Robust recognition of zinc binding sites in proteins

A role and mechanism for redox sensing by SENP1 in β-cell responses to high fat feeding

Pancreatic β-cells respond to metabolic stress by upregulating insulin secretion, however the underlying mechanisms remain unclear. Here we show, in β-cells from overweight humans without diabetes and mice fed a high-fat diet for 2 days, insulin exocytosis and secretion are enhanced without increased Ca2+ influx. RNA-seq of sorted β-cells suggests altered metabolic pathways early following high fat diet, where we find increased basal oxygen consumption and proton leak, but a more reduced cytosolic redox state. Increased β-cell exocytosis after 2-day high fat diet is dependent on this reduced intracellular redox state and requires the sentrin-specific SUMO-protease-1. Mice with either pancreas- or β-cell-specific deletion of this fail to up-regulate exocytosis and become rapidly glucose intolerant after 2-day high fat diet. Mechanistically, redox-sensing by the SUMO-protease requires a thiol group at C535 which together with Zn+-binding suppresses basal protease activity and unrestrained β-cell exocytosis, and increases enzyme sensitivity to regulation by redox signals.

Identifying and sharing per-and polyfluoroalkyl substances hot-spot areas and exposures in drinking water

Exposure to per- and polyfluoroalkyl substances (PFAS) in drinking water is widely recognized as a public health concern. Decision-makers who are responsible for managing PFAS drinking water risks lack the tools to acquire the information they need. In response to this need, we provide a detailed description of a Kentucky dataset that allows decision-makers to visualize potential hot-spot areas and evaluate drinking water systems that may be susceptible to PFAS contamination. The dataset includes information extracted from publicly available sources to create five different maps in ArcGIS Online and highlights potential sources of PFAS contamination in the environment in relation to drinking water systems. As datasets of PFAS drinking water sampling continue to grow as part of evolving regulatory requirements, we used this Kentucky dataset as an example to promote the reuse of this dataset and others like it. We incorporated the FAIR (Findable, Accessible, Interoperable, and Reusable) principles by creating a Figshare item that includes all data and associated metadata with these five ArcGIS maps.

The structure of neurofibromin isoform 2 reveals different functional states

The autosomal dominant monogenetic disease neurofibromatosis type 1 (NF1) affects approximately one in 3,000 individuals and is caused by mutations in the NF1 tumour suppressor gene, leading to dysfunction in the protein neurofibromin (Nf1)^1,2. As a GTPase-activating protein, a key function of Nf1 is repression of the Ras oncogene signalling cascade. We determined the human Nf1 dimer structure at an overall resolution of 3.3 Å. The cryo-electron microscopy structure reveals domain organization and structural details of the Nf1 exon 23a splicing³ isoform 2 in a closed, self-inhibited, Zn-stabilized state and an open state. In the closed conformation, HEAT/ARM core domains shield the GTPase-activating protein-related domain (GRD) so that Ras binding is sterically inhibited. In a distinctly different, open conformation of one protomer, a large-scale movement of the GRD occurs, which is necessary to access Ras, whereas Sec14-PH reorients to allow interaction with the cellular membrane⁴. Zn incubation of Nf1 leads to reduced Ras-GAP activity with both protomers in the self-inhibited, closed conformation stabilized by a Zn binding site between the N-HEAT/ARM domain and the GRD–Sec14-PH linker. The transition between closed, self-inhibited states of Nf1 and open states provides guidance for targeted studies deciphering the complex molecular mechanism behind the widespread neurofibromatosis syndrome and Nf1 dysfunction in carcinogenesis.

Cryo-EM structure of Nf1 protein is reported, revealing closed and open conformations that regulate interaction with Ras oncogene, setting the stage for understanding the mechanistic action of Nf1 and how disease mutations lead to dysfunction.

Transcriptomic insights into the zinc homeostasis of MCF-7 breast cancer cells via next-generation RNA sequencing

A significant gap in the knowledge of zinc homeostasis exists for breast cancer cells. In this study, we investigated the transcriptomic response of the luminal breast cancer cells (MCF-7) to the exposure of extracellular zinc using next-generation RNA sequencing. The dataset was collected for three time points (T0, T30, and T120) in the time course of zinc treatment, which revealed the dramatic increase, up to 869-fold, of the gene expression for metallothioneins (MT1B, MT1F, MT1X, and MT2A) and the zinc exporter ZnT1 (SLC30A1) at T30, continuingly through to T120. The similar dynamic expression pattern was found for the autophagy-related gene (VMP1) and numerous genes for zinc finger proteins (e.g. RNF165, ZNF365, ZBTB2, SNAI1, ZNF442, ZNF547, ZNF563, and ZNF296). These findings point to the all-hands-on-deck strategy adopted by the cancer cells for maintaining zinc homeostasis. The stress responsive genes encoding heat shock proteins (HSPA1A, HSPA1B, HSPA1L, HSPA4L, HSPA6, HSPA8, HSPH1, HSP90AA1, and HSP90AB1) and the MTF-1 biomarker genes (AKR1C2, CLU, ATF3, GDF15, HMOX1, MAP1A, MAFG, SESN2, and UBC) were also differentially up-regulated at T120, suggesting a role of heat shock proteins and the MTF-1 related stress proteins in dealing with zinc exposure. It is for the first time that the gene encoding Polo-like kinase 2 (PLK2) was found to be involved in zinc-related response. The top differentially expressed genes were validated by qRT-PCR and further extended to the basal type breast cancer cells (MDA-MB-231). It was found that the expression level of SLC30A1 in MDA-MB-231 was higher than MCF-7 in response to zinc exposure. Taken together, the findings contribute to our knowledge and understanding of zinc homeostasis in breast cancer cells.

Transition metal cation inhibition of Mycobacterium tuberculosis esterase RV0045C

Mycobacterium tuberculosis virulence is highly metal-dependent with metal availability modulating the shift from the dormant to active states of M. tuberculosis infection. Rv0045c from M. tuberculosis is a proposed metabolic serine hydrolase whose folded stability is dependent on divalent metal concentration. Herein, we measured the divalent metal inhibition profile of the enzymatic activity of Rv0045c and found specific divalent transition metal cations (Cu²⁺  ≥ Zn²⁺  > Ni²⁺  > Co²⁺ ) strongly inhibited its enzymatic activity. The metal cations bind allosterically, largely affecting values for k_cat rather than K_M . Removal of the artificial N-terminal 6xHis-tag did not change the metal-dependent inhibition, indicating that the allosteric inhibition site is native to Rv0045c. To isolate the site of this allosteric regulation in Rv0045c, the structures of Rv0045c were determined at 1.8 Å and 2.0 Å resolution in the presence and absence of Zn²⁺ with each structure containing a previously unresolved dynamic loop spanning the binding pocket. Through the combination of structural analysis with and without zinc and targeted mutagenesis, this metal-dependent inhibition was traced to multiple chelating residues (H202A/E204A) on a flexible loop, suggesting dynamic allosteric regulation of Rv0045c by divalent metals. Although serine hydrolases like Rv0045c are a large and diverse enzyme superfamily, this is the first structural confirmation of allosteric regulation of their enzymatic activity by divalent metals.

Neuropeptides: Roles and Activities as Metal Chelators in Neurodegenerative Diseases

Neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are characterized by deposits of amyloid proteins. The homeostasis of metal ions is crucial for the normal biological functions in the brain. However, in AD and PD, the imbalance of metal ions leads to formation of amyloid deposits. In the past four decades, there has been extensive effort to design compound agents than can chelate metal ions with the aim of preventing the formation of the amyloid deposits. Unfortunately, the compounds to date that were designed were not successful candidates to be used in clinical trials. Neuropeptides are small molecules that are produced and released by neurons. It has been shown that neuropeptides have neuroprotective effects in the brain and reduce the formation of amyloid deposits. This Review Article is focused on the function of neuropeptides as metal chelators. Experimental and computational studies demonstrated that neuropeptides could bind metal ions, such as Cu²⁺ and Zn²⁺. This Review Article provides perspectives and initiates future studies to investigate the role of neuropeptides as metal chelators in neurodegenerative diseases.

Inactivation of parathyroid hormone: perspectives of drug discovery to combating hyperparathyroidism

Hormonal coordination is tightly regulated within the human body and thus regulates human physiology. The parathyroid hormone (PTH), a member of the endocrine system, regulates the calcium and phosphate level within the human body. Under non-physiological conditions, PTH levels get upregulated (hyperparathyroidism) or downregulated (hypoparathyroidism) due to external or internal factors. In the case of hyperparathyroidism, elevated PTH stimulates cellular receptors present in the bones, kidneys, and intestines to increase the blood calcium level, leading to calcium deposition. This eventually causes various symptoms including kidney stones. Currently, there is no known medication that directly targets PTH in order to suppress its function. Therefore, it is of great interest to find novel small molecules or any other means that can modulate PTH function. The molecular signaling of PTH starts by binding of its N-terminus to the G-protein coupled PTH1/2 receptor. Therefore, any intervention that affects the N-terminus of PTH could be a lead candidate for treating hyperparathyroidism. As a proof-of-concept, there are various possibilities to inhibit molecular PTH function by (i) a small molecule, (ii) N-terminal PTH phosphorylation, (iii) fibril formation and (iv) residue-specific mutations. These modifications put PTH into an inactive state, which will be discussed in detail in this review article. We anticipate that exploring small molecules or other means that affect the N-terminus of PTH could be lead candidates in combating hyperparathyroidism.

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.

The potential of putative zinc-binding motifs of haemagglutinin (HA) protein for categorization and prediction of pathogenicity of H5 subtypes of avian influenza virus

In the present study, we used the potential of bioinformatics and computational analysis to predict the existence and biological relevance of zinc finger (ZF) motifs in heamagglutinin (HA) protein of Avian Influenza (AI) virus. Sequence data of Avian Influenza (AI) viruses were retrieved from accessible databases (GenBank, GISAID, IRD) and analyzed for the existence, as well as functional prediction of the putative zinc finger or ''zinc-binding'' motif(s) of HA protein. It is hypothesized that the ZF motif(s) in HA of AI virus can be used as a ''novel'' biomarker for categorization of the virus and/or its virulence. As a model for analysis, we used the H5 subtypes of highly pathogenic, non-pathogenic and low pathogenic avian influenza (HPAI, NPAI and LPAI) viruses of H5N1 and H5N2 of avian and human origins. Interestingly, our method of characterization using the zinc-finger agrees with the existing classification in distinguishing between highly pathogenic and non-pathogenic or low pathogenic subtypes. The new method also clearly distinguished between low and non-pathogenic strains of H5N2 and H5N1 which are indistinguishable by the existing method that utilizes the sequence of the polybasic amino acids of the proteolytic cleavage site for pathogenicity. It is hypothesized that zinc through the activities of zinc-binding proteins modulates the virulence property of the viral subtypes. Our observation further revealed that only the HA protein among the eight encoded proteins of influenza viruses contain high numbers of Cys-His residues. It is expected that the information gathered from the analysis of the data will be useful to generate more research hypotheses/designs that will give further insight towards the identification and control of avian influenza virus through the molecular manipulation of zinc finger motifs present in viral HA protein.

Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle

The prediction of ion ligand–binding residues in protein sequences is a challenging work that contributes to understand the specific functions of proteins in life processes. In this article, we selected binding residues of 14 ion ligands as research objects, including four acid radical ion ligands and 10 metal ion ligands. Based on the amino acid sequence information, we selected the composition and position conservation information of amino acids, the predicted structural information, and physicochemical properties of amino acids as basic feature parameters. We then performed a statistical analysis and reclassification for dihedral angle and proposed new methods on the extraction of feature parameters. The methods mainly included applying information entropy on the extraction of polarization charge and hydrophilic–hydrophobic information of amino acids and using position weight matrices on the extraction of position conservation information. In the prediction model, we used the random forest algorithm and obtained better prediction results than previous works. With the independent test, the Matthew's correlation coefficient and accuracy of 10 metal ion ligand–binding residues were larger than 0.07 and 52%, respectively; the corresponding evaluation values of four acid radical ion ligand–binding residues were larger than 0.15 and 86%, respectively. Further, we classified and combined the phi and psi angles and optimized prediction model for each ion ligand–binding residue.

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.

Prediction of Protein Metal Binding Sites Using Deep Neural Networks

Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which vital function is fulfilled. The prediction of metal-binding in proteins will be considered as a step-in function assignment for new proteins, which helps to obtain functional proteins in genomic studies, is critical to protein function annotation and drug discovery. Computational predictions made by using machine learning methods from the data obtained from amino acid sequences are widely used in the protein metal-binding and various bioinformatics fields. In this work, we present three different deep learning architectures for prediction of metal-binding of Histidines (HIS) and Cysteines (CYS) amino acids. These architectures are as follows: 2D Convolutional Neural Network, Long-Short Term Memory and Recurrent Neural Network. Their comparison is carried out on the three different sets of attributes derived from a public dataset of protein sequences. These three sets of features extracted from the protein sequence were obtained using the PAM scoring matrix, protein composition server, and binary representation methods. The results show that a better performance for prediction of protein metal- binding sites is obtained through Convolutional Neural Network architecture.

DNA-protein interaction: identification, prediction and data analysis

Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA-protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA-protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.

Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins

With more than a third of the genome encoding for metal-containing biomolecules, the in silico prediction of how metal ions bind to proteins is crucial in chemistry, biology, and medicine. To date, algorithms for metal-binding site prediction are mainly based on sequence analysis. Those methods have reached enough quality to predict the correct region of the protein and the coordinating residues involved in metal-binding, but they do not provide three-dimensional (3D) models. On the contrary, the prediction of accurate 3D models for protein-metal adducts by structural bioinformatics and molecular modeling techniques is still a challenge. Here, we present an update of our multipurpose molecular modeling suite, GaudiMM, to locate metal-binding sites in proteins. The approach is benchmarked on 105 X-ray structures with resolution lower than 2.0 Å. Results predict the correct binding site of the metal in the biological scaffold for all the entries in the data set. Generated 3D models of the protein-metal coordination complexes reach root-mean-square deviation values under 1.0 Å between calculated and experimental structures. The whole process is purely based on finding poses that satisfy metal-derived geometrical rules without needing sequence or fine electronic inputs. Additional post-optimizations, including receptor flexibility, have been tested and suggest that more extensive searches, required when the host structures present a low level of pre-organization, are also possible. With this new update, GaudiMM is now able to look for metal-binding sites in biological scaffolds and clearly shows how explicitly considering the geometric particularities of the first coordination sphere of the metal in a docking process provides excellent results.

Predicting protein-ligand binding residues with deep convolutional neural networks

Background

Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categorized as sequence-based or 3D-structure-based methods. All these methods are based on traditional machine learning. In a series of binding residue prediction tasks, 3D-structure-based methods are widely superior to sequence-based methods. However, due to the great number of proteins with known amino acid sequences, sequence-based methods have considerable room for improvement with the development of deep learning. Therefore, prediction of protein-ligand binding residues with deep learning requires study.

Results

In this study, we propose a new sequence-based approach called DeepCSeqSite for ab initio protein-ligand binding residue prediction. DeepCSeqSite includes a standard edition and an enhanced edition. The classifier of DeepCSeqSite is based on a deep convolutional neural network. Several convolutional layers are stacked on top of each other to extract hierarchical features. The size of the effective context scope is expanded as the number of convolutional layers increases. The long-distance dependencies between residues can be captured by the large effective context scope, and stacking several layers enables the maximum length of dependencies to be precisely controlled. The extracted features are ultimately combined through one-by-one convolution kernels and softmax to predict whether the residues are binding residues. The state-of-the-art ligand-binding method COACH and some of its submethods are selected as baselines. The methods are tested on a set of 151 nonredundant proteins and three extended test sets. Experiments show that the improvement of the Matthews correlation coefficient (MCC) is no less than 0.05. In addition, a training data augmentation method that slightly improves the performance is discussed in this study.

Conclusions

Without using any templates that include 3D-structure data, DeepCSeqSite significantlyoutperforms existing sequence-based and 3D-structure-based methods, including COACH. Augmentation of the training sets slightly improves the performance. The model, code and datasets are available at https://github.com/yfCuiFaith/DeepCSeqSite.

Characterizing metal-binding sites in proteins with X-ray crystallography

Metals have crucial roles in many physiological, pathological, toxicological, pharmaceutical, and diagnostic processes. Proper handling of metal-containing macromolecule samples for structural studies is not trivial, and failure to handle them properly is often a source of irreproducibility caused by issues such as pH changes, incorporation of unexpected metals, or oxidization/reduction of the metal. This protocol outlines the guidelines and best practices for characterizing metal-binding sites in protein structures and alerts experimenters to potential pitfalls during the preparation and handling of metal-containing protein samples for X-ray crystallography studies. The protocol features strategies for controlling the sample pH and the metal oxidation state, recording X-ray fluorescence (XRF) spectra, and collecting diffraction data sets above and below the corresponding metal absorption edges. This protocol should allow experimenters to gather sufficient evidence to unambiguously determine the identity and location of the metal of interest, as well as to accurately characterize the coordinating ligands in the metal binding environment within the protein. Meticulous handling of metal-containing macromolecule samples as described in this protocol should enhance experimental reproducibility in biomedical sciences, especially in X-ray macromolecular crystallography. For most samples, the protocol can be completed within a period of 7-190 d, most of which (2-180 d) is devoted to growing the crystal. The protocol should be readily understandable to structural biologists, particularly protein crystallographers with an intermediate level of experience.

A small-molecule acts as a 'roadblock' on DNA, hampering its fundamental processes

DNA replication, RNA and protein synthesis are the most fundamental housekeeping processes involved in an organism's growth. Failure or dysregulation of these pathways are often deleterious to life. Therefore, selective inhibition of such processes can be crucial for the inhibition of the growth of any cell, including cancer cells, pathogenic bacteria or other deadly microbes. In the present study, a Zn²⁺ complex is shown to act as a roadblock of DNA. The Zn²⁺ complex inhibited DNA taq polymerase activity under the in vitro conditions of polymerase chain reaction (PCR). Under in vivo conditions, it readily crosses the cell wall of gram-negative bacteria (Escherichia coli), leading to the reduction of RNA levels as well as protein content. Growth of pathogenic bacteria (e.g., Staphylococcus aureus and Pseudomonas aeruginosa) was also significantly retarded. The Zn²⁺ complex binds to the grooves of the DNA without inducing conformational changes or exhibiting chemical nuclease activity. To the best current knowledge, this is first coordination complex exhibiting a 'roadblock' property under both in vitro and in vivo conditions (show at all three levels - DNA, RNA and protein). The label-free approach used in this study may offer an alternative route towards fighting pathogenic bacteria or cancer cells by hampering fundamental cellular processes.

Identification of metal ion binding sites based on amino acid sequences

The identification of metal ion binding sites is important for protein function annotation and the design of new drug molecules. This study presents an effective method of analyzing and identifying the binding residues of metal ions based solely on sequence information. Ten metal ions were extracted from the BioLip database: Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+. The analysis showed that Zn2+, Cu2+, Fe2+, Fe3+, and Co2+ were sensitive to the conservation of amino acids at binding sites, and promising results can be achieved using the Position Weight Scoring Matrix algorithm, with an accuracy of over 79.9% and a Matthews correlation coefficient of over 0.6. The binding sites of other metals can also be accurately identified using the Support Vector Machine algorithm with multifeature parameters as input. In addition, we found that Ca2+ was insensitive to hydrophobicity and hydrophilicity information and Mn2+ was insensitive to polarization charge information. An online server was constructed based on the framework of the proposed method and is freely available at http://60.31.198.140:8081/metal/HomePage/HomePage.html.

Direct Binding of the pH-Regulated Protein 1 (Pra1) from Candida albicans Inhibits Cytokine Secretion by Mouse CD4+ T Cells

Opportunistic infections with the saprophytic yeast Candida albicans are a major cause of morbidity in immunocompromised patients. While the interaction of cells and molecules of innate immunity with C. albicans has been studied to great depth, comparatively little is known about the modulation of adaptive immunity by C. albicans. In particular, direct interaction of proteins secreted by C. albicans with CD4⁺ T cells has not been studied in detail. In a first screening approach, we identified the pH-regulated antigen 1 (Pra1) as a molecule capable of directly binding to mouse CD4⁺ T cells in vitro. Binding of Pra1 to the T cell surface was enhanced by extracellular Zn²⁺ ions which Pra1 is known to scavenge from the host in order to supply the fungus with Zn²⁺. In vitro stimulation assays using highly purified mouse CD4⁺ T cells showed that Pra1 increased proliferation of CD4⁺ T cells in the presence of plate-bound anti-CD3 monoclonal antibody. In contrast, secretion of effector cytokines such as IFNγ and TNF by CD4⁺ T cells upon anti-CD3/ anti-CD28 mAb as well as cognate antigen stimulation was reduced in the presence of Pra1. By secreting Pra1 C. albicans, thus, directly modulates and partially controls CD4⁺ T cell responses as shown in our in vitro assays.

Computational approaches for de novo design and redesign of metal-binding sites on proteins

Metal ions play pivotal roles in protein structure, function and stability. The functional and structural diversity of proteins in nature expanded with the incorporation of metal ions or clusters in proteins. Approximately one-third of these proteins in the databases contain metal ions. Many biological and chemical processes in nature involve metal ion-binding proteins, aka metalloproteins. Many cellular reactions that underpin life require metalloproteins. Most of the remarkable, complex chemical transformations are catalysed by metalloenzymes. Realization of the importance of metal-binding sites in a variety of cellular events led to the advancement of various computational methods for their prediction and characterization. Furthermore, as structural and functional knowledgebase about metalloproteins is expanding with advances in computational and experimental fields, the focus of the research is now shifting towards de novo design and redesign of metalloproteins to extend nature’s own diversity beyond its limits. In this review, we will focus on the computational toolbox for prediction of metal ion-binding sites, de novo metalloprotein design and redesign. We will also give examples of tailor-made artificial metalloproteins designed with the computational toolbox.

Accumulation of cholesterol and increased demand for zinc in serum-deprived RPE cells

PURPOSE

Having observed that confluent ARPE-19 cells (derived from human RPE) survive well in high-glucose serum-free medium (SFM) without further feeding for several days, we investigated the expression profile of RPE cells under the same conditions.

METHODS

Expression profiles were examined with microarray and quantitative PCR (qPCR) analyses, followed by western blot analysis of key regulated proteins. The effects of low-density lipoprotein (LDL) and zinc supplementation were examined with qPCR. Immunofluorescence was used to localize the LDL receptor and to examine LDL uptake. Cellular cholesterol levels were measured with filipin binding. Expression patterns in primary fetal RPE cells were compared using qPCR.

RESULTS

Microarray analyses of gene expression in ARPE-19, confirmed with qPCR, showed upregulation of lipid and cholesterol biosynthesis pathways in SFM. At the protein level, the cholesterol synthesis control factor SRBEF2 was activated, and other key lipid synthesis proteins increased. Supplementation of SFM with LDL reversed the upregulation of lipid and cholesterol synthesis genes, but not of cholesterol transport genes. The LDL receptor relocated to the plasma membrane, and LDL uptake was activated by day 5-7 in SFM, suggesting increased demand for cholesterol. Confluent ARPE-19 cells in SFM accumulated intracellular cholesterol, compared with cells supplemented with serum, over 7 days. Over the same time course in SFM, the expression of metallothioneins decreased while the major zinc transporter was upregulated, consistent with a parallel increase in demand for zinc. Supplementation with zinc reversed expression changes for metallothionein genes, but not for other zinc-related genes. Similar patterns of regulation were also seen in primary fetal human RPE cells in SFM.

CONCLUSIONS

ARPE-19 cells respond to serum deprivation and starvation with upregulation of the lipid and cholesterol pathways, accumulation of intracellular cholesterol, and increased demand for zinc. Similar trends are seen in primary fetal RPE cells. Cholesterol accumulation basal to RPE is a prominent feature of age-related macular degeneration (AMD), while dietary zinc is protective. It is conceivable that accumulating defects in Bruch's membrane and dysfunction of the choriocapillaris could impede transport between RPE and vasculature in AMD. Thus, this pattern of response to serum deprivation in RPE-derived cells may have relevance for some aspects of the progression of AMD.

Identification of Novel Abiotic Stress Proteins in Triticum aestivum Through Functional Annotation of Hypothetical Proteins

Cereal grain bread wheat (T. aestivum) is an important source of food and belongs to Poaceae family. Hypothetical proteins (HPs), i.e., proteins with unknown functions, share a substantial portion of wheat proteomes and play important roles in growth and physiology of plant system. Several functional annotations studies utilizing the protein sequences for characterization of role of individual protein in physiology of plant systems were being reported in recent past. In this study, an integrated pipeline of software/servers has been used for the identification and functional annotation of 124 unique HPs of T. aestivum considering available data in NCBI till date. All HPs were broadly annotated, out of which functions of 77 HPs were successfully assigned with high confidence level. Precisely functional annotation of remaining 47 HPs is also characterized with low confidence. Several latest versions of protein family databases, pathways information, genomics context methods and in silico tools were utilized to identify and assign function for individual HPs. Annotation result of several HPs mainly belongs to cellular protein, metabolic enzymes, binding proteins, transmembrane proteins, transcription factors and photosystem regulator proteins. Subsequently, functional analysis has revealed the role of few HPs in abiotic stress, which were further verified by phylogenetic analysis. The functionally associated proteins with each of above-mentioned abiotic stress-related proteins were identified through protein-protein interaction network analysis. The outcome of this study may be helpful for formulating general set pipeline/protocols for a better understanding of the role of HPs in physiological development of various plant systems.

Potential DNA binding and nuclease functions of ComEC domains characterized in silico

Bacterial competence, which can be natural or induced, allows the uptake of exogenous double stranded DNA (dsDNA) into a competent bacterium. This process is known as transformation. A multiprotein assembly binds and processes the dsDNA to import one strand and degrade another yet the underlying molecular mechanisms are relatively poorly understood. Here distant relationships of domains in Competence protein EC (ComEC) of Bacillus subtilis (Uniprot: P39695) were characterized. DNA-protein interactions were investigated in silico by analyzing models for structural conservation, surface electrostatics and structure-based DNA binding propensity; and by data-driven macromolecular docking of DNA to models. Our findings suggest that the DUF4131 domain contains a cryptic DNA-binding OB fold domain and that the β-lactamase-like domain is the hitherto cryptic competence nuclease. Proteins 2016; 84:1431-1442. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Interaction between IGFBP7 and insulin: a theoretical and experimental study

Insulin-like growth factor binding protein 7 (IGFBP7) can bind to insulin with high affinity which inhibits the early steps of insulin action. Lack of recognition mechanism impairs our understanding of insulin regulation before it binds to insulin receptor. Here we combine computational simulations with experimental methods to investigate the interaction between IGFBP7 and insulin. Molecular dynamics simulations indicated that His200 and Arg198 in IGFBP7 were key residues. Verified by experimental data, the interaction remained strong in single mutation systems R198E and H200F but became weak in double mutation system R198E-H200F relative to that in wild-type IGFBP7. The results and methods in present study could be adopted in future research of discovery of drugs by disrupting protein-protein interactions in insulin signaling. Nevertheless, the accuracy, reproducibility, and costs of free-energy calculation are still problems that need to be addressed before computational methods can become standard binding prediction tools in discovery pipelines.

Small Molecule Inhibited Parathyroid Hormone Mediated cAMP Response by N-Terminal Peptide Binding

Ligand binding to certain classes of G protein coupled receptors (GPCRs) stimulates the rapid synthesis of cAMP through G protein. Human parathyroid hormone (PTH), a member of class B GPCRs, binds to its receptor via its N–terminal domain, thereby activating the pathway to this secondary messenger inside cells. Presently, GPCRs are the target of many pharmaceuticals however, these drugs target only a small fraction of structurally known GPCRs (about 10%). Coordination complexes are gaining interest due to their wide applications in the medicinal field. In the present studies we explored the potential of a coordination complex of Zn(II) and anthracenyl–terpyridine as a modulator of the parathyroid hormone response. Preferential interactions at the N–terminal domain of the peptide hormone were manifested by suppressed cAMP generation inside the cells. These observations contribute a regulatory component to the current GPCR–cAMP paradigm, where not the receptor itself, but the activating hormone is a target. To our knowledge, this is the first report about a coordination complex modulating GPCR activity at the level of deactivating its agonist. Developing such molecules might help in the control of pathogenic PTH function such as hyperparathyroidism, where control of excess hormonal activity is essentially required.

Transition Metal Homeostasis

This chapter focuses on transition metals. All transition metal cations are toxic-those that are essential for Escherichia coli and belong to the first transition period of the periodic system of the element and also the "toxic-only" metals with higher atomic numbers. Common themes are visible in the metabolism of these ions. First, there is transport. High-rate but low-affinity uptake systems provide a variety of cations and anions to the cells. Control of the respective systems seems to be mainly through regulation of transport activity (flux control), with control of gene expression playing only a minor role. If these systems do not provide sufficient amounts of a needed ion to the cell, genes for ATP-hydrolyzing high-affinity but low-rate uptake systems are induced, e.g., ABC transport systems or P-type ATPases. On the other hand, if the amount of an ion is in surplus, genes for efflux systems are induced. By combining different kinds of uptake and efflux systems with regulation at the levels of gene expression and transport activity, the concentration of a single ion in the cytoplasm and the composition of the cellular ion "bouquet" can be rapidly adjusted and carefully controlled. The toxicity threshold of an ion is defined by its ability to produce radicals (copper, iron, chromate), to bind to sulfide and thiol groups (copper, zinc, all cations of the second and third transition period), or to interfere with the metabolism of other ions. Iron poses an exceptional metabolic problem due its metabolic importance and the low solubility of Fe(III) compounds, combined with the ability to cause dangerous Fenton reactions. This dilemma for the cells led to the evolution of sophisticated multi-channel iron uptake and storage pathways to prevent the occurrence of unbound iron in the cytoplasm. Toxic metals like Cd2+ bind to thiols and sulfide, preventing assembly of iron complexes and releasing the metal from iron-sulfur clusters. In the unique case of mercury, the cation can be reduced to the volatile metallic form. Interference of nickel and cobalt with iron is prevented by the low abundance of these metals in the cytoplasm and their sequestration by metal chaperones, in the case of nickel, or by B12 and its derivatives, in the case of cobalt. The most dangerous metal, copper, catalyzes Fenton-like reactions, binds to thiol groups, and interferes with iron metabolism. E. coli solves this problem probably by preventing copper uptake, combined with rapid efflux if the metal happens to enter the cytoplasm.

Genome-wide computational determination of the human metalloproteome

Accurate prediction of protein function in humans is important for understanding biological processes at the molecular level in biomedicine and drug design. Over a third of proteins are commonly held to bind metal, and ∼10% of human proteins, to bind zinc. Therefore, an initial step in protein function prediction frequently involves predicting metal ion binding. In recent years, methods have been developed to predict a set of residues in 3D space forming the metal-ion binding site, often with a high degree of accuracy. Here, using extensions of these methods, we provide an extensive list of human proteins and their putative metal ion binding site residues, using translated gene sequences derived from the complete, resolved human genome. Under conditions of ∼90% selectivity, over 900 new human putative metal ion binding proteins are identified. A statistical analysis of resolved metal ion binding sites in the human metalloproteome is furnished and the importance of remote homology analysis is demonstrated. As an example, a novel metal-ion binding site involving a complex of a botulinum substrate with its inhibitor is presented. On the basis of the location of the predicted site and the interactions of the contacting residues at the complex interface, we postulate that metal ion binding in this region could influence complex formation and, consequently, the functioning of the protein. Thus, this work provides testable hypotheses about novel functions of known proteins.

A survey of computational intelligence techniques in protein function prediction

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.

Structural determinants allowing transferase activity in SENSITIVE TO FREEZING 2, classified as a family I glycosyl hydrolase

SENSITIVE TO FREEZING 2 (SFR2) is classified as a family I glycosyl hydrolase but has recently been shown to have galactosyltransferase activity in Arabidopsis thaliana. Natural occurrences of apparent glycosyl hydrolases acting as transferases are interesting from a biocatalysis standpoint, and knowledge about the interconversion can assist in engineering SFR2 in crop plants to resist freezing. To understand how SFR2 evolved into a transferase, the relationship between its structure and function are investigated by activity assay, molecular modeling, and site-directed mutagenesis. SFR2 has no detectable hydrolase activity, although its catalytic site is highly conserved with that of family 1 glycosyl hydrolases. Three regions disparate from glycosyl hydrolases are identified as required for transferase activity as follows: a loop insertion, the C-terminal peptide, and a hydrophobic patch adjacent to the catalytic site. Rationales for the effects of these regions on the SFR2 mechanism are discussed.

Crystal structure of human CRMP-4: correction of intensities for lattice-translocation disorder

Collapsin response mediator proteins (CRMPs) are cytosolic phosphoproteins that are mainly involved in neuronal cell development. In humans, the CRMP family comprises five members. Here, crystal structures of human CRMP-4 in a truncated and a full-length version are presented. The latter was determined from two types of crystals, which were either twinned or partially disordered. The crystal disorder was coupled with translational NCS in ordered domains and manifested itself with a rather sophisticated modulation of intensities. The data were demodulated using either the two-lattice treatment of lattice-translocation effects or a novel method in which demodulation was achieved by independent scaling of several groups of intensities. This iterative protocol does not rely on any particular parameterization of the modulation coefficients, but uses the current refined structure as a reference. The best results in terms of R factors and map correlation coefficients were obtained using this new method. The determined structures of CRMP-4 are similar to those of other CRMPs. Structural comparison allowed the confirmation of known residues, as well as the identification of new residues, that are important for the homo- and hetero-oligomerization of these proteins, which are critical to nerve-cell development. The structures provide further insight into the effects of medically relevant mutations of the DPYSL-3 gene encoding CRMP-4 and the putative enzymatic activities of CRMPs.

Metal-mediated modulation of streptococcal cysteine protease activity and its biological implications

Streptococcal cysteine protease (SpeB), the major secreted protease produced by group A streptococcus (GAS), cleaves both host and bacterial proteins and contributes importantly to the pathogenesis of invasive GAS infections. Modulation of SpeB expression and/or its activity during invasive GAS infections has been shown to affect bacterial virulence and infection severity. Expression of SpeB is regulated by the GAS CovR-CovS two-component regulatory system, and we demonstrated that bacteria with mutations in the CovR-CovS two-component regulatory system are selected for during localized GAS infections and that these bacteria lack SpeB expression and exhibit a hypervirulent phenotype. Additionally, in a separate study, we showed that expression of SpeB can also be modulated by human transferrin- and/or lactoferrin-mediated iron chelation. Accordingly, the goal of this study was to investigate the possible roles of iron and other metals in modulating SpeB expression and/or activity in a manner that would potentiate bacterial virulence. Here, we report that the divalent metals zinc and copper inhibit SpeB activity at the posttranslational level. Utilizing online metal-binding site prediction servers, we identified two putative metal-binding sites in SpeB, one of which involves the catalytic-dyad residues (47)Cys and (195)His. Based on our findings, we propose that zinc and/or copper availability in the bacterial microenvironment can modulate the proteolytic activity of SpeB in a manner that preserves the integrity of several other virulence factors essential for bacterial survival and dissemination within the host and thereby may exacerbate the severity of invasive GAS infections.

The Application of Nafion Metal Catalyst Free Carbon Nanotube Modified Gold Electrode: Voltammetric Zinc Detection in Serum

Metal catalyst free carbon nanotube (MCFCNT) whiskers were first used as an electrode modification material on a gold electrode surface for zinc voltammetric measurements. A composite film of Nafion and MCFCNT whiskers was applied to a gold electrode surface to form a mechanically stable sensor. The sensor was then used for zinc detection in both acetate buffer solution and extracted bovine serum solution. A limit of detection of 53 nM was achieved for a 120 s deposition time. The zinc in bovine serum was extracted via a double extraction procedure using dithizone in chloroform as a zinc chelating ligand. The modified electrode was found to be both reliable and sensitive for zinc measurements in both matrices.

Computationally characterizing and comprehensive analysis of zinc-binding sites in proteins

Zinc is one of the most essential metals utilized by organisms, and zinc-binding proteins play an important role in a variety of biological processes such as transcription regulation, cell metabolism and apoptosis. Thus, characterizing the precise zinc-binding sites is fundamental to an elucidation of the biological functions and molecular mechanisms of zinc-binding proteins. Using systematic analyses of structural characteristics, we observed that 4-residue and 3-residue zinc-binding sites have distinctly specific geometric features. Based on the results, we developed the novel computational program Geometric REstriction for Zinc-binding (GRE4Zn) to characterize the zinc-binding sites in protein structures, by restricting the distances between zinc and its coordinating atoms. The comparison between GRE4Zn and analogous tools revealed that it achieved a superior performance. A large-scale prediction for structurally characterized proteins was performed with this powerful predictor, and statistical analyses for the results indicated zinc-binding proteins have come to be significantly involved in more complicated biological processes in higher species than simpler species during the course of evolution. Further analyses suggested that zinc-binding proteins are preferentially implicated in a variety of diseases and highly enriched in known drug targets, and the prediction of zinc-binding sites can be helpful for the investigation of molecular mechanisms. In this regard, these prediction and analysis results should prove to be highly useful be helpful for further biomedical study and drug design. The online service of GRE4Zn is freely available at: http://biocomp.ustc.edu.cn/gre4zn/. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

MOTIF DISCOVERY WITH DATA MINING IN 3D PROTEIN STRUCTURE DATABASES: DISCOVERY, VALIDATION AND PREDICTION OF THE U-SHAPE ZINC BINDING ("HUF-ZINC") MOTIF

Data mining in protein databases, derivatives from more fundamental protein 3D structure and sequence databases, has considerable unearthed potential for the discovery of sequence motif—structural motif—function relationships as the finding of the U-shape (Huf-Zinc) motif, originally a small student's project, exemplifies. The metal ion zinc is critically involved in universal biological processes, ranging from protein-DNA complexes and transcription regulation to enzymatic catalysis and metabolic pathways. Proteins have evolved a series of motifs to specifically recognize and bind zinc ions. Many of these, so called zinc fingers, are structurally independent globular domains with discontinuous binding motifs made up of residues mostly far apart in sequence. Through a systematic approach starting from the BRIX structure fragment database, we discovered that there exists another predictable subset of zinc-binding motifs that not only have a conserved continuous sequence pattern but also share a characteristic local conformation, despite being included in totally different overall folds. While this does not allow general prediction of all Zn binding motifs, a HMM-based web server, Huf-Zinc, is available for prediction of these novel, as well as conventional, zinc finger motifs in protein sequences. The Huf-Zinc webserver can be freely accessed through this URL ( http://mendel.bii.a-star.edu.sg/METHODS/hufzinc/ ).

An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins

Zinc-binding proteins are the most abundant metalloproteins in the Protein Data Bank where the zinc ions usually have catalytic, regulatory or structural roles critical for the function of the protein. Accurate prediction of zinc-binding sites is not only useful for the inference of protein function but also important for the prediction of 3D structure. Here, we present a new integrative framework that combines multiple sequence and structural properties and graph-theoretic network features, followed by an efficient feature selection to improve prediction of zinc-binding sites. We investigate what information can be retrieved from the sequence, structure and network levels that is relevant to zinc-binding site prediction. We perform a two-step feature selection using random forest to remove redundant features and quantify the relative importance of the retrieved features. Benchmarking on a high-quality structural dataset containing 1,103 protein chains and 484 zinc-binding residues, our method achieved >80% recall at a precision of 75% for the zinc-binding residues Cys, His, Glu and Asp on 5-fold cross-validation tests, which is a 10%-28% higher recall at the 75% equal precision compared to SitePredict and zincfinder at residue level using the same dataset. The independent test also indicates that our method has achieved recall of 0.790 and 0.759 at residue and protein levels, respectively, which is a performance better than the other two methods. Moreover, AUC (the Area Under the Curve) and AURPC (the Area Under the Recall-Precision Curve) by our method are also respectively better than those of the other two methods. Our method can not only be applied to large-scale identification of zinc-binding sites when structural information of the target is available, but also give valuable insights into important features arising from different levels that collectively characterize the zinc-binding sites. The scripts and datasets are available at http://protein.cau.edu.cn/zincidentifier/.

Defining and searching for structural motifs using DeepView/Swiss-PdbViewer

Background

Today, recognition and classification of sequence motifs and protein folds is a mature field, thanks to the availability of numerous comprehensive and easy to use software packages and web-based services. Recognition of structural motifs, by comparison, is less well developed and much less frequently used, possibly due to a lack of easily accessible and easy to use software.

Results

In this paper, we describe an extension of DeepView/Swiss-PdbViewer through which structural motifs may be defined and searched for in large protein structure databases, and we show that common structural motifs involved in stabilizing protein folds are present in evolutionarily and structurally unrelated proteins, also in deeply buried locations which are not obviously related to protein function.

Conclusions

The possibility to define custom motifs and search for their occurrence in other proteins permits the identification of recurrent arrangements of residues that could have structural implications. The possibility to do so without having to maintain a complex software/hardware installation on site brings this technology to experts and non-experts alike.

Structure and activity of the only human RNase T2

Mutations in the gene of human RNase T2 are associated with white matter disease of the human brain. Although brain abnormalities (bilateral temporal lobe cysts and multifocal white matter lesions) and clinical symptoms (psychomotor impairments, spasticity and epilepsy) are well characterized, the pathomechanism of RNase T2 deficiency remains unclear. RNase T2 is the only member of the Rh/T2/S family of acidic hydrolases in humans. In recent years, new functions such as tumor suppressing properties of RNase T2 have been reported that are independent of its catalytic activity. We determined the X-ray structure of human RNase T2 at 1.6 Å resolution. The α+β core fold shows high similarity to those of known T2 RNase structures from plants, while, in contrast, the external loop regions show distinct structural differences. The catalytic features of RNase T2 in presence of bivalent cations were analyzed and the structural consequences of known clinical mutations were investigated. Our data provide further insight into the function of human RNase T2 and may prove useful in understanding its mode of action independent of its enzymatic activity.

The zinc dyshomeostasis hypothesis of Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia in the elderly. Hallmark AD neuropathology includes extracellular amyloid plaques composed largely of the amyloid-β protein (Aβ), intracellular neurofibrillary tangles (NFTs) composed of hyper-phosphorylated microtubule-associated protein tau (MAP-tau), and microtubule destabilization. Early-onset autosomal dominant AD genes are associated with excessive Aβ accumulation, however cognitive impairment best correlates with NFTs and disrupted microtubules. The mechanisms linking Aβ and NFT pathologies in AD are unknown. Here, we propose that sequestration of zinc by Aβ-amyloid deposits (Aβ oligomers and plaques) not only drives Aβ aggregation, but also disrupts zinc homeostasis in zinc-enriched brain regions important for memory and vulnerable to AD pathology, resulting in intra-neuronal zinc levels, which are either too low, or excessively high. To evaluate this hypothesis, we 1) used molecular modeling of zinc binding to the microtubule component protein tubulin, identifying specific, high-affinity zinc binding sites that influence side-to-side tubulin interaction, the sensitive link in microtubule polymerization and stability. We also 2) performed kinetic modeling showing zinc distribution in extra-neuronal Aβ deposits can reduce intra-neuronal zinc binding to microtubules, destabilizing microtubules. Finally, we 3) used metallomic imaging mass spectrometry (MIMS) to show anatomically-localized and age-dependent zinc dyshomeostasis in specific brain regions of Tg2576 transgenic, mice, a model for AD. We found excess zinc in brain regions associated with memory processing and NFT pathology. Overall, we present a theoretical framework and support for a new theory of AD linking extra-neuronal Aβ amyloid to intra-neuronal NFTs and cognitive dysfunction. The connection, we propose, is based on β-amyloid-induced alterations in zinc ion concentration inside neurons affecting stability of polymerized microtubules, their binding to MAP-tau, and molecular dynamics involved in cognition. Further, our theory supports novel AD therapeutic strategies targeting intra-neuronal zinc homeostasis and microtubule dynamics to prevent neurodegeneration and cognitive decline.

Identification of a specific region in the e1 fusion protein involved in zinc inhibition of semliki forest virus fusion

The enveloped alphaviruses infect cells via a low-pH-triggered membrane fusion reaction mediated by the viral transmembrane protein E1. During fusion, E1 inserts into the target membrane and refolds to a hairpin-like postfusion conformation in which domain III (DIII) and the juxtamembrane stem pack against a central core trimer. Although zinc has previously been shown to cause a striking block in alphavirus fusion with liposome target membranes, the mechanism of zinc's effect on the E1 fusion protein is not understood. Here we developed a cell culture system to study zinc inhibition of fusion and infection of the alphavirus Semliki Forest virus (SFV). Inclusion of 2 mM ZnCl(2) in the pH 5.75 fusion buffer caused a decrease of ∼5 logs in SFV fusion at the plasma membrane. Fusion was also inhibited by nickel, a chemically related transition metal. Selection for SFV zinc resistance identified a key histidine residue, H333 on E1 DIII, while other conserved E1 histidine residues were not involved. An H333N mutation conferred resistance to both zinc and nickel, with properties in keeping with the known pH-dependent chelation of these metals by histidine. Biochemical studies demonstrated that zinc strongly inhibits formation of the postfusion E1 trimer in wild-type SFV but not in an H333 mutant. Together our results suggest that zinc acts by blocking the fold-back of DIII via its interaction with H333.

Predicting metal-binding sites from protein sequence

Prediction of binding sites from sequence can significantly help toward determining the function of uncharacterized proteins on a genomic scale. The task is highly challenging due to the enormous amount of alternative candidate configurations. Previous research has only considered this prediction problem starting from 3D information. When starting from sequence alone, only methods that predict the bonding state of selected residues are available. The sole exception consists of pattern-based approaches, which rely on very specific motifs and cannot be applied to discover truly novel sites. We develop new algorithmic ideas based on structured-output learning for determining transition-metal-binding sites coordinated by cysteines and histidines. The inference step (retrieving the best scoring output) is intractable for general output types (i.e., general graphs). However, under the assumption that no residue can coordinate more than one metal ion, we prove that metal binding has the algebraic structure of a matroid, allowing us to employ a very efficient greedy algorithm. We test our predictor in a highly stringent setting where the training set consists of protein chains belonging to SCOP folds different from the ones used for accuracy estimation. In this setting, our predictor achieves 56 percent precision and 60 percent recall in the identification of ligand-ion bonds.

Solution structure of the Mycobacterium tuberculosis EsxG·EsxH complex: functional implications and comparisons with other M. tuberculosis Esx family complexes

Mycobacterium tuberculosis encodes five type VII secretion systems that are responsible for exporting a number of proteins, including members of the Esx family, which have been linked to tuberculosis pathogenesis and survival within host cells. The gene cluster encoding ESX-3 is regulated by the availability of iron and zinc, and secreted protein products such as the EsxG·EsxH complex have been associated with metal ion acquisition. EsxG and EsxH have previously been shown to form a stable 1:1 heterodimeric complex, and here we report the solution structure of the complex, which features a core four-helix bundle decorated at both ends by long, highly flexible, N- and C-terminal arms that contain a number of highly conserved residues. Despite clear similarities in the overall backbone fold to the EsxA·EsxB complex, the structure reveals some striking differences in surface features, including a potential protein interaction site on the surface of the EsxG·EsxH complex. EsxG·EsxH was also found to contain a specific Zn²⁺ binding site formed from a cluster of histidine residues on EsxH, which are conserved across obligate mycobacterial pathogens including M. tuberculosis and Mycobacterium leprae. This site may reflect an essential role in zinc ion acquisition or point to Zn²⁺-dependent regulation of its interaction with functional partner proteins. Overall, the surface features of both the EsxG·EsxH and the EsxA·EsxB complexes suggest functions mediated via interactions with one or more target protein partners.