D3DistalMutation: a Database to Explore the Effect of Distal Mutations on Enzyme Activity

Engineering Alcohol Dehydrogenase for Efficient Catalytic Synthesis of Ethyl (R)-4-Chloro-3-hydroxybutyrate

Ethyl (R)-4-chloro-3-hydroxybutyrate [(R)-CHBE] is an intermediate with high value in medicine and pesticide applications. Alcohol dehydrogenase serves as an excellent biocatalyst during the synthesis of (R)-CHBE. However, the lack of effective engineering methods limits its wider application. In this study, the sequence-modeling-docking-principle (SMDP) method was used to screen enzymes with catalytic activity. Three protein modification strategies were established for the active center, substrate channel, and distal hotspot to enhance the catalytic efficiency of alcohol dehydrogenase LCRIII. Substrate batch replenishment was used to alleviate substrate inhibition. Subsequently, optimal mutant M3 (W151F-S167A-F215Y) was successfully obtained with a specific enzyme activity of 23.00 U/mg and kcat/Km of 11.22 (mM-1·min-1), which were 4.55- and 3.98-fold higher than those of the wild type, respectively. (R)-CHBE was prepared using M3 and GDH at 298.21 g/L (>99% e.e.). This study provides a promising approach for the protein engineering modification of alcohol dehydrogenase and industrial-scale production of (R)-CHBE.

Inside-Out Rational Design of Ornithine Cyclodeaminase RlOCD from Rhizobium leguminosarum by a Multiregion Synergy Strategy for Efficient Synthesis of l-Pipecolic Acid

Lysine cyclodeaminase (LCD)-mediated synthesis of l-pipecolic acid (l-PA) from l-lysine (l-Lys) is a promising approach. However, only one LCD has been reported, and its inadequate activity limits industrial applications. To address this problem, a substrate analogue-guided enzyme mining strategy was employed. A novel ornithine cyclodeaminase (OCD) from Rhizobium leguminosarum (RlOCD) was identified in combination with directed macrogenomic approaches. RlOCD displayed a conversion rate of 28% at a substrate loading as high as 1000 mM. A multiregion synergy strategy consisting of pocket reshaping, dynamical cross-correlation matrix-guided coevolutionary design, and surface modification was used to design RlOCD from the inside-out. A quadruple mutant (V93C/L119C/I170T/R90L) designated Mu4 with significantly increased activity was obtained, which showed a 28.46-fold increase in the catalytic efficiency. The conversion of Mu4 was 91% within 10 h at 1000 mM (146.19 g L-1) loading. The space-time yield of 282.1 g L-1 d-1 is the highest level ever reported. Molecular dynamics simulations and interaction analyses revealed that efficient pocket expansion and unique conformational rearrangements increased the affinity for the substrate, resulting in a more catalytically active conformation. This study expands the toolbox for the production of l-PA and demonstrates the effectiveness and potential of Mu4 for its production.

Enhanced Production, Enzymatic Activity, and Thermostability of an α-Amylase from Bacillus amyloliquefaciens in Lactococcus lactis

A novel α-amylase gene (BAA) from Bacillus amyloliquefaciens was cloned into Lactococcus lactis, designing two recombinant α-amylases to facilitate extracellular secretion. Following optimizing the expression conditions, the highest yield of BAA (88.12 mmol/L) was achieved upon 36 h induction and 5 ng/mL nisin concentration. Determining the enzymatic properties of BAA revealed its poor stability and activity at high temperatures, hindering its widespread application. Therefore, we used computer-aided design to generate a mutant, S275L, which exhibited significantly improved activity and thermostability: an 18.7% increase in enzymatic activity (3767.38 U/mg), a 10 °C increase in optimal temperature, and a 49.2% improvement in stability at 60 °C. Molecular dynamics simulations and force analysis confirmed these enhancements. Finally, the mutant S275L's potential was further analyzed for starch hydrolysis on poultry feed. Therefore, the mutant S275L holds promising as an enzyme agent for enhanced feed digestibility and quality.

A comparative study of two α-L-rhamnosidases with high sequence identity

The GH78 α-L-rhamnosidase from Aspergillus tubingensis (AT-Rha) was proved to be a new clade of Aspergillus α-L-rhamnosidases in the previous study. A putative α-L-rhamnosidase from A. kawachii IFO 4308 (AK-Rha) has 92 % identity in amino acid sequence with AT-Rha. In this study, AK-Rha was expressed in P. pastoris and characterized. Similar to AT-rRha, the recombinant AK-Rha (AK-rRha) showed a narrow substrate specificity to naringin. Interestingly, the enzyme activity of AK-rRha was 0.816 U/mg toward naringin, significantly lower than 125.142 U/mg of AT-rRha. Their large differences in catalytic efficiency was mainly due to their differences in kcat values between AK-rRha (0.67 s-1) and AT-rRha (4.89 × 104 s-1). The molecular dynamics simulation exhibited that the overall conformation of AK-Rha was rigid and that of AT-Rha was flexible; the Loop Y-L located above the catalytic domain formed different steric hindrances to naringin, and interacted with the flavonoid matrices at different strengths. The polar solvation energy analysis implied that the glycosidic bond was more easily hydrolysed in AT-Rha. The comparative study verified that the main feature of AK-Rha and AT-Rha represented Aspergillus α-L-rhamnosidase was the narrow substrate specificity toward naringin, and provided an insight of the relationships between their catalytic abilities and structures.

Dynamical responses predict a distal site that modulates activity in an antibiotic resistance enzyme

β-Lactamases, which hydrolyse β-lactam antibiotics, are key determinants of antibiotic resistance. Predicting the sites and effects of distal mutations in enzymes is challenging. For β-lactamases, the ability to make such predictions would contribute to understanding activity against, and development of, antibiotics and inhibitors to combat resistance. Here, using dynamical non-equilibrium molecular dynamics (D-NEMD) simulations combined with experiments, we demonstrate that intramolecular communication networks differ in three class A SulpHydryl Variant (SHV)-type β-lactamases. Differences in network architecture and correlated motions link to catalytic efficiency and β-lactam substrate spectrum. Further, the simulations identify a distal residue at position 89 in the clinically important Klebsiella pneumoniae carbapenemase 2 (KPC-2), as a participant in similar networks, suggesting that mutation at this position would modulate enzyme activity. Experimental kinetic, biophysical and structural characterisation of the naturally occurring, but previously biochemically uncharacterised, KPC-2G89D mutant with several antibiotics and inhibitors reveals significant changes in hydrolytic spectrum, specifically reducing activity towards carbapenems without effecting major structural or stability changes. These results show that D-NEMD simulations can predict distal sites where mutation affects enzyme activity. This approach could have broad application in understanding enzyme evolution, and in engineering of natural and de novo enzymes.

EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity

Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enhancement of one often leads to the reduction of the other, also called the trade-off mechanism. Although dozens of methods that predict the change of protein stability upon mutations have been developed, the prediction of the effect on activity is still in its early stage. Therefore, developing a fast and accurate method to predict the impact of the mutations on enzyme activity is helpful for enzyme design and understanding of the trade-off mechanism. Here, we introduce a novel approach, EnzyACT, a deep learning method that fuses graph technique and protein embedding to predict activity changes upon single or multiple mutations. Our model combines graph-based techniques and language models to predict the activity changes. Moreover, EnzyACT is trained on a new curated data set including both single- and multiple-point mutations. When benchmarked on multiple independent data sets, it shows uniform performance on problems affected by mutations. This work also provides insights into the impact of distant mutations within activity design, which could also be useful for predicting catalytic residues and developing improved enzyme-engineering strategies.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Unraveling the atomic mechanisms underlying glyphosate insensitivity in EPSPS: implications of distal mutations

5-enolpyruvyl shikimate-3-phosphate synthase (EPSPS), as an indispensable enzyme in the shikimate pathway, is the specific target of grasser killer glyphosate (GPJ). GPJ is a competitive inhibitor of phosphoenolpyruvate (PEP), which is the natural substrate of EPSPS. A novel Ls-EPSPS gene variant discovered from Liliaceae, named ELs-EPSPS, includes five distal mutations, E112V, D142N, T351S, D425G, and R496G, endowing high GPJ insensitivity. However, the implicit molecular mechanism of the enhanced tolerance/insensitivity of GPJ in ELs-EPSPS is not fully understood. Herein, we try to interpret the hidden molecular mechanism using computational methods. Computational results reveal the enhanced flexibility of apo EPSPS upon mutations. The enhanced affinity of the initial binding substrate shikimate-3-phosphate (S3P), and the higher probability of second ligands PEP/GPJ entering the pocket are observed in the ELs-EPSPS-S3P system. Docking and MD results further confirmed the decreased GPJ-induced EPSPS inhibition upon mutations. And, the alterations of K98 and R179 side-chain orientations upon mutations are detrimental to GPJ binding at the active site. Additionally, the oscillation of side chain K98, in charge of PEP location, improves the proximity effect for substrates in the dual-substrate systems upon mutations. Our results clarify that the enhanced GPJ tolerance of EPSPS is achieved from decreased competitive inhibition of GPJ at the atomic perspective, and this finding further contributes to the cultivation of EPSPS genes with higher GPJ tolerance/insensitivity and a mighty renovation for developing glyphosate-resistant crops.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

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.

Removal of substrate inhibition of Acinetobacter baumannii xanthine oxidase by point mutation at Gln-201 enables efficient reduction of purine content in fish sauce

Xanthine oxidase is an oxidase that has a molybdopterin structure with substrate inhibition. Here, we show that a single point mutation (Q201) in the Acinetobacter baumannii xanthine oxidase (AbXOD) obtained mutant Q201E (k cat =799.44 s-1, no inhibition) with high enzyme activity and decrease of substrate inhibition in 5 mmol/L high substrate model, and which cause two loops structure change at active center, characterized by complete loss of substrate inhibition without reduction of enzymatic activity. Molecular docking results showed that the change of flexible loop increased the affinity between substrate and enzyme, and the formation of a π-π bond and two hydrogen bonds made the substrate more stable in the active center. Ultimately, Q201E can still maintain better enzyme activity under high purine content (an approximately 7-fold improvement over the wild-type), indicating a broader application prospect in the manufacture of low-purine food.

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

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

Association Analysis between Genetic Variants of elovl5a and elovl5b and Poly-Unsaturated Fatty Acids in Common Carp (Cyprinus carpio)

Simple Summary

PUFAs have an essential impact on human health, but their availability constitutes a critical bottleneck in food production. Although fish is the traditional source of PUFAs, it is limited by the stagnation of fisheries. Many studies aim to increase the PUFA products of fish. Genetic markers are efficient in aquaculture breeding. Fatty acid desaturase 2 (fads2) and elongase 5 (elovl5) are the rate-limiting enzymes in the synthesis of PUFAs. The allo-tetraploid common carp is able to biosynthesize endogenous PUFAs. However, selective breeding common carp with high PUFA contents was hindered due to a lack of effective molecular markers. For future breeding common carp capable of producing endogenous PUFAs more effectively, we previously identified the polymorphisms in the coding regions of two duplicated fads2, fads2a and fads2b. However, the polymorphisms in the duplicated elovl5, elovl5a and elovl5b, were not detected. This study screened the genetic variants in the coding regions of elovl5a and elovl5b. Moreover, the joint effects of multiple coding SNPs in fads2b and elovl5b, two major genes regulating the PUFA biosynthesis, were evidenced with the increased explained percentages of the PUFA contents. These polymorphisms in these two genes were used to evaluate the breeding values of PUFAs. These SNPs would be potential markers for future selection to improve the PUFA contents in common carp.

Abstract

The allo-tetraploid common carp, one widely cultured food fish, is able to produce poly-unsaturated fatty acids (PUFAs). The genetic markers on the PUFA contents for breeding was limited. The polymorphisms in elovl5a and elovl5b, the rate-limiting enzymes in the PUFA biosynthesis, have not been investigated yet. Herein, we identified one coding SNP (cSNP) in elovl5a associated with the content of one PUFA and two cSNPs in elovl5b with the contents of eight PUFAs. The heterozygous genotypes in these three loci were associated with higher contents than the homozygotes. Together with previously identified two associated cSNPs in fads2b, we found the joint effect of these four cSNPs in fads2b and elovl5b on the PUFA contents with the increased explained percentages of PUFA contents. The genotype combinations of more heterozygotes were associated with higher PUFA contents than the other combinations. Using ten genomic selection programs with all cSNPs in fads2b and elovl5b, we obtained the high and positive correlations between the phenotypes and the estimated breeding values of eight PUFAs. These results suggested that elovl5b might be the major gene corresponding to common carp PUFA contents compared with elovl5a. The cSNP combinations in fads2b and elovl5b and the optimal genomic selection program will be used in the future selection breeding to improve the PUFA contents of common carp.