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

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.Communicated by Ramaswamy H. Sarma.

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.