Prediction of transition metal-binding sites from apo protein structures

The study of iron- and copper-binding proteome of Fusarium oxysporum and its effector candidates

Fusarium oxysporum f.sp. lycopersici is a phytopathogen which causes vascular wilt disease in tomato plants. The survival tactics of both pathogens and hosts depend on intricate interactions between host plants and pathogenic microbes. Iron-binding proteins (IBPs) and copper-binding proteins (CBPs) play a crucial role in these interactions by participating in enzyme reactions, virulence, metabolism, and transport processes. We employed high-throughput computational tools at the sequence and structural levels to investigate the IBPs and CBPs of F. oxysporum. A total of 124 IBPs and 37 CBPs were identified in the proteome of Fusarium. The ranking of amino acids based on their affinity for binding with iron is Glu > His> Asp > Asn > Cys, and for copper is His > Asp > Cys respectively. The functional annotation, determination of subcellular localization, and Gene Ontology analysis of these putative IBPs and CBPs have unveiled their potential involvement in a diverse array of cellular and biological processes. Three iron-binding glycosyl hydrolase family proteins, along with four CBPs with carbohydrate-binding domains, have been identified as potential effector candidates. These proteins are distinct from the host Solanum lycopersicum proteome. Moreover, they are known to be located extracellularly and function as enzymes that degrade the host cell wall during pathogen-host interactions. The insights gained from this report on the role of metal ions in plant-pathogen interactions can help develop a better understanding of their fundamental biology and control vascular wilt disease in tomato plants.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

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

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach

In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.

Co-evolution-based prediction of metal-binding sites in proteomes by machine learning

Metal ions have various important biological roles in proteins, including structural maintenance, molecular recognition and catalysis. Previous methods of predicting metal-binding sites in proteomes were based on either sequence or structural motifs. Here we developed a co-evolution-based pipeline named 'MetalNet' to systematically predict metal-binding sites in proteomes. We applied MetalNet to proteomes of four representative prokaryotic species and predicted 4,849 potential metalloproteins, which substantially expands the currently annotated metalloproteomes. We biochemically and structurally validated previously unannotated metal-binding sites in several proteins, including apo-citrate lyase phosphoribosyl-dephospho-CoA transferase citX, an Escherichia coli enzyme lacking structural or sequence homology to any known metalloprotein (Protein Data Bank (PDB) codes: 7DCM and 7DCN ). MetalNet also successfully recapitulated all known zinc-binding sites from the human spliceosome complex. The pipeline of MetalNet provides a unique and enabling tool for interrogating the hidden metalloproteome and studying metal biology.

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/.

HIT-2: Implementing machine learning algorithms to treat bound ions in biomolecules

Electrostatic features are fundamental to protein functions and protein-protein interactions. Studying highly charged biomolecules is challenging given the heterogeneous distribution of the ionic cloud around such biomolecules. Here we report a new computational method, Hybridizing Ions Treatment-2 (HIT-2), which is used to model biomolecule-bound ions using the implicit solvation model. By modeling ions, HIT-2 allows the user to calculate important electrostatic features of the biomolecules. HIT-2 applies an efficient algorithm to calculate the position of bound ions from molecular dynamics simulations. Modeling parameters were optimized by machine learning methods from thousands of datasets. The optimized parameters produced results with errors lower than 0.2 Å. The testing results on bound Ca2+ and Zn2+ in NAMD simulations also proved that HIT-2 can effectively identify bound ion types, numbers, and positions. Also, multiple tests performed on HIT-2 suggest the method can handle biomolecules that undergo remarkable conformational changes. HIT-2 can significantly improve electrostatic calculations for many problems in computational biophysics.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

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

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

mebipred: identifying metal binding potential in protein sequence

Motivation

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

Results

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

Availability and implementation

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

Supplementary information

Supplementary data are available at Bioinformatics online.

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.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

The moonlighting activities of dihydrolipoamide dehydrogenase: Biotechnological and biomedical applications

Dihydrolipoamide dehydrogenase (DLDH) is a homodimeric flavin-dependent enzyme that catalyzes the NAD⁺ -dependent oxidation of dihydrolipoamide. The enzyme is part of several multi-enzyme complexes such as the Pyruvate Dehydrogenase system that transforms pyruvate into acetyl-co-A. Concomitantly with its redox activity, DLDH produces Reactive Oxygen Species (ROS), which are involved in cellular apoptotic processes. DLDH possesses several moonlighting functions. One of these is the capacity to adhere to metal-oxides surfaces. This was first exemplified by the presence of an exocellular form of the enzyme on the cell-wall surface of Rhodococcus ruber. This capability was evolutionarily conserved and identified in the human, mitochondrial, DLDH. The enzyme was modified with Arg-Gly-Asp (RGD) groups, which enabled its interaction with integrin-rich cancer cells followed by "integrin-assisted-endocytosis." This allowed harnessing the enzyme for cancer therapy. Combining the TiO₂ -binding property with DLDH's ROS-production, enabled us to develop several medical applications including improving oesseointegration of TiO₂ -based implants and photodynamic treatment for melanoma. The TiO₂ -binding sites of both the bacterial and human DLDH's were identified on the proteins' molecules at regions that overlap with the binding site of E3-binding protein (E3BP). This protein is essential in forming the multiunit structure of PDC. Another moonlighting activity of DLDH, which is described in this Review, is its DNA-binding capacity that may affect DNA chelation and shredding leading to apoptotic processes in living cells. The typical ROS-generation by DLDH, which occurs in association with its enzymatic activity and its implications in cancer and apoptotic cell death are also discussed.

Interplay between central carbon metabolism and metal homeostasis in mycobacteria and other human pathogens

Bacterial nutrition is a fundamental aspect of pathogenesis. While the host environment is in principle nutrient-rich, hosts have evolved strategies to interfere with nutrient acquisition by pathogens. In turn, pathogens have developed mechanisms to circumvent these restrictions. Changing the availability of bioavailable metal ions is a common strategy used by hosts to limit bacterial replication. Macrophages and neutrophils withhold iron, manganese, and zinc ions to starve bacteria. Alternatively, they can release manganese, zinc, and copper ions to intoxicate microorganisms. Metals are essential micronutrients and participate in catalysis, macromolecular structure, and signalling. This review summarises our current understanding of how central carbon metabolism in pathogens adapts to local fluctuations in free metal ion concentrations. We focus on the transcriptomics and proteomics data produced in studies of the iron-sparing response in Mycobacterium tuberculosis, the etiological agent of tuberculosis, and consequently generate a hypothetical model linking trehalose accumulation, succinate secretion and substrate-level phosphorylation in iron-starved M. tuberculosis. This review also aims to highlight a large gap in our knowledge of pathogen physiology: the interplay between metal homeostasis and central carbon metabolism, two cellular processes which are usually studied separately. Integrating metabolism and metal biology would allow the discovery of new weaknesses in bacterial physiology, leading to the development of novel and improved antibacterial therapies.

Identification of a copper ion recognition peptide sequence in the subunit II of cytochrome c oxidase: a combined theoretical and experimental study

The role of the pentapeptide, NHSFM, derived from the surface exposed part of the metal ion binding loop of the subunit II of cytochrome c oxidase on the maturation of the binuclear purple CuA center of the enzyme has been investigated using several experimental and computational methods. The copper ion was found to form 1:1 complex of the pentapeptide with a binding constant ~ 10⁴ M^-1 to 10⁵ M^-1, where a 4 ligand coordination from the peptide in a type 2 copper center was revealed. The pH dependence of the metal-peptide was associated with a [Formula: see text] of ~ 10 suggesting deprotonation of the N-terminal amine. EXAFS studies as well as DFT calculations of the metal-peptide complexes revealed pH dependent changes in the metal-ligand bond distances. Spectroscopic properties of the metal peptides calculated from TDDFT studies agreed with the experimental results. Restrained molecular dynamics (RMD) simulations indicated coordination of a carbonyl oxygen from the asparagine (N) side chain and of water molecules apart from histidine (H), methionine (M) and terminal amine of asparagine (N) in a distorted square planar geometry of Cu-NHSFM. Analyses of the backbone distances as well as B-factors for the metal peptide suggested that the peptide backbone becomes more compact and rigid on binding of the metal ion. This indicated that binding of copper ion to this pentapeptide in the protein possibly cause movement of the protein backbone bringing other coordinating residues closer to the copper ion, and thus helping in sequential uptake of copper ions to the protein.

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.

Torpedo californica acetylcholinesterase is stabilized by binding of a divalent metal ion to a novel and versatile 4D motif

Stabilization of Torpedo californica acetylcholinesterase by the divalent cations Ca⁺², Mg⁺², and Mn⁺² was investigated. All three substantially protect the enzyme from thermal inactivation. Electron paramagnetic resonance revealed one high‐affinity binding site for Mn⁺² and several much weaker sites. Differential scanning calorimetry showed a single irreversible thermal transition. All three cations raise both the temperature of the transition and the activation energy, with the transition becoming more cooperative. The crystal structures of the Ca⁺² and Mg⁺² complexes with Torpedo acetylcholinesterase were solved. A principal binding site was identified. In both cases, it consists of four aspartates (a 4D motif), within which the divalent ion is embedded, together with several water molecules. It makes direct contact with two of the aspartates, and indirect contact, via waters, with the other two. The 4D motif has been identified in 31 acetylcholinesterase sequences and 28 butyrylcholinesterase sequences. Zebrafish acetylcholinesterase also contains the 4D motif; it, too, is stabilized by divalent metal ions. The ASSAM server retrieved 200 other proteins that display the 4D motif, in many of which it is occupied by a divalent cation. It is a very versatile motif, since, even though tightly conserved in terms of RMSD values, it can contain from one to as many as three divalent metal ions, together with a variable number of waters. This novel motif, which binds primarily divalent metal ions, is shared by a broad repertoire of proteins. An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:Protein_Science:3.

PDB‐ID(s): 7B38, 7B8E and 7B2W;

Physicochemical Characterisation of KEIF-The Intrinsically Disordered N-Terminal Region of Magnesium Transporter A

Magnesium transporter A (MgtA) is an active transporter responsible for importing magnesium ions into the cytoplasm of prokaryotic cells. This study focuses on the peptide corresponding to the intrinsically disordered N-terminal region of MgtA, referred to as KEIF. Primary-structure and bioinformatic analyses were performed, followed by studies of the undisturbed single chain using a combination of techniques including small-angle X-ray scattering, circular dichroism spectroscopy, and atomistic molecular-dynamics simulations. Moreover, interactions with large unilamellar vesicles were investigated by using dynamic light scattering, laser Doppler velocimetry, cryogenic transmission electron microscopy, and circular dichroism spectroscopy. KEIF was confirmed to be intrinsically disordered in aqueous solution, although extended and containing little β-structure and possibly PPII structure. An increase of helical content was observed in organic solvent, and a similar effect was also seen in aqueous solution containing anionic vesicles. Interactions of cationic KEIF with anionic vesicles led to the hypothesis that KEIF adsorbs to the vesicle surface through electrostatic and entropic driving forces. Considering this, there is a possibility that the biological role of KEIF is to anchor MgtA in the cell membrane, although further investigation is needed to confirm this hypothesis.

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.

SXGBsite: Prediction of Protein-Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting

The prediction of protein-ligand binding sites is important in drug discovery and drug design. Protein-ligand binding site prediction computational methods are inexpensive and fast compared with experimental methods. This paper proposes a new computational method, SXGBsite, which includes the synthetic minority over-sampling technique (SMOTE) and the Extreme Gradient Boosting (XGBoost). SXGBsite uses the position-specific scoring matrix discrete cosine transform (PSSM-DCT) and predicted solvent accessibility (PSA) to extract features containing sequence information. A new balanced dataset was generated by SMOTE to improve classifier performance, and a prediction model was constructed using XGBoost. The parallel computing and regularization techniques enabled high-quality and fast predictions and mitigated overfitting caused by SMOTE. An evaluation using 12 different types of ligand binding site independent test sets showed that SXGBsite performs similarly to the existing methods on eight of the independent test sets with a faster computation time. SXGBsite may be applied as a complement to biological experiments.

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.

In silico Study of Iron, Zinc and Copper Binding Proteins of Pseudomonas syringae pv. lapsa: Emphasis on Secreted Metalloproteins

The phytopathogenic bacteria, Pseudomonas syringae pv. lapsa (P. syringae pv. lapsa) infects the staple food crop wheat. Metalloproteins play important roles in plant-pathogen interactions. Hence, the present work is aimed to predict and analyze the iron (Fe), zinc (Zn), and copper (Cu) binding proteins of P. syringae pv. lapsa which help in its growth, adaptation, survival and pathogenicity. A total of 232 Fe, 307 Zn, and 38 Cu-binding proteins have been identified. The functional annotation, subcellular localization and gene ontology enriched network analysis revealed their role in wide range of biological activities of the phytopathogen. Among the identified metalloproteins, a total of 29 Fe-binding, 31 Zn-binding, and 5 Cu-binding proteins were found to be secreted in nature. These putative secreted metalloproteins may perform diverse cellular and biological functions ranging from transport, response to oxidative stress, proteolysis, antimicrobial resistance, metabolic processes, protein folding and DNA repair. The observations obtained here may provide initial information required to draft new schemes to control microbial infections of staple food crops and will further help in developing sustainable agriculture.

Cadmium-induced conformational changes in type 2 metallothionein of medicinal plant Coptis japonica: insights from molecular dynamics studies of apo, partially and fully metalated forms

Plants play an important role in the removal of excess heavy metals from soil and water. Medicinal plants can also have non-traditional use in phytoremediation technologies. Among the heavy metals, Cadmium (Cd) is the most abundant and readily taken up by the crop plants. Plant metallothioneins (MTs) are small proteins having cysteine-rich residues and appear to play key roles in metal homoeostasis. Plant metallothionein 2 (MT 2) from Coptis japonica (Gold-thread; CjMT 2) is a typical member of this subfamily and features two cysteine-rich regions containing eight and six cysteine residues, respectively, separated by 42 amino acids long linker region. In-silico analysis of MT 2 protein sequences of C. japonica was performed. In this study, ab initio methods were utilised for the prediction of three-dimensional structure of CjMT 2. After structure validation, heavy metal-binding sites were predicted for the selected modelled structures of CjMT 2. To obtain Cd_i-CjMT 2 (i = 1-7), metalated complex individual docking experiments were performed. The stability of the metalated docked structures was assessed by molecular dynamics (MD) simulation studies. Our study showed that CjMT 2 binds up to 4 Cd²⁺ ions in two distinct domains: a N-terminal β-domain that binds to 2 Cd²⁺ ions and a C-terminal α-domain that binds with 2 Cd²⁺ ions. Our analysis revealed that Cys residues of alpha and beta domain and some residues of spacer region of CjMT 2 protein might be important for the cadmium interaction. MD simulation studies provided insight into metal-induced conformational changes and mechanism of metalation of CjMT 2, an intrinsically disordered protein. This study provides useful insights into mechanism of cadmium-type 2 metallothionein interaction.

ATPbind: Accurate Protein-ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons

Protein-ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there is no method that can optimally identify ATP binding sites for different proteins. In this study, we report a new composite predictor, ATPbind, for ATP binding sites by integrating the outputs of two template-based predictors (i.e., S-SITE and TM-SITE) and three discriminative sequence-driven features of proteins: position specific scoring matrix, predicted secondary structure, and predicted solvent accessibility. In ATPbind, we assembled multiple support vector machines (SVMs) based on a random undersampling technique to cope with the serious imbalance phenomenon between the numbers of ATP binding sites and of non-ATP binding sites. We also constructed a new gold-standard benchmark data set consisting of 429 ATP binding proteins from the PDB database to evaluate and compare the proposed ATPbind with other existing predictors. Starting from a query sequence and predicted I-TASSER models, ATPbind can achieve an average accuracy of 72%, covering 62% of all ATP binding sites while achieving a Matthews correlation coefficient value that is significantly higher than that of other state-of-the-art predictors.

Prediction of zinc binding sites in proteins using sequence derived information

Zinc is one the most abundant catalytic cofactor and also an important structural component of a large number of metallo-proteins. Hence prediction of zinc metal binding sites in proteins can be a significant step in annotation of molecular function of a large number of proteins. Majority of existing methods for zinc-binding site predictions are based on a data-set of proteins, which has been compiled nearly a decade ago. Hence there is a need to develop zinc-binding site prediction system using the current updated data to include recently added proteins. Herein, we propose a support vector machine-based method, named as ZincBinder, for prediction of zinc metal-binding site in a protein using sequence profile information. The predictor was trained using fivefold cross validation approach and achieved 85.37% sensitivity with 86.20% specificity during training. Benchmarking on an independent non-redundant data-set, which was not used during training, showed better performance of ZincBinder vis-à-vis existing methods. Executable versions, source code, sample datasets, and usage instructions are available at http://proteininformatics.org/mkumar/znbinder/.

Identification of Protein-Ligand Binding Sites by Sequence Information and Ensemble Classifier

Identifying protein-ligand binding sites is an important process in drug discovery and structure-based drug design. Detecting protein-ligand binding sites is expensive and time-consuming by traditional experimental methods. Hence, computational approaches provide many effective strategies to deal with this issue. Recently, lots of computational methods are based on structure information on proteins. However, these methods are limited in the common scenario, where both the sequence of protein target is known and sufficient 3D structure information is available. Studies indicate that sequence-based computational approaches for predicting protein-ligand binding sites are more practical. In this paper, we employ a novel computational model of protein-ligand binding sites prediction, using protein sequence. We apply the Discrete Cosine Transform (DCT) to extract feature from Position-Specific Score Matrix (PSSM). In order to improve the accuracy, Predicted Relative Solvent Accessibility (PRSA) information is also utilized. The predictor of protein-ligand binding sites is built by employing the ensemble weighted sparse representation model with random under-sampling. To evaluate our method, we conduct several comprehensive tests (12 types of ligands testing sets) for predicting protein-ligand binding sites. Results show that our method achieves better Matthew's correlation coefficient (MCC) than other outstanding methods on independent test sets of ATP (0.506), ADP (0.511), AMP (0.393), GDP (0.579), GTP (0.641), Mg²⁺ (0.317), Fe³⁺ (0.490) and HEME (0.640). Our proposed method outperforms earlier predictors (the performance of MCC) in 8 of the 12 ligands types.

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.

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.

Reduced Glutathione Mediates Pheno-Ultrastructure, Kinome and Transportome in Chromium-Induced Brassica napus L

Chromium (Cr) as a toxic metal is widely used for commercial purposes and its residues have become a potential environmental threat to both human and plant health. Oilseed rape (Brassica napus L.) is one of the candidate plants that can absorb the considerable quantity of toxic metals from the soil. Here, we used two cultivars of B. napus cvs. ZS 758 (metal-tolerant) and Zheda 622 (metal-susceptible) to investigate the phenological attributes, cell ultrastructure, protein kinases (PKs) and molecular transporters (MTs) under the combined treatments of Cr stress and reduced glutathione (GSH). Seeds of these cultivars were grown in vitro at different treatments i.e., 0, 400 μM Cr, and 400 μM Cr + 1 mM GSH in control growth chamber for 6 days. Results had confirmed that Cr significantly reduced the plant length, stem and root, and fresh biomass such as leaf, stem and root. Cr noticeably caused the damages in leaf mesophyll cells. Exogenous application of GSH significantly recovered both phenological and cell structural damages in two cultivars under Cr stress. For the PKs, transcriptomic data advocated that Cr stress alone significantly increased the gene expressions of BnaA08g16610D, BnaCnng19320D, and BnaA08g00390D over that seen in controls (Ck). These genes encoded both nucleic acid and transition metal ion binding proteins, and protein kinase activity (PKA) and phosphotransferase activities in both cultivars. Similarly, the presence of Cr revealed elite MT genes [BnaA04g26560D, BnaA02g28130D, and BnaA02g01980D (novel)] that were responsible for water transmembrane transporter activity. However, GSH in combination with Cr stress significantly up-regulated the genes for PKs [such as BnaCnng69940D (novel) and BnaC08g49360D] that were related to PKA, signal transduction, and oxidoreductase activities. For MTs, BnaC01g29930D and BnaA07g14320D were responsible for secondary active transmembrane transporter and protein transporter activities that were expressed more in GSH treatment than either Ck or Cr-treated cells. In general, it can be concluded that cultivar ZS 758 is more tolerant toward Cr-induced stress than Zheda 622.

MIB: Metal Ion-Binding Site Prediction and Docking Server

The structure of a protein determines its biological function(s) and its interactions with other factors; the binding regions tend to be conserved in sequence and structure, and the interacting residues involved are usually in close 3D space. The Protein Data Bank currently contains more than 110 000 protein structures, approximately one-third of which contain metal ions. Identifying and characterizing metal ion-binding sites is thus essential for investigating a protein's function(s) and interactions. However, experimental approaches are time-consuming and costly. The web server reported here was built to predict metal ion-binding residues and to generate the predicted metal ion-bound 3D structure. Binding templates have been constructed for regions that bind 12 types of metal ion-binding residues have been used to construct binding templates. The templates include residues within 3.5 Å of the metal ion, and the fragment transformation method was used for structural comparison between query proteins and templates without any data training. Through the adjustment of scoring functions, which are based on the similarity of structure and binding residues. Twelve kinds of metal ions (Ca²⁺, Cu²⁺, Fe³⁺, Mg²⁺, Mn²⁺, Zn²⁺, Cd²⁺, Fe²⁺, Ni²⁺, Hg²⁺, Co²⁺, and Cu⁺) binding residues prediction are supported. MIB also provides the metal ions docking after prediction. The MIB server is available at http://bioinfo.cmu.edu.tw/MIB/ .

Multiple Evolutionary Origins of Ubiquitous Cu2+ and Zn2+ Binding in the S100 Protein Family

The S100 proteins are a large family of signaling proteins that play critical roles in biology and disease. Many S100 proteins bind Zn2+, Cu2+, and/or Mn2+ as part of their biological functions; however, the evolutionary origins of binding remain obscure. One key question is whether divalent transition metal binding is ancestral, or instead arose independently on multiple lineages. To tackle this question, we combined phylogenetics with biophysical characterization of modern S100 proteins. We demonstrate an earlier origin for established S100 subfamilies than previously believed, and reveal that transition metal binding is widely distributed across the tree. Using isothermal titration calorimetry, we found that Cu2+ and Zn2+ binding are common features of the family: the full breadth of human S100 paralogs-as well as two early-branching S100 proteins found in the tunicate Oikopleura dioica-bind these metals with μM affinity and stoichiometries ranging from 1:1 to 3:1 (metal:protein). While binding is consistent across the tree, structural responses to binding are quite variable. Further, mutational analysis and structural modeling revealed that transition metal binding occurs at different sites in different S100 proteins. This is consistent with multiple origins of transition metal binding over the evolution of this protein family. Our work reveals an evolutionary pattern in which the overall phenotype of binding is a constant feature of S100 proteins, even while the site and mechanism of binding is evolutionarily labile.

Minimal Functional Sites in Metalloproteins and Their Usage in Structural Bioinformatics

Metal ions play a functional role in numerous biochemical processes and cellular pathways. Indeed, about 40% of all enzymes of known 3D structure require a metal ion to be able to perform catalysis. The interactions of the metals with the macromolecular framework determine their chemical properties and reactivity. The relevant interactions involve both the coordination sphere of the metal ion and the more distant interactions of the so-called second sphere, i.e., the non-bonded interactions between the macromolecule and the residues coordinating the metal (metal ligands). The metal ligands and the residues in their close spatial proximity define what we call a minimal functional site (MFS). MFSs can be automatically extracted from the 3D structures of metal-binding biological macromolecules deposited in the Protein Data Bank (PDB). They are 3D templates that describe the local environment around a metal ion or metal cofactor and do not depend on the overall macromolecular structure. MFSs provide a different view on metal-binding proteins and nucleic acids, completely focused on the metal. Here we present different protocols and tools based upon the concept of MFS to obtain deeper insight into the structural and functional properties of metal-binding macromolecules. We also show that structure conservation of MFSs in metalloproteins relates to local sequence similarity more strongly than to overall protein similarity.

Constructing query-driven dynamic machine learning model with application to protein-ligand binding sites prediction

We are facing an era with annotated biological data rapidly and continuously generated. How to effectively incorporate new annotated data into the learning step is crucial for enhancing the performance of a bioinformatics prediction model. Although machine-learning-based methods have been extensively used for dealing with various biological problems, existing approaches usually train static prediction models based on fixed training datasets. The static approaches are found having several disadvantages such as low scalability and impractical when training dataset is huge. In view of this, we propose a dynamic learning framework for constructing query-driven prediction models. The key difference between the proposed framework and the existing approaches is that the training set for the machine learning algorithm of the proposed framework is dynamically generated according to the query input, as opposed to training a general model regardless of queries in traditional static methods. Accordingly, a query-driven predictor based on the smaller set of data specifically selected from the entire annotated base dataset will be applied on the query. The new way for constructing the dynamic model enables us capable of updating the annotated base dataset flexibly and using the most relevant core subset as the training set makes the constructed model having better generalization ability on the query, showing "part could be better than all" phenomenon. According to the new framework, we have implemented a dynamic protein-ligand binding sites predictor called OSML (On-site model for ligand binding sites prediction). Computer experiments on 10 different ligand types of three hierarchically organized levels show that OSML outperforms most existing predictors. The results indicate that the current dynamic framework is a promising future direction for bridging the gap between the rapidly accumulated annotated biological data and the effective machine-learning-based predictors. OSML web server and datasets are freely available at: http://www.csbio.sjtu.edu.cn/bioinf/OSML/ for academic use.

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.

Interaction of frataxin, an iron binding protein, with IscU of Fe-S clusters biogenesis pathway and its upregulation in AmpB resistant Leishmania donovani

Leishmania donovani is a unicellular protozoon parasite that causes visceral leishmaniasis (VL), which is a fatal disease if left untreated. Certain Fe-S proteins of the TCA cycle and respiratory chain have been found in the Leishmania parasite but the precise mechanisms for their biogenesis and the maturation of Fe-S clusters remains unknown. Fe-S clusters are ubiquitous cofactors of proteins that perform critical cellular functions. The clusters are biosynthesized by the mitochondrial Iron-Sulphur Cluster (ISC) machinery with core protein components that include the catalytic cysteine desulphurase IscS, the scaffold proteins IscU and IscA, and frataxin as an iron carrier/donor. However, no information regarding frataxin, its regulation, or its role in drug resistance is available for the Leishmania parasite. In this study, we characterized Ld-frataxin to investigate its role in the ISC machinery of L. donovani. We expressed and purified the recombinant Ld-frataxin protein and observed its interaction with Ld-IscU by co-purification and pull-down assay. Furthermore, we observed that the cysteine desulphurase activity of the purified Ld-IscS protein was stimulated in the presence of Ld-frataxin and Ld-IscU, particularly in the presence of iron; neither Ld-frataxin nor Ld-IscU alone had significant effects on Ld-IscS activity. Interestingly, RT-PCR and western blotting showed that Ld-frataxin is upregulated in AmpB-resistant isolates compared to sensitive strains, which may support higher Fe-S protein activity in AmpB-resistant L. donovani. Additionally, Ld-frataxin was localized in the mitochondria, as revealed by digitonin fractionation and indirect immunofluorescence. Thus, our results suggest the role of Ld-frataxin as an iron binding/carrier protein for Fe-S cluster biogenesis that physically interacts with other core components of the ISC machinery within the mitochondria.

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.

Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.

An integrative computational model for large-scale identification of metalloproteins in microbial genomes: a focus on iron-sulfur cluster proteins

Metalloproteins represent a ubiquitous group of molecules which are crucial to the survival of all living organisms. While several metal-binding motifs have been defined, it remains challenging to confidently identify metalloproteins from primary protein sequences using computational approaches alone. Here, we describe a comprehensive strategy based on a machine learning approach to design and assess a penalized generalized linear model. We used this strategy to detect members of the iron-sulfur cluster protein family. A new category of descriptors, whose profile is based on profile hidden Markov models, encoding structural information was combined with public descriptors into a linear model. The model was trained and tested on distinct datasets composed of well-characterized iron-sulfur protein sequences, and the resulting model provided higher sensitivity compared to a motif-based approach, while maintaining a good level of specificity. Analysis of this linear model allows us to detect and quantify the contribution of each descriptor, providing us with a better understanding of this complex protein family along with valuable indications for further experimental characterization. Two newly-identified proteins, YhcC and YdiJ, were functionally validated as genuine iron-sulfur proteins, confirming the prediction. The computational model was then applied to over 550 prokaryotic genomes to screen for iron-sulfur proteomes; the results are publicly available at: . This study represents a proof-of-concept for the application of a penalized linear model to identify metalloprotein superfamilies on a large-scale. The application employed here, screening for iron-sulfur proteomes, provides new candidates for further biochemical and structural analysis as well as new resources for an extensive exploration of iron-sulfuromes in the microbial world.

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.

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.