MetalPDB: a database of metal sites in biological macromolecular structures

Insights into the differential proteome landscape of a newly isolated Paramecium multimicronucleatum in response to cadmium stress

Employing microbial systems for the bioremediation of contaminated waters represent a potential option, however, limited understanding of the underlying mechanisms hampers the implication of microbial-mediated bioremediation. The omics tools offer a promising approach to explore the molecular basis of the bioremediation process. Here, a mass spectrometry-based quantitative proteome profiling approach was conducted to explore the differential protein levels in cadmium-treated Paramecium multimicronucleatum. The Proteome Discoverer software was used to identify and quantify differentially abundant proteins. The proteome profiling generated 7,416 peptide spectral matches, yielding 2824 total peptides, corresponding to 989 proteins. The analysis revealed that 29 proteins exhibited significant (p ≤ 0.05) differential levels, including a higher abundance of 6 proteins and reduced levels of 23 proteins in Cd2+ treated samples. These differentially abundant proteins were associated with stress response, energy metabolism, protein degradation, cell growth, and hormone processing. Briefly, a comprehensive proteome profile in response to cadmium stress of a newly isolated Paramecium has been established that will be useful in future studies identifying critical proteins involved in the bioremediation of metals in ciliates. SIGNIFICANCE: Ciliates are considered a good biological indicator of chemical pollution and relatively sensitive to heavy metal contamination. A prominent ciliate, Paramecium is a promising candidate for the bioremediation of polluted water. The proteins related to metal resistance in Paramecium species are still largely unknown and need further exploration. In order to identify and reveal the proteins related to metal resistance in Paramecia, we have reported differential protein abundance in Paramecium multimicronucleatum in response to cadmium stress. The proteins found in our study play essential roles during stress response, hormone processing, protein degradation, energy metabolism, and cell growth. It seems likely that Paramecia are not a simple sponge for metals but they could also transform them into less toxic derivatives or by detoxification by protein binding. This data will be helpful in future studies to identify critical proteins along with their detailed mechanisms involved in the bioremediation and detoxification of metal ions in Paramecium species.

A database overview of metal-coordination distances in metalloproteins

Metalloproteins are ubiquitous in all living organisms and take part in a very wide range of biological processes. For this reason, their experimental characterization is crucial to obtain improved knowledge of their structure and biological functions. The three-dimensional structure represents highly relevant information since it provides insight into the interaction between the metal ion(s) and the protein fold. Such interactions determine the chemical reactivity of the bound metal. The available PDB structures can contain errors due to experimental factors such as poor resolution and radiation damage. A lack of use of distance restraints during the refinement and validation process also impacts the structure quality. Here, the aim was to obtain a thorough overview of the distribution of the distances between metal ions and their donor atoms through the statistical analysis of a data set based on more than 115 000 metal-binding sites in proteins. This analysis not only produced reference data that can be used by experimentalists to support the structure-determination process, for example as refinement restraints, but also resulted in an improved insight into how protein coordination occurs for different metals and the nature of their binding interactions. In particular, the features of carboxylate coordination were inspected, which is the only type of interaction that is commonly present for nearly all metals.

MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks

The chemical properties of metal complexes are strongly dependent on the number and geometrical arrangement of ligands coordinated to the metal center. Existing methods for determining either coordination number or geometry rely on a trade-off between accuracy and computational costs, which hinders their application to the study of large structure data sets. Here, we propose MetalHawk (https://github.com/vrettasm/MetalHawk), a machine learning-based approach to perform simultaneous classification of metal site coordination number and geometry through artificial neural networks (ANNs), which were trained using the Cambridge Structural Database (CSD) and Metal Protein Data Bank (MetalPDB). We demonstrate that the CSD-trained model can be used to classify sites belonging to the most common coordination numbers and geometry classes with balanced accuracy equal to 96.51% for CSD-deposited metal sites. The CSD-trained model was also found to be capable of classifying bioinorganic metal sites from the MetalPDB database, with balanced accuracy equal to 84.29% on the whole PDB data set and to 91.66% on manually reviewed sites in the PDB validation set. Moreover, we report evidence that the output vectors of the CSD-trained model can be considered as a proxy indicator of metal-site distortions, showing that these can be interpreted as a low-dimensional representation of subtle geometrical features present in metal site structures.

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify

Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.

Characterization of the Zinc Uptake Repressor (Zur) from Acinetobacter baumannii

Bacterial cells tightly regulate the intracellular concentrations of essential transition metal ions by deploying a panel of metal-regulated transcriptional repressors and activators that bind to operator-promoter regions upstream of regulated genes. Like other zinc uptake regulator (Zur) proteins, Acinetobacter baumannii Zur represses transcription of its regulon when ZnII is replete and binds more weakly to DNA when ZnII is limiting. Previous studies established that Zur proteins are homodimeric and harbor at least two metal sites per protomer or four per dimer. CdII X-ray absorption spectroscopy (XAS) of the Cd2Zn2 AbZur metalloderivative with CdII bound to the allosteric sites reveals a S(N/O)3 first coordination shell. Site-directed mutagenesis suggests that H89 and C100 from the N-terminal DNA binding domain and H107 and E122 from the C-terminal dimerization domain comprise the regulatory metal site. KZn for this allosteric site is 6.0 (±2.2) × 1012 M-1 with a functional "division of labor" among the four metal ligands. N-terminal domain ligands H89 and C100 contribute far more to KZn than H107 and E122, while C100S AbZur uniquely fails to bind to DNA tightly as measured by an in vitro transcription assay. The heterotropic allosteric coupling free energy, ΔGc, is negative, consistent with a higher KZn for the AbZur-DNA complex and defining a bioavailable ZnII set-point of ≈6 × 10-14 M. Small-angle X-ray scattering (SAXS) experiments reveal that only the wild-type Zn homodimer undergoes allosteric switching, while the C100S AbZur fails to switch. These data collectively suggest that switching to a high affinity DNA-binding conformation involves a rotation/translation of one protomer relative to the other in a way that is dependent on the integrity of C100. We place these findings in the context of other Zur proteins and Fur family repressors more broadly.

Differences in heavy metal binding to cysteine-containing coiled-coil peptides

One third of all structurally characterised proteins contain a metal; however, the interplay between metal-binding and peptide/protein folding has yet to be fully elucidated. To better understand how metal binding affects peptide folding, a range of metals should be studied within a specific scaffold. To this end, we modified a histidine-containing coiled-coil peptide to create a cysteine-containing scaffold, named CX3C, which was designed to bind heavy metal ions. In addition, we generated a peptide named CX2C, which contains a binding site more commonly found in natural proteins. Using a combination of analytical techniques including circular dichroism (CD) spectroscopy, UV-Vis spectroscopy and size-exclusion chromatography coupled to multi-angle light scattering (SEC-MALS), we examined the differences in the metal-binding properties of the two peptides. Both peptides are largely unfolded in the apo state due to the disruption of the hydrophobic core by inclusion of the polar cysteine residues. However, this unfolding is overcome by the addition of Cd(II), Pb(II) and Hg(II), and helical assemblies are formed. Both peptides have differing affinities for these metal ions, a fact likely attributed to the differing sizes of the ions. We also show that the oligomerisation state of the peptide complexes and the coordination geometries of the metal ions differ between the two peptide scaffolds. These findings highlight that subtle changes in the primary structure of a peptide can have considerable implications for metal binding.

Thermodynamics of Metal-Acetate Interactions

Metal ions play crucial roles in protein- and ligand-mediated interactions. They not only act as catalysts to facilitate biological processes but are also important as protein structural elements. Accurately predicting metal ion interactions in computational studies has always been a challenge, and various methods have been suggested to improve these interactions. One such method is the 12-6-4 Lennard-Jones (LJ)-type nonbonded model. Using this model, it has been possible to successfully reproduce the experimental properties of metal ions in aqueous solution. The model includes induced dipole interactions typically ignored in the standard 12-6 LJ nonbonded model. In this we expand the applicability of this model to metal ion-carboxylate interactions. Using 12-6-4 parameters that reproduce the solvation free energies of the metal ions leads to an overestimation of metal ion-acetate interactions, thus, prompting us to fine-tune the model to specifically handle the latter. We also show that the standard 12-6 LJ model significantly falls short in reproducing the experimental binding free energy between acetate and 11 metal ions (Ni(II), Mg(II), Cu(II), Zn(II), Co(II), Cu(I), Fe(II), Mn(II), Cd(II), Ca(II), and Ag(I)). In this study, we describe optimized C4 parameters for the 12-6-4 LJ nonbonded model to be used with three widely employed water models (Transferable Intermolecular Potential with 3 Points (TIP3P), Simple Point Charge Extended (SPC/E), and Optimal Point Charge (OPC) water models). These parameters can accurately match the experimental binding free energy between 11 metal ions and acetate. These parameters can be applied to the study of metalloproteins and transition metal ion channels and transporters, as acetate serves as a representative of the negatively charged amino acid side chains from aspartate and glutamate.

Fe(II), Mn(II), and Zn(II) Binding to the C-Terminal Region of FeoB Protein: An Insight into the Coordination Chemistry and Specificity of the Escherichia coli Fe(II) Transporter

The interactions between two peptide ligands [Ac763CCAASTTGDCH773 (P1) and Ac743RRARSRVDIELLATRKSVSSCCAASTTGDCH773 (P2)] derived from the cytoplasmic C-terminal region of Eschericha coli FeoB protein and Fe(II), Mn(II), and Zn(II) ions were investigated. The Feo system is regarded as the most important bacterial Fe(II) acquisition system, being one of the key virulence factors, especially in anaerobic conditions. Located in the inner membrane of Gram-negative bacteria, FeoB protein transports Fe(II) from the periplasm to the cytoplasm. Despite its crucial role in bacterial pathogenicity, the mechanism in which the metal ion is trafficked through the membrane is not yet elucidated. In the gammaproteobacteria class, the cytoplasmic C-terminal part of FeoB contains conserved cysteine, histidine, and glutamic and aspartic acid residues, which could play a vital role in Fe(II) binding in the cytoplasm, receiving the metal ion from the transmembrane helices. In this work, we characterized the complexes formed between the whole cytosolic C-terminal sequence of E. coli FeoB (P2) and its key polycysteine region (P1) with Fe(II), Mn(II), and Zn(II) ions, exploring the specificity of the C-terminal region of FeoB. With the help of a variety of potentiometric, spectroscopic (electron paramagnetic resonance and NMR), and spectrometric (electrospray ionization mass spectrometry) techniques and molecular dynamics, we propose the metal-binding modes of the ligands, compare their affinities toward the metal ions, and discuss the possible physiological role of the C-terminal region of E. coli FeoB.

Hunting down zinc(II)-binding sites in proteins with distance matrices

MOTIVATION

In recent years, high-throughput sequencing technologies have made available the genome sequences of a huge variety of organisms. However, the functional annotation of the encoded proteins often still relies on low-throughput and costly experimental studies. Bioinformatics approaches offer a promising alternative to accelerate this process. In this work, we focus on the binding of zinc(II) ions, which is needed for 5%-10% of any organism's proteins to achieve their physiologically relevant form.

RESULTS

To implement a predictor of zinc(II)-binding sites in the 3D structures of proteins, we used a neural network, followed by a filter of the network output against the local structure of all known sites. The latter was implemented as a function comparing the distance matrices of the Cα and Cβ atoms of the sites. We called the resulting tool Master of Metals (MOM). The structural models for the entire proteome of an organism generated by AlphaFold can be used as input to our tool in order to achieve annotation at the whole organism level within a few hours. To demonstrate this, we applied MOM to the yeast proteome, obtaining a precision of about 76%, based on data for homologous proteins.

AVAILABILITY AND IMPLEMENTATION

Master of Metals has been implemented in Python and is available at https://github.com/cerm-cirmmp/Master-of-metals.

New Perspectives on Food Matrix Modulation of Food Allergies: Immunomodulation and Component Interactions

Food allergy is a multifactorial interplay process influenced not only by the structure and function of the allergen itself but also by other components of the food matrix. For food, before it is thoroughly digested and absorbed, numerous factors make the food matrix constantly change. This will also lead to changes in the chemistry, biochemical composition, and structure of the various components in the matrix, resulting in multifaceted effects on food allergies. In this review, we reveal the relationship between the food matrix and food allergies and outline the immune role of the components in the food matrix, while highlighting the ways and pathways in which the components in the food matrix interact and their impact on food allergies. The in-depth study of the food matrix will essentially explore the mechanism of food allergies and bring about new ideas and breakthroughs for the prevention and treatment of food allergies.

Bond clusters control rupture force limit in shear loaded histidine-Ni2+ metal-coordinated proteins

Dynamic noncovalent interactions are pivotal to the structure and function of biological proteins and have been used in bioinspired materials for similar roles. Metal-coordination bonds, in particular, are especially tunable and enable control over static and dynamic properties when incorporated into synthetic materials. Despite growing efforts to engineer metal-coordination bonds to produce strong, tough, and self-healing materials, the systematic characterization of the exact contribution of these bonds towards mechanical strength and the effect of geometric arrangements is missing, limiting the full design potential of these bonds. In this work, we engineer the cooperative rupture of metal-coordination bonds to increase the rupture strength of metal-coordinated peptide dimers. Utilizing all-atom steered molecular dynamics simulations on idealized bidentate histidine-Ni²⁺ coordinated peptides, we show that histidine-Ni²⁺ bonds can rupture cooperatively in groups of two to three bonds. We find that there is a strength limit, where adding additional coordination bonds does not contribute to the additional increase in the protein rupture strength, likely due to the highly heterogeneous rupture behavior exhibited by the coordination bonds. Further, we show that this coordination bond limit is also found natural metal-coordinated biological proteins. Using these insights, we quantitatively suggest how other proteins can be rationally designed with dynamic noncovalent interactions to exhibit cooperative bond breaking behavior. Altogether, this work provides a quantitative analysis of the cooperativity and intrinsic strength limit for metal-coordination bonds with the aim of advancing clear guiding molecular principles for the mechanical design of metal-coordinated materials.

The Metal-binding Protein Atlas (MbPA): an integrated database for curating metalloproteins in all aspects

Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106373 entries and 440187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.

Copper Reductase Activity and Free Radical Chemistry by Cataract-Associated Human Lens γ-Crystallins

Cataracts are caused by high-molecular-weight aggregates of human eye lens proteins that scatter light, causing lens opacity. Metal ions have emerged as important potential players in the etiology of cataract disease, as human lens γ-crystallins are susceptible to metal-induced aggregation. Here, the interaction of Cu²⁺ ions with γD-, γC-, and γS-crystallins, the three most abundant γ-crystallins in the lens, has been evaluated. Cu²⁺ ions induced non-amyloid aggregation in all three proteins. Solution turbidimetry, sodium dodecyl sulfate poly(acrylamide) gel electrophoresis (SDS-PAGE), circular dichroism, and differential scanning calorimetry showed that the mechanism for Cu-induced aggregation involves: (i) loss of β-sheet structure in the N-terminal domain; (ii) decreased thermal and kinetic stability; (iii) formation of metal-bridged species; and (iv) formation of disulfide-bridged dimers. Isothermal titration calorimetry (ITC) revealed distinct Cu²⁺ binding affinities in the γ-crystallins. Electron paramagnetic resonance (EPR) revealed two distinct Cu²⁺ binding sites in each protein. Spin quantitation demonstrated the reduction of γ-crystallin-bound Cu²⁺ ions to Cu⁺ under aerobic conditions, while X-ray absorption spectroscopy (XAS) confirmed the presence of linear or trigonal Cu⁺ binding sites in γ-crystallins. Our EPR and XAS studies revealed that γ-crystallins' Cu²⁺ reductase activity yields a protein-based free radical that is likely a Tyr-based species in human γD-crystallin. This unique free radical chemistry carried out by distinct redox-active Cu sites in human lens γ-crystallins likely contributes to the mechanism of copper-induced aggregation. In the context of an aging human lens, γ-crystallins could act not only as structural proteins but also as key players for metal and redox homeostasis.

A Comparison of Bonded and Nonbonded Zinc(II) Force Fields with NMR Data

Classical molecular dynamics (MD) simulations are widely used to inspect the behavior of zinc(II)-proteins at the atomic level, hence the need to properly model the zinc(II) ion and the interaction with its ligands. Different approaches have been developed to represent zinc(II) sites, with the bonded and nonbonded models being the most used. In the present work, we tested the well-known zinc AMBER force field (ZAFF) and a recently developed nonbonded force field (NBFF) to assess how accurately they reproduce the dynamic behavior of zinc(II)-proteins. For this, we selected as benchmark six zinc-fingers. This superfamily is extremely heterogenous in terms of architecture, binding mode, function, and reactivity. From repeated MD simulations, we computed the order parameter (S2) of all backbone N-H bond vectors in each system. These data were superimposed to heteronuclear Overhauser effect measurements taken by NMR spectroscopy. This provides a quantitative estimate of the accuracy of the FFs in reproducing protein dynamics, leveraging the information about the protein backbone mobility contained in the NMR data. The correlation between the MD-computed S2 and the experimental data indicated that both tested FFs reproduce well the dynamic behavior of zinc(II)-proteins, with comparable accuracy. Thus, along with ZAFF, NBFF represents a useful tool to simulate metalloproteins with the advantage of being extensible to diverse systems such as those bearing dinuclear metal sites.

Classifying metal-binding sites with neural networks

To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal-binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine-tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe-2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub-classifications of the [2Fe-2S] cofactor: (1) a standard [2Fe-2S] cofactor and (2) a Rieske [2Fe-2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal-binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal-binding site identification in proteins.

Metal-induced structural variability of mononuclear metal-binding sites from a database perspective

Metalloproteins are ubiquitous in all kingdoms of life. Their role and function are tightly related to the local structure of the metal-binding site. In this regard, the MetalPDB database is an invaluable tool since it stores the 3D structure of metal-binding sites and of their corresponding apo forms. In this work, we exploited MetalPDB to compute extensive statistics over >3000 clusters of mononuclear sites about the rearrangements occurring upon change in metalation state. For each cluster, we matched the holo and apo sites so that it was possible to average the distances between all possible pairs of Cα and donor atoms and thus quantitatively assess structural variations by computing the Δ values (mean apo distance - mean holo distance). For most of the structures the backbone is rigid with little to no rearrangement, while donor atoms experience significant changes of their relative position when the metal is removed. Sodium and potassium sites are an exception to this general observation. This is most likely caused by their preference for coordination by the main-chain oxygen atoms, making the rearrangement of donor atoms superimposable to that of the backbone. Magnesium and calcium show a different behavior, despite their chemical similarity: calcium sites undergo a larger reorganization upon metalation although both metals have similar percentage of backbone oxygen as donor atoms. We ascribe this observation to the structural and energetic factors regulating the selectivity for calcium over magnesium.

Rapid diagnosis of malaria by chemometric peak-free LIBS of trace biometals in blood

Laser Induced Breakdown Spectroscopy (LIBS) trace atomic species of diseased biofluids are subtle (peak-free) in complex spectra. Trace analysis requires a considerable push in analytical strategy. Enabling LIBS with chemometrics can help identify, extract, analyze and interpret the trace species' spectral signatures to give an insight on the biophysiological status of the bodies from which the biofluids originate. We report on the trace quantitative performance of peak-free LIBS enabled by chemometrics modelling using principal components analysis (PCA) for direct artificial neural network (ANN)-based analysis of Cu, Zn, Fe and Mg in Plasmodium falciparum-infected blood in the context of rapid spectral diagnosis of malaria utilizing the biometals as the disease biomarkers. Only one standard is required in this method-to delineate the analyte spectral regions (feature selection) and to test for accuracy. Based on the alteration of the biometal levels and their multivariate and correlational patterns in cultured blood, peripheral finger blood drops dried directly on Nucleopore membrane filters was accurately discriminated as either malaria-infected or healthy. Further the morphological evolution of Plasmodium was accurately predicted using spectral features of the biometals wherein high negative correlations between Fe (- 0.775) and Zn (- 0.881) and high positive correlations between Cu (0.892) and Mg (0.805) with parasitemia were observed. During the first 96 h of malaria infection Cu increases profoundly (from 328 to 1999 ppb) while Fe, Zn and Mg decrease (from 1206 to 674 ppb), (from 1523 to 499 ppb) and (from 23,880 to 19,573 ppb) respectively. Compared with healthy, Plasmodium falciparum-infected blood has high Cu but low levels of Fe, Zn and Mg. Cu and Zn are highly (≥ 0.9) positively correlated while Fe and Cu as well as Zn and Cu are highly (≥ 0.9) negatively correlated. Chemometric peak-free LIBS showed the potential for direct rapid malaria diagnostics in blood based on the levels, alterations and multivariate associations of the trace biometals which are used as biomarkers of the disease.

Design of a Flexible, Zn-Selective Protein Scaffold that Displays Anti-Irving-Williams Behavior

Selective metal binding is a key requirement not only for the functions of natural metalloproteins but also for the potential applications of artificial metalloproteins in heterogeneous environments such as cells and environmental samples. The selection of transition-metal ions through protein design can, in principle, be achieved through the appropriate choice and the precise positioning of amino acids that comprise the primary metal coordination sphere. However, this task is made difficult by the intrinsic flexibility of proteins and the fact that protein design approaches generally lack the sub-Å precision required for the steric selection of metal ions. We recently introduced a flexible/probabilistic protein design strategy (MASCoT) that allows metal ions to search for optimal coordination geometry within a flexible, yet covalently constrained dimer interface. In an earlier proof-of-principle study, we used MASCoT to generate an artificial metalloprotein dimer, (AB)₂, which selectively bound Co^II and Ni^II over Cu^II (as well as other first-row transition-metal ions) through the imposition of a rigid octahedral coordination geometry, thus countering the Irving-Williams trend. In this study, we set out to redesign (AB)₂ to examine the applicability of MASCoT to the selective binding of other metal ions. We report here the design and characterization of a new flexible protein dimer, B₂, which displays Zn^II selectivity over all other tested metal ions including Cu^II both in vitro and in cellulo. Selective, anti-Irving-Williams Zn^II binding by B₂ is achieved through the formation of a unique trinuclear Zn coordination motif in which His and Glu residues are rigidly placed in a tetrahedral geometry. These results highlight the utility of protein flexibility in the design and discovery of selective binding motifs.

Relations between Structure and Zn(II) Binding Affinity Shed Light on the Mechanisms of Rad50 Hook Domain Functioning and Its Phosphorylation

The metal binding at protein–protein interfaces is still uncharted territory in intermolecular interactions. To date, only a few protein complexes binding Zn(II) in an intermolecular manner have been deeply investigated. The most notable example of such interfaces is located in the highly conserved Rad50 protein, part of the Mre11-Rad50-Nbs1 (MRN) complex, where Zn(II) is required for homodimerization (Zn(Rad50)2). The high stability of Zn(Rad50)2 is conserved not only for the protein derived from the thermophilic archaeon Pyrococcus furiosus (logK12 = 20.95 for 130-amino-acid-long fragment), which was the first one studied, but also for the human paralog studied here (logK12 = 19.52 for a 183-amino-acid-long fragment). As we reported previously, the extremely high stability results from the metal-coupled folding process where particular Rad50 protein fragments play a critical role. The sequence–structure–stability analysis based on human Rad50 presented here separates the individual structural components that increase the stability of the complex, pointing to amino acid residues far away from the Zn(II) binding site as being largely responsible for the complex stabilization. The influence of the individual components is very well reflected by the previously published crystal structure of the human Rad50 zinc hook (PDB: 5GOX). In addition, we hereby report the effect of phosphorylation of the zinc hook domain, which exerts a destabilizing effect on the domain. This study identifies factors governing the stability of metal-mediated protein–protein interactions and illuminates their molecular basis.

The zinc proteome of SARS-CoV-2

Zinc is an essential element for human health. Among its many functions, zinc(II) modulates the immune response to infections and, at high concentrations or in the presence of ionophores, inhibits the replication of various RNA viruses. Structural biology studies on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed that zinc(II) is the most common metal ion that binds to viral proteins. However, the number of zinc(II)-binding sites identified by experimental methods is far from exhaustive, as metal ions may be lost during protein purification protocols. To better define the zinc(II)-binding proteome of coronavirus, we leveraged the wealth of deposited structural data and state-of-the-art bioinformatics methods. Through this in silico approach, 15 experimental zinc(II) sites were identified and a further 22 were predicted in Spike, open reading frame (ORF)3a/d, ORF8, and several nonstructural proteins, highlighting an essential role of zinc(II) in viral replication. Furthermore, the structural relationships between viral and eukaryotic sites (typically zinc fingers) indicate that SARS-CoV-2 can compete with human proteins for zinc(II) binding. Given the double-edged effect of zinc(II) ions, both essential and toxic to coronavirus, only the complete elucidation of the structural and regulatory zinc(II)-binding sites can guide selective antiviral strategies based on zinc supplementation.

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications

All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures.

Learning to Identify Physiological and Adventitious Metal-Binding Sites in the Three-Dimensional Structures of Proteins by Following the Hints of a Deep Neural Network

Thirty-eight percent of protein structures in the Protein Data Bank contain at least one metal ion. However, not all these metal sites are biologically relevant. Cations present as impurities during sample preparation or in the crystallization buffer can cause the formation of protein-metal complexes that do not exist in vivo. We implemented a deep learning approach to build a classifier able to distinguish between physiological and adventitious zinc-binding sites in the 3D structures of metalloproteins. We trained the classifier using manually annotated sites extracted from the MetalPDB database. Using a 10-fold cross validation procedure, the classifier achieved an accuracy of about 90%. The same neural classifier could predict the physiological relevance of non-heme mononuclear iron sites with an accuracy of nearly 80%, suggesting that the rules learned on zinc sites have general relevance. By quantifying the relative importance of the features describing the input zinc sites from the network perspective and by analyzing the characteristics of the MetalPDB datasets, we inferred some common principles. Physiological sites present a low solvent accessibility of the aminoacids forming coordination bonds with the metal ion (the metal ligands), a relatively large number of residues in the metal environment (≥20), and a distinct pattern of conservation of Cys and His residues in the site. Adventitious sites, on the other hand, tend to have a low number of donor atoms from the polypeptide chain (often one or two). These observations support the evaluation of the physiological relevance of novel metal-binding sites in protein structures.

Role of water-bridged interactions in metal ion coupled protein allostery

Allosteric communication between distant parts of proteins controls many cellular functions, in which metal ions are widely utilized as effectors to trigger the allosteric cascade. Due to the involvement of strong coordination interactions, the energy landscape dictating the metal ion binding is intrinsically rugged. How metal ions achieve fast binding by overcoming the landscape ruggedness and thereby efficiently mediate protein allostery is elusive. By performing molecular dynamics simulations for the Ca²⁺ binding mediated allostery of the calmodulin (CaM) domains, each containing two Ca²⁺ binding helix-loop-helix motifs (EF-hands), we revealed the key role of water-bridged interactions in Ca²⁺ binding and protein allostery. The bridging water molecules between Ca²⁺ and binding residue reduces the ruggedness of ligand exchange landscape by acting as a lubricant, facilitating the Ca²⁺ coupled protein allostery. Calcium-induced rotation of the helices in the EF-hands, with the hydrophobic core serving as the pivot, leads to exposure of hydrophobic sites for target binding. Intriguingly, despite being structurally similar, the response of the two symmetrically arranged EF-hands upon Ca²⁺ binding is asymmetric. Breakage of symmetry is needed for efficient allosteric communication between the EF-hands. The key roles that water molecules play in driving allosteric transitions are likely to be general in other metal ion mediated protein allostery.

Natural proteins often utilize allostery in executing a variety of functions. Metal ions are typical cofactors to trigger the allosteric cascade. In this work, using the Ca²⁺ sensor protein calmodulin as the model system, we revealed crucial roles of water-bridged interactions in the metal ion coupled protein allostery. The coordination of the Ca²⁺ to the binding site involves an intermediate in which the water molecule bridges the Ca²⁺ and the liganding residue. The bridging water reduces the free energy barrier height of ligand exchange, therefore facilitating the ligand exchange and allosteric coupling by acting as a lubricant. We also showed that the response of the two symmetrically arranged EF-hand motifs of CaM domains upon Ca²⁺ binding is asymmetric, which is directly attributed to the differing dehydration process of the Ca²⁺ ions and is needed for efficient allosteric communication.

mebipred: identifying metal binding potential in protein sequence

Motivation

metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability.

Results

we developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred’s utility in analyzing microbiome metal requirements.

Availability and implementation

mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

The Fe-S proteome of Escherichia coli: prediction, function and fate

Iron-sulfur (Fe-S) clusters are inorganic ubiquitous and ancient cofactors. Fe-S bound proteins contribute to most cellular processes, including DNA replication and integrity, genetic expression and regulation, metabolism, biosynthesis and most bioenergetics systems. Also, Fe-S proteins hold a great biotechnological potential in metabolite and chemical production, including antibiotics. From classic biophysics and spectroscopy methodologies to recent development in bioinformatics, including structural modeling and chemoproteomics, our capacity to predict and identify Fe-S proteins has spectacularly increased over the recent years. Here, these developments are presented and collectively used to update the composition of Escherichia coli Fe-S proteome, for which we predict 181 occurrences, i.e. 40 more candidates than in our last catalog (Py and Barras, 2010), and equivalent to 4% of its total proteome. Besides, Fe-S clusters can be targeted by redox active compounds or reactive oxygen and nitrosative species, and even be destabilized by contaminant metals. Accordingly, we discuss how cells handle damaged Fe-S proteins, i.e. degradation, recycling or repair.

Porphyrin in prebiotic catalysis: Ascertaining a route for the emergence of early metalloporphyrins

Metal ions are known to catalyze certain prebiotic reactions. However, the transition from metal ions to extant metalloenzymes remains unclear. Porphyrins are found ubiquitously in the catalytic core of many ancient metalloenzymes. In this study, we evaluated the influence of porphyrin-based organic scaffold, on the catalysis, emergence and putative molecular evolution of prebiotic metalloporphyrins. We studied the effect of porphyrins on the transition metal ion-mediated oxidation of hydroquinone (HQ). We report a change in the catalytic activity of the metal ions in the presence of porphyrin. This was observed to be facilitated by the coordination between metal ions and porphyrins or by the formation of non-coordinated complexes. The metal-porphyrin complexes also oxidized NADH, underscoring its versatility at oxidizing more than one substrate. Our study highlights the selective advantage that some of the metal ions would have had in the presence of porphyrin, underscoring their role in shaping the evolution of protometalloenzymes.

MeCOM: A Method for Comparing Three-Dimensional Metalloenzyme Active Sites

Since metalloenzymes are a large collection of metal ion(s) dependent enzymes, comparison analyses of metalloenzyme active sites are critical for metalloenzyme de novo design, function investigation, and inhibitor development. Here, we report a method named MeCOM for comparing metalloenzyme active sites. It is characterized by metal ion(s) centric active site recognition and three-dimensional superimposition using α-carbon or pharmacophore features. The test results revealed that for the given metalloenzymes, MeCOM could effectively recognize the active sites, extract active site features, and superimpose the active sites; it also could correctly identify similar active sites, differentiate dissimilar active sites, and evaluate the similarity degree. Moreover, MeCOM showed potential to establish new associations between structurally distinct metalloenzymes by active site comparison. MeCOM is freely available at https://mecom.ddtmlab.org.

Ten things I `hate' about refinement

Macromolecular refinement is an optimization process that aims to produce the most likely macromolecular structural model in the light of experimental data. As such, macromolecular refinement is one of the most complex optimization problems in wide use. Macromolecular refinement programs have to deal with the complex relationship between the parameters of the atomic model and the experimental data, as well as a large number of types of prior knowledge about chemical structure. This paper draws attention to areas of unfinished business in the field of macromolecular refinement. In it, we describe ten refinement topics that we think deserve attention and discuss directions leading to macromolecular refinement software that would make the best use of modern computer resources to meet the needs of structural biologists of the twenty-first century.

Zn-Induced Interactions Between SARS-CoV-2 orf7a and BST2/Tetherin

We present in this work a first X-ray Absorption Spectroscopy study of the interactions of Zn with human BST2/tetherin and SARS-CoV-2 orf7a proteins as well as with some of their complexes. The analysis of the XANES region of the measured spectra shows that Zn binds to BST2, as well as to orf7a, thus resulting in the formation of BST2-orf7a complexes. This structural information confirms the the conjecture, recently put forward by some of the present Authors, according to which the accessory orf7a (and possibly also orf8) viral protein are capable of interfering with the BST2 antiviral activity. Our explanation for this behavior is that, when BST2 gets in contact with Zn bound to the orf7a Cys15 ligand, it has the ability of displacing the metal owing to the creation of a new disulfide bridge across the two proteins. The formation of this BST2-orf7a complex destabilizes BST2 dimerization, thus impairing the antiviral activity of the latter.

virusMED: an atlas of hotspots of viral proteins

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

Nitrite reductase activity within an antiparallel de novo scaffold

Copper nitrite reductase (CuNiR) is a copper enzyme that converts nitrite to nitric oxide and is an important part of the global nitrogen cycle in bacteria. The relatively simple CuHis₃ binding site of the CuNiR active site has made it an enticing target for small molecule modeling and de novo protein design studies. We have previously reported symmetric CuNiR models within parallel three stranded coiled coil systems, with activities that span a range of three orders of magnitude. In this report, we investigate the same CuHis₃ binding site within an antiparallel three helical bundle scaffold, which allows the design of asymmetric constructs. We determine that a simple CuHis₃ binding site can be designed within this scaffold with enhanced activity relative to the comparable construct in parallel coiled coils. Incorporating more complex designs or repositioning this binding site can decrease this activity as much as 15 times. Comparing these constructs, we reaffirm a previous result in which a blue shift in the 1s to 4p transition energy determined by Cu(I) X-ray absorption spectroscopy is correlated with an enhanced activity within imidazole-based constructs. With this step and recent successful electron transfer site designs within this scaffold, we are one step closer to a fully functional de novo designed nitrite reductase.

Comparative Genomic Analysis of the DUF34 Protein Family Suggests Role as a Metal Ion Chaperone or Insertase

Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as "GTP cyclohydrolase I type 2" through electronic propagation based on one study. Here, the annotation status of this protein family was examined through a comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox, and universal stress responses.

Titratable transmembrane residues and a hydrophobic plug are essential for manganese import via the Bacillus anthracis ABC transporter MntBC-A

All extant life forms require trace transition metals (e.g., Fe^2/3+, Cu^1/2+, and Mn²⁺) to survive. However, as these are environmentally scarce, organisms have evolved sophisticated metal uptake machineries. In bacteria, high-affinity import of transition metals is predominantly mediated by ABC transporters. During bacterial infection, sequestration of metal by the host further limits the availability of these ions, and accordingly, bacterial ABC transporters (importers) of metals are key virulence determinants. However, the structure–function relationships of these metal transporters have not been fully elucidated. Here, we used metal-sensitivity assays, advanced structural modeling, and enzymatic assays to study the ABC transporter MntBC-A, a virulence determinant of the bacterial human pathogen Bacillus anthracis. We find that despite its broad metal-recognition profile, MntBC-A imports only manganese, whereas zinc can function as a high-affinity inhibitor of MntBC-A. Computational analysis shows that the transmembrane metal permeation pathway is lined with six titratable residues that can coordinate the positively charged metal, and mutagenesis studies show that they are essential for manganese transport. Modeling suggests that access to these titratable residues is blocked by a ladder of hydrophobic residues, and ATP-driven conformational changes open and close this hydrophobic seal to permit metal binding and release. The conservation of this arrangement of titratable and hydrophobic residues among ABC transporters of transition metals suggests a common mechanism. These findings advance our understanding of transmembrane metal recognition and permeation and may aid the design and development of novel antibacterial agents.

Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem

Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is critical for understanding the vast majority of cellular processes, with direct applications in health and food sciences. Since 2010, the WeNMR project (www.wenmr.eu) has implemented numerous web-based services to facilitate the use of advanced computational tools by researchers in the field, using the high throughput computing infrastructure provided by EGI. These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4,000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. The current set of active WeNMR portals are described, together with the complex backend machinery that allows distributed computing resources to be harvested efficiently.

AncPhore: A versatile tool for anchor pharmacophore steered drug discovery with applications in discovery of new inhibitors targeting metallo-β-lactamases and indoleamine/tryptophan 2,3-dioxygenases

We herein describe AncPhore, a versatile tool for drug discovery, which is characterized by pharmacophore feature analysis and anchor pharmacophore (i.e., most important pharmacophore features) steered molecular fitting and virtual screening. Comparative analyses of numerous protein–ligand complexes using AncPhore revealed that anchor pharmacophore features are biologically important, commonly associated with protein conservative characteristics, and have significant contributions to the binding affinity. Performance evaluation of AncPhore showed that it had substantially improved prediction ability on different types of target proteins including metalloenzymes by considering the specific contributions and diversity of anchor pharmacophore features. To demonstrate the practicability of AncPhore, we screened commercially available chemical compounds and discovered a set of structurally diverse inhibitors for clinically relevant metallo-β-lactamases (MBLs); of them, 4 and 6 manifested potent inhibitory activity to VIM-2, NDM-1 and IMP-1 MBLs. Crystallographic analyses of VIM-2:4 complex revealed the precise inhibition mode of 4 with VIM-2, highly consistent with the defined anchor pharmacophore features. Besides, we also identified new hit compounds by using AncPhore for indoleamine/tryptophan 2,3-dioxygenases (IDO/TDO), another class of clinically relevant metalloenzymes. This work reveals anchor pharmacophore as a valuable concept for target-centered drug discovery and illustrates the potential of AncPhore to efficiently identify new inhibitors for different types of protein targets.

Protein sequence design with deep generative models

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.

Proteomic response of the marine ammonia-oxidising archaeon Nitrosopumilus maritimus to iron limitation reveals strategies to compensate for nutrient scarcity

Dissolved iron (Fe) is vanishingly low in the oceans, with ecological success conferred to microorganisms that can restructure their biochemistry to maintain high growth rates during Fe scarcity. Chemolithoautotrophic ammonia-oxidising archaea (AOA) are highly abundant in the oceans, constituting ~30% of cells below the photic zone. Here we examine the proteomic response of the AOA isolate Nitrosopumilus maritimus to growth-limiting Fe concentrations. Under Fe limitation, we observed a significant reduction in the intensity of Fe-dense ferredoxins associated with respiratory complex I whilst complex III and IV proteins with more central roles in the electron transport chain remain unchanged. We concomitantly observed an increase in the intensity of Fe-free functional alternatives such as flavodoxin and plastocyanin, thioredoxin and alkyl hydroperoxide which are known to mediate electron transport and reactive oxygen species detoxification, respectively. Under Fe limitation, we found a marked increase in the intensity of the ABC phosphonate transport system (Phn), highlighting an intriguing link between Fe and P cycling in N. maritimus. We hypothesise that an elevated uptake of exogenous phosphonates under Fe limitation may either supplement N. maritimus' endogenous methylphosphonate biosynthesis pathway - which requires Fe - or enhance the production of phosphonate-containing exopolysaccharides known to efficiently bind environmental Fe.

The symmetric designer protein Pizza as a scaffold for metal coordination

Symmetric proteins are currently of interest as they allow creation of larger assemblies and facilitate the incorporation of metal ions in the larger complexes. Recently this was demonstrated by the biomineralization of the cadmium-chloride nanocrystal via the Pizza designer protein. However, the mechanism behind this formation remained unclear. Here, we set out to investigate the mechanism driving the formation of this nanocrystal via truncation, mutation, and circular permutations. In addition, the interaction of other biologically relevant metal ions with these symmetric proteins to form larger symmetric complexes was also studied. The formation of the initial nanocrystal is shown to originate from steric strain, where His 58 induces a different rotameric conformation on His 73, thereby distorting an otherwise perfect planar ring of alternating cadmium and chlorine ions, resulting in the smallest nanocrystal. Similar highly symmetric complexes were also observed for the other biological relevant metal ions. However, the flexibility of the coordinating histidine residues allows each metal ion to adopt its preferred geometry leading to either monomeric or dimeric β-propeller units, where the metal ions are located at the interface between both propeller units. These results demonstrate that symmetric proteins are not only interesting to generate larger assemblies, but are also the perfect scaffold to create more complex metal based assemblies. Such metal protein assemblies may then find applications in bionanotechnology or biocatalysis.

InterMetalDB: A Database and Browser of Intermolecular Metal Binding Sites in Macromolecules with Structural Information

InterMetalDB is a free-of-charge database and browser of intermolecular metal binding sites that are present on the interfaces of macromolecules forming larger assemblies based on structural information deposited in Protein Data Bank (PDB). It can be found and freely used at https://intermetaldb.biotech.uni.wroc.pl/. InterMetalDB collects the interfacial binding sites with involvement of metal ions and clusters them on the basis of 50% sequence similarity and the nearest metal environment (5 Å radius). The data are available through the web interface where they can be queried, viewed, and downloaded. Complexity of the query depends on the user, because the questions in the query are connected with each other by a logical AND. InterMetalDB offers several useful options for filtering records including searching for structures by particular parameters such as structure resolution, structure description, and date of deposition. Records can be filtered by coordinated metal ion, number of bound amino acid residues, coordination sphere, and other features. InterMetalDB is regularly updated and will continue to be regularly updated with new content in the future. InterMetalDB is a useful tool for all researchers interested in metalloproteins, protein engineering, and metal-driven oligomerization.

BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

With a large amount of research dedicated to decoding how metallic species bind to proteins, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal-binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parameterization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 90 metal-binding X-ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems whose structures (either experimental or theoretical) are not optimal for metal-binding sites. We report here its application on three different challenging cases: (i) the modulation of metal-binding sites during conformational transition in human serum albumin, (ii) the identification of possible routes of metal migration in hemocyanins, and (iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. BioMetAll is an open-source application available at https://github.com/insilichem/biometall.

High-throughput mutagenesis reveals unique structural features of human ADAR1

Adenosine Deaminases that act on RNA (ADARs) are enzymes that catalyze adenosine to inosine conversion in dsRNA, a common form of RNA editing. Mutations in the human ADAR1 gene are known to cause disease and recent studies have identified ADAR1 as a potential therapeutic target for a subset of cancers. However, efforts to define the mechanistic effects for disease associated ADAR1 mutations and the rational design of ADAR1 inhibitors are limited by a lack of structural information. Here, we describe the combination of high throughput mutagenesis screening studies, biochemical characterization and Rosetta-based structure modeling to identify unique features of ADAR1. Importantly, these studies reveal a previously unknown zinc-binding site on the surface of the ADAR1 deaminase domain which is important for ADAR1 editing activity. Furthermore, we present structural models that explain known properties of this enzyme and make predictions about the role of specific residues in a surface loop unique to ADAR1.

PyDISH: database and analysis tools for heme porphyrin distortion in heme proteins

Heme participates in a wide range of biological functions such as oxygen transport, electron transport, oxygen reduction, transcriptional regulation and so on. While the mechanism of each function has been investigated for many heme proteins, the origin of the diversity of the heme functions is still unclear and a crucial scientific issue. We have constructed a database of heme proteins, named Python-based database and analyzer for DIStortion of Heme porphyrin (PyDISH), which also contains some analysis tools. The aim of PyDISH is to integrate the information on the structures of hemes and heme proteins and the functions of heme proteins. This database will provide the structure-function relationships focusing on heme porphyrin distortion and lead to the elucidation of the origin of the functional diversity of heme proteins. In addition, the insights obtained from the database can be used for the design of protein function. PyDISH contains the structural data of more than 13 000 hemes extracted from the Protein Data Bank, including heme porphyrin distortion, axial ligands coordinating to the heme and the orientation of the propionate sidechains of heme. PyDISH also has information about the protein domains, including Uniprot ID, protein fold by CATH ID, organism, coordination distance and so on. The analytical tools implemented in PyDISH allow users to not only browse and download the data but also analyze the structures of heme porphyrin by using the analytical tools implemented in PyDISH. PyDISH users will be able to utilize the obtained results for the design of protein function. Database URL: http://pydish.bio.info.hiroshima-cu.ac.jp/.

Harnessing Peptides against lead pollution and poisoning: Achievements and prospects

Among the broad applicability of peptides in numerous aspects of life and technologies, their interactions with lead (Pb), one of the most harmful substances to the environment and health, are constantly explored. So far, peptides were developed for environmental remediation of Pb-contaminations by various strategies such as hydrogelation and surface display. They were also designed for Pb detection and sensing by electrochemical and fluorescent methods and for modeling natural proteins that involve in mechanisms by which Pb is toxic. This review aims at summarizing selected examples of these applications, manifesting the enormous potential of peptides in the combat against Pb pollution. Nevertheless, the absence of new medicinal treatments against Pb poisoning that are based on peptides is noticeable. An overview of previous achievements utilizing Pb-peptide interactions towards various goals is presented and can be therefore leveraged to construct a useful toolbox for the design of smart peptides as next-generation therapeutics against Pb.

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes

BACKGROUND

The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work.

RESULTS

Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings.

CONCLUSION

We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.

SrnR from Streptomyces griseus is a nickel-binding transcriptional activator

Nickel ions are crucial components for the catalysis of biological reactions in prokaryotic organisms. As an uncontrolled nickel trafficking is toxic for living organisms, nickel-dependent bacteria have developed tightly regulated strategies to maintain the correct intracellular metal ion quota. These mechanisms require transcriptional regulator proteins that respond to nickel concentration, activating or repressing the expression of specific proteins related to Ni(II) metabolism. In Streptomyces griseus, a Gram-positive bacterium used for antibiotic production, SgSrnR and SgSrnQ regulate the nickel-dependent antagonistic expression of two superoxide dismutase (SOD) enzymes, a Ni-SOD and a FeZn-SOD. According to a previously proposed model, SgSrnR and SgSrnQ form a protein complex in which SgSrnR works as repressor, binding directly to the promoter of the gene coding for FeZn-SOD, while SgSrnQ is the Ni(II)-dependent co-repressor. The present work focuses on the determination of the biophysical and functional properties of SgSrnR. The protein was heterologously expressed and purified from Escherichia coli. The structural and metal-binding analysis, carried out by circular dichroism, light scattering, fluorescence and isothermal titration calorimetry, showed that the protein is a well-structured homodimer, able to bind nickel with moderate affinity. DNase I footprinting and β-galactosidase gene reporter assays revealed that apo-SgSrnR is able to bind its DNA operator and activates a transcriptional response. The structural and functional properties of this protein are discussed relatively to its role as a Ni(II)-dependent sensor.

Structural basis of microcystinase activity for biodegrading microcystin-LR

Microcystinase (MlrA) catalyzes the first and most important biodegradation step of hepatotoxic microcystin-LR (MC-LR) produced and released from cyanobacterial cells, and the underlying catalytic mechanism is not completely understood yet. MlrA was postulated previously to be a metalloprotease with an active site of H²⁶⁰AIH²⁶³NE²⁶⁵, a variant of the common metal-binding motif of HEXXH. Through comparison with representative modes in HEXXH-containing metalloproteases, molecular dynamics simulation, homology modeling, and docking, the active sites of MlrA involved in the MC-LR biodegradation by Sphingomonas sp. USTB-05 were predicted. Site-directed mutants of MlrA were constructed for verification then. The results show that MlrA is likely not a metalloprotease, but a glutamate protease belonging to type II CAAX prenyl endopeptidases. Combined with the biodegradation of MC-LR by MlrA and its mutants, a complete enzymatic mechanism for MC-LR biodegradation by MlrA is proposed: Glu¹⁷² and His²⁰⁵ activate a water molecule facilitating a nucleophilic attack on the Adda-Arg peptide bond of MC-LR; Trp¹⁷⁶ and Trp²⁰¹ contact the carboxylate side chain of Glu¹⁷²and, by raising its pKa potentially, accelerate the reaction rates; His²⁶⁰ and Asn²⁶⁴ (located in the previous postulated active center of H²⁶⁰AIH²⁶³NE²⁶⁵) function as an oxyanion hole to stabilize the transition states. This study reveals the enzymatic mechanism of MlrA for catalyzing MC-LR in both the representative modes and the experiments of site-directed mutagenesis.

Catalytic Cycle of Neisseria meningitidis CMP-Sialic Acid Synthetase Illustrated by High-Resolution Protein Crystallography

Cytidine 5'-monophosphate (CMP)-sialic acid synthetase (CSS) is an essential enzyme involved in the biosynthesis of carbohydrates and glycoconjugates containing sialic acids, a class of α-keto acids that are generally terminal key recognition residues by many proteins that play important biological and pathological roles. The CSS from Neisseria meningitidis (NmCSS) has been commonly used with other enzymes such as sialic acid aldolase and/or sialyltransferase in synthesizing a diverse array of compounds containing sialic acid or its naturally occurring and non-natural derivatives. To better understand its catalytic mechanism and substrate promiscuity, four NmCSS crystal structures trapped at various stages of the catalytic cycle with bound substrates, substrate analogues, and products have been obtained and are presented here. These structures suggest a mechanism for an "open" and "closed" conformational transition that occurs as sialic acid binds to the NmCSS/cytidine-5'-triphosphate (CTP) complex. The closed conformation positions critical residues to help facilitate the nucleophilic attack of sialic acid C2-OH to the α-phosphate of CTP, which is also aided by two observed divalent cations. Product formation drives the active site opening, promoting the release of products.