Derivatives of Natural Organocatalytic Cofactors and Artificial Organocatalytic Cofactors as Catalysts in Enzymes

Catalytically active non‐metal cofactors in enzymes carry out a variety of different reactions. The efforts to develop derivatives of naturally occurring cofactors such as flavins or pyridoxal phosphate and the advances to design new, non‐natural cofactors are reviewed here. We report the status quo for enzymes harboring organocatalysts as derivatives of natural cofactors or as artificial ones and their application in the asymmetric synthesis of various compounds

Computational design of a homotrimeric metalloprotein with a trisbipyridyl core

Metal-chelating heteroaryl small molecules have found widespread use as building blocks for coordination-driven, self-assembling nanostructures. The metal-chelating noncanonical amino acid (2,2'-bipyridin-5yl)alanine (Bpy-ala) could, in principle, be used to nucleate specific metalloprotein assemblies if introduced into proteins such that one assembly had much lower free energy than all alternatives. Here we describe the use of the Rosetta computational methodology to design a self-assembling homotrimeric protein with [Fe(Bpy-ala)3]2+ complexes at the interface between monomers. X-ray crystallographic analysis of the homotrimer showed that the design process had near-atomic-level accuracy: The all-atom rmsd between the design model and crystal structure for the residues at the protein interface is ∼1.4 Å. These results demonstrate that computational protein design together with genetically encoded noncanonical amino acids can be used to drive formation of precisely specified metal-mediated protein assemblies that could find use in a wide range of photophysical applications.</p

Accelerating Biocatalysis Discovery with Machine Learning: A New Era in Enzyme Engineering new title based on editors feedback: Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design

New emerging computational tools promise to revolutionize protein engineering for biocatalysis application and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers to navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This perspective aims to discuss and also to celebrate these innovative new approaches in protein science by highlighting their potential on selected recent developments and applications and offering thoughts on future opportunities. It also critically assesses the technology’s limitations, unanswered questions and unmet challenges

The Essential Functional Interplay of the Catalytic Groups in Acid Phosphatase

Cooperative interplay between the functional devices of a preorganized active site is fundamental to enzyme catalysis. A deepened understanding of this phenomenon is central to elucidating the remarkable efficiency of natural enzymes, and provides an essential benchmark for enzyme design and engineering. Here, we study the functional interconnectedness of the catalytic nucleophile (His18) in an acid phosphatase by analyzing the consequences of its replacement with aspartate. We present crystallographic, biochemical and computational evidence for a conserved mechanistic pathway via a phospho-enzyme intermediate on Asp18. Linear free-energy relationships for phosphoryl transfer from phosphomonoester substrates to His18/Asp18 provide evidence for cooperative interplay between the nucleophilic and general-acid catalytic groups in the wildtype enzyme, and its substantial loss in the H18D variant. As an isolated factor of phosphatase efficiency, the advantage of a histidine compared to an aspartate nucleophile is around 10^4-fold. Cooperativity with the catalytic acid adds ≥10^2-fold to that advantage. Empirical valence bond simulations of phosphoryl transfer from glucose 1-phosphate to His and Asp in the enzyme explain the loss of activity of the Asp18 enzyme through a combination of impaired substrate positioning in the Michaelis complex, as well as a shift from early to late protonation of the leaving group in the H18D variant. The evidence presented furthermore suggests that the cooperative nature of catalysis distinguishes the enzymatic reaction from the corresponding reaction in solution and is enabled by the electrostatic preorganization of the active site. Our results reveal sophisticated discrimination in multifunctional catalysis of a highly proficient phosphatase active site

Essential Functional Interplay of the Catalytic Groups in Acid Phosphatase

The cooperative interplay between the functional devices of a preorganized active site is fundamental to enzyme catalysis. An in-depth understanding of this phenomenon is central to elucidating the remarkable efficiency of natural enzymes and provides an essential benchmark for enzyme design and engineering. Here, we study the functional interconnectedness of the catalytic nucleophile (His18) in an acid phosphatase by analyzing the consequences of its replacement with aspartate. We present crystallographic, biochemical, and computational evidence for a conserved mechanistic pathway via a phospho-enzyme intermediate on Asp18. Linear free-energy relationships for phosphoryl transfer from phosphomonoester substrates to His18/Asp18 provide evidence for the cooperative interplay between the nucleophilic and general-acid catalytic groups in the wild-type enzyme, and its substantial loss in the H18D variant. As an isolated factor of phosphatase efficiency, the advantage of a histidine compared to an aspartate nucleophile is similar to 10(4)-fold. Cooperativity with the catalytic acid adds &gt;= 10(2)-fold to that advantage. Empirical valence bond simulations of phosphoryl transfer from glucose 1-phosphate to His and Asp in the enzyme explain the loss of activity of the Asp18 enzyme through a combination of impaired substrate positioning in the Michaelis complex, as well as a shift from early to late protonation of the leaving group in the H18D variant. The evidence presented furthermore suggests that the cooperative nature of catalysis distinguishes the enzymatic reaction from the corresponding reaction in solution and is enabled by the electrostatic preorganization of the active site. Our results reveal sophisticated discrimination in multifunctional catalysis of a highly proficient phosphatase active site

The crystal structure of D-threonine aldolase from Alcaligenes xylosoxidans provides insight into a metal ion assisted PLP-dependent mechanism.

Threonine aldolases catalyze the pyridoxal phosphate (PLP) dependent cleavage of threonine into glycine and acetaldehyde and play a major role in the degradation of this amino acid. In nature, L- as well as D-specific enzymes have been identified, but the exact physiological function of D-threonine aldolases (DTAs) is still largely unknown. Both types of enantio-complementary enzymes have a considerable potential in biocatalysis for the stereospecific synthesis of various β-hydroxy amino acids, which are valuable building blocks for the production of pharmaceuticals. While several structures of L-threonine aldolases (LTAs) have already been determined, no structure of a DTA is available to date. Here, we report on the determination of the crystal structure of the DTA from Alcaligenes xylosoxidans (AxDTA) at 1.5 Å resolution. Our results underline the close relationship of DTAs and alanine racemases and allow the identification of a metal binding site close to the PLP-cofactor in the active site of the enzyme which is consistent with the previous observation that divalent cations are essential for DTA activity. Modeling of AxDTA substrate complexes provides a rationale for this metal dependence and indicates that binding of the β-hydroxy group of the substrate to the metal ion very likely activates this group and facilitates its deprotonation by His193. An equivalent involvement of a metal ion has been implicated in the mechanism of a serine dehydratase, which harbors a metal ion binding site in the vicinity of the PLP cofactor at the same position as in DTA. The structure of AxDTA is completely different to available structures of LTAs. The enantio-complementarity of DTAs and LTAs can be explained by an approximate mirror symmetry of crucial active site residues relative to the PLP-cofactor

A Novel High-Throughput Nanopore-Sequencing-Based Strategy for Rapid and Automated S-Protein Typing of SARS-CoV-2 Variants

Rapid molecular surveillance of SARS-CoV-2 S-protein variants leading to immune escape and/or increased infectivity is of utmost importance. Among global bottlenecks for variant monitoring in diagnostic settings are sequencing and bioinformatics capacities. In this study, we aimed to establish a rapid and user-friendly protocol for high-throughput S-gene sequencing and subsequent automated identification of variants. We designed two new primer pairs to amplify only the immunodominant part of the S-gene for nanopore sequencing. Furthermore, we developed an automated “S-Protein-Typer” tool that analyzes and reports S-protein mutations on the amino acid level including a variant of concern indicator. Validation of our primer panel using SARS-CoV-2-positive respiratory specimens covering a broad Ct range showed successful amplification for 29/30 samples. Restriction to the region of interest freed sequencing capacity by a factor of 12–13, compared with whole-genome sequencing. Using either the MinION or Flongle flow cell, our sequencing strategy reduced the time required to identify SARS-CoV-2 variants accordingly. The S-Protein-Typer tool identified all mutations correctly when challenged with our sequenced samples and 50 deposited sequences covering all VOCs (December 2021). Our proposed S-protein variant screening offers a simple, more rapid, and low-cost entry into NGS-based SARS-CoV-2 analysis, compared with current whole-genome approaches