PROSS 2: a new server for the design of stable and highly expressed protein variants

Multidimensional computational strategies enhance the thermostability of alpha-galactosidase

Alpha-Galactosidase has significant industrial application value in food processing, animal nutrition and medical applications. Microbial-derived α-galactosidases predominate industrial implementation due to high productivity, yet their inherent thermal instability necessitates systematic protein engineering. In this study, we established a dual-strategy protein engineering framework to enhance the thermostability of Aspergillus tubingensis α-galactosidase (AtWU_04653). Strategy I employed integrative computational design tools (ABACUS2/PROSS/DBD2) for mutational library construction, which yielded the dominant mutant A169P exhibiting remarkable performance: 78.52 % enhancement in thermal half-life at 55 °C (pH 4.0) and 52.04 % increase in catalytic efficiency (kcat /Km). Strategy II implemented a physics-based computational methodology combining GROMACS molecular dynamics simulations with Rosetta unfolding free energy calculations and SPIRED machine learning predictions, successfully deriving three stabilized variants (E429I, N380L, T64P) displaying 57.33 %, 67.17 %, and 41.34 % extended half-lives respectively. Notably, E429I and T64P demonstrated concurrent 85.25 % and 65.90 % catalytic activity augmentation (kcat /Km). Both strategies achieved substantial reduction in experimental screening workload while enabling synergistic thermostability-activity optimization. This study uses sequence conservation analysis, unfolding free energy calculation, molecular dynamics simulation, and innovative protein prediction models to establish multidimensional computational strategies for designing mutants, providing new and important technical references for computational design and functional optimization of enzymes.

Functional characterization and protein engineering of glycosyltransferase for 2"-O-xylosylation of ginsenoside Rg3

The limited abundance of xylosylated ginsenosides and the lack of efficient biocatalysts hinder their pharmacological exploration. This study identified a 2"-O-xylosyltransferase (PnUGT57) from Panax notoginseng that catalyzes the conversion of ginsenoside Rg3 to notoginsenoside ST4. Wild-type PnUGT57 preferred UDP-xylose over UDP-glucose and UDP-rhamnose and displayed limited thermostability (t1/2 = 6.73 h at 30 °C). To enhance UDP-xylose specificity, sequence-guided mutagenesis generated the C140A variant, which achieved remarkable UDP-xylose specificity (100 % conversion) with a 1.34-fold increase in catalytic efficiency while showing weak activity toward UDP-glucose (8.7 %) and UDP-rhamnose (5.2 %) activity. The F367A mutant possesses only xylosyltransferase activity but with reduced catalytic efficiency (0.3-fold of the WT). Molecular docking revealed that the enhanced UDP-xylose specificity in C140A and F367A resulted from the loss of key hydrogen bonding and hydrophobic interactions. To improve thermostability, computational design produced a triple mutant (P101S/L200C/G255D) with an 8.58-fold longer half-life (57.76 h), attributed to optimized surface charge distribution and improved hydration layer formation, as confirmed by molecular dynamics simulation. The combinatorial mutant C140A/P101S/L200C/G255D synergistically improved UDP-xylose specificity, thermostability, and catalytic efficiency, enabling efficient ST4 biosynthesis. This study elucidates the catalytic mechanism of PnUGT57 and presents engineered variants as promising biocatalysts for sustainable ginsenoside production.

The use of isomerases and epimerases for the production of the functional sugars mannose, allulose and tagatose from Fructose

Fructose, a common monosaccharide in nature extensively utilized in the food industry, poses a risk of elevated chronic disease incidence with excessive consumption. The global movement for a healthier living has sparked a quest for sugar reduction in foodstuff. The growing concern regarding the adverse impact of excessive sugar consumption on public health has led to significant interest in investigating healthier sugar alternatives. Research efforts have refocused on converting fructose into high-value, reduced-calorie functional sugars. Fructose can undergo direct conversion into three such sugars-mannose, allulose, and tagatose-via a streamlined bioconversion process. Allulose and tagatose, epimers of fructose, are derivable directly from fructose through C-3 and C-4 epimerization processes, whereas mannose, the aldose isomer of fructose, can be synthesized via isomerization pathways. This article aims to present recent advancements in the physiological functions, production methods, and applications of functional sugars derived from fructose. Particularly, it focuses on the bioproduction of mannose, allulose, and tagatose from fructose, encompassing discussions on the recent progress in the related isomerases and epimerases, such as mannose isomerase/lyxose isomerase, ketose 3-epimerase, and tagatose 4-epimerase. This review will provide a fresh perspective on the high-value biological utilization of fructose resources.

Effect of Fold-Promoting Mutation and Signal Peptide Screening on Recombinant Glucan 1,4-Alpha-maltohydrolase Secretion in Pichia pastoris

Glucan 1,4-alpha-maltohydrolase (3.2.1.133, GMH) is an important biocatalyst in the baking industry, which could delay the retrogradation of bread and improve its cold-storage durability. In the present study, a newly cloned Thgmh was characterized and secreted by Pichia pastoris (Komagataella pastoris). After computationally assisted rational design that promotes peptide folding, the maltogenic activity in supernatant was enhanced 1.6-fold in comparison with the base strain. The signal leading sequence screening and the gene dosage increment further improved secretion by approximately 6.4-fold. The purified rationally designed ThGMHs exhibited maximal activity against soluble starch at pH 7.0 and 60 ℃, and maltose is the main catalytic product. In a 5-L bioreactor, conventional fed-batch fermentation resulted in 6130 U mL-1 extracellular maltogenic activity. Therefore, a promising strain for GMH production was developed, which provides a useful reference for the secretory production of other industrial enzymes.

ProCeSa: Contrast-Enhanced Structure-Aware Network for Thermostability Prediction with Protein Language Models

Proteins play a fundamental role in biology, and their thermostability is essential for their proper functionality. The precise measurement of thermostability is crucial, traditionally relying on resource-intensive experiments. Recent advances in deep learning, particularly in protein language models (PLMs), have significantly accelerated the progress in protein thermostability prediction. These models utilize various biological characteristics or deep representations generated by PLMs to represent the protein sequences. However, effectively incorporating structural information, based on the PLM embeddings, while not considering atomic protein structures, remains an open and formidable challenge. Here, we propose a novel Protein Contrast-enhanced Structure-Aware (ProCeSa) model that seamlessly integrates both sequence and structural information extracted from PLMs to enhance thermostability prediction. Our model employs a contrastive learning scheme guided by the categories of amino acid residues, allowing it to discern intricate patterns within protein sequences. Rigorous experiments conducted on publicly available data sets establish the superiority of our method over state-of-the-art approaches, excelling in both classification and regression tasks. Our results demonstrate that ProCeSa addresses the complex challenge of predicting protein thermostability by utilizing PLM-derived sequence embeddings, without requiring access to atomic structural data.

Stabilized designs of the malaria adhesin protein PvRBP2b for use as a potential diagnostic for Plasmodium vivax

Plasmodium vivax is emerging as the most prevalent species causing malaria outside Africa. Most P. vivax infections are relapses due to the reactivation of the dormant liver stage parasites (hypnozoites). Hypnozoites are a major reservoir for transmission but undetectable by commercial diagnostic tests. Antibodies against P. vivax reticulocyte-binding protein 2b (PvRBP2b) are among the most reliable serological biomarkers for recent P. vivax infections in the prior 9 months and act as indirect biomarkers for risk of relapse. We sought to design stabilized variants of PvRBP2b, under stringent conditions of minimally perturbing the solvent-accessible surfaces to maintain its antigenicity profile. Furthermore, for some of the designs, due to limited diversity of natural PvRBP2b homologs, we combined AI-based ProteinMPNN and PROSS atomistic design calculations. The best, bearing 19 core mutations relative to PvRBP2b, expressed 16-fold greater amounts (up to 11 mg/l), and had 14 °C higher thermal tolerance than the parental protein. Critically, the stabilized designs retained binding to naturally acquired human mAbs with nanomolar affinities, suggesting that the immunologically competent surfaces were retained as was confirmed by crystallographic analyses. Using longitudinal observational cohorts from malaria endemic regions of Thailand, Brazil, and the Solomon Islands, we show that antibody responses against the designs are highly correlated with those against the parental protein and can classify individuals as recently infected with P. vivax. This efficient computational stability design methodology can be used to enhance the biophysical properties of other recalcitrant proteins for use as diagnostics or vaccine immunogens.

Thermostability Improvement of the Chitinase from Bacillus circulans for Efficient Chitin Oligosaccharide Production via Computational Design

The chitinase A1 from Bacillus circulans WL-12 (BcChiA1) exhibits promising potential for producing chitin oligosaccharides (CHOs), while its application is limited by its poor thermal stability. In this study, a set of thermostable variants were obtained by modifying BcChiA1 using a comprehensive strategy based on a computer-aided design. A combination of five beneficial single-point mutations (S67G/K177R/A220V/N257Y/N271E) to BcChiA1 generated a markedly improved variant, Mu5. Mu5 exhibited a half-life of 295 min at 60 °C, which was 59 times higher than that of BcChiA1. Furthermore, Mu5 was reused for chitin conversion, releasing 86.14 ± 3.73 mM of CHOs after five reaction cycles. Molecular dynamics simulation and structural analysis revealed that these enhancements were driven by increased structural rigidity and compactness, resulting in a protein conformation that was less prone to thermal denaturation. This combined approach through computational design yielded a thermostable BcChiA1 variant, potentially facilitating CHOs production in economical way.

Improvement in the Thermal Stability of IsMHETase by Sequence and Structure-Guided Calculation

In the degradation of poly(ethylene terephthalate) (PET), mono(2-hydroxyethyl) terephthalate (MHET) hydrolase (IsMHETase) plays a crucial role in the complete degradation of PET. Although IsMHETase was discovered concurrently with IsPETase, its structural and functional properties are not well understood. To enhance the thermal stability of IsMHETase, we selected six homologous proteins that share the closest evolutionary relationship for structure-based protein rational design, all exhibiting over 60% amino acid sequence identity with IsMHETase. Using FireProt, PROSS, and Consensus analysis, we identified the key mutation sites of IsMHETase. Sequence and structural analyses indicate that, among these seven proteins, all amino acids within 5 Å of the substrate-binding site are identical, with the exception of Ser131 and Phe415. Additionally, the amino acids within a 4 Å range of the catalytic triad are nearly identical. Through integrated free energy calculations, phylogenetic tree analysis, sequence analysis, and conservation analysis, we have identified a variant with four key mutations (termed IsMHETase-M1: N156G, T159V, E110A, A493P) that exhibits improved thermal stability. The selection of mutations during the protein modification process often requires considerable time. Our predictions have established a foundation for the rational design of IsMHETase and its homologous proteins.

Codeine 3-O-demethylase catalyzed biotransformation of morphinan alkaloids in Escherichia coli: site directed mutagenesis of terminal residues improves enzyme expression, stability and biotransformation yield

The cultivation of opium poppy is the only commercially viable source of most morphinan alkaloids. Bioproduction of morphinan alkaloids in recombinant whole-cell systems provides a promising alternate source of these valuable compounds. The enzyme codeine 3-O-demethylase can transform morphinan alkaloids by O-demethylation and has been applied in single step biotransformation reactions or as part of larger biosynthetic cascade, however, the productivity for these reactions remains low and suboptimal enzyme properties could be improved. This mutagenesis study targeted non-conserved N-and C-terminal residues, which were replaced with the equivalent residues from enzyme thebaine 6-O-demethylase. Whole cell biotransformation performance was significantly improved in Escherichia coli expressing codeine 3-O-demethylase mutants, with a ~ 2.8-fold increase in the production of oripavine from thebaine and ~ 1.3-fold increase in the production of morphine from codeine. Statistical analysis of biotransformation yield, enzyme expression and stability, predicted using changes in Gibbs free energy (ΔΔG) with deep-learning-based model DDmut, suggested that altered enzyme stability and/or expression of soluble protein may contribute to the observed improvements in biotransformation. This approach could be beneficial for screening future codeine 3-O-demethylase mutations and for other enzymes.

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Notably, both technologies have the potential to impact drug discovery as any therapeutic protein target can now be modelled, allowing the de novo design of peptide binders and the identification of small molecule ligands through in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design that are enabled by this transformative shift in protein science.

Increased thermal stability and catalytic efficiency of 3-ketosteroid Δ1-dehydrogenase5 from Arthrobacter simplex significantly reduces enzyme dosage in prednisone acetate biosynthesis

The 3-ketosteroid-Δ1-dehydrogenase5 (KsdD5) from Arthrobacter simplex converts cortisone acetate to prednisone acetate, an important step in steroid catabolism. To achieve sustainable and efficient enzyme production, we employed computer-aided screening, structural analysis, and combinatorial experiments to identify engineered KsdD5 variants (M1 and M3) with dual advantages of stability and active sites. M1 had a 8.2-fold longer half-life (19.6 h at 30 °C) than KsdD5-WT, an 11.8 °C higher half-inactivation temperature (T5015min), and a 10.6 °C higher melting temperature (Tm). M3 had 3.82-fold higher catalytic activity than WT, a 3.9-fold longer half-life at 30 °C, and higher T5015min and Tm by 14 °C and 6.9 °C, respectively. Furthermore, kinetic and microscale thermophoresis analyses revealed M3 exhibited higher catalytic efficiency due to its larger enzymatic channel. Molecular dynamics simulations showed M1 promoted tighter secondary structure packing, reduced residue flexibility, and increased hydrogen bond formation, ensuring enzyme stability and activity at elevated temperatures. Under industrial conditions, M1 converted >96 % cortisone acetate within 12 h at 30 °C with a 60 g·L-1 substrate dosage and 6 g·L-1 cell mass, whereas the M3 conversion rate was 95 %. This study demonstrates a robust strategy for developing efficient enzyme mutants, facilitating sustainable industrial production of prednisone acetate with a minimal enzyme dosage.

Enzymatic Routes to Designer Hemicelluloses for Use in Biobased Materials

Various enzymes can be used to modify the structure of hemicelluloses directly in vivo or following extraction from biomass sources, such as wood and agricultural residues. Generally, these enzymes can contribute to designer hemicelluloses through four main strategies: (1) enzymatic hydrolysis such as selective removal of side groups by glycoside hydrolases (GH) and carbohydrate esterases (CE), (2) enzymatic cross-linking, for instance, the selective addition of side groups by glycosyltransferases (GT) with activated sugars, (3) enzymatic polymerization by glycosynthases (GS) with activated glycosyl donors or transglycosylation, and (4) enzymatic functionalization, particularly via oxidation by carbohydrate oxidoreductases and via amination by amine transaminases. Thus, this Perspective will first highlight enzymes that play a role in regulating the degree of polymerization and side group composition of hemicelluloses, and subsequently, it will explore enzymes that enhance cross-linking capabilities and incorporate novel chemical functionalities into saccharide structures. These enzymatic routes offer a precise way to tailor the properties of hemicelluloses for specific applications in biobased materials, contributing to the development of renewable alternatives to conventional materials derived from fossil fuels.

Improving the catalytic activity and thermostability of Aspergillus niger xylanase through computational design

Xylanase plays the most important role in catalyzing xylan to xylose moieties. GH11 xylanases have been widely used in many fields, but most GH11 xylanases are mesophilic enzymes. To improve the catalytic activity and thermostability of Aspergillus niger xylanase (Xyn-WT), we predicted potential key mutation sites of Xyn-WT through multiple computer-aided enzyme engineering strategies. We introduce a simple and economical Ni affinity chromatography purification method to obtain high-purity xylanase and its mutants. Ten mutants (Xyn-A, Xyn-B, Xyn-C, E45T, Q93R, E45T/Q93R, A161P, Xyn-D, Xyn-E, Xyn-F) were identified. Among the ten mutants, four (Xyn-A, Xyn-C, A161P, Xyn-F) presented improved thermal stability and activity, with Xyn-F(A161P/E45T/Q93R) being the most thermally stable and active. Compared with Xyn-WT, after heat treatment at 55 °C and 60 °C for 10 min, the remaining enzyme activity of Xyn-F was 12 and 6 times greater than that of Xyn-WT, respectively, and Xyn-F was approximately 1.5 times greater than Xyn-WT when not heat treated. The pH adaptation of Xyn-F was also significantly enhanced. In summary, an improved catalytic activity and thermostability of the design variant Xyn-F has been reported.

Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase

Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent years, machine learning (ML)-guided protein engineering has become an attractive tool because of its ability to comprehensively explore the sequence space of enzymes and discover superior mutants. This study employed ML to perform combinatorial mutation design on the pectin lyase PMGL-Ba from Bacillus licheniformis, aiming to improve its thermostability. First, 18 single-point mutants with enhanced thermostability were identified through semi-rational design. Subsequently, the initial library containing a small number of low-order mutants was utilized to construct an ML model to explore the combinatorial sequence space (theoretically 196,608 mutants) of single-point mutants. The results showed that the ML-predicted second library was successfully enriched with highly thermostable combinatorial mutants. After one iteration of learning, the best-performing combinatorial mutant in the third library, P36, showed a 67-fold and 39-fold increase in half-life at 75 °C and 80 °C, respectively, as well as a 2.1-fold increase in activity. Structural analysis and molecular dynamics simulations provided insights into the improved performance of the engineered enzyme.

Structures of the Varicella Zoster Virus Glycoprotein E and Epitope Mapping of Vaccine-Elicited Antibodies

Background: Varicella zoster virus (VZV) is the causative agent for chickenpox and herpes zoster (HZ, shingles). HZ is a debilitating disease affecting elderly and immunocompromised populations. Glycoprotein E (gE) is indispensable for viral replication and cell-to-cell spread and is the primary target for anti-VZV antibodies. Importantly, gE is the sole antigen in Shingrix, a highly efficacious, AS01B-adjuvanted vaccine approved in multiple countries for the prevention of HZ, yet the three-dimensional (3D) structure of gE remains elusive. Objectives: We sought to determine the structure of VZV gE and to understand in detail its interactions with neutralizing antibodies. Methods: We used X-ray crystallography and cryo-electron microscopy to elucidate structures of gE bound by recombinant Fabs of antibodies previously elicited through vaccination with Zostavax, a live, attenuated vaccine. Results: The 3D structures resolve distinct central and C-terminal antigenic domains, presenting an array of diverse conformational epitopes. The central domain has two beta-sheets and two alpha helices, including an IgG-like fold. The C-terminal domain exhibits 3 beta-sheets and an Ig-like fold and high structural similarity to HSV1 gE. Conclusions: gE from VZV-infected cells elicits a human antibody response with a preference for the gI binding domain of gE. These results yield insights to VZV gE structure and immunogenicity, provide a framework for future studies, and may guide the design of additional herpesvirus vaccine antigens. Teaser: Structures of varicella zoster virus glycoprotein E reveal distinct antigenic domains and define epitopes for vaccine-elicited human antibodies.

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

One-shot design elevates functional expression levels of a voltage-gated potassium channel

Membrane proteins play critical physiological roles as receptors, channels, pumps, and transporters. Despite their importance, however, low expression levels often hamper the experimental characterization of membrane proteins. We present an automated and web-accessible design algorithm called mPROSS (https://mPROSS.weizmann.ac.il), which uses phylogenetic analysis and an atomistic potential, including an empirical lipophilicity scale, to improve native-state energy. As a stringent test, we apply mPROSS to the Kv1.2-Kv2.1 paddle chimera voltage-gated potassium channel. Four designs, encoding 9-26 mutations relative to the parental channel, were functional and maintained potassium-selective permeation and voltage dependence in Xenopus oocytes with up to 14-fold increase in whole-cell current densities. Additionally, single-channel recordings reveal no significant change in the channel-opening probability nor in unitary conductance, indicating that functional expression levels increase without impacting the activity profile of individual channels. Our results suggest that the expression levels of other dynamic channels and receptors may be enhanced through one-shot design calculations.

GMMA Can Stabilize Proteins Across Different Functional Constraints

Stabilizing proteins without otherwise hampering their function is a central task in protein engineering and design. PYR1 is a plant hormone receptor that has been engineered to bind diverse small molecule ligands. We sought a set of generalized mutations that would provide stability without affecting functionality for PYR1 variants with diverse ligand-binding capabilities. To do this we used a global multi-mutant analysis (GMMA) approach, which can identify substitutions that have stabilizing effects and do not lower function. GMMA has the added benefit of finding substitutions that are stabilizing in different sequence contexts and we hypothesized that applying GMMA to PYR1 with different functionalities would identify this set of generalized mutations. Indeed, conducting FACS and deep sequencing of libraries for PYR1 variants with two different functionalities and applying a GMMA analysis identified 5 substitutions that, when inserted into four PYR1 variants that each bind a unique ligand, provided an increase of 2-6 °C in thermal inactivation temperature and no decrease in functionality.

Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint

Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.

Computer-aided design to enhance the stability of aldo-keto reductase KdAKR

The aldo-keto reductase (AKR) KdAKR from Kluyvermyces dobzhanskii can reduce t-butyl 6-chloro-(5S)-hydroxy-3-oxohexanoate ((5S)-CHOH) to t-butyl 6-chloro-(3R,5S)-dihydroxyhexanoate ((3R,5S)-CDHH), which is the key chiral intermediate of rosuvastatin. Herein, a computer-aided design that combined the use of PROSS platform and consensus design was employed to improve the stability of a previously constructed mutant KdAKRM6 . Experimental verification revealed that S196C, T232A, V264I and V45L produced improved thermostability and activity. The "best" mutant KdAKRM10 (KdAKRM6 -S196C/T232A/V264I/V45L) was constructed by combining the four beneficial mutations, which displayed enhanced thermostability. Its T50 15 and Tm values were increased by 10.2 and 10.0°C, respectively, and half-life (t1/2 ) at 40°C was increased by 17.6 h. Additionally, KdAKRM10 demonstrated improved resistance to organic solvents compared to that of KdAKRM6 . Structural analysis revealed that the increased number of hydrogen bonds and stabilized hydrophobic core contributed to the rigidity of KdAKRM10 , thus improving its stability. The results validated the feasibility of the computer-aided design strategy in improving the stability of AKRs.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2 ) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Computational redesign of taxane-10β-hydroxylase for de novo biosynthesis of a key paclitaxel intermediate

Paclitaxel (Taxol®) is the most popular anticancer diterpenoid predominantly present in Taxus. The core skeleton of paclitaxel is highly modified, but researches on the cytochrome P450s involved in post-modification process remain exceedingly limited. Herein, the taxane-10β-hydroxylase (T10βH) from Taxus cuspidata, which is the third post-modification enzyme that catalyzes the conversion of taxadiene-5α-yl-acetate (T5OAc) to taxadiene-5α-yl-acetoxy-10β-ol (T10OH), was investigated in Escherichia coli by combining computation-assisted protein engineering and metabolic engineering. The variant of T10βH, M3 (I75F/L226K/S345V), exhibited a remarkable 9.5-fold increase in protein expression, accompanied by respective 1.3-fold and 2.1-fold improvements in turnover frequency (TOF) and total turnover number (TTN). Upon integration into the engineered strain, the variant M3 resulted in a substantial enhancement in T10OH production from 0.97 to 2.23 mg/L. Ultimately, the titer of T10OH reached 3.89 mg/L by fed-batch culture in a 5-L bioreactor, representing the highest level reported so far for the microbial de novo synthesis of this key paclitaxel intermediate. This study can serve as a valuable reference for further investigation of other P450s associated with the artificial biosynthesis of paclitaxel and other terpenoids. KEY POINTS: • The T10βH from T. cuspidata was expressed and engineered in E. coli unprecedentedly. • The expression and activity of T10βH were improved through protein engineering. • De novo biosynthesis of T10OH was achieved in E. coli with a titer of 3.89 mg/L.

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.

FireProt 2.0: web-based platform for the fully automated design of thermostable proteins

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

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.

Designed active-site library reveals thousands of functional GFP variants

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.

Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.

Computational design and molecular dynamics simulations suggest the mode of substrate binding in ceramide synthases

Until now, membrane-protein stabilization has relied on iterations of mutations and screening. We now validate a one-step algorithm, mPROSS, for stabilizing membrane proteins directly from an AlphaFold2 model structure. Applied to the lipid-generating enzyme, ceramide synthase, 37 designed mutations lead to a more stable form of human CerS2. Together with molecular dynamics simulations, we propose a pathway by which substrates might be delivered to the ceramide synthases.

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies

Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolutionary-based and physics-inspired approaches. The former infer characteristic sequence features shared by sets of evolutionary-related proteins, such as conserved or coevolving positions, and recombine them to generate candidates with similar structure and function. The latter approaches estimate key biochemical properties, such as structure free energy, conformational entropy, or binding affinities using machine learning surrogates, and optimize them to yield improved designs. Here, we review recent progress along both tracks, discuss their strengths and weaknesses, and highlight opportunities for synergistic approaches.

Stairway to Stereoisomers: Engineering Short- and Medium-Chain Ketoreductases To Produce Chiral Alcohols

The short- and medium-chain dehydrogenase/reductase superfamilies are responsible for most chiral alcohol production in laboratories and industries. In nature, they participate in diverse roles such as detoxification, housekeeping, secondary metabolite production, and catalysis of several chemicals with commercial and environmental significance. As a result, they are used in industries to create biopolymers, active pharmaceutical intermediates (APIs), and are also used as components of modular enzymes like polyketide synthases for fabricating bioactive molecules. Consequently, random, semi-rational and rational engineering have helped transform these enzymes into product-oriented efficient catalysts. The rise of newer synthetic chemicals and their enantiopure counterparts has proved challenging, and engineering them has been the subject of numerous studies. However, they are frequently limited to the synthesis of a single chiral alcohol. The study attempts to defragment and describe hotspots of engineering short- and medium-chain dehydrogenases/reductases for the production of chiral synthons.

Recent and future perspectives on engineering interferons and other cytokines as therapeutics

As crucial mediators and regulators of our immune system, cytokines are involved in a broad range of biological processes and are implicated in various disease pathologies. The field of cytokine therapeutics has gained much momentum from the maturation of conventional protein engineering methodologies such as structure-based designs and/or directed evolution, which is further aided by the advent of in silico protein designs and characterization. Just within the past 5 years, there has been an explosion of proof-of-concept, preclinical, and clinical studies that utilize an armory of protein engineering methods to develop cytokine-based drugs. Here, we highlight the key engineering strategies undertaken by recent studies that aim to improve the pharmacodynamic and pharmacokinetic profile of interferons and other cytokines as therapeutics.

Biocatalytic Approaches to Building Blocks for Enzymatic and Chemical Glycan Synthesis

While the field of biocatalysis has bloomed over the past 20-30 years, advances in the understanding and improvement of carbohydrate-active enzymes, in particular, the sugar nucleotides involved in glycan building block biosynthesis, have progressed relatively more slowly. This perspective highlights the need for further insight into substrate promiscuity and the use of biocatalysis fundamentals (rational design, directed evolution, immobilization) to expand substrate scopes toward such carbohydrate building block syntheses and/or to improve enzyme stability, kinetics, or turnover. Further, it explores the growing premise of using biocatalysis to provide simple, cost-effective access to stereochemically defined carbohydrate materials, which can undergo late-stage chemical functionalization or automated glycan synthesis/polymerization.

Designed High-Redox Potential Laccases Exhibit High Functional Diversity

White-rot fungi secrete an impressive repertoire of high-redox potential laccases (HRPLs) and peroxidases for efficient oxidation and utilization of lignin. Laccases are attractive enzymes for the chemical industry due to their broad substrate range and low environmental impact. Since expression of functional recombinant HRPLs is challenging, however, iterative-directed evolution protocols have been applied to improve their expression, activity, and stability. We implement a rational, stabilize-and-diversify strategy to two HRPLs that we could not functionally express. First, we use the PROSS stability-design algorithm to allow functional expression in yeast. Second, we use the stabilized enzymes as starting points for FuncLib active-site design to improve their activity and substrate diversity. Four of the FuncLib-designed HRPLs and their PROSS progenitor exhibit substantial diversity in reactivity profiles against high-redox potential substrates, including lignin monomers. Combinations of 3-4 subtle mutations that change the polarity, solvation, and sterics of the substrate-oxidation site result in orders of magnitude changes in reactivity profiles. These stable and versatile HRPLs are a step toward generating an effective lignin-degrading consortium of enzymes that can be secreted from yeast. The stabilize-and-diversify strategy can be applied to other challenging enzyme families to study and expand the utility of natural enzymes.

Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations

Many proteins have low thermodynamic stability, which can lead to low expression yields and limit functionality in research, industrial and clinical settings. This article introduces two, web-based tools that use the high-resolution structure of a protein along with the Rosetta molecular modeling program to predict stabilizing mutations. The protocols were recently applied to three genetically and structurally distinct proteins and successfully predicted mutations that improved thermal stability and/or protein yield. In all three cases, combining the stabilizing mutations raised the protein unfolding temperatures by more than 20°C. The first protocol evaluates point mutations and can generate a site saturation mutagenesis heatmap. The second identifies mutation clusters around user-defined positions. Both applications only require a protein structure and are particularly valuable when a deep multiple sequence alignment is not available. These tools were created to simplify protein engineering and enable research that would otherwise be infeasible due to poor expression and stability of the native molecule.

Glycine Substitution of Residues with Unfavored Dihedral Angles Improves Protein Thermostability

Single mutations that can substantially enhance stability are highly desirable for protein engineering. However, it is generally rare for this kind of mutant to emerge from directed evolution experiments. This study used computational approaches to identify hotspots in a diacylglycerol-specific lipase for mutagenesis with functional hotspot and sequence consensus strategies, followed by ∆∆G calculations for all possible mutations using the Rosetta ddg_monomer protocol. Single mutants with significant ∆∆G changes (≤−2.5 kcal/mol) were selected for expression and characterization. Three out of seven tested mutants showed a significantly enhanced thermostability, with Q282W and A292G in the catalytic pocket and D245G located on the opposite surface of the protein. Remarkably, A292G increased the T5015 (the temperature at which 50% of the enzyme activity was lost after a 15 min of incubation) by ~7 °C, concomitant with a twofold increase in enzymatic activity at the optimal reaction temperature. Structural analysis showed that both A292 and D245 adopted unfavored dihedral angles in the wild-type (WT) enzyme. Substitution of them by glycine might release a steric strain to increase the stability. In sum, substitution by glycine might be a promising strategy to improve protein thermostability.

Stable and Functionally Diverse Versatile Peroxidases Designed Directly from Sequences

White-rot fungi secrete a repertoire of high-redox potential oxidoreductases to efficiently decompose lignin. Of these enzymes, versatile peroxidases (VPs) are the most promiscuous biocatalysts. VPs are attractive enzymes for research and industrial use but their recombinant production is extremely challenging. To date, only a single VP has been structurally characterized and optimized for recombinant functional expression, stability, and activity. Computational enzyme optimization methods can be applied to many enzymes in parallel but they require accurate structures. Here, we demonstrate that model structures computed by deep-learning-based ab initio structure prediction methods are reliable starting points for one-shot PROSS stability-design calculations. Four designed VPs encoding as many as 43 mutations relative to the wildtype enzymes are functionally expressed in yeast, whereas their wildtype parents are not. Three of these designs exhibit substantial and useful diversity in their reactivity profiles and tolerance to environmental conditions. The reliability of the new generation of structure predictors and design methods increases the scale and scope of computational enzyme optimization, enabling efficient discovery and exploitation of the functional diversity in natural enzyme families directly from genomic databases.

CDR1 Composition Can Affect Nanobody Recombinant Expression Yields

The isolation of nanobodies from pre-immune libraries by means of biopanning is a straightforward process. Nevertheless, the recovered candidates often require optimization to improve some of their biophysical characteristics. In principle, CDRs are not mutated because they are likely to be part of the antibody paratope, but in this work, we describe a mutagenesis strategy that specifically addresses CDR1. Its sequence was identified as an instability hot spot by the PROSS program, and the available structural information indicated that four CDR1 residues bound directly to the antigen. We therefore modified the loop flexibility with the addition of an extra glycine rather than by mutating single amino acids. This approach significantly increased the nanobody yields but traded-off with moderate affinity loss. Accurate modeling coupled with atomistic molecular dynamics simulations enabled the modifications induced by the glycine insertion and the rationale behind the engineering design to be described in detail.

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.