Prediction of metal ion-binding sites in proteins using the fragment transformation method

Conserved cysteine residues in Kaposi's sarcoma herpesvirus ORF34 are necessary for viral production and viral pre-initiation complex formation

Kaposi's sarcoma herpesvirus (KSHV) ORF34 plays a significant role as a component of the viral pre-initiation complex (vPIC), which is indispensable for late gene expression across beta and gamma herpesviruses. Although the key role of ORF34 within the vPIC and its function as a hub protein have been recognized, further clarification regarding its specific contribution to vPIC functionality and interactions with other components is required. This study employed a deep-learning algorithm-assisted structural model of ORF34, revealing highly conserved amino acid residues across human beta- and gamma-herpesviruses localized in structured domains. Thus, we engineered ORF34 alanine-scanning mutants by substituting conserved residues with alanine. These mutants were evaluated for their ability to interact with other vPIC factors and restore viral production in cells harboring the ORF34-deficient KSHV-BAC. Our experimental results highlight the crucial role of the 4 cysteine residues conserved in ORF34: a tetrahedral arrangement consisting of a pair of C-Xn-C consensus motifs. This suggests the potential incorporation of metal cations in interacting with ORF24 and ORF66 vPIC components, facilitating late gene transcription, and promoting overall virus production by capturing metal cations. In summary, our findings underline the essential role of conserved cysteines in KSHV ORF34 for effective vPIC assembly and viral replication, thereby enhancing our understanding of the complex interplay between the vPIC components.

Metagenomic exploration of cold-active enzymes for detergent applications: Characterization of a novel, cold-active and alkali-stable GH8 endoglucanase from ikaite columns in SW Greenland

Microbial communities from extreme environments are largely understudied, but are essential as producers of metabolites, including enzymes, for industrial processes. As cultivation of most microorganisms remains a challenge, culture-independent approaches for enzyme discovery in the form of metagenomics to analyse the genetic potential of a community are rapidly becoming the way forward. This study focused on analysing a metagenome from the cold and alkaline ikaite columns in Greenland, identifying 282 open reading frames (ORFs) that encoded putative carbohydrate-modifying enzymes with potential applications in, for example detergents and other processes where activity at low temperature and high pH is desired. Seventeen selected ORFs, representing eight enzyme families were synthesized and expressed in two host organisms, Escherichia coli and Aliivibrio wodanis. Aliivibrio wodanis demonstrated expression of a more diverse range of enzyme classes compared to E. coli, emphasizing the importance of alternative expression systems for enzymes from extremophilic microorganisms. To demonstrate the validity of the screening strategy, we chose a recombinantly expressed cellulolytic enzyme from the metagenome for further characterization. The enzyme, Cel240, exhibited close to 40% of its relative activity at low temperatures (4°C) and demonstrated endoglucanase characteristics, with a preference for cellulose substrates. Despite low sequence similarity with known enzymes, computational analysis and structural modelling confirmed its cellulase-family affiliation. Cel240 displayed activity at low temperatures and good stability at 25°C, activity at alkaline pH and increased activity in the presence of CaCl2, making it a promising candidate for detergent and washing industry applications.

Copper-binding proteins and exonic splicing enhancers and silencers

Eukaryotic DNA codes not only for proteins but contains a wealth of information required for accurate splicing of messenger RNA precursors and inclusion of constitutively or alternatively spliced exons in mature transcripts. This "auxiliary" splicing code has been characterized as exonic splicing enhancers and silencers (ESE and ESS). The exact interplay between protein and splicing codes is, however, poorly understood. Here, we show that exons encoding copper-coordinating amino acids in human cuproproteins lack ESEs and/or have an excess of ESSs, yet RNA sequencing and expressed sequence tags data show that they are more efficiently included in mature transcripts by the splicing machinery than average exons. Their largely constitutive inclusion in messenger RNA is facilitated by stronger splice sites, including polypyrimidine tracts, consistent with an important role of the surrounding intron architecture in ensuring high expression of metal-binding residues during evolution. ESE/ESS profiles of codons and entire exons that code for copper-coordinating residues were very similar to those encoding residues that coordinate zinc but markedly different from those that coordinate calcium. Together, these results reveal how the traditional and auxiliary splicing motifs responded to constraints of metal coordination in proteins.

Role of Histidine 310 in Amydetes vivianii firefly luciferase pH and metal sensitivities and improvement of its color tuning properties

Firefly luciferases emit yellow-green light and are pH-sensitive, changing the bioluminescence color to red in the presence of heavy metals, acidic pH and high temperatures. These pH and metal-sensitivities have been recently harnessed for intracellular pH indication and toxic metal biosensing. However, whereas the structure of the pH sensor and the metal binding site, which consists mainly of two salt bridges that close the active site (E311/R337 and H310/E354), has been identified, the specific role of residue H310 in pH and metal sensing is still under debate. The Amydetes vivianii firefly luciferase has one of the lowest pH sensitivities among the group of pH-sensitive firefly luciferases, displaying high bioluminescent activity and special spectral selectivity for cadmium and mercury, which makes it a promising analytical reagent. Using site-directed mutagenesis, we have investigated in detail the role of residue H310 on pH and metal sensitivity in this luciferase. Negatively charged residues at position 310 increase the pH sensitivity and metal sensitivity; H310G considerably increases the size of the cavity, severely impacting the activity, H310R closes the cavity, and H310F considerably decreases both pH and metal sensitivities. However, no substitution completely abolished pH and metal sensitivities. The results indicate that the presence of negatively charged and basic side chains at position 310 is important for pH sensitivity and metals coordination, but not essential, indicating that the remaining side chains of E311 and E354 may still coordinate some metals in this site. Furthermore, a metal binding site search predicted that H310 mutations decrease the affinity mainly for Zn, Ni and Hg but less for Cd, and revealed the possible existence of additional binding sites for Zn, Ni and Hg.

Purification and characterization of glutamate dehydrogenase from rainbow trout (Oncorhynchus mykiss) liver and molecular docking studies

Glutamate dehydrogenase (GDH) participates in the energy metabolism of proteins and the synthesis of metabolites important for the organism. In this study, GDH enzyme was purified from the liver of rainbow trout (Oncorhynchus mykiss) by 2',5'-ADP Sepharose 4B affinity chromatography in one step. As a result of this purification process, GDH enzyme was purified 171-fold with 5.83 U/mg protein-specific activity. The characterization experiments presented that the storage stability of the purified GDH enzyme was determined as -80°C; optimum temperature 40°C; it was determined that the optimum ionic strength was 100 mM phosphate buffer and the optimum pH was 8.00. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and PAGE studies showed that the natural molar mass of the purified GDH enzyme was 346.74 kDa, and the molar mass of its subunits was 53.71 kDa. Km and Vmax values for substrates and coenzymes of GDH enzyme purified from rainbow trout liver were calculated, and the lowest Km value was found in NAD+ (1.86 mM) and the highest Vmax value in NH4 + (1.79 U/mL). The effects of some metal ions, vitamins, and solvents on the activity of the purified GDH enzyme were investigated and also IC50 values and inhibition types. The metal ion with the lowest IC50 value is Ag+ (8.65 ± 1.68 μM), and the vitamin is B6 (0.77 ± 0.04 mM). The binding affinities of inhibitors were investigated with molecular docking, based on the conformational state of GDH.

Screening, separation and identification of metal-chelating peptides for nutritional, cosmetics and pharmaceutical applications

Metal-chelating peptides, which form metal-peptide coordination complexes with various metal ions, can be used as biofunctional ingredients notably to enhance human health and prevent diseases. This review aims to discuss recent insights into food-derived metal-chelating peptides, the strategies set up for their discovery, their study, and identification. After understanding the overall properties of metal-chelating peptides, their production from food-derived protein sources and their potential applications will be discussed, particularly in nutritional, cosmetics and pharmaceutical fields. In addition, the review provides an overview of the last decades of progress in discovering food-derived metal-chelating peptides, addressing several screening, separation and identification methodologies. Furthermore, it emphasizes the methods used to assess peptide-metal interaction, allowing for better understanding of chemical and thermodynamic parameters associated with the formation of peptide-metal coordination complexes, as well as the specific amino acid residues that play important roles in the metal ion coordination.

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

Exonic splicing code and coordination of divalent metals in proteins

Exonic sequences contain both protein-coding and RNA splicing information but the interplay of the protein and splicing code is complex and poorly understood. Here, we have studied traditional and auxiliary splicing codes of human exons that encode residues coordinating two essential divalent metals at the opposite ends of the Irving-Williams series, a universal order of relative stabilities of metal-organic complexes. We show that exons encoding Zn2+-coordinating amino acids are supported much less by the auxiliary splicing motifs than exons coordinating Ca2+. The handicap of the former is compensated by stronger splice sites and uridine-richer polypyrimidine tracts, except for position -3 relative to 3' splice junctions. However, both Ca2+ and Zn2+ exons exhibit close-to-constitutive splicing in multiple tissues, consistent with their critical importance for metalloprotein function and a relatively small fraction of expendable, alternatively spliced exons. These results indicate that constraints imposed by metal coordination spheres on RNA splicing have been efficiently overcome by the plasticity of exon-intron architecture to ensure adequate metalloprotein expression.

Photocatalytic Reduction of Methylene Blue by Surface-Engineered Recombinant Escherichia coli as a Whole-Cell Biocatalyst

A novel Escherichia coli strain, created by engineering its cell surface with a cobalt-binding peptide CP1, was investigated in this study. The recombinant strain, pBAD30-YiaT-CP1, was structurally modeled to determine its cobalt-binding affinity. Furthermore, the effectiveness and specificity of pBAD30-CP1 in adsorbing and extracting cobalt from artificial wastewater polluted with the metal were investigated. The modified cells were subjected to cobalt concentrations (0.25 mM to 1 mM) and pH levels (pH 3, 5, 7, and 9). When exposed to a pH of 7 and a cobalt concentration of 1 mM, the pBAD30-CP1 strain had the best cobalt recovery efficiency, measuring 1468 mol/g DCW (Dry Cell Weight). Furthermore, pBAD30-CP1 had a higher affinity for cobalt than nickel and manganese. Field Emission Scanning Electron Microscopy (FE-SEM), Transmission Electron Microscopy (TEM), and Energy-Dispersive X-ray Spectroscopy (EDS) were used to examine the physiochemical parameters of the recombinant cells after cobalt adsorption. These approaches revealed the presence of cobalt in a bound state on the cell surface in the form of nanoparticles. In addition, the cobalt-binding recombinant strains were used in the photocatalytic reduction of methylene blue, which resulted in a 59.52% drop in the observed percentage. This study shows that modified E. coli strains have the potential for efficient cobalt recovery and application in environmental remediation operations.

Molecular dynamics of JUNO-IZUMO1 complexation suggests biologically relevant mechanisms in fertilization

JUNO-IZUMO1 binding is the first known physical link created between the sperm and egg membranes in fertilization, however, how this initiates sperm-egg fusion remains elusive. As advanced structural insights will help to combat the infertility crisis, or advance fertility control, we employed all-atom Molecular Dynamics (MD) to derive dynamic structural insights that are difficult to obtain experimentally. We found that the hydrated JUNO-IZUMO1 interface is composed of a large set of short-lived non-covalent interactions. The contact interface is destabilized by strategically located point mutations, as well as by Zn2+ ions, which shift IZUMO1 into the non-binding "boomerang" conformation. We hypothesize that the latter might explain how the transient zinc spark, as released after sperm entry into the oocyte, might contribute to block polyspermy. To address a second mystery, we performed another set of simulations, as it was previously suggested that JUNO in solution is unable to bind to folate despite it belonging to the folate receptor family. MD now suggests that JUNO complexation with IZUMO1 opens up the binding pocket thereby enabling folate insertion. Our MD simulations thus provide crucial new hypotheses how the dynamics of the JUNO-IZUMO1 complex upon solvation might regulate fertility.

Prediction of Protein Ion-Ligand Binding Sites with ELECTRA

Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it is becoming crucial to identify ion-binding sites. The accurate identification of protein-ion binding sites helps us to understand proteins' biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion-binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32-, SO42-, PO43-, NO2-). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46%, respectively, in the F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools, producing prediction results for a hundred sequences for a specific ion in under ten minutes.

Insulin-cobalt core-shell nanoparticles for receptor-targeted bioimaging and diabetic wound healing

Diabetic wounds represent a major issue in medical care and need advanced therapeutic and tissue imaging systems for better management. The utilization of nano-formulations involving proteins like insulin and metal ions plays significant roles in controlling wound outcomes by decreasing inflammation or reducing microbial load. This work reports the easy one-pot synthesis of extremely stable, biocompatible, and highly fluorescent insulin-cobalt core-shell nanoparticles (ICoNPs) with enhanced quantum yield for their highly specific receptor-targeted bioimaging and normal and diabetic wound healing in vitro (HEKa cell line). The particles were characterized using physicochemical properties, biocompatibility, and wound healing applications. FTIR bands at 670.35 cm^-1, 849.79, and 973.73 indicating the Co-O bending, CoO-OH bond, and Co-OH bending, respectively, confirm the protein-metal interactions, which is further supported by the Raman spectra. In silico studies indicate the presence of cobalt binding sites on the insulin chain B at 8 GLY, 9 SER, and 10 HIS positions. The particles exhibit a magnificent loading efficiency of 89.48 ± 0.049% and excellent release properties (86.54 ± 2.15% within 24 h). Further, based on fluorescent properties, the recovery process can be monitored under an appropriate setup, and the binding of ICoNPs to insulin receptors was confirmed by bioimaging. This work helps synthesize effective therapeutics with numerous wound-healing promoting and monitoring applications.

Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins

Metal ions are essential cofactors for many proteins and play a crucial role in many applications such as enzyme design or design of protein-protein interactions because they are biologically abundant, tether to the protein using strong interactions, and have favorable catalytic properties. Computational design of metalloproteins is however hampered by the complex electronic structure of many biologically relevant metals such as zinc . In this work, we develop two tools - Metal3D (based on 3D convolutional neural networks) and Metal1D (solely based on geometric criteria) to improve the location prediction of zinc ions in protein structures. Comparison with other currently available tools shows that Metal3D is the most accurate zinc ion location predictor to date with predictions within 0.70 ± 0.64 Å of experimental locations. Metal3D outputs a confidence metric for each predicted site and works on proteins with few homologes in the protein data bank. Metal3D predicts a global zinc density that can be used for annotation of computationally predicted structures and a per residue zinc density that can be used in protein design workflows. Currently trained on zinc, the framework of Metal3D is readily extensible to other metals by modifying the training data.

Molecular characterization of Satsuma mandarin (Citrus unshiu Marc.) VASCULAR PLANT ONE-ZINC FINGER2 (CuVOZ2) interacting with CuFT1 and CuFT3

Shortening the juvenility is a burning issue in breeding fruit trees such as Satsuma mandarin (Citrus unshiu Marc.). Decreasing the breeding period requires a comprehensive understanding of the flowering process in woody plants. Throughout the Arabidopsis flowering system, FLOWERING LOCUS T (FT) interacts with other transcription factors (TFs) and functions as a transmissible floral inducer. In a previous study, a VASCULAR PLANT ONE-ZINC FINGER1 (VOZ1)-like TF from the Satsuma mandarin, CuVOZ1, showed protein-protein interaction with two citrus FTs in a yeast two-hybrid (Y2H) system and precocious flowering in Arabidopsis. In this study, another VOZ, CuVOZ2, was isolated from the Satsuma mandarin 'Aoshima' and protein-protein interaction was confirmed between CuVOZ2 and CuFTs. No apical meristem (NAM) and zinc coordination motifs were identified within the N-terminal of CuVOZ2. Docking simulation predicted that interactions between CuVOZ2 and CuFTs might occur in domain B of CuVOZ2, which contains a zinc finger motif. According to docking predictions, the distances between the amino acid residues involved ranged from 1.09 to 4.37 Å, indicating weak Van der Waals forces in the interaction. Cys216, Cys221, Cys235, and His239 in CuVOZ2 were suggested to bond with a Zn2+ in the Zn coordination motif. Ectopic expression of 35SΩ:CuVOZ2 in Arabidopsis affected the flowering time, length of inflorescence and internode, and number of siliques, suggesting that CuVOZ2 might regulate both vegetative and reproductive development, act as a trigger for early flowering, and be involved in the elongation of inflorescence possibly in a slightly different way than CuVOZ1.

SCAMPER: Accurate Type-Specific Prediction of Calcium-Binding Residues Using Sequence-Derived Features

Understanding molecular mechanisms involved in calcium-protein interactions and modeling corresponding docking rely on the accurate identification of calcium-binding residues (CaBRs). The defects of experimentally annotating protein functions enhances the development of computational approaches that correctly identify calcium-binding interactions. Studies have reported that current methods severely cross-predict residues that interact with other types of molecules (e.g., nucleic acids, proteins, and small ligands) as CaBRs. In this study, a novel predictor named SCAMPER (Selective CAlciuM-binding PrEdictoR) is proposed for the accurate and specific prediction of CaBRs. SCAMPER is designed using newly compiled dataset with complete UniProt sequences and annotations, which include calcium-binding, nucleic acid-binding, protein-binding, and small ligand-binding residues. We use a novel designed two-layer scheme to perform predictions as well as penalize cross-predictions. Empirical tests on an independent test dataset reveals that the proposed method significantly outperforms state-of-the-art predictors. SCAMPER is proved to be capable of distinguishing CaBRs from different types of metal-ion binding residues. We further perform CaBRs predictions on the whole human proteome, and use the results to hypothesize calcium-binding proteins (CaBPs). The latest experimental verified CaBPs and GO analysis prove the accuracy of our predictions. We implement the proposed method and share the data at http://www.inforstation.com/webservers/SCAMPER/.

The putative metal-binding proteome of the Coronaviridae family

Metalloproteins are well-known for playing various physicochemical processes in all life forms, including viruses. Some life-threatening viruses (such as some members of the Coronaviridae family of viruses) are emerged and remerged frequently and are rapidly transmitted throughout the globe. This study aims to identify and characterize the metal-binding proteins (MBPs) of the Coronaviridae family of viruses and further provides insight into the MBP's role in sustaining and propagating viruses inside a host cell and in the outer environment. In this study, the available proteome of the Coronaviridae family was exploited. Identified potential MBPs were analyzed for their functional domains, structural aspects, and subcellular localization. We also demonstrate phylogenetic aspects of all predicted MBPs among other Coronaviridae family members to understand the evolutionary trend among their respective hosts. A total of 256 proteins from 51 different species of coronaviruses are predicted as MBPs. These MBPs perform various key roles in the replication and survival of viruses within the host cell. Cysteine, aspartic acid, threonine, and glutamine are key amino acid residues interacting with respective metal ions. Our observations also indicate that the metalloproteins of this family of viruses circulated and evolved in different hosts, which supports the zoonotic nature of coronaviruses. The comprehensive information on MBPs of the Coronaviridae family may be further helpful in designing novel therapeutic metalloprotein targets. Moreover, the study of viral MBPs can also help to understand the roles of MBPs in virus pathogenesis and virus-host interactions.

A computational study of metal ions interaction with amyloid-β 1-42 peptide structure in hyperpyrexia: Implications for Alzheimer disease

Given the current context of the SARS-CoV-19 pandemic, among the interfering risky factors with the Aβ peptide aggregation in the brains of Alzheimer's disease (AD) patients can be hyperpyrexia and increased intracranial pressure (ICP). According to our hypothesis on the relationship between hyperpyrexia and cognitive decline in AD, two models of Aβ peptides were used in this study: the structure of AD amyloid beta-peptide and near-atomic resolution fibril structures of the Aβ peptide. Therefore, the binding templates were constructed for Aβ peptide regions able to bind 9 different metal ions. The fragment transformation method was used for the structural comparison between Aβ chains. Molecular dynamics simulation (MDS) was applied using the Nose-Poincare-Anderson equation to generate a theoretically correct NPT (isothermal-isobaric ensemble). The smallest dissimilarities were observed in the case of Cu⁺ binding potential followed by Co²⁺, both with similar variation. Structural changes have also occurred as a result of the dynamic simulation. All these changes suggest an aggravating factor in both hyperpyretic and AD conditions. Our findings suggest that elevated temperature and increased intracranial pressure rise the effect of peptide aggregation, by converting α-helix motif to β-sheet and random coil conformation, which are related to the formation of senile plaques in AD brains.

Thermoadaptation in an Ancestral Diterpene Cyclase by Altered Loop Stability

Thermostability is the key to maintain the structural integrity and catalytic activity of enzymes in industrial biotechnological processes, such as terpene cyclase-mediated generation of medicines, chiral synthons, and fine chemicals. However, affording a large increase in the thermostability of enzymes through site-directed protein engineering techniques can constitute a challenge. In this paper, we used ancestral sequence reconstruction to create a hyperstable variant of the ent-copalyl diphosphate synthase PtmT2, a terpene cyclase involved in the assembly of antibiotics. Molecular dynamics simulations on the μs timescale were performed to shed light on possible molecular mechanisms contributing to activity at an elevated temperature and the large 40 °C increase in melting temperature observed for an ancestral variant of PtmT2. In silico analysis revealed key differences in the flexibility of a loop capping the active site, between extant and ancestral proteins. For the modern enzyme, the loop collapses into the active site at elevated temperatures, thus preventing biocatalysis, whereas the loop remains in a productive conformation both at ambient and high temperatures in the ancestral variant. Restoring a Pro loop residue introduced in the ancestral variant to the corresponding Gly observed in the extant protein led to reduced catalytic activity at high temperatures, with only moderate effects on the melting temperature, supporting the importance of the flexibility of the capping loop in thermoadaptation. Conversely, the inverse Gly to Pro loop mutation in the modern enzyme resulted in a 3-fold increase in the catalytic rate. Despite an overall decrease in maximal activity of ancestor compared to wild type, its increased thermostability provides a robust backbone amenable for further enzyme engineering. Our work cements the importance of loops in enzyme catalysis and provides a molecular mechanism contributing to thermoadaptation in an ancestral enzyme.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn²⁺, Cu²⁺, Fe²⁺, Fe³⁺, Co²⁺, Mn²⁺, Ca²⁺, Mg²⁺, Na⁺, and K⁺). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the S_n and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm

Background

Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.

Results

In this paper, Mg²⁺ and Ca²⁺ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm.

Conclusions

An efficient method for predicting Mg²⁺ and Ca²⁺ ligand binding sites was presented.

Global Mapping of Metalloproteomes

Metalloproteins play diverse and critical functions in all living systems, and their dysfunctional forms are closely related to many human diseases. The development of methods that enable comprehensive mapping of metalloproteome is of great interest to help elucidate crucial roles of metalloproteins in both physiology and pathology, as well as to discover new metalloproteins. We herein briefly review recent progress in the field of metalloproteomics and provide future outlooks.

Application of green synthesised copper iodide particles on cotton fabric-protective face mask material against COVID-19 pandemic

Microorganisms cause variety of diseases that constitutes a severe threat to mankind. Due to the upsurge of many infectious diseases, there is a high requirement and demand for the development of safety products finished with antimicrobial properties. The study involves the antimicrobial activity of natural cotton coated with copper iodide capped with Hibiscus rosa-sinensis L. flower extract (CuI-FE) which is rich in anthocyanin, cyanidin-3-sophoroside by ultrasonication method. The coated and uncoated cotton fabric was characterised through XRD, SEM, AFM, tensile strength and UV-Visible spectroscopic techniques. XRD confirmed the formation of CuI particles, SEM showed that CuI-FE was prismatic in shape. The average size of CuI-FE particles was found to be 552.45 nm. Anti-bacterial studies showed copper iodide particles to be a potent antimicrobial agent. AFM images confirmed the rupture of bacterial cell walls in the presence of prismatic CuI-FE. In-vitro cytotoxicity investigation of CuI-FE was performed against cancer and spleen cell lines to evaluate the cell viability. Cytotoxicity analysis revealed the IC50 value of 233.93 μg/mL in the presence of CuI-FE. Molecular docking study was also carried out to understand the interaction of CuI-FE with COVID-19 main protease. This paper has given an insight on the usage of CuI-FE coated on the cotton fabric that has proved to have strong inhibition against the nano ranged bacterial, cancerous cell line and a strong interaction with the COVID-19 protease. Such eco-friendly material will provide a safe environment even after the disposable of medical waste from the infectious diseases like influenza and current pandemic like COVID-19.

Structure prediction of honey bee vitellogenin: a multi-domain protein important for insect immunity

Vitellogenin (Vg) has been implicated as a central protein in the immunity of egg‐laying animals. Studies on a diverse set of species suggest that Vg supports health and longevity through binding to pathogens. Specific studies of honey bees (Apis mellifera) further indicate that the vitellogenin (vg) gene undergoes selection driven by local pathogen pressures. Determining the complete 3D structure of full‐length Vg (flVg) protein will provide insights regarding the structure–function relationships underlying allelic variation. Honey bee Vg has been described in terms of function, and two subdomains have been structurally described, while information about the other domains is lacking. Here, we present a structure prediction, restrained by experimental data, of flVg from honey bees. To achieve this, we performed homology modeling and used AlphaFold before using a negative‐stain electron microscopy map to restrict, orient, and validate our 3D model. Our approach identified a highly conserved Ca²⁺‐ion‐binding site in a von Willebrand factor domain that might be central to Vg function. Thereafter, we used rigid‐body fitting to predict the relative position of high‐resolution domains in a flVg model. This mapping represents the first experimentally validated full‐length protein model of a Vg protein and is thus relevant for understanding Vg in numerous species. Our results are also specifically relevant to honey bee health, which is a topic of global concern due to rapidly declining pollinator numbers.

Transcriptome analysis of rice response to blast fungus identified core genes involved in immunity

Rice blast disease caused by the filamentous Ascomycetous fungus Magnaporthe oryzae is a major threat to rice production worldwide. The mechanisms underlying rice resistance to M. oryzae, such as transcriptional reprogramming and signalling networks, remain elusive. In this study, we carried out an in-depth comparative transcriptome study on the susceptible and resistant rice cultivars in response to M. oryzae. Our analysis highlighted that rapid, high-amplitude transcriptional reprogramming was important for rice defence against blast fungus. Ribosome- and protein translation-related genes were significantly enriched among differentially expressed genes (DEGs) at 12 hpi in both cultivars, indicating that the protein translation machinery is regulated in the activation of immunity in rice. Furthermore, we identified a core set of genes that are involved in the rice response to both biotic and abiotic stress. More importantly, among the core genes, we demonstrated that the metallothionein OsMT1a and OsMT1b genes positively regulated rice resistance while a peroxidase gene Perox4 negatively regulated rice resistance to M. oryzae. Our study provides novel insight into transcriptional reprogramming and serves as a valuable resource for functional studies on rice immune signalling components in resistance to blast disease.

Involvement of different hemoprotein thiol groups of Oncorhynchus mykiss in cadmium toxicity

BACKGROUND

Cadmium is considered the seventh most toxic heavy metal as per ATSDR ranking but its mechanism of toxicity is debated. Recently, we evaluated the effects of this metal on the erythrocyte of teleost fish (Oncorhynchus mykiss) leading us to hypothesize that the pro-oxidant activity of cadmium is not linked to mitochondria but more likely to haemoglobin. In this context, the main aim of this work was to detect the ability of Cd to induce structural perturbation in haemoproteins that present different structures and thus different functional properties and to identify what sites of interaction are mainly involved.

METHODS

The effect of Cd on the structural destabilization of the different haemoproteins was followed spectrophometrically through their precipitation. In addition, the sites of interaction between the different haemoproteins and bivalent cadmium ions were identified by MIB server followed by molecular docking/molecular dynamics simulations both in the dimeric and tetrameric associations.

RESULTS

Cadmium does not influence the autoxidation rate of Mb, HbA and trout HbI. However, the presence of this metal accelerates the precipitation process in trout HbIV in a dose-dependent manner. Moreover, the presence of 1-10-50-250-500-1000 μM GSH, a chelating agent, reduces the ability of cadmium to accelerate the denaturation process although it is not able to completely prevent it. In order to explain the experimental results, a computational investigations was carried out to identify the cadmium cation affinity for the studied haemoglobins and myoglobin, both in their dimeric and tetrameric forms. As a result, the highest affinity cadmium binding sites for fish HbIV are located at the interface between tetramer-tetramer association, indicating that the cation can assist supramolecular protein aggregations and induce complex precipitation. For mammalian Hb, Mb and fish HbI computational investigation did not detect any site where Cd could to induce such aggregation, in line with the experimental results.

CONCLUSION

The present study provides new information on the mechanisms of toxicity of cadmium by specific interaction with trout O. mykiss haemoglobin component.

An analysis of inhibition of the severe acute respiratory syndrome coronavirus 2 RNA-dependent RNA polymerase by zinc ion: an in silico approach

Background: Coronavirus disease 2019 is caused by exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was reported that Zn²⁺ is an inhibitor of severe acute respiratory syndrome coronavirus (SARS-CoV). We hypothesize that the same applies to the newly discovered SARS-CoV-2. Material & methods: We compared the structure of RNA-dependent RNA polymerase between SARS-CoV and SARS-CoV-2. The RdRp’s binding to Zn²⁺ was studied by metal ion-binding site prediction and docking server. Results: Several regions containing key residues were detected. The functional aspartic acid residues RdRp, 618D, 760D and 761D were among the predicted Zn²⁺-binding residues. Conclusion: The most probable mechanism of inhibition of RdRp by Zn²⁺ is binding to the active aspartic acid triad while other binding sites can further destabilize the enzyme or interfere with the fidelity-check mechanism.

The most probable mechanism of inhibition of RNA polymerase by Zn²⁺ is binding to the active aspartic acid triad while other binding sites can further destabilize the enzyme or interfere with the fidelity-check mechanism. #Zinc #SARS_CoV_2 #COVID_19.

Inferring metal binding sites in flexible regions of proteins

Metal ions are central to the molecular function of many proteins. Thus their knowledge in experimentally determined structure is important; however, such structures often lose bound metal ions during sample preparation. Identification of these metal-binding site(s) becomes difficult when the receptor is novel and/or their conformations differ in the bound/unbound states. Locating such sites in theoretical models also poses a challenge due to the uncertainties with side-chain modeling. We address the problem by employing the Geometric Hashing algorithm to create a template library of functionally important binding sites and match query structures with the available templates. The matching is done on the structure ensemble obtained from coarse-grained molecular dynamics simulation, where metal-specific amino acids are screened to infer the true site. Test on 1347 non-redundant monomer protein structures show that Ca²⁺ , Zn²⁺ , Mg²⁺ , Cu²⁺ , and Fe³⁺ binding site residues can be classified at 0.92, 0.95, 0.80, 0.90, and 0.92 aggregate performance (out of 1) across all possible thresholds. The performance for Ca²⁺ and Zn²⁺ is notably superior in comparison to state-of-the-art methods like IonCom and MIB. Specific case studies show that additionally predicted metal-binding site residues in proteins have features necessary for ion binding. These include new sites not predicted by other methods. The use of coarse-grained dynamics thus provides a generalized approach to improve metal-binding site prediction. The work is expected to contribute to improving our ability to correctly predict protein molecular function where knowledge of metal binding is a key requirement.

Restriction of an intron size en route to endothermy

Ca2+-insensitive and -sensitive E1 subunits of the 2-oxoglutarate dehydrogenase complex (OGDHC) regulate tissue-specific NADH and ATP supply by mutually exclusive OGDH exons 4a and 4b. Here we show that their splicing is enforced by distant lariat branch points (dBPs) located near the 5' splice site of the intervening intron. dBPs restrict the intron length and prevent transposon insertions, which can introduce or eliminate dBP competitors. The size restriction was imposed by a single dominant dBP in anamniotes that expanded into a conserved constellation of four dBP adenines in amniotes. The amniote clusters exhibit taxon-specific usage of individual dBPs, reflecting accessibility of their extended motifs within a stable RNA hairpin rather than U2 snRNA:dBP base-pairing. The dBP expansion took place in early terrestrial species and was followed by a uridine enrichment of large downstream polypyrimidine tracts in mammals. The dBP-protected megatracts permit reciprocal regulation of exon 4a and 4b by uridine-binding proteins, including TIA-1/TIAR and PUF60, which promote U1 and U2 snRNP recruitment to the 5' splice site and BP, respectively, but do not significantly alter the relative dBP usage. We further show that codons for residues critically contributing to protein binding sites for Ca2+ and other divalent metals confer the exon inclusion order that mirrors the Irving-Williams affinity series, linking the evolution of auxiliary splicing motifs in exons to metallome constraints. Finally, we hypothesize that the dBP-driven selection for Ca2+-dependent ATP provision by E1 facilitated evolution of endothermy by optimizing the aerobic scope in target tissues.

Identification of zinc-binding peptides in ADAM17-inhibiting whey protein hydrolysates using IMAC-Zn2+ coupled with shotgun peptidomics

Abstract

Food components possessing zinc ligands can be used to inhibit zinc-dependent enzymes. In this study, zinc-binding peptides were derived from whey protein hydrolysates, and their ultrafiltration (> 1 and < 1 kDa) fractions, produced with Esperase (WPH-Esp), Everlase and Savinase. Immobilized metal affinity chromatography (IMAC-Zn2+) increased the zinc-binding capacity of the peptide fraction (83%) when compared to WPH-Esp (23%) and its < 1 kDa fraction (40%). The increased zinc-binding capacity of the sample increased the inhibitory activity against the zinc-dependent “a disintegrin and metalloproteinase 17”. LC-MS/MS analysis using a shotgun peptidomics approach resulted in the identification of 24 peptides originating from bovine β-lactoglobulin, α-lactalbumin, serum albumin, β-casein, κ-casein, osteopontin-k, and folate receptor-α in the fraction. The identified peptides contained different combinations of the strong zinc-binding group of residues, His+Cys, Asp+Glu and Phe+Tyr, although Cys residues were absent in the sequences. In silico predictions showed that the IMAC-Zn2+ peptides were non-toxins. However, the peptides possessed poor drug-like and pharmacokinetic properties; this was possibly due to their long chain lengths (5–19 residues). Taken together, this work provided an array of food peptide-based zinc ligands for further investigation of structure-function relationships and development of nutraceuticals against inflammatory and other zinc-related diseases.

Graphical abstract

Protection of lead-induced cytotoxicity using paramagnetic nickel–insulin quantum clusters

Pb-toxicity is associated with inflammation which leads to delay in wound healing. Pb²⁺ utilizes calcium ion channels to enter the cell. Therefore, to achieve effective healing in a Pb-poisoned system, capturing Pb²⁺ from the circulatory system would be an effective approach without hampering the activity of the calcium ion channel. In this work insulin–nickel fluorescent quantum clusters (INiQCs) have been synthesized and used for the specific detection of Pb²⁺ ions in vitro and in cell-free systems. INiQCs (0.09 μM) can detect Pb²⁺ concentrations as low as 10 pM effectively in a cell-free system using the fluorescence turn-off method. In vitro INiQCs (0.45 μM) can detect Pb²⁺ concentrations as low as 1 μM. INiQCs also promote wound healing which can easily be monitored using the bright fluorescence of INiQCs. INiQCs also help to overcome the wound recovery inhibitory effect of Pb²⁺in vitro using lead nitrate. This work helps to generate effective biocompatible therapeutics for wound recovery in Pb²⁺ poisoned individuals.

Receptor targeted ferromagnetic Insulin–Nickel Quantum fluorescence Clusters (INiQCs) can specifically detect Pb²⁺ and prevents Pb²⁺ poisoning.

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.

Bioengineering of non-pathogenic Escherichia coli to enrich for accumulation of environmental copper

Heavy metal sequestration from industrial wastes and agricultural soils is a long-standing challenge. This is more critical for copper since copper pollution is hazardous both for the environment and for human health. In this study, we applied an integrated approach of Darwin’s theory of natural selection with bacterial genetic engineering to generate a biological system with an application for the accumulation of Cu²⁺ ions. A library of recombinant non-pathogenic Escherichia coli strains was engineered to express seven potential Cu²⁺ binding peptides encoded by a ‘synthetic degenerate’ DNA motif and fused to Maltose Binding Protein (MBP). Most of these peptide-MBP chimeras conferred tolerance to high concentrations of copper sulphate, and in certain cases in the order of 160-fold higher than the recognised EC₅₀ toxic levels of copper in soils. UV–Vis spectroscopic analysis indicated a molar ratio of peptide-copper complexes, while a combination of bioinformatics-based structure modelling, Cu²⁺ ion docking, and MD simulations of peptide-MBP chimeras corroborated the extent of Cu²⁺ binding among the peptides. Further, in silico analysis predicted the peptides possessed binding affinity toward a broad range of divalent metal ions. Thus, we report on an efficient, cost-effective, and environment-friendly prototype biological system that is potentially capable of copper bioaccumulation, and which could easily be adapted for the removal of other hazardous heavy metals or the bio-mining of rare metals.

Lupinus albus γ-Conglutin, a Protein Structurally Related to GH12 Xyloglucan-Specific Endo-Glucanase Inhibitor Proteins (XEGIPs), Shows Inhibitory Activity against GH2 β-Mannosidase

γ-conglutin (γC) is a major protein of Lupinus albus seeds, but its function is still unknown. It shares high structural similarity with xyloglucan-specific endo-glucanase inhibitor proteins (XEGIPs) and, to a lesser extent, with Triticum aestivum endoxylanase inhibitors (TAXI-I), active against fungal glycoside hydrolases GH12 and GH11, respectively. However, γC lacks both these inhibitory activities. Since β-galactomannans are major components of the cell walls of endosperm in several legume plants, we tested the inhibitory activity of γC against a GH2 β-mannosidase (EC 3.2.1.25). γC was actually able to inhibit the enzyme, and this effect was enhanced by the presence of zinc ions. The stoichiometry of the γC/enzyme interaction was 1:1, and the calculated K_i was 1.55 μM. To obtain further insights into the interaction between γC and β-mannosidase, an in silico structural bioinformatic approach was followed, including some docking analyses. By and large, this work describes experimental findings that highlight new scenarios for understanding the natural role of γC. Although structural predictions can leave space for speculative interpretations, the full complexity of the data reported in this work allows one to hypothesize mechanisms of action for the basis of inhibition. At least two mechanisms seem plausible, both involving lupin-γC-peculiar structures.

Diversity, structure and regulation of microbial metallothionein: metal resistance and possible applications in sequestration of toxic metals

Metallothioneins (MTs) are a group of cysteine-rich, universal, low molecular weight proteins distributed widely in almost all major taxonomic groups ranging from tiny microbes to highly organized vertebrates. The primary function of this protein is storage, transportation and binding of metals, which enable microorganisms to detoxify heavy metals. In the microbial world, these peptides were first identified in a cyanobacterium Synechococcus as the SmtA protein which exhibits high affinity towards rising level of zinc and cadmium to preserve metal homeostasis in a cell. In yeast, MTs aid in reserving copper and confer protection against copper toxicity by chelating excess copper ions in a cell. Two MTs, CUP1 and Crs5, originating from Saccharomyces cerevisiae predominantly bind to copper though are capable of binding with zinc and cadmium ions. MT superfamily 7 is found in ciliated protozoa which show high affinity towards copper and cadmium. Several tools and techniques, such as western blot, capillary electrophoresis, inductively coupled plasma, atomic emission spectroscopy and high performance liquid chromatography, have been extensively utilized for the detection and quantification of microbial MTs which are utilized for the efficient remediation and sequestration of heavy metals from a contaminated environment.

Human γS-Crystallin-Copper Binding Helps Buffer against Aggregation Caused by Oxidative Damage

Divalent metal cations can play a role in protein aggregation diseases, including cataract. Here we compare the aggregation of human γS-crystallin, a key structural protein of the eye lens, via mutagenesis, ultraviolet light damage, and the addition of metal ions. All three aggregation pathways result in globular, amorphous-looking structures that do not elongate into fibers. We also investigate the molecular mechanism underlying copper(II)-induced aggregation. This work was motivated by the observation that zinc(II)-induced aggregation of γS-crystallin is driven by intermolecular bridging of solvent-accessible cysteine residues, while in contrast, copper(II)-induced aggregation of this protein is exacerbated by the removal of solvent-accessible cysteines via mutation. Here we find that copper(II)-induced aggregation results from a complex mechanism involving multiple interactions with the protein. The initial protein-metal interactions result in the reduction of Cu(II) to Cu(I) with concomitant oxidation of γS-crystallin. In addition to the intermolecular disulfides that represent a starting point for aggregation, intramolecular disulfides also occur in the cysteine loop, a region of the N-terminal domain that was previously found to mediate the early stages of cataract formation. This previously unobserved ability of γS-crystallin to transfer disulfides intramolecularly suggests that it may serve as an oxidation sink for the lens after glutathione levels have become depleted during aging. γS-Crystallin thus serves as the last line of defense against oxidation in the eye lens, a result that underscores the chemical functionality of this protein, which is generally considered to play a purely structural role.

New insights into the zinc-α2-glycoprotein (ZAG) scaffold and its metal ions binding abilities using spectroscopic techniques

AIMS

Zinc-α2-glycoprotein (ZAG) is soluble lipid mobilizing protein and a noval adipokine associated with cancer cachexia. ZAG is an omnipresent protein and represent a fold of MHC class I proteins. Although ZAG's metal binding capacity has already been reported, no other metal has been mapped to date besides the complex formation with zinc.

MAIN METHODOLOGY

In this study, fluorescence emission spectroscopy and mass spectrometry (MALDI-TOF) were employed to define the putative interaction sites and their accessibility for the biologically important metals of Irving William Series.

KEY FINDINGS

Several hotspot residues in the ZAG scaffold involved in these interactions were mapped and their binding affinity score for each metal has been determined. Thebinding abilities of these sites and aggregation propensities of ZAG were monitored by fluorescence emission spectroscopy.

SIGNIFICANCE

The prediction of such binding affinity with metals on the active sites and its impact on the conformational states to accelerate aggregation was discussed as an important finding that may be involved in several other biochemical processes such as lipid binding, β-adrenergic receptors, cancer cachexia and association with plasma cholesterol and obesity.

The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility

Many proteins realize their special functions by binding with specific metal ion ligands during a cell's life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work. We have presented an improved computational approach for predicting the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Ca2+, Mg2+, Mn2+, Na+, and K+) by adding reclassified relative solvent accessibility (RSA). The best accuracy of fivefold cross-validation was higher than 77.9%, which was about 16% higher than the previous result on the same dataset. It was found that different reclassification of the RSA information can make different contributions to the identification of specific ligand binding residues. Our study has provided an additional understanding of the effect of the RSA on the identification of metal ion ligand binding residues.

Improving the binding affinity estimations of protein-ligand complexes using machine-learning facilitated force field method

Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-metallo and metallo PL complexes. Bappl+ outperforms other state-of-the-art scoring functions, achieving a high Pearson correlation coefficient of up to ~ 0.76 with low standard deviations. The biggest contributors to the increased performance are the use of a machine-learning model and the enlarged training dataset. We have also evaluated the performance of Bappl+ on target-specific proteins, which highlighted the limitations of our function and provides a way for further improvements. We believe that Bappl+ methodology could prove valuable in ranking candidate molecules against a target metallo or non-metallo protein by reliably predicting their binding affinities, thus helping in the drug discovery process.

Candida antarctica Lipase A-Based Enantiorecognition of a Highly Strained 4-Dibenzocyclooctynol (DIBO) Used for PET Imaging

The enantiomers of aromatic 4-dibenzocyclooctynol (DIBO), used for radiolabeling and subsequent conjugation of biomolecules to form radioligands for positron emission tomography (PET), were separated by kinetic resolution using lipase A from Candida antarctica (CAL-A). In optimized conditions, (R)-DIBO [(R)-1, ee 95%] and its acetylated (S)-ester [(S)-2, ee 96%] were isolated. In silico docking results explained the ability of CAL-A to differentiate the enantiomers of DIBO and to accommodate various acyl donors. Anhydrous MgCl₂ was used for binding water from the reaction medium and, thus, for obtaining higher conversion by preventing hydrolysis of the product (S)-2 into the starting material. Since the presence of hydrated MgCl₂·6H₂O also allowed high conversion or effect on enantioselectivity, Mg²⁺ ion was suspected to interact with the enzyme. Binding site predictions indicated at least two sites of interest; one in the lid domain at the bottom of the acyl binding pocket and another at the interface of the hydrolase and flap domains, just above the active site.

A Novel Legionella Genomic Island Encodes a Copper-Responsive Regulatory System and a Single Icm/Dot Effector Protein Transcriptionally Activated by Copper

Legionella pneumophila is an intracellular human pathogen that utilizes amoebae as its environmental host. The adaptation of L. pneumophila to the intracellular environment requires coordination of expression of its multicomponent pathogenesis system, which is composed of a secretion system and effector proteins. However, the regulatory factors controlling the expression of this pathogenesis system are only partially uncovered. Here, we discovered a novel regulatory system that is activated by copper and controls the expression of a single effector protein. The genes encoding both the regulatory system and the effector protein are located on a genomic island that undergoes horizontal gene transfer within the Legionella genus. This regulator-effector genomic island represents the first reported case of local regulation of effectors in Legionella. The discovery of this regulatory mechanism is an important step forward in the understanding of how the regulatory network of effectors functions and evolves in the Legionella genus.

The intracellular pathogen Legionella pneumophila utilizes the Icm/Dot type IV secretion system to translocate >300 effector proteins into host cells during infection. The regulation of some of these effector-encoding genes was previously shown to be coordinated by several global regulators, including three two-component systems (TCSs) found in all the Legionella species examined. Here, we describe the first Legionella genomic island encoding a single Icm/Dot effector and a dedicated TCS, which regulates its expression. This genomic island, which we named Lci, undergoes horizontal gene transfer in the Legionella genus, and the TCS encoded from this island (LciRS) is homologous to TCSs that control the expression of various metal resistance systems found in other bacteria. We found that the L. pneumophila sensor histidine kinase LciS is specifically activated by copper via a unique, small periplasmic sensing domain. Upon activation by LciS, the response regulator LciR directly binds to a conserved regulatory element and activates the expression of the adjacently located lciE effector-encoding gene. Thus, LciR represents the first local regulator of effectors identified in L. pneumophila. Moreover, we found that the expression of the lciRS operon is repressed by the Fis1 and Fis3 regulators, leading to Fis-mediated effects on copper induction of LciE and silencing of the expression of this genomic island in the absence of copper. This island represents a novel type of effector regulation in Legionella, shedding new light on the ways by which the Legionella pathogenesis system evolves its effector repertoire and expands its activating signals.

Insulin-copper quantum clusters preparation and receptor targeted bioimaging

Protein embedded fluorescence quantum clusters (QCs) have received a great amount of interest among the researchers because of their high aqueous solubility, stability, cost efficiency, and target specificity. Considerable advancement has happened in making functional quantum clusters with target specificity. This work reports the simple synthesis of insulin protected copper quantum clusters (ICuQCs) and its receptor-targeted bioimaging applications. The preparation of copper quantum clusters (CuQCs) was done simply by one-pot synthesis method by changing the pH of the insulin protein firstly to 10.5 basic pH than physiological pH. At physiological pH, the mixture incubated in oven 37 ⁰C at 240 rpm has been developed to process initially polydisperse, non-fluorescent, and unstable CuDs into monodispersed (∼2-3 nm), highly fluorescent, and extremely stable ICuQCs in the same phase (aqueous) using insulin as protein. HRTEM image show uniform distribution of CuDs within the protein matrix. Metal ion binding site prediction and docking server (MIB) results show that chain B of insulin contains 3 templates contains 5 amino acid residues which bind with Cu²⁺ metal ion. Groove 1 contains GLY8 and HIS10 bind has the highest binding potential towards Cu metal ions. The methodology adopted in this study should largely contribute to the practical applications of this new class of QCs. In view of the protein protection, coupled with direct synthesis and easy functionalization, this hybrid QC-protein system is expected to have numerous optical and bioimaging applications in the future.

Recognizing ion ligand binding sites by SMO algorithm

Background

In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function.

Results

In this study, four acid radical ion ligands (NO₂⁻,CO₃²⁻,SO₄²⁻,PO₄³⁻) and ten metal ion ligands (Zn²⁺,Cu²⁺,Fe²⁺,Fe³⁺,Ca²⁺,Mg²⁺,Mn²⁺,Na⁺,K⁺,Co²⁺) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation.

Conclusions

An efficient method for predicting ion ligand binding sites was presented.

Bioinformatic Exploration of Metal-Binding Proteome of Zoonotic Pathogen Orientia tsutsugamushi

Metal ions are involved in many essential biological processes and are crucial for the survival of all organisms. Identification of metal-binding proteins (MBPs) of human affecting pathogens may provide the blueprint for understanding biological metal usage and their putative roles in pathogenesis. This study is focused on the analysis of MBPs from Orientia tsutsugamushi (Ott), a causal agent of scrub typhus in humans. A total of 321 proteins were predicted as putative MBPs, based on sequence search and three-dimensional structure analysis. Majority of proteins could bind with magnesium, and the order of metal binding was Mg > Ca > Zn > Mn > Fe > Cd > Ni > Co > Cu, respectively. The predicted MBPs were functionally classified into nine broad classes. Among them, gene expression and regulation, metabolism, cell signaling, and transport classes were dominant. It was noted that the putative MBPs were localized in all subcellular compartments of Ott, but majorly found in the cytoplasm. Additionally, it was revealed that out of 321 predicted MBPs 245 proteins were putative bacterial toxins and among them, 98 proteins were nonhomologous to human proteome. Sixty putative MBPs showed the ability to interact with drug or drug-like molecules, which indicate that they may be used as broad-spectrum drug targets. These predicted MBPs from Ott could play vital role(s) in various cellular activities and virulence, hence may serve as plausible therapeutic targets to design metal-based drugs to curtail its infection.

Zinc binding proteome of a phytopathogen Xanthomonas translucens pv. undulosa

Xanthomonas translucens pv. undulosa (Xtu) is a proteobacteria which causes bacterial leaf streak (BLS) or bacterial chaff disease in wheat and barley. The constant competition for zinc (Zn) metal nutrients contributes significantly in plant-pathogen interactions. In this study, we have employed a systematic in silico approach to study the Zn-binding proteins of Xtu. From the whole proteome of Xtu, we have identified approximately 7.9% of proteins having Zn-binding sequence and structural motifs. Further, 115 proteins were found homologous to plant-pathogen interaction database. Among these 115 proteins, 11 were predicted as putative secretory proteins. The functional diversity in Zn-binding proteins was revealed by functional domain, gene ontology and subcellular localization analysis. The roles of Zn-binding proteins were found to be varied in the range from metabolism, proteolysis, protein biosynthesis, transport, cell signalling, protein folding, transcription regulation, DNA repair, response to oxidative stress, RNA processing, antimicrobial resistance, DNA replication and DNA integration. This study provides preliminary information on putative Zn-binding proteins of Xtu which may further help in designing new metal-based antimicrobial agents for controlling BLS and bacterial chaff infections on staple crops.

Calcium-ligand variants of the myocilin olfactomedin propeller selected from invertebrate phyla reveal cross-talk with N-terminal blade and surface helices

Olfactomedins are a family of modular proteins found in multicellular organisms that all contain five-bladed β-propeller olfactomedin (OLF) domains. In support of differential functions for the OLF propeller, the available crystal structures reveal that only some OLF domains harbor an internal calcium-binding site with ligands derived from a triad of residues. For the myocilin OLF domain (myoc-OLF), ablation of the ion-binding site (triad Asp, Asn, Asp) by altering the coordinating residues affects the stability and overall structure, in one case leading to misfolding and glaucoma. Bioinformatics analysis reveals a variety of triads with possible ion-binding characteristics lurking in OLF domains in invertebrate chordates such as Arthropoda (Asp-Glu-Ser), Nematoda (Asp-Asp-His) and Echinodermata (Asp-Glu-Lys). To test ion binding and to extend the observed connection between ion binding and distal structural rearrangements, consensus triads from these phyla were installed in the myoc-OLF. All three protein variants exhibit wild-type-like or better stability, but their calcium-binding properties differ, concomitant with new structural deviations from wild-type myoc-OLF. Taken together, the results indicate that calcium binding is not intrinsically destabilizing to myoc-OLF or required to observe a well ordered side helix, and that ion binding is a differential feature that may underlie the largely elusive biological function of OLF propellers.

The iron-regulated vacuolar Legionella pneumophila MavN protein is a transition-metal transporter

Legionella pneumophila causes a potentially fatal form of pneumonia by replicating within macrophages in the Legionella-containing vacuole (LCV). Bacterial survival and proliferation within the LCV rely on hundreds of secreted effector proteins comprising high functional redundancy. The vacuolar membrane-localized MavN, hypothesized to support iron transport, is unique among effectors because loss-of-function mutations result in severe intracellular growth defects. We show here an iron starvation response by L. pneumophila after infection of macrophages that was prematurely induced in the absence of MavN, consistent with MavN granting access to limiting cellular iron stores. MavN cysteine accessibilities to a membrane-impermeant label were determined during macrophage infections, revealing a topological pattern supporting multipass membrane transporter models. Mutations to several highly conserved residues that can take part in metal recognition and transport resulted in defective intracellular growth. Purified MavN and mutant derivatives were directly tested for transporter activity after heterologous purification and liposome reconstitution. Proteoliposomes harboring MavN exhibited robust transport of Fe²⁺, with the severity of defect of most mutants closely mimicking the magnitude of defects during intracellular growth. Surprisingly, MavN was equivalently proficient at transporting Fe²⁺, Mn²⁺, Co²⁺, or Zn²⁺ Consequently, flooding infected cells with either Mn²⁺ or Zn²⁺ allowed collaboration with iron to enhance intracellular growth of L. pneumophila ΔmavN strains, indicating a clear role for MavN in transporting each of these ions. These findings reveal that MavN is a transition-metal-ion transporter that plays a critical role in response to iron limitation during Legionella infection.

AMMOS2: a web server for protein-ligand-water complexes refinement via molecular mechanics

AMMOS2 is an interactive web server for efficient computational refinement of protein-small organic molecule complexes. The AMMOS2 protocol employs atomic-level energy minimization of a large number of experimental or modeled protein-ligand complexes. The web server is based on the previously developed standalone software AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of protein-ligand interactions at five levels of flexibility of the protein receptor. The new version 2 of AMMOS implemented in the AMMOS2 web server allows the users to include explicit water molecules and individual metal ions in the protein-ligand complexes during minimization. The web server provides comprehensive analysis of computed energies and interactive visualization of refined protein-ligand complexes. The ligands are ranked by the minimized binding energies allowing the users to perform additional analysis for drug discovery or chemical biology projects. The web server has been extensively tested on 21 diverse protein-ligand complexes. AMMOS2 minimization shows consistent improvement over the initial complex structures in terms of minimized protein-ligand binding energies and water positions optimization. The AMMOS2 web server is freely available without any registration requirement at the URL: http://drugmod.rpbs.univ-paris-diderot.fr/ammosHome.php.

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods

The zinc (Zn²⁺) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in protein structures can provide potential clues for elucidation of protein folding and functions. However, determining zinc-binding residues by experimental means is usually lab-intensive and associated with high cost in most cases. In this context, the development of computational tools for identifying zinc-binding sites is highly desired, especially in the current post-genomic era. In this work, we developed a novel zinc-binding site prediction method by combining several intensively-trained machine learning models. To establish an accurate and generative method, we downloaded all zinc-binding proteins from the Protein Data Bank and prepared a non-redundant dataset. Meanwhile, a well-prepared dataset by other groups was also used. Then, effective and complementary features were extracted from sequences and three-dimensional structures of these proteins. Moreover, several well-designed machine learning models were intensively trained to construct accurate models. To assess the performance, the obtained predictors were stringently benchmarked using the diverse zinc-binding sites. Furthermore, several state-of-the-art in silico methods developed specifically for zinc-binding sites were also evaluated and compared. The results confirmed that our method is very competitive in real world applications and could become a complementary tool to wet lab experiments. To facilitate research in the community, a web server and stand-alone program implementing our method were constructed and are publicly available at . The downloadable program of our method can be easily used for the high-throughput screening of potential zinc-binding sites across proteomes.