-translational modifications

Enzymatic degradation of lignocelluloses into fermentable sugars to produce biofuels and other biomaterials is critical for environmentally sustainable development and energy resource supply. However, there are problems in enzymatic cellulose hydrolysis, such as the complex cellulase composition, low degradation efficiency, high production cost, and post-translational modifications (PTMs), all of which are closely related to specific characteristics of cellulases that remain unclear. These problems hinder the practical application of cellulases. Due to the rapid development of computer technology in recent years, computer-aided protein engineering is being widely used, which also brings new opportunities for the development of cellulases. Especially in recent years, a large number of studies have reported on the application of computer-aided protein engineering in the development of cellulases; however, these articles have not been systematically reviewed. This article focused on the aspect of protein engineering and PTMs of fungal cellulases. In this manuscript, the latest literatures and the distribution of potential sites of cellulases for engineering have been systematically summarized, which provide reference for further improvement of cellulase properties. KEY POINTS: •Rational design based on virtual mutagenesis can improve cellulase properties. •Modifying protein side chains and glycans helps obtain superior cellulases. •N-terminal glutamine-pyroglutamate conversion stabilizes fungal cellulases.

Production of microhomogeneous glycopeptide by a mutated NGT according FuncLib with unique sugar as substrate

N-glycosylation is one of the most important post-translational modifications of proteins. Cytoplasmic soluble N-glycosyltransferase (NGT) exists in bacteria, which is able to transfer different monosaccharide from sugar nucleotide to the NXS/T(X ≠ Pro) consensus sequence in a polypeptide. At present, the NGT enzymes reported could transfer a variety of different sugars to protein, which will lead to the heterogeneity of the sugar chain and the complexity and instability of the structure and function of glycopeptides. According to the FuncLib algorithm, we obtained mutant ApNGT-P1 from ApNGT (the NGT from Actinobacillus pleuropneumoniae) with increased substrate specificity. Compared with the wild-type ApNGT, mutant ApNGT-P1 could only utilize UDP-Glc as sugar donors. The optimum temperature of ApNGT-P1 was about 40 °C and the optimum pH was 7.5-8.0 in PBS buffer. ApNGT-P1 exhibited better tolerance for K⁺, Mn²⁺, Ca²⁺, and Mg²⁺, but was strongly inhibited by Na⁺, Cu²⁺ and Zn²⁺. The mutant can be applied to the efficient production of glycosylated peptides or proteins with uniform glucose at their glycosylation sites. Besides, this work provided a feasible pathway for further studies on the improving donor substrates selectivity of NGTs.

Stabilization of the SARS-CoV-2 Receptor Binding Domain by Protein Core Redesign and Deep Mutational Scanning.. [PMID: 34845448 PMCID: PMC8629191 DOI: 10.1101/2021.11.22.469552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Stabilizing antigenic proteins as vaccine immunogens or diagnostic reagents is a stringent case of protein engineering and design as the exterior surface must maintain recognition by receptor(s) and antigen—specific antibodies at multiple distinct epitopes. This is a challenge, as stability-enhancing mutations must be focused on the protein core, whereas successful computational stabilization algorithms typically select mutations at solvent-facing positions. In this study we report the stabilization of SARS-CoV-2 Wuhan Hu-1 Spike receptor binding domain (S RBD) using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L RBD mutations, maintains recognition by the receptor ACE2 and a panel of different anti-RBD monoclonal antibodies, is between 1–2°C more thermally stable than the original RBD using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original RBD. Our approach could be applied to the computational stabilization of a wide range of proteins without requiring detailed knowledge of active sites or binding epitopes, particularly powerful for cases when there are multiple or unknown binding sites.

Biocatalytic Alkylation Chemistry: Building Molecular Complexity with High Selectivity

Biocatalysis has traditionally been viewed as a field that primarily enables access to chiral centers. This includes the synthesis of chiral alcohols, amines and carbonyl compounds, often through functional group interconversion via hydrolytic or oxidation-reduction reactions. This limitation is partly being overcome by the design and evolution of new enzymes. Here, we provide an overview of a recently thriving research field that we summarize as biocatalytic alkylation chemistry. In the past 3-4 years, numerous new enzymes have been developed that catalyze sp3 C-C/N/O/S bond formations. These enzymes utilize different mechanisms to generate molecular complexity by coupling simple fragments with high activity and selectivity. In many cases, the engineered enzymes perform reactions that are difficult or impossible to achieve with current small-molecule catalysts such as organocatalysts and transition-metal complexes. This review further highlights that the design of new enzyme function is particularly successful when off-the-shelf synthetic reagents are utilized to access non-natural reactive intermediates. This underscores how biocatalysis is gradually moving to a field that build molecules through selective bond forming reactions.

Enzyme engineering and in vivo testing of a formate reduction pathway

Formate is an attractive feedstock for sustainable microbial production of fuels and chemicals, but its potential is limited by the lack of efficient assimilation pathways. The reduction of formate to formaldehyde would allow efficient downstream assimilation, but no efficient enzymes are known for this transformation. To develop a 2-step formate reduction pathway, we screened natural variants of acyl-CoA synthetase (ACS) and acylating aldehyde dehydrogenase (ACDH) for activity on one-carbon substrates and identified active and highly expressed homologs of both enzymes. We then performed directed evolution, increasing ACDH-specific activity by 2.5-fold and ACS lysate activity by 5-fold. To test for the in vivo activity of our pathway, we expressed it in a methylotroph which can natively assimilate formaldehyde. Although the enzymes were active in cell extracts, we could not detect formate assimilation into biomass, indicating that further improvement will be required for formatotrophy. Our work provides a foundation for further development of a versatile pathway for formate assimilation.

Substrate Analogues for the Enzyme-Catalyzed Detoxification of the Organophosphate Nerve Agents-Sarin, Soman, and Cyclosarin

The G-type nerve agents, sarin (GB), soman (GD), and cyclosarin (GF), are among the most toxic compounds known. Much progress has been made in evolving the enzyme phosphotriesterase (PTE) from Pseudomonas diminuta for the decontamination of the G-agents; however, the extreme toxicity of the G-agents makes the use of substrate analogues necessary. Typical analogues utilize a chromogenic leaving group to facilitate high-throughput screening, and substitution of an O-methyl for the P-methyl group found in the G-agents, in an effort to reduce toxicity. Till date, there has been no systematic evaluation of the effects of these substitutions on catalytic activity, and the presumed reduction in toxicity has not been tested. A series of 21 G-agent analogues, including all combinations of O-methyl, p-nitrophenyl, and thiophosphate substitutions, have been synthesized and evaluated for their ability to unveil the stereoselectivity and catalytic activity of PTE variants against the authentic G-type nerve agents. The potential toxicity of these analogues was evaluated by measuring the rate of inactivation of acetylcholinesterase (AChE). All of the substitutions reduced inactivation of AChE by more than 100-fold, with the most effective being the thiophosphate analogues, which reduced the rate of inactivation by about 4-5 orders of magnitude. The analogues were found to reliably predict changes in catalytic activity and stereoselectivity of the PTE variants and led to the identification of the BHR-30 variant, which has no apparent stereoselectivity against GD and a k_cat/K_m of 1.4 × 10⁶, making it the most efficient enzyme for GD decontamination reported till date.

Exploiting enzyme evolution for computational protein design

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.

Computationally designed pyocyanin demethylase acts synergistically with tobramycin to kill recalcitrant Pseudomonas aeruginosa biofilms

Pseudomonas aeruginosa is an opportunistic human pathogen that develops difficult-to-treat biofilms in immunocompromised individuals, cystic fibrosis patients, and in chronic wounds. P. aeruginosa has an arsenal of physiological attributes that enable it to evade standard antibiotic treatments, particularly in the context of biofilms where it grows slowly and becomes tolerant to many drugs. One of its survival strategies involves the production of the redox-active phenazine, pyocyanin, which promotes biofilm development. We previously identified an enzyme, PodA, that demethylated pyocyanin and disrupted P. aeruginosa biofilm development in vitro. Here, we asked if this protein could be used as a potential therapeutic for P. aeruginosa infections together with tobramycin, an antibiotic typically used in the clinic. A major roadblock to answering this question was the poor yield and stability of wild-type PodA purified from standard Escherichia coli overexpression systems. We hypothesized that the insufficient yields were due to poor packing within PodA's obligatory homotrimeric interfaces. We therefore applied the protein design algorithm, AffiLib, to optimize the symmetric core of this interface, resulting in a design that incorporated five mutations leading to a 20-fold increase in protein yield from heterologous expression and purification and a substantial increase in stability to environmental conditions. The addition of the designed PodA with tobramycin led to increased killing of P. aeruginosa cultures under oxic and hypoxic conditions in both the planktonic and biofilm states. This study highlights the potential for targeting extracellular metabolites to assist the control of P. aeruginosa biofilms that tolerate conventional antibiotic treatment.

Biorefinery Gets Hot: Thermophilic Enzymes and Microorganisms for Second-Generation Bioethanol Production

To mitigate the current global energy and the environmental crisis, biofuels such as bioethanol have progressively gained attention from both scientific and industrial perspectives. However, at present, commercialized bioethanol is mainly derived from edible crops, thus raising serious concerns given its competition with feed production. For this reason, lignocellulosic biomasses (LCBs) have been recognized as important alternatives for bioethanol production. Because LCBs supply is sustainable, abundant, widespread, and cheap, LCBs-derived bioethanol currently represents one of the most viable solutions to meet the global demand for liquid fuel. However, the cost-effective conversion of LCBs into ethanol remains a challenge and its implementation has been hampered by several bottlenecks that must still be tackled. Among other factors related to the challenging and variable nature of LCBs, we highlight: (i) energy-demanding pretreatments, (ii) expensive hydrolytic enzyme blends, and (iii) the need for microorganisms that can ferment mixed sugars. In this regard, thermophiles represent valuable tools to overcome some of these limitations. Thus, the aim of this review is to provide an overview of the state-of-the-art technologies involved, such as the use of thermophilic enzymes and microorganisms in industrial-relevant conditions, and to propose possible means to implement thermophiles into second-generation ethanol biorefineries that are already in operation.

The beauty of biocatalysis: sustainable synthesis of ingredients in cosmetics

Covering: 2015 up to July 2021The market for cosmetics is consumer driven and the desire for green, sustainable and natural ingredients is increasing. The use of isolated enzymes and whole-cell organisms to synthesise these products is congruent with these values, especially when combined with the use of renewable, recyclable or waste feedstocks. The literature of biocatalysis for the synthesis of ingredients in cosmetics in the past five years is herein reviewed.

The evolving art of creating genetic diversity: From directed evolution to synthetic biology

The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.

From Natural Methylation to Versatile Alkylations Using Halide Methyltransferases

Halide methyltransferases (HMTs) enable the enzymatic synthesis of S-adenosyl-l-methionine (SAM) from S-adenosyl-l-homocysteine (SAH) and methyl iodide. Characterisation of a range of naturally occurring HMTs and subsequent protein engineering led to HMT variants capable of synthesising ethyl, propyl, and allyl analogues of SAM. Notably, HMTs do not depend on chemical synthesis of methionine analogues, as required by methionine adenosyltransferases (MATs). However, at the moment MATs have a much broader substrate scope than the HMTs. Herein we provide an overview of the discovery and engineering of promiscuous HMTs and how these strategies will pave the way towards a toolbox of HMT variants for versatile chemo- and regioselective biocatalytic alkylations.

Biomolecular Recognition of the Glycan Neoantigen CA19-9 by Distinct Antibodies

Glycans decorate the cell surface, secreted glycoproteins and glycolipids, and altered glycans are often found in cancers. Despite their high diagnostic and therapeutic potential, however, glycans are polar and flexible molecules that are quite challenging for the development and design of high-affinity binding antibodies. To understand the mechanisms by which glycan neoantigens are specifically recognized by antibodies, we analyze the biomolecular recognition of the tumor-associated carbohydrate antigen CA19-9 by two distinct antibodies using X-ray crystallography. Despite the potential plasticity of glycans and the very different antigen-binding surfaces presented by the antibodies, both structures reveal an essentially identical extended CA19-9 conformer, suggesting that this conformer's stability selects the antibodies. Starting from the bound structure of one of the antibodies, we use the AbLIFT computational algorithm to design a variant with seven core mutations in the variable domain's light-heavy chain interface that exhibits tenfold improved affinity for CA19-9. The results reveal strategies used by antibodies to specifically recognize glycan antigens and show how automated antibody-optimization methods may be used to enhance the clinical potential of existing antibodies.

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.

Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO₂ or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

Data-driven computational protein design

Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.

Breakthroughs in computational design methods open up new frontiers for de novo protein engineering

Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5 years.

Community-Wide Experimental Evaluation of the PROSS Stability-Design Method

Recent years have seen a dramatic improvement in protein-design methodology. Nevertheless, most methods demand expert intervention, limiting their widespread adoption. By contrast, the PROSS algorithm for improving protein stability and heterologous expression levels has been successfully applied to a range of challenging enzymes and binding proteins. Here, we benchmark the application of PROSS as a stand-alone tool for protein scientists with no or limited experience in modeling. Twelve laboratories from the Protein Production and Purification Partnership in Europe (P4EU) challenged the PROSS algorithm with 14 unrelated protein targets without support from the PROSS developers. For each target, up to six designs were evaluated for expression levels and in some cases, for thermal stability and activity. In nine targets, designs exhibited increased heterologous expression levels either in prokaryotic and/or eukaryotic expression systems under experimental conditions that were tailored for each target protein. Furthermore, we observed increased thermal stability in nine of ten tested targets. In two prime examples, the human Stem Cell Factor (hSCF) and human Cadherin-Like Domain (CLD12) from the RET receptor, the wild type proteins were not expressible as soluble proteins in E. coli, yet the PROSS designs exhibited high expression levels in E. coli and HEK293 cells, respectively, and improved thermal stability. We conclude that PROSS may improve stability and expressibility in diverse cases, and that improvement typically requires target-specific expression conditions. This study demonstrates the strengths of community-wide efforts to probe the generality of new methods and recommends areas for future research to advance practically useful algorithms for protein science.

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.

Engineered Enzymes Enable Selective N-Alkylation of Pyrazoles With Simple Haloalkanes

Selective alkylation of pyrazoles could solve a challenge in chemistry and streamline synthesis of important molecules. Here we report catalyst‐controlled pyrazole alkylation by a cyclic two‐enzyme cascade. In this enzymatic system, a promiscuous enzyme uses haloalkanes as precursors to generate non‐natural analogs of the common cosubstrate S‐adenosyl‐l‐methionine. A second engineered enzyme transfers the alkyl group in highly selective C−N bond formations to the pyrazole substrate. The cosubstrate is recycled and only used in catalytic amounts. Key is a computational enzyme‐library design tool that converted a promiscuous methyltransferase into a small enzyme family of pyrazole‐alkylating enzymes in one round of mutagenesis and screening. With this enzymatic system, pyrazole alkylation (methylation, ethylation, propylation) was achieved with unprecedented regioselectivity (>99 %), regiodivergence, and in a first example on preparative scale.

Understanding substrate binding and the role of gatekeeping residues in PigC access tunnels

Prodigiosin ligase PigC has been engineered by semi-rational design to accept short chain-pyrroles, providing molecular understanding of access tunnels and the substrate-binding pocket.

The AbDesign computational pipeline for modular backbone assembly and design of binders and enzymes

The functional sites of many protein families are dominated by diverse backbone regions that lack secondary structure (loops) but fold stably into their functionally competent state. Nevertheless, the design of structured loop regions from scratch, especially in functional sites, has met with great difficulty. We therefore developed an approach, called AbDesign, to exploit the natural modularity of many protein families and computationally assemble a large number of new backbones by combining naturally occurring modular fragments. This strategy yielded large, atomically accurate, and highly efficient proteins, including antibodies and enzymes exhibiting dozens of mutations from any natural protein. The combinatorial backbone‐conformation space that can be accessed by AbDesign even for a modestly sized family of homologs may exceed the diversity in the entire PDB, providing the sub‐Ångstrom level of control over the positioning of active‐site groups that is necessary for obtaining highly active proteins. This manuscript describes how to implement the pipeline using code that is freely available at https://github.com/Fleishman‐Lab/AbDesign_for_enzymes.

Deploying Microbial Synthesis for Halogenating and Diversifying Medicinal Alkaloid Scaffolds

Plants produce some of the most potent therapeutics and have been used for thousands of years to treat human diseases. Today, many medicinal natural products are still extracted from source plants at scale as their complexity precludes total synthesis from bulk chemicals. However, extraction from plants can be an unreliable and low-yielding source for human therapeutics, making the supply chain for some of these life-saving medicines expensive and unstable. There has therefore been significant interest in refactoring these plant pathways in genetically tractable microbes, which grow more reliably and where the plant pathways can be more easily engineered to improve the titer, rate and yield of medicinal natural products. In addition, refactoring plant biosynthetic pathways in microbes also offers the possibility to explore new-to-nature chemistry more systematically, and thereby help expand the chemical space that can be probed for drugs as well as enable the study of pharmacological properties of such new-to-nature chemistry. This perspective will review the recent progress toward heterologous production of plant medicinal alkaloids in microbial systems. In particular, we focus on the refactoring of halogenated alkaloids in yeast, which has created an unprecedented opportunity for biosynthesis of previously inaccessible new-to-nature variants of the natural alkaloid scaffolds.

Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily

To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADOs), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts, we discovered latent ADO-like function across the ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of Escherichia coli class I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families.

Ketosynthase Domain Constrains the Design of Polyketide Synthases

Modular polyketide synthases (PKSs) produce complex, bioactive secondary metabolites in assembly line-like multistep reactions. Longstanding efforts to produce novel, biologically active compounds by recombining intact modules to new modular PKSs have mostly resulted in poorly active chimeras and decreased product yields. Recent findings demonstrate that the low efficiencies of modular chimeric PKSs also result from rate limitations in the transfer of the growing polyketide chain across the noncognate module:module interface and further processing of the non-native polyketide substrate by the ketosynthase (KS) domain. In this study, we aim at disclosing and understanding the low efficiency of chimeric modular PKSs and at establishing guidelines for modular PKSs engineering. To do so, we work with a bimodular PKS testbed and systematically vary substrate specificity, substrate identity, and domain:domain interfaces of the KS involved reactions. We observe that KS domains employed in our chimeric bimodular PKSs are bottlenecks with regards to both substrate specificity as well as interaction with the acyl carrier protein (ACP). Overall, our systematic study can explain in quantitative terms why early oversimplified engineering strategies based on the plain shuffling of modules mostly failed and why more recent approaches show improved success rates. We moreover identify two mutations of the KS domain that significantly increased turnover rates in chimeric systems and interpret this finding in mechanistic detail.

Enhancing organophosphate hydrolase efficacy via protein engineering and immobilization strategies

Organophosphorus compounds (OPs), developed as pesticides and chemical warfare agents, are extremely toxic chemicals that pose a public health risk. Of the different detoxification strategies, organophosphate-hydrolyzing enzymes have attracted much attention, providing a potential route for detoxifying those exposed to OPs. Phosphotriesterase (PTE), also known as organophosphate hydrolase (OPH), is one such enzyme that has been extensively studied as a catalytic bioscavenger. In this review, we will discuss the protein engineering of PTE aimed toward improving the activity and stability of the enzyme. In order to make enzyme utilization in OP detoxification more favorable, enzyme immobilization provides an effective means to increase enzyme activity and stability. Here, we present several such strategies that enhance the storage and operational stability of PTE/OPH.

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.

A mixture of three engineered phosphotriesterases enables rapid detoxification of the entire spectrum of known threat nerve agents

Nerve agents are organophosphates (OPs) that potently inhibit acetylcholinesterase, and their enzymatic detoxification has been a long-standing goal. Nerve agents vary widely in size, charge, hydrophobicity and the cleavable ester bond. A single enzyme is therefore unlikely to efficiently hydrolyze all agents. Here, we describe a mixture of three previously developed variants of the bacterial phosphotriesterase (Bd-PTE) that are highly stable and nearly sequence identical. This mixture enables effective detoxification of a broad spectrum of known threat agents-GA (tabun), GB (sarin), GD (soman), GF (cyclosarin), VX and Russian-VX. The potential for dimer dissociation and exchange that could inactivate Bd-PTE has minimal impact, and the three enzyme variants are as active in a mixture as they are individually. To our knowledge, this engineered enzyme 'cocktail' comprises the first solution for enzymatic detoxification of the entire range of threat nerve agents.

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (∼2 × 104 M-1 s-1 and ∼102 s-1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ∼2 kcal mol-1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme design.

Practically useful protein-design methods combining phylogenetic and atomistic calculations

Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.

Rozhkova A, N. Zorov I, Korotkova O, P. Sinitsyn A, Schwaneberg U, D. Davari M. Engineering Robust Cellulases for Tailored Lignocellulosic Degradation Cocktails

Lignocellulosic biomass is a most promising feedstock in the production of second-generation biofuels. Efficient degradation of lignocellulosic biomass requires a synergistic action of several cellulases and hemicellulases. Cellulases depolymerize cellulose, the main polymer of the lignocellulosic biomass, to its building blocks. The production of cellulase cocktails has been widely explored, however, there are still some main challenges that enzymes need to overcome in order to develop a sustainable production of bioethanol. The main challenges include low activity, product inhibition, and the need to perform fine-tuning of a cellulase cocktail for each type of biomass. Protein engineering and directed evolution are powerful technologies to improve enzyme properties such as increased activity, decreased product inhibition, increased thermal stability, improved performance in non-conventional media, and pH stability, which will lead to a production of more efficient cocktails. In this review, we focus on recent advances in cellulase cocktail production, its current challenges, protein engineering as an efficient strategy to engineer cellulases, and our view on future prospects in the generation of tailored cellulases for biofuel production.

Computer-Assisted Recombination (CompassR) Teaches us How to Recombine Beneficial Substitutions from Directed Evolution Campaigns

A main remaining challenge in protein engineering is how to recombine beneficial substitutions. Systematic recombination studies show that poorly performing variants are usually obtained after recombination of 3 to 4 beneficial substitutions. This limits researchers in exploiting nature's potential in generating better enzymes. The Computer-assisted Recombination (CompassR) strategy provides a selection guide for beneficial substitutions that can be recombined to gradually improve enzyme performance by analysis of the relative free energy of folding (ΔΔGfold ). The performance of CompassR was evaluated by analysis of 84 recombinants located on 13 positions of Bacillus subtilis lipase A. The finally obtained variant F17S/V54K/D64N/D91E had a 2.7-fold improved specific activity in 18.3 % (v/v) 1-butyl-3-methylimidazolium chloride ([BMIM][Cl]). In essence, the deducted CompassR rule allows recombination of beneficial substitutions in an iterative manner and empowers researchers to generate better enzymes in a time-efficient manner.

Design of a basigin-mimicking inhibitor targeting the malaria invasion protein RH5

Many human pathogens use host cell-surface receptors to attach and invade cells. Often, the host-pathogen interaction affinity is low, presenting opportunities to block invasion using a soluble, high-affinity mimic of the host protein. The Plasmodium falciparum reticulocyte-binding protein homolog 5 (RH5) provides an exciting candidate for mimicry: it is highly conserved and its moderate affinity binding to the human receptor basigin (KD  ≥1 μM) is an essential step in erythrocyte invasion by this malaria parasite. We used deep mutational scanning of a soluble fragment of human basigin to systematically characterize point mutations that enhance basigin affinity for RH5 and then used Rosetta to design a variant within the sequence space of affinity-enhancing mutations. The resulting seven-mutation design exhibited 1900-fold higher affinity (KD approximately 1 nM) for RH5 with a very slow binding off rate (0.23 h-1 ) and reduced the effective Plasmodium growth-inhibitory concentration by at least 10-fold compared to human basigin. The design provides a favorable starting point for engineering on-rate improvements that are likely to be essential to reach therapeutically effective growth inhibition.

Enzymatic Bioremediation of Organophosphate Compounds-Progress and Remaining Challenges

Organophosphate compounds are ubiquitously employed as agricultural pesticides and maintained as chemical warfare agents by several nations. These compounds are highly toxic, show environmental persistence and accumulation, and contribute to numerous cases of poisoning and death each year. While their use as weapons of mass destruction is rare, these never fully disappear into obscurity as they continue to be tools of fear and control by governments and terrorist organizations. Beyond weaponization, their wide-scale dissemination as agricultural products has led to environmental accumulation and intoxication of soil and water across the globe. Therefore, there is a dire need for rapid and safe agents for environmental bioremediation, personal decontamination, and as therapeutic detoxicants. Organophosphate hydrolyzing enzymes are emerging as appealing targets to satisfy decontamination needs owing to their ability to hydrolyze both pesticides and nerve agents using biologically-derived materials safe for both the environment and the individual. As the release of genetically modified organisms is not widely accepted practice, researchers are exploring alternative strategies of organophosphate bioremediation that focus on cell-free enzyme systems. In this review, we first discuss several of the more prevalent organophosphorus hydrolyzing enzymes along with research and engineering efforts that have led to an enhancement in their activity, substrate tolerance, and stability. In the later half we focus on advances achieved through research focusing on enhancing the catalytic activity and stability of phosphotriesterase, a model organophosphate hydrolase, using various approaches such as nanoparticle display, DNA scaffolding, and outer membrane vesicle encapsulation.

A lipophilicity-based energy function for membrane-protein modelling and design

Membrane-protein design is an exciting and increasingly successful research area which has led to landmarks including the design of stable and accurate membrane-integral proteins based on coiled-coil motifs. Design of topologically more complex proteins, such as most receptors, channels, and transporters, however, demands an energy function that balances contributions from intra-protein contacts and protein-membrane interactions. Recent advances in water-soluble all-atom energy functions have increased the accuracy in structure-prediction benchmarks. The plasma membrane, however, imposes different physical constraints on protein solvation. To understand these constraints, we recently developed a high-throughput experimental screen, called dsTβL, and inferred apparent insertion energies for each amino acid at dozens of positions across the bacterial plasma membrane. Here, we express these profiles as lipophilicity energy terms in Rosetta and demonstrate that the new energy function outperforms previous ones in modelling and design benchmarks. Rosetta ab initio simulations starting from an extended chain recapitulate two-thirds of the experimentally determined structures of membrane-spanning homo-oligomers with <2.5Å root-mean-square deviation within the top-predicted five models (available online: http://tmhop.weizmann.ac.il). Furthermore, in two sequence-design benchmarks, the energy function improves discrimination of stabilizing point mutations and recapitulates natural membrane-protein sequences of known structure, thereby recommending this new energy function for membrane-protein modelling and design.

Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces

Antibodies developed for research and clinical applications may exhibit suboptimal stability, expressibility, or affinity. Existing optimization strategies focus on surface mutations, whereas natural affinity maturation also introduces mutations in the antibody core, simultaneously improving stability and affinity. To systematically map the mutational tolerance of an antibody variable fragment (Fv), we performed yeast display and applied deep mutational scanning to an anti-lysozyme antibody and found that many of the affinity-enhancing mutations clustered at the variable light-heavy chain interface, within the antibody core. Rosetta design combined enhancing mutations, yielding a variant with tenfold higher affinity and substantially improved stability. To make this approach broadly accessible, we developed AbLIFT, an automated web server that designs multipoint core mutations to improve contacts between specific Fv light and heavy chains (http://AbLIFT.weizmann.ac.il). We applied AbLIFT to two unrelated antibodies targeting the human antigens VEGF and QSOX1. Strikingly, the designs improved stability, affinity, and expression yields. The results provide proof-of-principle for bypassing laborious cycles of antibody engineering through automated computational affinity and stability design.

Towards descriptor of elementary functions for protein design

We review studies of the protein evolution that help to formulate rules for protein design. Acknowledging the fundamental importance of Dayhoff's provision on the emergence of functional proteins from short peptides, we discuss multiple evidences of the omnipresent partitioning of protein globules into structural/functional units, using which greatly facilitates the engineering and design efforts. Closed loops and elementary functional loops, which are descendants of ancient ring-like peptides that formed fist protein domains in agreement with Dayhoff's hypothesis, can be considered as basic units of protein structure and function. We argue that future developments in protein design approaches should consider descriptors of the elementary functions, which will help to complement designed scaffolds with functional signatures and flexibility necessary for their functions.