A De Novo Designed Esterase with p-Nitrophenyl Acetate Hydrolysis Activity

Antiproliferative activities through accelerating autophagic flux by basidalin and its analogs in human cancer cells

Basidalin, isolated from the basidiomycete Leucoagaricus naucina, has previously demonstrated antibacterial and antitumor properties against murine cancer cells in vivo, but its effects on human cancer cells remain unknown. In this study, we found that basidalin possesses antiproliferative activity against human cancer cell lines. To elucidate the antiproliferative mechanism of basidalin, we focused on autophagy. Treatment with basidalin led to an increase in LC3-II expression level, and accelerated autophagic flux through an mTOR-independent pathway. Moreover, according to the structure-activity relationship analysis-including newly synthesized basidalin analogs-the formyl group, not the amino group, contributes to the antiproliferative activities of basidalin against human cancer cells. Additionally, the antiproliferative activity of basidalin analogs was strongly correlated with autophagy-inducing activity, indicating that basidalin exhibits antiproliferative activity through autophagy induction. These data suggest that basidalin, characterized by its ability to upregulate autophagic flux, emerges as a novel anticancer drug.

Molecular Cloning, Characterization, and Application of Organic Solvent-Stable and Detergent-Compatible Thermostable Alkaline Protease from Geobacillus thermoglucosidasius SKF4

Several thermostable proteases have been identified, yet only a handful have undergone the processes of cloning, comprehensive characterization, and full exploitation in various industrial applications. Our primary aim in this study was to clone a thermostable alkaline protease from a thermophilic bacterium and assess its potential for use in various industries. The research involved the amplification of the SpSKF4 protease gene, a thermostable alkaline serine protease obtained from the Geobacillus thermoglucosidasius SKF4 bacterium through polymerase chain reaction (PCR). The purified recombinant SpSKF4 protease was characterized, followed by evaluation of its possible industrial applications. The analysis of the gene sequence revealed an open reading frame (ORF) consisting of 1,206 bp, coding for a protein containing 401 amino acids. The cloned gene was expressed in Escherichia coli. The molecular weight of the enzyme was measured at 28 kDa using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The partially purified enzyme has its highest activity at a pH of 10 and a temperature of 80°C. In addition, the enzyme showed a half-life of 15 h at 80°C, and there was a 60% increase in its activity at 10 mM Ca2+ concentration. The activity of the protease was completely inhibited (100%) by phenylmethylsulfonyl fluoride (PMSF); however, the addition of sodium dodecyl sulfate (SDS) resulted in a 20% increase in activity. The enzyme was also stable in various organic solvents and in certain commercial detergents. Furthermore, the enzyme exhibited strong potential for industrial use, particularly as a detergent additive and for facilitating the recovery of silver from X-ray film.

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.

Comparative gut proteomics study revealing adaptive physiology of Eurasian spruce bark beetle, Ips typographus (Coleoptera: Scolytinae)

The bark beetle, Ips typographus (L.), is a major pest of Norway spruce, Picea abies (L.), causing enormous economic losses globally. The adult stage of the I. typographus has a complex life cycle (callow and sclerotized); the callow beetles feed ferociously, whereas sclerotized male beetles are more aggressive and pioneers in establishing new colonies. We conducted a comparative proteomics study to understand male and female digestion and detoxification processes in callow and sclerotized beetles. Proteome profiling was performed using high-throughput liquid chromatography-mass spectrometry. A total of >3000 proteins were identified from the bark beetle gut, and among them, 539 were differentially abundant (fold change ±2, FDR <0.05) between callow and sclerotized beetles. The differentially abundant proteins (DAPs) mainly engage with binding, catalytic activity, anatomical activity, hydrolase activity, metabolic process, and carbohydrate metabolism, and hence may be crucial for growth, digestion, detoxification, and signalling. We validated selected DAPs with RT-qPCR. Gut enzymes such as NADPH-cytochrome P450 reductase (CYC), glutathione S-transferase (GST), and esterase (EST) play a crucial role in the I. typographus for detoxification and digesting of host allelochemicals. We conducted enzyme activity assays with them and observed a positive correlation of CYC and GST activities with the proteomic results, whereas EST activity was not fully correlated. Furthermore, our investigation revealed that callow beetles had an upregulation of proteins associated with juvenile hormone (JH) biosynthesis and chitin metabolism, whereas sclerotized beetles exhibited an upregulation of proteins linked to fatty acid metabolism and the TCA cycle. These distinctive patterns of protein regulation in metabolic and functional processes are specific to each developmental stage, underscoring the adaptive responses of I. typographicus in overcoming conifer defences and facilitating their survival. Taken together, it is the first gut proteomic study comparing males and females of callow and sclerotized I. typographus, shedding light on the adaptive ecology at the molecular level. Furthermore, the information about bark beetle handling of nutritionally limiting and defence-rich spruce phloem diet can be utilized to formulate RNAi-mediated beetle management.

Designing Efficient Enzymes: Eight Predicted Mutations Convert a Hydroxynitrile Lyase into an Efficient Esterase

Hydroxynitrile lyase from rubber tree (HbHNL) shares 45% identical amino acid residues with the homologous esterase from tobacco, SABP2, but the two enzymes catalyze different reactions. The x-ray structures reveal a serine-histidine-aspartate catalytic triad in both enzymes along with several differing amino acid residues within the active site. Previous exchange of three amino acid residues in the active site of HbHNL with the corresponding amino acid residue in SABP2 (T11G-E79H-K236M) created variant HNL3, which showed low esterase activity toward p-nitrophenyl acetate. Further structure comparison reveals additional differences surrounding the active site. HbHNL contains an improperly positioned oxyanion hole residue and differing solvation of the catalytic aspartate. We hypothesized that correcting these structural differences would impart good esterase activity on the corresponding HbHNL variant. To predict the amino acid substitutions needed to correct the structure, we calculated shortest path maps for both HbHNL and SABP2, which reveal correlated movements of amino acids in the two enzymes. Replacing four amino acid residues (C81L-N104T-V106F-G176S) whose movements are connected to the movements of the catalytic residues yielded variant HNL7TV (stabilizing substitution H103V was also added), which showed an esterase catalytic efficiency comparable to that of SABP2. The x-ray structure of an intermediate variant, HNL6V, showed an altered solvation of the catalytic aspartate and a partially corrected oxyanion hole. This dramatic increase in catalytic efficiency demonstrates the ability of shortest path maps to predict which residues outside the active site contribute to catalytic activity.

De Novo Computational Design of a Lipase with Hydrolysis Activity towards Middle-Chained Fatty Acid Esters

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta "inside-out" protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD₁ exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD₁-M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate-3.34 times higher than that of 1a8uD₁. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD₁ and the redesigned 1a8uD₁-M8 were devised from scratch. More importantly, the designed 1a8uD₁-M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Design of novel enzyme biocatalysts for industrial bioprocess: Harnessing the power of protein engineering, high throughput screening and synthetic biology

Biocatalysts have wider applications in various industries. Biocatalysts are generating bigger attention among researchers due to their unique catalytic properties like activity, specificity and stability. However the industrial use of many enzymes is hindered by low catalytic efficiency and stability during industrial processes. Properties of enzymes can be altered by protein engineering. Protein engineers are increasingly study the structure-function characteristics, engineering attributes, design of computational tools for enzyme engineering, and functional screening processes to improve the design and applications of enzymes. The potent and innovative techniques of enzyme engineering deliver outstanding opportunities for tailoring industrially important enzymes for the versatile production of biochemicals. An overview of the current trends in enzyme engineering is explored with important representative examples.