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

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

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

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

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

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.

An acidic loop within the human soluble CD23 protein may direct the interaction between sCD23 and the αXβ2 integrin

CD23 is involved in a myriad of immune reactions. It is not only a receptor for IgE, but also functions in the regulation of IgE synthesis, isotype switching in B cells, and induction of the inflammatory response. These effector functions of CD23 arise through its interaction with another leukocyte-specific cell surface receptor - the β₂ integrin subfamily. It has been shown that CD23 is also capable of interacting with the β₃ and β₅ integrin β-subunit of integrins via a basic RKC motif in a metal cation-independent fashion. In this study the interaction was probed for whether or not the RKC motif governs the interaction between CD23 and the α_Xβ₂ integrin as well. This was done by performing bioinformatic docking predictions between CD23 and α_Xβ₂ integrin αI domain and SPR spectroscopy analysis of the interaction. This revealed that in the absence of cations, the RKC motif is involved in interaction with the integrin αI domain. However, in the presence of divalent metal cations the interaction showed the involvement of a novel acidic motif within the CD23 protein. This same pattern of interaction was seen in docking predictions between CD23 and the β₃I-like domain. This study thus presents an alternative site as a possible contributor to the CD23-integrin interaction exhibiting cation-dependence.

Assessing the Binding Performance of Amyloid-Carbon Membranes toward Heavy Metal Ions

Amyloid-carbon hybrid membranes have exceptional performance in removing heavy metal ions from water because of the presence of multiple binding sites on the amyloid fibrils, but the binding process is still not fully understood. To understand the mechanisms of amyloid-metal ion binding, we perform adsorption isotherms on a model system given by β-lactoglobulin amyloid fibrils and four representative heavy metal ions: chromium (Cr), nickel (Ni), silver (Ag), and platinum (Pt). Furthermore, to get a comprehensive thermodynamic picture of the binding process between amino acid residues and heavy metals, we here use isothermal titration calorimetry on native β-lactoglobulin monomers and amyloid fibrils exposed to the two model metal ions, that is, silver and chromium. A conclusive thermodynamic insight on the binding process emerges by direct measurements of enthalpy and entropy changes, association binding constant, and average number of binding sites of the protein monomer and amyloid fibril. As a result of the strong amyloid binding affinity between amino acids and metal ions, when the protein is converted into amyloid fibrils and assembled into membranes, the resulting amyloid-activated carbon hybrids remove all the tested heavy metals with efficiencies beyond 99%. Importantly, the efficiency remains stable during several consecutive cycles, demonstrating a high adsorption capacity and a long lifetime and reusability of the membranes. The recovery of adsorbed precious metal ions converted into elemental metals is shown to be a general feature of these membranes, with platinum and silver successfully recovered from saturated hybrid membranes by a simple thermal reduction. The separation performance, evaluated on real electroplating industrial wastewater containing chromium and nickel, is found to exceed 99% at a permeability as high as 2.92 × 10^-16 m², that is, at least 4 orders of magnitude higher than typical nanofiltration membranes, conclusively validating the technology under stringent real conditions.

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.

Protein phenotype diagnosis of autosomal dominant calmodulin mutations causing irregular heart rhythms

The life-threatening group of irregular cardiac rhythmic disorders also known as Cardiac Arrhythmias (CA) are caused by mutations in highly conserved Calmodulin (CALM/CaM) genes. Herein, we present a multidimensional approach to diagnose changes in phenotypic, stability, and Ca²⁺ ion binding properties of CA-causing mutations. Mutation pathogenicity was determined by diverse computational machine learning approaches. We further modeled the mutations in 3D protein structure and analyzed residue level phenotype plasticity. We have also examined the influence of torsion angles, number of H-bonds, and free energy dynamics on the stability, near-native simulation dynamic potential of residue fluctuations in protein structures, Ca²⁺ ion binding potentials, of CaM mutants. Our study recomends to use M-CAP method for measuring the pathogenicity of CA causing CaM variants. Interestingly, most CA-causing variants we analyzed, exists in either third (V/H-96, S/I-98, V-103) or fourth (G/V-130, V/E/H-132, H-134, P-136, G-141, and L-142) EF-hands located in carboxyl domains of the CaM molecule. We observed that the minor structural fluctuations caused by these variants are likely tolerable owing to the highly flexible nature of calmodulin's globular domains. However, our molecular docking results supports that these variants disturb the affinity of CaM toward Ca²⁺ ions and corroborate previous findings from functional studies. Taken together, these computational findings can explain the molecular reasons for subtle changes in structure, flexibility, and stability aspects of mutant CaM molecule. Our comprehensive molecular scanning approach demonstrates the utility of computational methods in quick preliminary screening of CA- CaM mutations before undertaking time consuming and complicated functional laboratory assays.

2,2'-Bipyridine and hydrazide containing peptides for cyclization and complex quaternary structural control

A synthetic peptide containing two N_ε-methyl lysines (Ac-K(N_ε-Me)GYTGYTGK(N_ε-Me)D-OH) was alkylated with bipyridine (bipy) ligands substituted at the fifth (MP-5) and sixth (MP-6) positions, thereby creating Ac-K(N_ε-Me, N_ε-Bipy)GYTGYTGK(N_ε-Me, N_ε-Bipy)D-OH. Peptides with 6-position bipyridine did not bind to Fe²⁺ and Zn²⁺. Peptides with 5-position bipyridine bound these metals, and in the presence of one equivalent of a free bipy derivative folded into a macrocycle. Further, the free bipy derivative could also contain a cyclized peptide derived from hydrazone formation, resulting in complex but controlled quaternary peptide structure.

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.

Byssus Structure and Protein Composition in the Highly Invasive Fouling Mussel Limnoperna fortunei

Biofouling mediated by byssus adhesion in invasive bivalves has become a global environmental problem in aquatic ecosystems, resulting in negative ecological and economic consequences. Previous studies suggested that mechanisms responsible for byssus adhesion largely vary among bivalves, but it is poorly understood in freshwater species. Understanding of byssus structure and protein composition is the prerequisite for revealing these mechanisms. Here, we used multiple methods, including scanning electron microscope, liquid chromatography–tandem mass spectrometry, transcriptome sequencing, real-time quantitative PCR, inductively coupled plasma mass spectrometry, to investigate structure, and protein composition of byssus in the highly invasive freshwater mussel Limnoperna fortunei. The results indicated that the structure characteristics of adhesive plaque, proximal and distal threads were conducive to byssus adhesion, contributing to the high biofouling capacity of this species. The 3,4-dihydroxyphenyl-α-alanine (Dopa) is a major post-transnationally modification in L. fortunei byssus. We identified 16 representative foot proteins with typical repetitive motifs and conserved domains by integrating transcriptomic and proteomic approaches. In these proteins, Lfbp-1, Lffp-2, and Lfbp-3 were specially located in foot tissue and highly expressed in the rapid byssus formation period, suggesting the involvement of these foot proteins in byssus production and adhesion. Multiple metal irons, including Ca²⁺, Mg²⁺, Zn²⁺, Al³⁺, and Fe³⁺, were abundant in both foot tissue and byssal thread. The heavy metals in these irons may be directly accumulated by L. fortunei from surrounding environments. Nevertheless, some metal ions (e.g., Ca²⁺) corresponded well with amino acid preferences of L. fortunei foot proteins, suggesting functional roles of these metal ions by interacting with foot proteins in byssus adhesion. Overall, this study provides structural and molecular bases of adhesive mechanisms of byssus in L. fortunei, and findings here are expected to develop strategies against biofouling by freshwater organisms.

Toxic Effects and Molecular Mechanism of Different Types of Silver Nanoparticles to the Aquatic Crustacean Daphnia magna

Silver nanoparticles (AgNPs) have been assessed to have a high exposure risk for humans and aquatic organisms. Toxicity varies considerably between different types of AgNPs. This study aimed to investigate the toxic effects of AgNPs with different particle sizes (40 and 110 nm) and different surface coatings (sodium citrate and polyvinylpyrrolidone, PVP) on Daphnia magna and their mechanisms of action. The results revealed that the citrate-coated AgNPs were more toxic than PVP-coated AgNPs and that the 40 nm AgNPs were more toxic than the 110 nm AgNPs. Transcriptome analysis further revealed that the toxic effects of AgNPs on D. magna were related to the mechanisms of ion binding and several metabolic pathways, such as the "RNA polymerase" pathway and the "protein digestion and absorption" pathway. Moreover, the principal component analysis (PAC) results found that surface coating was the major factor that determines the toxicities compared to particle size. These results could help us better understand the possible mechanism of AgNP toxicity in aquatic invertebrates at the transcriptome level and establish an important foundation for revealing the broad impacts of nanoparticles on aquatic environments.

Synthesis of Hybrid-Polypeptides m-PEO-b-poly(His-co-Gly) and m-PEO-b-poly(His-co-Ala) and Study of Their Structure and Aggregation. Influence of Hydrophobic Copolypeptides on the Properties of Poly(L-histidine)

The highly diverse and sophisticated action of proteins results from their equally diverse primary structure, which along with the nature of interactions between the amino acids, defines the higher self-assembly of proteins. The interactions between amino acids can be very complicated, and their understanding is necessary in order to elucidate the protein structure-properties relationship. A series of well-defined hybrid-polypeptidic diblock copolymers of the type m-PEO-b-poly(His-co-Gly) and m-PEO-b-poly(His-co-Ala) was synthesized through the ring opening polymerization of the N-carboxyanhydrides of the corresponding amino acids, with a molar ratio of the hydrophobic peptide to histidine at 10%, 20% and 40%. The excellent purity of the monomers combined with the high vacuum techniques resulted in controlled polymerization with high molecular and compositional homogeneity. FT-IR, as well as circular dichroism, were employed to investigate the secondary structure of the polymers, while DLS, SLS and ζ-potential were utilized to study the aggregates formed in aqueous solutions, as well as their pH responsiveness. The results revealed that the randomly distributed monomeric units of glycine or alanine significantly influence L-histidine’s structure. Depending on the pH, aggregates with a different structure, different molecular characteristics and a different surface charge are formed, potentially leading to very interesting bioapplications.

Purification and biochemical characterization of a novel mesophilic glucoamylase from Aspergillus tritici WZ99

Glucoamylase, cleaving the nonreducing end of starch releasing glucose, is an important enzyme in starch processing. The optimal temperature for industrial glucoamylase activity is 60-70°C, which is not compatible with the optimal growth temperature for Saccharomyces cerevisiae. In this study, 26 fungal strains producing amylolytic activities that were more active at 30°C than at 60°C were isolated from 151 environmental samples. Fungal strain WZ99, producing extracellular amylolytic activities with the lowest optimal temperature at 40°C, was identified as Aspergillus tritici by analysis of morphological and molecular data. An extracellular glucoamylase was purified from A. tritici WZ99. The optimal pH of the enzyme was 4.0-5.0 and optimal temperature was 45°C. The glucoamylase was stable at pH 4.5-10.0 and below 40°C. Metal ions at four concentrations did not inhibit the enzyme activity. The glucoamylase contained a catalytic domain belonging to glycosyl hydrolase family 15 and thus was named as AtriGA15A. The enzyme shared the highest identity of 54% with a glucoamylase from Rasamsonia emersonii. This glucoamylase showing excellent comprehensive enzymatic characteristics might have potential applications in starch-based bioethanol production and starch processing.

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.

Candida albicans Sap6 amyloid regions function in cellular aggregation and zinc binding, and contribute to zinc acquisition

Candida albicans is an opportunistic fungal pathogen colonizing the oral cavity. C. albicans secreted aspartic protease Sap6 is important for virulence during oral candidiasis since it degrades host tissues to release nutrients and essential transition metals. We found that zinc specifically increased C. albicans autoaggregation induced by Sap6; and that Sap6 itself bound zinc ions. In silico analysis of Sap6 predicted four amyloidogenic regions that were synthesized as peptides (P1–P4). All peptides, as well as full length Sap6, demonstrated amyloid properties, and addition of zinc further increased amyloid formation. Disruption of amyloid regions by Congo red significantly reduced auotoaggregation. Deletion of C. albicans genes that control zinc acquisition in the ZAP1 regulon, including zinc transporters (Pra1 and Zrt1) and other zinc-regulated surface proteins, resulted in lower autoaggregation and reduction of surface binding of Sap6. Cells with high expression of PRA1 and ZRT1 also showed increased Sap6-mediated autoaggregation. C. albicans ∆sap6 deletion mutants failed to accumulate intracellular zinc comparable to ∆zap1, ∆zrt1, and ∆pra1 cells. Thus Sap6 is a multi-functional molecule containing amyloid regions that promotes autoaggregation and zinc uptake, and may serve as an additional system for the community acquisition of zinc.

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.

Understanding the functional properties of bio-inorganic nanoflowers as biocatalysts by deciphering the metal-binding sites of enzymes

The biomineralisation of metal phosphates is a promising approach to develop more efficient nanobiocatalysts; however, the interactions between the protein and the inorganic mineral are poorly understood. Elucidating which protein regions most likely participate in the mineral formation will guide the fabrication of more efficient biocatalysts based on metal-phosphate nanoflowers. We have biomineralised the lipase from Thermomyces lanuginosus using three calcium, zinc and copper phosphates to fabricate different types of bio-inorganic nanoflowers. To better understand how the biomineralisation process affects the enzyme properties, we have computationally predicted the protein regions with a higher propensity for binding Ca²⁺, Cu²⁺ and Zn²⁺. These binding sites can be considered as presumable nucleation points where the biomineralisation process starts and explain why different metals can form bio-inorganic nanoflowers of the same enzyme with different functional properties. The formation of calcium, copper and zinc phosphates in the presence of this lipase gives rise to nanoflowers with different morphologies and different enzymatic properties such as activity, stability, hyperactivation and activity-pH profile; these functional differences are supported by structural studies based on fluorescence spectroscopy and can be explained by the different locations of the predicted nucleation sites for the different metals. Among the three metals used herein, the mineralisation of this lipase with zinc-phosphate enables the fabrication of bio-inorganic nanoflowers 34 times more stable than the soluble enzyme. These bio-inorganic nanoflowers were reused for 8 reaction cycles achieving 100% yield in the hydrolysis of p-nitrophenol butyrate but losing more than 50% of their initial activity after 6 operational cycles. Finally, this heterogeneous biocatalyst was more active and enantioselective than the soluble enzyme (ee = 79%(R)) towards the kinetic resolution of rac-1-phenylethyl acetate yielding the R enantiomer with ee = 84%.

Protein ligand-specific binding residue predictions by an ensemble classifier

BACKGROUND

Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands.

RESULTS

In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4-19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction.

CONCLUSIONS

Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz  or request upon the corresponding author.

The metal face of protein tyrosine phosphatase 1B

A new paradigm in metallobiochemistry describes the activation of inactive metalloenzymes by metal ion removal. Protein tyrosine phosphatases (PTPs) do not seem to require a metal ion for enzymatic activity. However, both metal cations and metal anions modulate their enzymatic activity. One binding site is the phosphate binding site at the catalytic cysteine residue. Oxyanions with structural similarity to phosphate, such as vanadate, inhibit the enzyme with nanomolar to micromolar affinities. In addition, zinc ions (Zn2+) inhibit with picomolar to nanomolar affinities. We mapped the cation binding site close to the anion binding site and established a specific mechanism of inhibition occurring only in the closed conformation of the enzyme when the catalytic cysteine is phosphorylated and the catalytic aspartate moves into the active site. We discuss this dual inhibition by anions and cations here for PTP1B, the most thoroughly investigated protein tyrosine phosphatase. The significance of the inhibition in phosphorylation signaling is becoming apparent only from the functions of PTP1B in the biological context of metal cations as cellular signaling ions. Zinc ion signals complement redox signals but provide a different type of control and longer lasting inhibition on a biological time scale owing to the specificity and affinity of zinc ions for coordination environments. Inhibitor design for PTP1B and other PTPs is a major area of research activity and interest owing to their prominent roles in metabolic regulation in health and disease, in particular cancer and diabetes. Our results explain the apparent dichotomy of both cations (Zn2+) and oxyanions such as vanadate inhibiting PTP1B and having insulin-enhancing ("anti-diabetic") effects and suggest different approaches, namely targeting PTPs in the cell by affecting their physiological modulators and considering a metallodrug approach that builds on the knowledge of the insulin-enhancing effects of both zinc and vanadium compounds.

Redox Activity of Copper(II) Complexes with NSFRY Pentapeptide and Its Analogues

The influence of cation-π interactions on the electrochemical properties of copper(II) complexes with synthesized pentapeptide C-terminal fragment of Atrial Natriuretic Factor (ANF) hormone was studied in this work. Molecular modeling performed for Cu(II)-NSFRY-NH₂ complex indicated that the cation-π interactions between Tyr and Cu(II), and also between Phe-Arg led to specific conformation defined as peptide box, in which the metal cation is isolated from the solvent by peptide ligand. Voltammetry experiments enabled to compare the redox properties and stability of copper(II) complexes with NSFRY-NH₂ and its analogues (namely: NSFRA-NH₂, NSFRF-NH_2, NSAAY-NH₂, NSAAA-NH₂, AAAAA-NH₂) as well as to evaluate the contribution of individual amino acid residues to these properties. The obtained results led to the conclusion, that cation-π interactions play a crucial role in the effective stabilization of copper(II) complexes with the fragments of ANF peptide hormone and therefore could control the redox processes in other metalloproteins.

Analysis of Comparative Sequence and Genomic Data to Verify Phylogenetic Relationship and Explore a New Subfamily of Bacterial Lipases

Thermostable and organic solvent-tolerant enzymes have significant potential in a wide range of synthetic reactions in industry due to their inherent stability at high temperatures and their ability to endure harsh organic solvents. In this study, a novel gene encoding a true lipase was isolated by construction of a genomic DNA library of thermophilic Aneurinibacillus thermoaerophilus strain HZ into Escherichia coli plasmid vector. Sequence analysis revealed that HZ lipase had 62% identity to putative lipase from Bacillus pseudomycoides. The closely characterized lipases to the HZ lipase gene are from thermostable Bacillus and Geobacillus lipases belonging to the subfamily I.5 with ≤ 57% identity. The amino acid sequence analysis of HZ lipase determined a conserved pentapeptide containing the active serine, GHSMG and a Ca²⁺-binding motif, GCYGSD in the enzyme. Protein structure modeling showed that HZ lipase consisted of an α/β hydrolase fold and a lid domain. Protein sequence alignment, conserved regions analysis, clustal distance matrix and amino acid composition illustrated differences between HZ lipase and other thermostable lipases. Phylogenetic analysis revealed that this lipase represented a new subfamily of family I of bacterial true lipases, classified as family I.9. The HZ lipase was expressed under promoter P_lac using IPTG and was characterized. The recombinant enzyme showed optimal activity at 65°C and retained ≥ 97% activity after incubation at 50°C for 1h. The HZ lipase was stable in various polar and non-polar organic solvents.

Microbial Metalloproteomics

Metalloproteomics is a rapidly developing field of science that involves the comprehensive analysis of all metal-containing or metal-binding proteins in a biological sample. The purpose of this review is to offer a comprehensive overview of the research involving approaches that can be categorized as inductively coupled plasma (ICP)-MS based methods, X-ray absorption/fluorescence, radionuclide based methods and bioinformatics. Important discoveries in microbial proteomics will be reviewed, as well as the outlook to new emerging approaches and research areas.

Automatic generation of bioinformatics tools for predicting protein-ligand binding sites

MOTIVATION

Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful.

RESULTS

We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.

AVAILABILITY AND IMPLEMENTATION

The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux.

CONTACT

shimizu@bi.a.u-tokyo.ac.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MavN is a Legionella pneumophila vacuole-associated protein required for efficient iron acquisition during intracellular growth

Iron is essential for the growth and virulence of most intravacuolar pathogens. The mechanisms by which microbes bypass host iron restriction to gain access to this metal across the host vacuolar membrane are poorly characterized. In this work, we identify a unique intracellular iron acquisition strategy used by Legionella pneumophila. The bacterial Icm/Dot (intracellular multiplication/defect in organelle trafficking) type IV secretion system targets the bacterial-derived MavN (more regions allowing vacuolar colocalization N) protein to the surface of the Legionella-containing vacuole where this putative transmembrane protein facilitates intravacuolar iron acquisition. The ΔmavN mutant exhibits a transcriptional iron-starvation signature before its growth is arrested during the very early stages of macrophage infection. This intracellular growth defect is rescued only by the addition of excess exogenous iron to the culture medium and not a variety of other metals. Consistent with MavN being a translocated substrate that plays an exclusive role during intracellular growth, the mutant shows no defect for growth in broth culture, even under severe iron-limiting conditions. Putative iron-binding residues within the MavN protein were identified, and point mutations in these residues resulted in defects specific for intracellular growth that are indistinguishable from the ΔmavN mutant. This model of a bacterial protein inserting into host membranes to mediate iron transport provides a paradigm for how intravacuolar pathogens can use virulence-associated secretion systems to manipulate and acquire host iron.

Molecular characterization influencing metal resistance in the Cupriavidus/Ralstonia genomes

Our environment is stressed with a load of heavy and toxic metals. Microbes, abundant in our environment, are found to adapt well to this metal-stressed condition. A comparative study among five Cupriavidus/Ralstonia genomes can offer a better perception of their evolutionary mechanisms to adapt to these conditions. We have studied codon usage among 1051 genes common to all these organisms and identified 15 optimal codons frequently used in highly expressed genes present within 1051 genes. We found the core genes of Cupriavidus metallidurans CH34 have a different optimal codon choice for arginine, glycine and alanine in comparison with the other four bacteria. We also found that the synonymous codon usage bias within these 1051 core genes is highly correlated with their gene expression. This supports that translational selection drives synonymous codon usage in the core genes of these genomes. Synonymous codon usage is highly conserved in the core genes of these five genomes. The only exception among them is C. metallidurans CH34. This genomewide shift in synonymous codon choice in C. metallidurans CH34 may have taken place due to the insertion of new genes in its genomes facilitating them to survive in heavy metal containing environment and the co-evolution of the other genes in its genome to achieve a balance in gene expression. Structural studies indicated the presence of a longer N-terminal region containing a copper-binding domain in the cupC proteins of C. metallidurans CH3 that helps it to attain higher binding efficacy with copper in comparison with its orthologs.

Structural basis for the recognition of mycolic acid precursors by KasA, a condensing enzyme and drug target from Mycobacterium tuberculosis

The survival of Mycobacterium tuberculosis depends on mycolic acids, very long α-alkyl-β-hydroxy fatty acids comprising 60-90 carbon atoms. However, despite considerable efforts, little is known about how enzymes involved in mycolic acid biosynthesis recognize and bind their hydrophobic fatty acyl substrates. The condensing enzyme KasA is pivotal for the synthesis of very long (C38-42) fatty acids, the precursors of mycolic acids. To probe the mechanism of substrate and inhibitor recognition by KasA, we determined the structure of this protein in complex with a mycobacterial phospholipid and with several thiolactomycin derivatives that were designed as substrate analogs. Our structures provide consecutive snapshots along the reaction coordinate for the enzyme-catalyzed reaction and support an induced fit mechanism in which a wide cavity is established through the concerted opening of three gatekeeping residues and several α-helices. The stepwise characterization of the binding process provides mechanistic insights into the induced fit recognition in this system and serves as an excellent foundation for the development of high affinity KasA inhibitors.

Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering

Accurately identifying the protein-ligand binding sites or pockets is of significant importance for both protein function analysis and drug design. Although much progress has been made, challenges remain, especially when the 3D structures of target proteins are not available or no homology templates can be found in the library, where the template-based methods are hard to be applied. In this paper, we report a new ligand-specific template-free predictor called TargetS for targeting protein-ligand binding sites from primary sequences. TargetS first predicts the binding residues along the sequence with ligand-specific strategy and then further identifies the binding sites from the predicted binding residues through a recursive spatial clustering algorithm. Protein evolutionary information, predicted protein secondary structure, and ligand-specific binding propensities of residues are combined to construct discriminative features; an improved AdaBoost classifier ensemble scheme based on random undersampling is proposed to deal with the serious imbalance problem between positive (binding) and negative (nonbinding) samples. Experimental results demonstrate that TargetS achieves high performances and outperforms many existing predictors. TargetS web server and data sets are freely available at: http://www.csbio.sjtu.edu.cn/bioinf/TargetS/ for academic use.

Inferences on the biochemical and environmental regulation of universal stress proteins from Schistosomiasis parasites

BACKGROUND

Human schistosomiasis is a freshwater snail-transmitted disease caused by parasitic flatworms of the Schistosoma genus. Schistosoma haematobium, Schistosoma mansoni, and Schistosoma japonicum are the three major species infecting humans. These parasites undergo a complex developmental life cycle, in which they encounter a plethora of environmental signals. The presence of genes encoding the universal stress protein (USP) domain in the genomes of Schistosoma spp. suggests these flatworms are equipped to respond to unfavorable conditions. Though data on gene expression is available for USP genes, their biochemical and environmental regulation are incompletely understood. The identification of additional regulatory molecules for Schistosoma. USPs, which may be present in the human, snail, or water environments, could also be useful for schistosomiasis interventions.

METHODS

We developed a protocol that includes a visual analytics stage to facilitate integration, visualization, and decision making, from the results of sequence analyses and data collection on a set of 13 USPs from S. mansoni and S. japonicum.

RESULTS

Multiple sequence alignment identified conserved sites that could be key residues regulating the function of USPs of the Schistosoma spp. Based on the consistency and completeness of sequence annotation, we prioritized for further research the gene for a 184-amino-acid-long USP that is present in the genomes of the three human-infecting Schistosoma spp. Calcium, zinc, and magnesium ions were predicted to interact with the protein product of the gene.

CONCLUSION

Given that the initial effects of praziquantel on schistosomes include the influx of calcium ions, additional investigations are required to (1) functionally characterize the interactions of calcium ions with the amino acid residues of Schistosoma USPs; and (2) determine the transcriptional response of Schistosoma. USP genes to praziquantel. The data sets produced, and the visual analytics views that were developed, can be easily reused to develop new hypotheses.

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.