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Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life.

An artificial metalloenzyme is compartmentalized and evolved in vivo for olefin metathesis—an archetypal organometallic reaction without equivalent in nature; the evolved metathase reveals broad substrate scope and compares favourably with commercial catalysts. Directed evolution of an artificial metalloenzyme Artificial metalloenzymes—made by incorporating an abiotic cofactor within a protein scaffold—have the potential to engineer non-natural in vivo reactions. To be of practical use, such catalysts must maintain their activity in a cellular environment, which means overcoming the tendency of introduced metal cofactors to be inhibited by cellular components. This paper demonstrates that directed evolution can overcome this difficulty. Markus Jeschek et al . report on the in vivo evolution of a ruthenium–protein complex that can catalyse olefin metathesis—an archetypal organometallic reaction with no equivalent in nature—in the periplasm of Escherichia coli . The evolved metathase compares favorably with commercial catalysts, shows activity for different metathesis substrates and can be further evolved in different directions by adjusting the protocol. The field of biocatalysis has advanced from harnessing natural enzymes to using directed evolution to obtain new biocatalysts with tailor-made functions 1 . Several tools have recently been developed to expand the natural enzymatic repertoire with abiotic reactions 2 , 3 . For example, artificial metalloenzymes, which combine the versatile reaction scope of transition metals with the beneficial catalytic features of enzymes, offer an attractive means to engineer new reactions. Three complementary strategies exist 3 : repurposing natural metalloenzymes for abiotic transformations 2 , 4 ; in silico metalloenzyme (re-)design 5 , 6 , 7 ; and incorporation of abiotic cofactors into proteins 8 , 9 , 10 , 11 . The third strategy offers the opportunity to design a wide variety of artificial metalloenzymes for non-natural reactions. However, many metal cofactors are inhibited by cellular components and therefore require purification of the scaffold protein 12 , 13 , 14 , 15 . This limits the throughput of genetic optimization schemes applied to artificial metalloenzymes and their applicability in vivo to expand natural metabolism. Here we report the compartmentalization and in vivo evolution of an artificial metalloenzyme for olefin metathesis, which represents an archetypal organometallic reaction 16 , 17 , 18 , 19 , 20 , 21 , 22 without equivalent in nature. Building on previous work 6 on an artificial metallohydrolase, we exploit the periplasm of Escherichia coli as a reaction compartment for the ‘metathase’ because it offers an auspicious environment for artificial metalloenzymes, mainly owing to low concentrations of inhibitors such as glutathione, which has recently been identified as a major inhibitor 15 . This strategy facilitated the assembly of a functional metathase in vivo and its directed evolution with substantially increased throughput compared to conventional approaches that rely on purified protein variants. The evolved metathase compares favourably with commercial catalysts, shows activity for different metathesis substrates and can be further evolved in different directions by adjusting the workflow. Our results represent the systematic implementation and evolution of an artificial metalloenzyme that catalyses an abiotic reaction in vivo , with potential applications in, for example, non-natural metabolism.

The engineering of catalysts with desirable properties can be accelerated by computer-aided design. To achieve this aim, features of molecular catalysts can be condensed into numerical descriptors that can then be used to correlate reactivity and structure. Based on such descriptors, we have introduced topographic steric maps that provide a three-dimensional image of the catalytic pocket—the region of the catalyst where the substrate binds and reacts—enabling it to be visualized and also reshaped by changing various parameters. These topographic steric maps, especially when used in conjunction with density functional theory calculations, enable catalyst structural modifications to be explored quickly, making the online design of new catalysts accessible to the wide chemical community. In this Perspective, we discuss the application of topographic steric maps either to rationalize the behaviour of known catalysts—from synthetic molecular species to metalloenzymes—or to design improved catalysts. The shape complementarity between the active site of a catalyst and a substrate influences how effectively a reaction can be catalysed. Computational tools can be used to visualize the shape around the active centre of a range of catalysts and the application of such approaches to rationalize the behaviour of known catalysts — and to design new ones — is discussed.

Enzyme biosensors are useful tools that can monitor rapid changes in metabolite levels in real-time. However, current approaches are largely constrained to metabolites within a limited chemical space. With the rising development of artificial metalloenzymes (ArM), a unique opportunity exists to design biosensors from the ground-up for metabolites that are difficult to detect using current technologies. Here we present the design and development of the ArM ethylene probe ( AEP ), where an albumin scaffold is used to solubilize and protect a quenched ruthenium catalyst. In the presence of the phytohormone ethylene, cross metathesis can occur to produce fluorescence. The probe can be used to detect both exogenous- and endogenous-induced changes to ethylene biosynthesis in fruits and leaves. Overall, this work represents an example of an ArM biosensor, designed specifically for the spatial and temporal detection of a biological metabolite previously not accessible using enzyme biosensors. Existing methods to detect ethylene in plant tissue typically require gas chromatography or use ethylene-dependent gene expression as a proxy. Here Vong et al . show that an artificial metalloenzyme-based ethylene probe can be used to detect ethylene in plants with improved spatiotemporal resolution.

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

Natural enzymes contain highly evolved active sites that lead to fast rates and high selectivities. Although artificial metalloenzymes have been developed that catalyze abiological transformations with high stereoselectivity, the activities of these artificial enzymes are much lower than those of natural enzymes. Here, we report a reconstituted artificial metalloenzyme containing an iridium porphyrin that exhibits kinetic parameters similar to those of natural enzymes. In particular, variants of the P450 enzyme CYP119 containing iridium in place of iron catalyze insertions of carbenes into C-H bonds with up to 98% enantiomeric 1 excess, 35,000 turnovers, and 2550 hours⁻¹ turnover frequency. This activity leads to intramolecular carbene insertions into unactivated C-H bonds and intermolecular carbene insertions into C-H bonds. These results lift the restrictions on merging chemical catalysis and biocatalysis to create highly active, productive, and selective metalloenzymes for abiological reactions.

De novo protein design has succeeded in generating a large variety of globular proteins, but the construction of protein scaffolds with cavities that could accommodate large signaling molecules, cofactors, and substrates remains an outstanding challenge. The long, often flexible loops that form such cavities in many natural proteins are difficult to precisely program and thus challenging for computational protein design. Here we describe an alternative approach to this problem. We fused two stable proteins with C2 symmetry—a de novo designed dimeric ferredoxin fold and a de novo designed TIM barrel—such that their symmetry axes are aligned to create scaffolds with large cavities that can serve as binding pockets or enzymatic reaction chambers. The crystal structures of two such designs confirm the presence of a 420 cubic Ångström chamber defined by the top of the designed TIM barrel and the bottom of the ferredoxin dimer. We functionalized the scaffold by installing a metal-binding site consisting of four glutamate residues close to the symmetry axis. The protein binds lanthanide ions with very high affinity as demonstrated by tryptophan-enhanced terbium luminescence. This approach can be extended to other metals and cofactors, making this scaffold a modular platform for the design of binding proteins and biocatalysts.

Divalent d-block metal cations (DDMCs), such as Fe, Zn and Mn, participate in many biological processes. Understanding how specific DDMCs are transported to and within the cell and what controls their binding selectivity to different proteins is crucial for defining the mechanisms of metalloproteins. To better understand such processes, we scanned the RCSB Protein Data Bank, performed a de novo structural-based comprehensive analysis of seven DDMCs and found their amino acid binding and coordination geometry propensities. We then utilized these results to characterize the correlation between metal selectivity, specific binding site composition and phylogenetic classification of the cation diffusion facilitator (CDF) protein family, a family of DDMC transporters found throughout evolution and sharing a conserved structure, yet with different members displaying distinct metal selectivity. Our analysis shows that DDMCs differ, at times significantly, in terms of their binding propensities, and that in each CDF clade, the metal selectivity-related binding site has a unique and conserved sequence signature. However, only limited correlation exists between the composition of the DDMC binding site in each clade and the metal selectivity shown by its proteins.

Cyanide (CN) is a well-known mitochondrial poison. CN poisoning may result from acute or long-term exposure to a number of CN compounds. Recent insight into the chemical affinities of the CN anion has increased our understanding of its toxicity and the mechanisms of antidotal actions, which, together with information on various exposure sources, are reviewed in the present article. A literature search in Scopus, Embase, Web of Science, PubMed, and Google Scholar for the period 2001–2024 revealed that the CN anion after exposure or degradation of CN compounds is distributed to vulnerable copper and iron-containing targets, especially in mitochondria, thus blocking the electron transport chain. Intake of cyanogenic compounds may exert subacute or chronic toxic effects, also because of the interaction with cobalt in vitamin B12. Antidotal agents exert their effects through the affinity of CN for cobalt- or iron-containing compounds. Research on CN interactions with metalloproteins may increase our insight into CN toxicity and efficient antidotal regimens.