From Bioorganic Models to Cells.

I endeavor to share how various choices-some deliberate, some unconscious-and the unmistakable influence of many others shaped my scientific pursuits. I am fascinated by how two long-term, major streams of my research, DNA replication and purine biosynthesis, have merged with unexpected interconnections. If I have imparted to many of the talented individuals who have passed through my lab a degree of my passion for uncloaking the mysteries hidden in scientific research and an understanding of the honesty and rigor it demands and its impact on the world community, then my mentorship has been successful.

Discovery of cytosine deaminases enables base-resolution methylome mapping using a single enzyme.

Deaminases have important uses in modification detection and genome editing. However, the range of applications is limited by the small number of characterized enzymes. To expand the toolkit of deaminases, we developed an in vitro approach that bypasses a major hurdle with their toxicity in cells. We assayed 175 putative cytosine deaminases on a variety of substrates and found a broad range of activity on double- and single-stranded DNA in various sequence contexts, including CpG-specific deaminases and enzymes without sequence preference. We also characterized enzyme selectivity across six DNA modifications and reported enzymes that do not deaminate modified cytosines. The detailed analysis of diverse deaminases opens new avenues for biotechnological and medical applications. As a demonstration, we developed SEM-seq, a non-destructive single-enzyme methylation sequencing method using a modification-sensitive double-stranded DNA deaminase. The streamlined protocol enables accurate, base-resolution methylome mapping of scarce biological material, including cell-free DNA and 10 pg input DNA.

Understanding the Chemistry and Biology of Glycosylation with Glycan Synthesis.

Glycoscience research has been significantly impeded by the complex compositions of the glycans present in biological molecules and the lack of convenient tools suitable for studying the glycosylation process and its function. Polysaccharides and glycoconjugates are not encoded directly by genes; instead, their biosynthesis relies on the differential expression of carbohydrate enzymes, resulting in heterogeneous mixtures of glycoforms, each with a distinct physiological activity. Access to well-defined structures is required for functional study, and this has been provided by chemical and enzymatic synthesis and by the engineering of glycosylation pathways. This review covers general methods for preparing glycans commonly found in mammalian systems and applying them to the synthesis of therapeutically significant glycoconjugates (glycosaminoglycans, glycoproteins, glycolipids, glycosylphosphatidylinositol-anchored proteins) and the development of carbohydrate-based vaccines.

Topography of histone H3-H4 interaction with the Hat1-Hat2 acetyltransferase complex.

Chaperones influence histone conformation and intermolecular interaction in multiprotein complexes, and the structures obtained with full-length histones often provide more accurate and comprehensive views. Here, our structure of the Hat1-Hat2 acetyltransferase complex bound to Asf1-H3-H4 shows that the core domains of H3 and H4 are involved in binding Hat1 and Hat2, and the N-terminal tail of H3 makes extensive interaction with Hat2. These findings expand the knowledge about histone-protein interaction and implicate a function of Hat2/RbAp46/48, which is a versatile histone chaperone found in many chromatin-associated complexes, in the passing of histones between chaperones.

Combining systems and synthetic biology for in vivo enzymology.

Enzymatic parameters are classically determined in vitro, under conditions that are far from those encountered in cells, casting doubt on their physiological relevance. We developed a generic approach combining tools from synthetic and systems biology to measure enzymatic parameters in vivo. In the context of a synthetic carotenoid pathway in Saccharomyces cerevisiae, we focused on a phytoene synthase and three phytoene desaturases, which are difficult to study in vitro. We designed, built, and analyzed a collection of yeast strains mimicking substantial variations in substrate concentration by strategically manipulating the expression of geranyl-geranyl pyrophosphate (GGPP) synthase. We successfully determined in vivo Michaelis-Menten parameters (KM, Vmax, and kcat) for GGPP-converting phytoene synthase from absolute metabolomics, fluxomics and proteomics data, highlighting differences between in vivo and in vitro parameters. Leveraging the versatility of the same set of strains, we then extracted enzymatic parameters for two of the three phytoene desaturases. Our approach demonstrates the feasibility of assessing enzymatic parameters directly in vivo, providing a novel perspective on the kinetic characteristics of enzymes in real cellular conditions.

The complex universe of inactive PARP1.

Poly(ADP-ribose) polymerase 1 (PARP1) is a crucial member of the PARP family, which modifies targets through ADP-ribosylation and plays key roles in a variety of biological processes. PARP inhibitors (PARPis) hinder ADP-ribosylation and lead to the retention of PARP1 at the DNA lesion (also known as trapping), which underlies their toxicity. However, inhibitors and mutations that make PARP1 inactive do not necessarily correlate with trapping potency, challenging the current understanding of inactivation-caused trapping. Recent studies on mouse models indicate that both trapping and non-trapping inactivating mutations of PARP1 lead to embryonic lethality, suggesting the unexpected toxicity of the current inhibition strategy. The allosteric model, complicated automodification, and various biological functions of PARP1 all contribute to the complexity of PARP1 inactivation.

ELTA: Enzymatic Labeling of Terminal ADP-Ribose.

ADP-ribosylation refers to the addition of one or more ADP-ribose groups onto proteins. The attached ADP-ribose monomers or polymers, commonly known as poly(ADP-ribose) (PAR), modulate the activities of the modified substrates or their binding affinities to other proteins. However, progress in this area is hindered by a lack of tools to investigate this protein modification. Here, we describe a new method named ELTA (enzymatic labeling of terminal ADP-ribose) for labeling free or protein-conjugated ADP-ribose monomers and polymers at their 2'-OH termini using the enzyme OAS1 and dATP. When coupled with various dATP analogs (e.g., radioactive, fluorescent, affinity tags), ELTA can be used to explore PAR biology with techniques routinely used to investigate DNA or RNA function. We demonstrate that ELTA enables the biophysical measurements of protein binding to PAR of a defined length, detection of PAR length from proteins and cells, and enrichment of sub-femtomole amounts of ADP-ribosylated peptides from cell lysates.

DNA-DISK: Automated end-to-end data storage via enzymatic single-nucleotide DNA synthesis and sequencing on digital microfluidics.

In the age of information explosion, the exponential growth of digital data far exceeds the capacity of current mainstream storage media. DNA is emerging as a promising alternative due to its higher storage density, longer retention time, and lower power consumption. To date, commercially mature DNA synthesis and sequencing technologies allow for writing and reading of information on DNA with customization and convenience at the research level. However, under the disconnected and nonspecialized mode, DNA data storage encounters practical challenges, including susceptibility to errors, long storage latency, resource-intensive requirements, and elevated information security risks. Herein, we introduce a platform named DNA-DISK that seamlessly streamlined DNA synthesis, storage, and sequencing on digital microfluidics coupled with a tabletop device for automated end-to-end information storage. The single-nucleotide enzymatic DNA synthesis with biocapping strategy is utilized, offering an ecofriendly and cost-effective approach for data writing. A DNA encapsulation using thermo-responsive agarose is developed for on-chip solidification, not only eliminating data clutter but also preventing DNA degradation. Pyrosequencing is employed for in situ and accurate data reading. As a proof of concept, DNA-DISK successfully stored and retrieved a musical sheet file (228 bits) with lower write-to-read latency (4.4 min of latency per bit) as well as superior automation compared to other platforms, demonstrating its potential to evolve into a DNA Hard Disk Drive in the future.

Protein click chemistry and its potential for medical applications.

A revolution in chemical biology occurred with the introduction of click chemistry. Click chemistry plays an important role in protein chemistry modifications, providing specific, sensitive, rapid, and easy-to-handle methods. Under physiological conditions, click chemistry often overlaps with bioorthogonal chemistry, defined as reactions that occur rapidly and selectively without interfering with biological processes. Click chemistry is used for the posttranslational modification of proteins based on covalent bond formations. With the contribution of click reactions, selective modification of proteins would be developed, representing an alternative to other technologies in preparing new proteins or enzymes for studying specific protein functions in different biological processes. Click-modified proteins have potential in diverse applications such as imaging, labeling, sensing, drug design, and enzyme technology. Due to the promising role of proteins in disease diagnosis and therapy, this review aims to highlight the growing applications of click strategies in protein chemistry over the last two decades, with a special emphasis on medicinal applications.

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs.

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting Km, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.

The logic of protein post-translational modifications (PTMs): Chemistry, mechanisms and evolution of protein regulation through covalent attachments.

Protein post-translational modifications (PTMs) play a crucial role in all cellular functions by regulating protein activity, interactions and half-life. Despite the enormous diversity of modifications, various PTM systems show parallels in their chemical and catalytic underpinnings. Here, focussing on modifications that involve the addition of new elements to amino-acid sidechains, I describe historical milestones and fundamental concepts that support the current understanding of PTMs. The historical survey covers selected key research programmes, including the study of protein phosphorylation as a regulatory switch, protein ubiquitylation as a degradation signal and histone modifications as a functional code. The contribution of crucial techniques for studying PTMs is also discussed. The central part of the essay explores shared chemical principles and catalytic strategies observed across diverse PTM systems, together with mechanisms of substrate selection, the reversibility of PTMs by erasers and the recognition of PTMs by reader domains. Similarities in the basic chemical mechanism are highlighted and their implications are discussed. The final part is dedicated to the evolutionary trajectories of PTM systems, beginning with their possible emergence in the context of rivalry in the prokaryotic world. Together, the essay provides a unified perspective on the diverse world of major protein modifications.

Dual factors required for cytochrome-P450-mediated hydrocarbon ring contraction in bacterial gibberellin phytohormone biosynthesis.

Cytochromes P450 (CYPs) are heme-thiolate monooxygenases that prototypically catalyze the insertion of oxygen into unactivated C-H bonds but are capable of mediating more complex reactions. One of the most remarked-upon alternative reactions occurs during biosynthesis of the gibberellin A (GA) phytohormones, involving hydrocarbon ring contraction with coupled aldehyde extrusion of ent-kaurenoic acid to form the first gibberellin intermediate. While the unusual nature of this reaction has long been noted, its mechanistic basis has remained opaque. Building on identification of the relevant CYP114 from bacterial GA biosynthesis, detailed structure-function studies are reported here, including development of in vitro assays as well as crystallographic analyses both in the absence and presence of substrate. These structures provided insight into enzymatic catalysis of this unusual reaction, as exemplified by identification of a key role for the "missing" acid from an otherwise highly conserved acid-alcohol pair of residues. Notably, the results demonstrate that ring contraction requires dual factors, both the use of a dedicated ferredoxin and absence of the otherwise conserved acidic residue, with exclusion of either limiting turnover to just the initiating and more straightforward hydroxylation. The results provide detailed insight into the enzymatic structure-function relationships underlying this fascinating reaction and support the use of a semipinacol mechanism for the unusual ring contraction reaction.

Allosteric communication between ligand binding domains modulates substrate inhibition in adenylate kinase.

Enzymes play a vital role in life processes; they control chemical reactions and allow functional cycles to be synchronized. Many enzymes harness large-scale motions of their domains to achieve tremendous catalytic prowess and high selectivity for specific substrates. One outstanding example is provided by the three-domain enzyme adenylate kinase (AK), which catalyzes phosphotransfer between ATP to AMP. Here we study the phenomenon of substrate inhibition by AMP and its correlation with domain motions. Using single-molecule FRET spectroscopy, we show that AMP does not block access to the ATP binding site, neither by competitive binding to the ATP cognate site nor by directly closing the LID domain. Instead, inhibitory concentrations of AMP lead to a faster and more cooperative domain closure by ATP, leading in turn to an increased population of the closed state. The effect of AMP binding can be modulated through mutations throughout the structure of the enzyme, as shown by the screening of an extensive AK mutant library. The mutation of multiple conserved residues reduces substrate inhibition, suggesting that substrate inhibition is an evolutionary well conserved feature in AK. Combining these insights, we developed a model that explains the complex activity of AK, particularly substrate inhibition, based on the experimentally observed opening and closing rates. Notably, the model indicates that the catalytic power is affected by the microsecond balance between the open and closed states of the enzyme. Our findings highlight the crucial role of protein motions in enzymatic activity.

Discovery of a class of glycosaminoglycan lyases with ultrabroad substrate spectrum and their substrate structure preferences.

Glycosaminoglycan (GAG) lyases are often strictly substrate specific, and it is especially difficult to simultaneously degrade GAGs with different types of glycosidic bonds. Herein, we found a new class of GAG lyases (GAGases) from different bacteria. These GAGases belong to polysaccharide lyase 35 family and share quite low homology with the identified GAG lyases. The most surprising thing is that GAGases can not only degrade three types of GAGs: hyaluronan, chondroitin sulfate, and heparan sulfate but also even one of them can also degrade alginate. Further investigation of structural preferences revealed that GAGases selectively act on GAG domains composed of non/6-O-/N-sulfated hexosamines and d-glucoronic acids as well as on alginate domains composed of d-mannuronic acids. In addition, GAG lyases were once speculated to have evolved from alginate lyases, but no transitional enzymes have been found. The discovery of GAGases not only broadens the category of GAG lyases, provides new enzymatic tools for the structural and functional studies of GAGs with specific structures, but also provides candidates for the evolution of GAG lyases.

Chemical diversity in angiosperms - monoterpene synthases control complex reactions that provide the precursors for ecologically and commercially important monoterpenoids.

Monoterpene synthases (MTSs) catalyze the first committed step in the biosynthesis of monoterpenoids, a class of specialized metabolites with particularly high chemical diversity in angiosperms. In addition to accomplishing a rate enhancement, these enzymes manage the formation and turnover of highly reactive carbocation intermediates formed from a prenyl diphosphate substrate. At each step along the reaction path, a cationic intermediate can be subject to cyclization, migration of a proton, hydride, or alkyl group, or quenching to terminate the sequence. However, enzymatic control of ligand folding, stabilization of specific intermediates, and defined quenching chemistry can maintain the specificity for forming a signature product. This review article will discuss our current understanding of how angiosperm MTSs control the reaction environment. Such knowledge allows inferences about the origin and regulation of chemical diversity, which is pertinent for appreciating the role of monoterpenoids in plant ecology but also for aiding commercial efforts that harness the accumulation of these specialized metabolites for the food, cosmetic, and pharmaceutical industries.

A serine carboxypeptidase-like acyltransferase catalyzes consecutive four-step reactions of hydrolyzable tannin biosynthesis in Camellia oleifera.

Hydrolyzable tannins (HTs), a class of polyphenolic compounds found in dicotyledonous plants, are widely used in food and pharmaceutical industries because of their beneficial effects on human health. Although the biosynthesis of simple HTs has been verified at the enzymatic level, relevant genes have not yet been identified. Here, based on the parent ion-fragment ion pairs in the feature fragment data obtained using UPLC-Q-TOF-/MS/MS, galloyl phenolic compounds in the leaves of Camellia sinensis and C. oleifera were analyzed qualitatively and quantitatively. Correlation analysis between the transcript abundance of serine carboxypeptidase-like acyltransferases (SCPL-ATs) and the peak area of galloyl products in Camellia species showed that SCPL3 expression was highly correlated with HT biosynthesis. Enzymatic verification of the recombinant protein showed that CoSCPL3 from C. oleifera catalyzed the four consecutive steps involved in the conversion of digalloylglucose to pentagalloylglucose. We also identified the residues affecting the enzymatic activity of CoSCPL3 and determined that SCPL-AT catalyzes the synthesis of galloyl glycosides. The findings of this study provide a target gene for germplasm innovation of important cash crops that are rich in HTs, such as C. oleifera, strawberry, and walnut.

Multiple Origins of Bioluminescence in Beetles and Evolution of Luciferase Function.

Bioluminescence in beetles has long fascinated biologists, with diverse applications in biotechnology. To date, however, our understanding of its evolutionary origin and functional variation mechanisms remains poor. To address these questions, we obtained high-quality reference genomes of luminous and nonluminous beetles in 6 Elateroidea families. We then reconstructed a robust phylogenetic relationship for all luminous families and related nonluminous families. Comparative genomic analyses and biochemical functional experiments suggested that gene evolution within Elateroidea played a crucial role in the origin of bioluminescence, with multiple parallel origins observed in the luminous beetle families. While most luciferase-like proteins exhibited a conserved nonluminous amino acid pattern (TLA346 to 348) in the luciferin-binding sites, luciferases in the different luminous beetle families showed divergent luminous patterns at these sites (TSA/CCA/CSA/LVA). Comparisons of the structural and enzymatic properties of ancestral, extant, and site-directed mutant luciferases further reinforced the important role of these sites in the trade-off between acyl-CoA synthetase and luciferase activities. Furthermore, the evolution of bioluminescent color demonstrated a tendency toward hypsochromic shifts and variations among the luminous families. Taken together, our results revealed multiple parallel origins of bioluminescence and functional divergence within the beetle bioluminescent system.

Arsenate reductase of Rufibacter tibetensis is a metallophosphoesterase evolved to catalyze redox reactions.

An arsenate reductase (Car1) from the Bacteroidetes species Rufibacter tibetensis 1351T was isolated from the Tibetan Plateau. The strain exhibits resistance to arsenite [As(III)] and arsenate [As(V)] and reduces As(V) to As(III). Here we shed light on the mechanism of enzymatic reduction by Car1. AlphaFold2 structure prediction, active site energy minimization, and steady-state kinetics of wild-type and mutant enzymes give insight into the catalytic mechanism. Car1 is structurally related to calcineurin-like metallophosphoesterases (MPPs). It functions as a binuclear metal hydrolase with limited phosphatase activity, particularly relying on the divalent metal Ni2+. As an As(V) reductase, it displays metal promiscuity and is coupled to the thioredoxin redox cycle, requiring the participation of two cysteine residues, Cys74 and Cys76. These findings suggest that Car1 evolved from a common ancestor of extant phosphatases by incorporating a redox function into an existing MPP catalytic site. Its proposed mechanism of arsenate reduction involves Cys74 initiating a nucleophilic attack on arsenate, leading to the formation of a covalent intermediate. Next, a nucleophilic attack of Cys76 leads to the release of As(III) and the formation of a surface-exposed Cys74-Cys76 disulfide, ready for reduction by thioredoxin.

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders.

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite-protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite-protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE)-based framework to predict MPI in genome-scale heterogeneous enzymatic reaction networks across ten organisms. By incorporating molecular features of metabolites and proteins as well as neighboring information in the MPI networks, our MPI-VGAE predictor achieved the best predictive performance compared to other machine learning methods. Moreover, when applying the MPI-VGAE framework to reconstruct hundreds of metabolic pathways, functional enzymatic reaction networks and a metabolite-metabolite interaction network, our method showed the most robust performance among all scenarios. To the best of our knowledge, this is the first MPI predictor by VGAE for enzymatic reaction link prediction. Furthermore, we implemented the MPI-VGAE framework to reconstruct the disease-specific MPI network based on the disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of novel enzymatic reaction links were identified. We further validated and explored the interactions of these enzymatic reactions using molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and facilitate the study of the disrupted metabolisms in diseases.