Abstract The AMP-forming acetyl-CoA synthetase is regulated by lysine acetylation both in bacteria and eukaryotes. However, the underlying mechanism is poorly understood. The Bacillus subtilis acetyltransferase AcuA and the AMP-forming acetyl-CoA synthetase AcsA form an AcuA•AcsA complex, dissociating upon lysine acetylation of AcsA by AcuA. Crystal structures of AcsA from Chloroflexota bacterium in the apo form and in complex with acetyl-adenosine-5′-monophosphate (acetyl-AMP) support the flexible C-terminal domain adopting different conformations. AlphaFold2 predictions suggest binding of AcuA stabilizes AcsA in an undescribed conformation. We show the AcuA•AcsA complex dissociates upon acetyl-coenzyme A (acetyl-CoA) dependent acetylation of AcsA by AcuA. We discover an intrinsic phosphotransacetylase activity enabling AcuA•AcsA generating acetyl-CoA from acetyl-phosphate (AcP) and coenzyme A (CoA) used by AcuA to acetylate and inactivate AcsA. Here, we provide mechanistic insights into the regulation of AMP-forming acetyl-CoA synthetases by lysine acetylation and discover an intrinsic phosphotransacetylase allowing modulation of its activity based on AcP and CoA levels.

Plastic-degrading enzymes facilitate the biocatalytic recycling of poly(ethylene terephthalate) (PET), a significant synthetic polymer, and substantial progress has been made in utilizing PET hydrolases for industrial applications. To fully exploit the potential of these enzymes, a deeper mechanistic understanding followed by targeted protein engineering is essential. Through advanced molecular dynamics simulations and free energy analysis methods, we elucidated the complete pathway from the initial binding of two PET hydrolases-the thermophilic leaf-branch compost cutinase (LCC) and polyester hydrolase 1 (PES-H1)-to an amorphous PET substrate, ultimately leading to a PET chain entering the active site in a hydrolyzable conformation. Our findings indicate that initial PET binding is nonspecific and driven by polar and hydrophobic interactions. We demonstrate that the subsequent entry of PET into the active site can occur via one of three key pathways, identifying barriers related to both PET-PET and PET-enzyme interactions, as well as specific residues highlighted through in silico and in vitro mutagenesis. These insights not only enhance our understanding of the mechanisms underlying PET degradation and facilitate the development of targeted enzyme enhancement strategies but also provide a novel framework applicable to enzyme studies across various disciplines.

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities. Machine learning approaches allow the creation of protein adaptive landscapes and the identification of catalytic modes using limited experimental data to establish relationships between enzyme, substrate and catalytic performance, and have been used for data-driven protein engineering to improve their catalytic activity and selectivity.image

Baeyer‐Villiger monooxygenases (BVMOs) are important flavin‐dependent enzymes which perform oxygen insertion reactions leading to valuable products. As reported in many studies, BVMOs are usually unstable during application, preventing a wider usage in biocatalysis. Here, we discovered a novel NADPH‐dependent BVMO which originates from Halopolyspora algeriensis using sequence similarity networks (SSNs). The enzyme is stable at temperatures between 10 °C to 30 °C up to five days after the purification, and yields the normal ester product. In this study, the substrate scope was investigated for a broad range of aliphatic ketones and the enzyme was biochemically characterized to identify optimum reaction conditions. The best substrate (86 % conversion) was 2‐dodecanone using purified enzyme. This novel BVMO could potentially be applied as part of an enzymatic cascade or in bioprocesses which utilize aliphatic alkanes as feedstock.

LARAsuite (https://gitlab.com/larasuite) is a free and open source research data management system that addresses the problematic of manual data insertion and metadata assignment into the corresponding databases by a radically automated processes. Data and Metadata is mainly not inserted by humans, but by the machines producing the data and automatically transferring this generated data and metadata to the LARA RDM system. This data transfer is achieved through the intensive utilisation of the free and open lab-automation standard SiLA (Standardisa- tion in Labautomation) (https://sila-standard.org), combined with a simple to note process description language (pythonLab) and orchestration, scheduling, data aggre- gation and evaluation. Many LARA instances can be selectively synchronised to form a decentralised network of data infrastructures across labs and institutions. Research data and meta-data can be queried by a SPARQL endpoint. As the LARAsuite comes close to an ideal FAIR-principles based RDM System1, is generic to the most common natural science applications, is open source (python based), modular and easy deploy- able, it can be used to showcase NFDI goals for a wide range of scientists

Enzyme activity data for biocatalytic applications are currently often not annotated with standardized conditions and terms. This makes it extremely hard to retrieve, compare, and reuse enzymatic data. With advances in the fields of artificial intelligence (AI) and machine learning (ML), the automated usability of data in the form of machine-readable annotations will play a crucial role for their success. It is becoming increasingly easy to retrieve complex data sets and extract relevant information; however, standardized data readability is a current limitation. In this contribution, we outline an iterative approach to develop standardized terms and create semantic relations (ontologies) to achieve this highly desirable goal of improving the discoverability, accessibility, interoperability, and reuse of digital resources in the field of biocatalysis.

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

Protein design is limited by the diversity of functional groups provided by the canonical protein 'building blocks'. Incorporating noncanonical amino acids (ncAAs) into enzymes enables a dramatic expansion of their catalytic features. For this, quick identification of fully translated and correctly folded variants is decisive. Herein, we report the engineering of the enantioselectivity of an esterase utilizing several ncAAs. Key for the identification of active and soluble protein variants was the use of the split-GFP method, which is crucial as it allows simple determination of the expression levels of enzyme variants with ncAA incorporations by fluorescence. Several identified variants led to improved enantioselectivity or even inverted enantiopreference in the kinetic resolution of ethyl 3-phenylbutyrate.

Carboxylic acid reductases (CARs) catalyze the direct adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide phosphate (NADPH) dependent reduction of carboxylic acids to their corresponding aldehydes. The identification and improvement of CARs by protein engineering is, however, severely limited by the lack of fast and generic methods to quantify aldehydes. Within this study, we applied a convenient high-throughput assay (HTA) based on amino benzamidoxime (ABAO) that allows the substrate-independent and chemoselective quantification of aldehydes. Random mutagenesis of the well-known CAR from Nocardia iowensis (CAR(Ni)) to improve its activity for sterically demanding 2-substituted benzoic acid derivatives was conducted in a K-M-dependent fashion, and the HTA applied in the presence of microbial cells. The study identified a hot spot in the active site of CAR(Ni) that increased the affinity to 2-methoxybenzoic acid 9-fold upon mutation from glutamine to proline (Q283P). The catalytic performance of CAR(NiQ283P) appeared to be significantly improved also for other substrates such as 2-substituted (2-Cl, 2-Br) as well as 3- and 4-substituted benzoic acids (3-OMe, 4-OMe), and even aliphatic octanoic acid.

Escherichia coli (E. coli) as heterologous host enables the recombinant expression of the desired protein in high amounts. Nevertheless, the expression in such a host, especially by utilizing a strong induction system, can result in insoluble and/or inactive protein fractions (inclusion bodies). Furthermore, the expression of different enzyme variants often leads to a diverse growth behavior of the E. coli clones resulting in the identification of false-positives when screening a mutant library. Thus, we developed a protocol for an optimal and reproducible protein expression in microtiter plates showcased for the expression of the cyclohexanone monooxygenase (CHMO) from Acinetobacter sp. NCIMB 9871. By emerging this protocol, several parameters concerning the expression medium, the cultivation temperatures, shaking conditions as well as time and induction periods for CHMO were investigated. We employed a microtiter plate shaker with humidity and temperature control (Cytomat™) (integrated in a robotic platform) to obtain an even growth and expression over the plates. Our optimized protocol provides a comprehensive overview of the key factors influencing a reproducible protein expression and this should serve as basis for the adaptation to other enzyme classes.

Chiral amines are important building blocks, especially for the pharmaceutical industry. Although amine transaminases (ATAs) are versatile enzymes to synthesize chiral amines, the wildtype enzymes do not accept ketones with two large substituents next to the carbonyl functionality. Using bioinformatic tools to design a seven-site mutant library followed by high-throughput screening, we were able to identify variants of the enzyme from Vibrio fluvialis (VF-ATA) with a widened binding pocket, as exemplified for a range of ketones. Three variants allowed the asymmetric synthesis of 2,2-dimethyl-1-phenylpropan-1-aminenot accessible by any wildtype ATA described so far. The best variant containing four mutations (L56V, W57C, F85V, V153A) gave 100% conversion of the ketone to yield the amine with an enantiomeric excess value >99%, notably with preference for the (R)-enantiomer. Insilico modeling enabled the reconstruction of the substrate binding mode to the newly evolved pocket and, hence, allowed explanation of the experimental results.

ABSTRACTQuorum sensing, the bacterial cell-cell communication by small molecules, controls important processes such as infection and biofilm formation. Therefore, it is a promising target with several therapeutic and technical applications besides its significant ecological relevance. Enzymes inactivatingN-acyl-l-homoserine lactones, the most common class of communication molecules among Gram-negative proteobacteria, mainly belong to the groups of quorum-quenching lactonases or quorum-quenching acylases. However, identification, characterization, and optimization of these valuable biocatalysts are based on a very limited number of fundamentally different methods with their respective strengths and weaknesses. Here, a (bio)chemical activity assay is described, which perfectly complements the other methods in this field. It enables continuous and high-throughput activity measurements of purified and unpurified quorum-quenching enzymes within several minutes. For this, the reaction products released by quorum-quenching lactonases and quorum-quenching acylases are converted either by a secondary enzyme or by autohydrolysis tol-homoserine. In turn,l-homoserine is detected by the previously described calcein assay, which is sensitive to α-amino acids with free N and C termini. Besides its establishment, the method was applied to the characterization of three previously undescribed quorum-quenching lactonases and variants thereof and to the identification of quorum-quenching acylase-expressingEscherichia coliclones in an artificial library. Furthermore, this study indicates that porcine aminoacylase 1 is not active towardN-acyl-l-homoserine lactones as published previously but instead converts the autohydrolysis productN-acyl-l-homoserine.IMPORTANCEIn this study, a novel method is presented for the identification, characterization, and optimization of quorum-quenching enzymes that are active towardN-acyl-l-homoserine lactones. These are the most common communication molecules among Gram-negative proteobacteria. The activity assay is a highly valuable complement to the available analytical tools in this field. It will facilitate studies on the environmental impact of quorum-quenching enzymes and contribute to the development of therapeutic and technical applications of this promising enzyme class.

Here, we report a widely and generally applicable strategy to obtain reliable information in high-throughput protein screenings of enzyme mutant libraries. The method is based on the usage of the split-GFP technology for the normalization of the expression level of each individual protein variant combined with activity measurements, thus resolving the important problems associated with the different solubility of each mutant and allowing the detection of previously invisible variants. The small size of the employed protein tag (16 amino acids) required for the reconstitution of the GFP fluorescence reduces possible interferences such as enzyme activity variations or solubility disturbances to a minimum. Specific enzyme activity measurements without purification, in situ soluble protein expression monitoring, and data normalization are the powerful outputs of this methodology, thus enabling the accurate identification of improved protein variants during high-throughput screening by substantially reducing the occurrence of false negatives and false positives.

Even though there is an urgent need for new antifungals with improved clinical properties, the substrate promiscuity of tailoring enzymes has been poorly studied as a source of new structural diversity for polyene macrolides. We explore the acceptance of different polyene macrolides by the glutamine amidotransferase PscA and the catalytic effect of different homologous P450 cytochromes on a common scaffold, the pimaricin precursor 4,5-desepoxypimaricin. By combining these two parallel strategies, we present three new pimaricin derivatives and a new lucensomycin variant. Our results show that P450 cytochromes devoted to the modification of the polyol region of polyene macrolides are not as substrate-specific as previously thought and highlight PscA as a versatile small-ring polyene-modifying enzyme that allows the preparation of new carboxamide derivatives. We also provide useful information for the future production of previously unconceived epoxidized polyene macrolide antifungals.