Exploiting enzyme evolution for computational protein design

Investigating the role of polar amino acids driven by evolution in the active site architecture of GH11 xylanase

Enzymes, as vital biomacromolecules, have developed significant plasticity, enabling adaptation to diverse environments and catalysis of numerous biochemical reactions. However, enzyme evolution is constrained by mutational limitations, as amino acid substitutions often impair structure or function, hampering optimization endeavors. To address this, we integrated structural bioinformatics with site-directed mutagenesis to investigate the evolutionary trends of four GH11 family xylanases (XynA, XynB, XynD, and XynE) from Aspergillus niger An76. Our analysis revealed that conserved residues in active sites are unevenly distributed, with highly conserved residues critical for catalysis and relatively conserved residues offering mutation potential. The mutation of Asp/Asn near catalytic residues at -1 subsite could not only alter the catalytic activity, but also shift the optimal pH by one unit. Additional mutants, including XynB-A143P, XynA-F142W, XynD-E20T, and XynD-E192Q, increased enzymatic activity by 17%, 46%, 82%, and 26%, respectively. More importantly, ancestral sequence reconstruction highlighted the importance of Arg at the -1 subsite of GH11 xylanases, and combinatorial mutation based on Y160R reinstated the pseudo-enzyme XynE's activity to 417.6 IU/mg. This study demonstrates the efficacy of evolutionary-informed mutagenesis for precise enzyme design, providing insights for optimizing GH11 family and other enzymes.

Multisite synergistic evolution of oleate hydratase via DCCM and the orthogonal location of distal sites

The evolution of enzymes through multisite combinations is often constrained by the dynamic correlations among residues, resulting in the challenge of achieving additive effects when combining single-site positive mutants. Nevertheless, the incorporation of distal-site residue mutants offers a promising avenue to unlock synergistic interactions, particularly in enzymes with channels that mediate substrate and product transport. While combinatorial mutagenesis typically requires exhaustive screening of extensive mutant libraries to identify superior variants. In this study, a multisite combination strategy using dynamic cross-correlation matrices (DCCM) iterative analysis, coupled with location orthogonal filtration (DCCM/OL) was established based on single-site mutants enhancing enzyme performance by engineering distal residues using the "Structure, SCANEER, and Sequence (3S)" approach. Thirteen beneficial single mutants of oleate hydratase from Staphylococcus aureus (SaOhy), which catalyzes the hydration of linoleic acid, were targeted using the "3S" approach. By employing the DCCM/OL strategy, the number of candidates in the combination mutation library for experimental screening were reduced from 60 to 5. And a triple-site mutant, SaOhy_L151V/I411L/V135A, was ultimately identified, which increased the catalytic efficiencies with linoleic acid and oleic acid by a 4- and 2.3-fold, respectively. The philosophy of multisite mutation based on distal residues using the DCCM/OL strategy effectively achieves an amplification of enzymatic activity with distinct substrates simultaneously. This study offers a promising strategy for the construction of a mutant smart library and multisite combinations to efficiently increase enzymatic performance.

Catalytic Mechanism of Gut Benzyl Ether Reductase for Efficient Bioconversion of Furofuran Lignans into Enterolignan Precursors

Enterolignan is a vital anticancer compound, and benzyl ether reductase (BER) plays a key role in its biosynthesis by facilitating lignan biotransformation. Using virtual alanine scanning and site-directed mutagenesis, we identified critical residues influencing BER activity in DSM 2243T. Mutations Y214A, K383A, and K395A led to a near-complete loss of enzymatic activity, highlighting their essential roles. Conversely, the E332Y, G393V, and L515A variants demonstrated over a 2-fold increase in catalytic efficiency compared to the wild-type BER. Molecular docking and dynamics simulations revealed that Y214 and K383 are involved in substrate recognition and binding, while K395, functioning as a catalytic base, forms a critical η3 loop (residues 389-396) that regulates the catalytic pocket's size and spatial resistance. In the wild-type BER, this loop moves inward by 5 Å upon substrate binding. However, in the E332Y, G393V, and L515A mutants, the loop shifts outward by 4.8, 6.1, and 5.6 Å, respectively, likely enhancing substrate accommodation and catalytic efficiency. This η3 loop movement also appears to influence hydride transfer from cofactors to pinoresinol, which is a crucial step in the catalytic mechanism. These findings offer valuable insights into BER's catalytic mechanism and lay a foundation for enzyme engineering to optimize enterolignan biomanufacturing.

Ancestral Sequence Reconstruction for Designing Biocatalysts and Investigating their Functional Mechanisms

Biocatalysis has emerged as a green approach for efficient and sustainable production in various industries. In recent decades, numerous advancements in computational and predictive approaches, including ancestral sequence reconstruction (ASR) have sparked a new wave for protein engineers to improve and expand biocatalyst capabilities. ASR is an evolution-based strategy that uses phylogenetic relationships among homologous extant sequences to probabilistically infer the most likely ancestral sequences. It has proven to be a powerful tool with applications ranging from creating highly stable enzymes for direct applications to preparing moderately active robust protein scaffolds for further enzyme engineering. Intriguingly, it can also provide insights into fundamental aspects that are challenging to study with extant (current) enzymes. This Perspective discusses a practical strategy for guiding enzyme engineers on how to embrace ASR as a practical or associated protocol for protein engineering and highlights recent examples of using ASR in various applications, including increasing thermostability, expanding promiscuity, fine-tuning selectivity and function, and investigating mechanistic and evolution aspects. We believe that the use of the ASR approach will continue to contribute to the ongoing development of the biocatalysis field. We have been in a "golden era" for biocatalysis in which numerous useful enzymes have been developed through many waves of enzyme engineering via advancements in computational methodologies.

Catalytic mechanism, computational design, and crystal structure of a highly specific and efficient benzoylecgonine hydrolase

Enzyme therapy for cocaine detoxification should break down both cocaine and its primary toxic metabolite, benzoylecgonine (BZE), which is also the main form of cocaine contaminant in the environment. An ideal BZE-metabolizing enzyme (BZEase) is expected to be highly efficient and selective in BZE hydrolysis. Here, BZEase4 was engineered from bacterial cocaine esterase (CocE) by our reactant state-based enzyme design theories (RED), which has a 34,977-fold improved substrate discrimination between BZE and the neurotransmitter acetylcholine (ACh), compared with wild-type CocE. Under the physiological concentrations of BZE and ACh, the reaction velocity of BZEase4 against BZE is 2.25 × 106-fold higher than it against ACh, suggesting BZEase4 has extremely high substrate selectivity for BZE over ACh to minimize the potential cholinergic side-effects. This study provides additional evidence supporting the further development of BZEase4 toward a promising therapeutic for cocaine overdose, a potentially effective and eco-friendly enzymatic method for BZE degradation in the environment.

Function and Evolution of the Plant MES Family of Methylesterases

Land plant evolution has been marked by numerous genetic innovations, including novel catalytic reactions. Plants produce various carboxyl methyl esters using carboxylic acids as substrates, both of which are involved in diverse biological processes. The biosynthesis of methyl esters is catalyzed by SABATH methyltransferases, and understanding of this family has broadened in recent years. Meanwhile, the enzymes catalyzing demethylation-known as methylesterases (MESs)-have received less attention. Here, we present a comprehensive review of the plant MES family, focusing on known biochemical and biological functions, and evolution in the plant kingdom. Thirty-two MES genes have been biochemically characterized, with substrates including methyl esters of plant hormones and several other specialized metabolites. One characterized member demonstrates non-esterase activity, indicating functional diversity in this family. MES genes regulate biological processes, including biotic and abiotic defense, as well as germination and root development. While MES genes are absent in green algae, they are ubiquitous among the land plants analyzed. Extant MES genes belong to three groups of deep origin, implying ancient gene duplication and functional divergence. Two of these groups have yet to have any characterized members. Much remains to be uncovered about the enzymatic functions, biological roles, and evolution of the MES family.

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

Strangers in a foreign land: 'Yeastizing' plant enzymes

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

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.

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.

Elucidating the Enzymatic Mechanism of Dihydrocoumarin Degradation: Insight into the Functional Evolution of Methyl-Parathion Hydrolase from QM/MM and MM MD Simulations

Methyl-parathion hydrolase (MPH), which evolved from dihydrocoumarin hydrolase, offers one of the most efficient enzymes for the hydrolysis of methyl-parathion. Interestingly, the substrate preference of MPH shifts from the methyl-parathion to the lactone dihydrocoumarin (DHC) after its mutation of five specific residues (R72L, L273F, L258H, T271I, and S193Δ, m5-MPH). Here, extensive QM/MM calculations and MM MD simulations have been used to delve into the structure-function relationship of MPH enzymes and plausible mechanisms for the chemical and nonchemical steps, including the transportation and binding of the substrate DHC to the active site, the hydrolysis reaction, and the product release. The results reveal that the five mutations remodel the active pocket and reposition DHC within the active site, leading to stronger enzyme-substrate interactions. The MM/GBSA-estimated binding free energies are about -20.7 kcal/mol for m5-MPH and -17.1 kcal/mol for wild-type MPH. Furthermore, this conformational adjustment of the protein may facilitate the chemical step of DHC hydrolysis and the product release, although there is a certain influence on the substrate transport. The hydrolytic reaction begins with the nucleophilic attack of the bridging OH- with the energy barriers of 22.0 and 18.0 kcal/mol for the wild-type and m5-MPH enzymes, respectively, which is rate-determining for the entire process. Unraveling these mechanistic intricacies may help in the understanding of the natural evolution of enzymes for diverse substrates and establish the enzyme structure-function relationship.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Sequence - dynamics - function relationships in protein tyrosine phosphatases

Protein tyrosine phosphatases (PTPs) are crucial regulators of cellular signaling. Their activity is regulated by the motion of a conserved loop, the WPD-loop, from a catalytically inactive open to a catalytically active closed conformation. WPD-loop motion optimally positions a catalytically critical residue into the active site, and is directly linked to the turnover number of these enzymes. Crystal structures of chimeric PTPs constructed by grafting parts of the WPD-loop sequence of PTP1B onto the scaffold of YopH showed WPD-loops in a wide-open conformation never previously observed in either parent enzyme. This wide-open conformation has, however, been observed upon binding of small molecule inhibitors to other PTPs, suggesting the potential of targeting it for drug discovery efforts. Here, we have performed simulations of both enzymes and show that there are negligible energetic differences in the chemical step of catalysis, but significant differences in the dynamical properties of the WPD-loop. Detailed interaction network analysis provides insight into the molecular basis for this population shift to a wide-open conformation. Taken together, our study provides insight into the links between loop dynamics and chemistry in these YopH variants specifically, and how WPD-loop dynamic can be engineered through modification of the internal protein interaction network.

Mono-phosphorylation at Ser4 of barrier-to-autointegration factor (Banf1) significantly reduces its DNA binding capability by inducing critical changes in its local conformation and DNA binding surface

Barrier-to-autointegration factor (Banf1) is a small DNA-bridging protein. The binding status of Banf1 to DNA is regulated by its N-terminal phosphorylation and dephosphorylation, which plays a critical role in cell proliferation. Banf1 can be phosphorylated at Ser4 into mono-phosphorylated Banf1, which is further phosphorylated at Thr3 to form di-phosphorylated Banf1. It was observed decades ago that mono-phosphorylated Banf1 cannot bind to DNA. However, the underlying molecular- and atomic-level mechanisms remain unclear. A clear understanding of these mechanisms will aid in interfering with the cell proliferation process for better global health. Herein, we explored the detailed atomic bases of unphosphorylated Banf1-DNA binding and how mono- and di-phosphorylation of Banf1 impair these atomic bases to eliminate its DNA-binding capability, followed by exploring the DNA-binding capability of mono- and di-phosphorylation Banf1, using comprehensive and systematic molecular modelling and molecular dynamics simulations. This work presented in detail the residue-level binding energies, hydrogen bonds and water bridges between Banf1 and DNA, some of which have not been reported. Moreover, we revealed that mono-phosphorylation of Banf1 causes its N-terminal secondary structure changes, which in turn induce significant changes in Banf1's DNA binding surface, thus eliminating its DNA-binding capability. At the atomic level, we also uncovered the alterations in interactions due to the induction of mono-phosphorylation that result in the N-terminal secondary structure changes of Banf1. Additionally, our modelling showed that phosphorylated Banf1 with their dominant N-terminal secondary structures bind to DNA with a significantly lower affinity and the docked binding pose are not stable in MD simulations. These findings help future studies in predicting effect of mutations in Banf1 on its DNA-binding capability and open a novel avenue for the development of therapeutics such as cancer drugs, targeting cell proliferation by inducing conformational changes in Banf1's N-terminal domain.

Loop dynamics and the evolution of enzyme activity

In the early 2000s, Tawfik presented his 'New View' on enzyme evolution, highlighting the role of conformational plasticity in expanding the functional diversity of limited repertoires of sequences. This view is gaining increasing traction with increasing evidence of the importance of conformational dynamics in both natural and laboratory evolution of enzymes. The past years have seen several elegant examples of harnessing conformational (particularly loop) dynamics to successfully manipulate protein function. This Review revisits flexible loops as critical participants in regulating enzyme activity. We showcase several systems of particular interest: triosephosphate isomerase barrel proteins, protein tyrosine phosphatases and β-lactamases, while briefly discussing other systems in which loop dynamics are important for selectivity and turnover. We then discuss the implications for engineering, presenting examples of successful loop manipulation in either improving catalytic efficiency, or changing selectivity completely. Overall, it is becoming clearer that mimicking nature by manipulating the conformational dynamics of key protein loops is a powerful method of tailoring enzyme activity, without needing to target active-site residues.

Exploring Oxidoreductases from Extremophiles for Biosynthesis in a Non-Aqueous System

Organic solvent tolerant oxidoreductases are significant for both scientific research and biomanufacturing. However, it is really challenging to obtain oxidoreductases due to the shortages of natural resources and the difficulty to obtained it via protein modification. This review summarizes the recent advances in gene mining and structure-functional study of oxidoreductases from extremophiles for non-aqueous reaction systems. First, new strategies combining genome mining with bioinformatics provide new insights to the discovery and identification of novel extreme oxidoreductases. Second, analysis from the perspectives of amino acid interaction networks explain the organic solvent tolerant mechanism, which regulate the discrete structure-functional properties of extreme oxidoreductases. Third, further study by conservation and co-evolution analysis of extreme oxidoreductases provides new perspectives and strategies for designing robust enzymes for an organic media reaction system. Furthermore, the challenges and opportunities in designing biocatalysis non-aqueous systems are highlighted.

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

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

The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database

The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.