Flexibility of metal binding sites in proteins on a database scale

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.

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

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

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

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

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.

Multimodal Response to Copper Binding in Superoxide Dismutase Dynamics

Copper/zinc superoxide dismutase (SOD) is a homodimeric metalloenzyme that has been extensively studied as a benchmark for structure-function relationships in proteins, in particular because of its implication in the familial form of the neurodegenerative disease amyotrophic lateral sclerosis. Here, we investigate microcrystalline preparations of two differently metalated forms of SOD, namely, the fully mature functional Cu,Zn state and the E,Zn-SOD state in which the Cu site is empty. By using solid-state NMR with fast magic-angle spinning (MAS) at high magnetic fields (¹H Larmor frequency of 800-1000 MHz), we quantify motions spanning a dynamic range from ns to ms. We determine that metal ion uptake does not act as a rigidification element but as a switch redistributing motional processes on different time scales, with coupling of the dynamics of histidine side chains and those of remote key backbone elements of the protein.

The role of membrane-bound metal ions in toxicity of a human cancer cell-active pore-forming toxin Cry41Aa from Bacillus thuringiensis

Bacillus thuringiensis crystal (Cry) proteins, used for decades as insecticidal toxins, are well known to be toxic to certain insects, but not to mammals. A novel group of Cry toxins called parasporins possess a strong cytocidal activity against some human cancer cells. Cry41Aa, or parasporin3, closely resembles commercially used insecticidal toxins and yet is toxic to the human hepatic cancer cell line HepG2, disrupting membranes of susceptible cells, similar to its insecticidal counterparts. In this study, we explore the protective effect that the common divalent metal chelator EGTA exerts on Cry41Aa's activity on HepG2 cells. Our results indicate that rather than interfering with a signalling pathway as a result of chelating cations in the medium, the chelator prevented the toxin's interaction with the membrane, and thus the subsequent steps of membrane damage and p38 phosphorylation, by removing cations bound to plasma membrane components. BAPTA and DTPA also inhibited Cry41Aa toxicity but at higher concentrations. We also show for the first time that Cry41Aa induces pore formation in planar lipid bilayers. This activity is not altered by EGTA, consistent with a biological context of chelation. Salt supplementation assays identified Ca²⁺, Mn²⁺ and Zn²⁺ as being able to reinstate Cry41Aa activity. Our data suggest the existence of one or more metal cation-dependent receptors in the Cry41Aa mechanism of action.

Development of METAL-ACTIVE SITE and ZINCCLUSTER tool to predict active site pockets

The advent of whole genome sequencing leads to increasing number of proteins with known amino acid sequences. Despite many efforts, the number of proteins with resolved three dimensional structures is still low. One of the challenging tasks the structural biologists face is the prediction of the interaction of metal ion with any protein for which the structure is unknown. Based on the information available in Protein Data Bank, a site (METALACTIVE INTERACTION) has been generated which displays information for significant high preferential and low-preferential combination of endogenous ligands for 49 metal ions. User can also gain information about the residues present in the first and second coordination sphere as it plays a major role in maintaining the structure and function of metalloproteins in biological system. In this paper, a novel computational tool (ZINCCLUSTER) is developed, which can predict the zinc metal binding sites of proteins even if only the primary sequence is known. The purpose of this tool is to predict the active site cluster of an uncharacterized protein based on its primary sequence or a 3D structure. The tool can predict amino acids interacting with a metal or vice versa. This tool is based on the occurrence of significant triplets and it is tested to have higher prediction accuracy when compared to that of other available techniques.

Biochemical characterization of functional domains of the chaperone Cosmc

Cosmc is an endoplasmic reticulum chaperone necessary for normal protein O-GalNAc glycosylation through regulation of T-synthase, its single client. Loss-of-function of Cosmc results in expression of the Tn antigen, which is associated with multiple human diseases including cancer. Despite intense interest in dysregulated expression of the Tn antigen, little is known about the structure and function of Cosmc, including domain organization, secondary structure, oligomerization, and co-factors. Limited proteolysis experiments show that Cosmc contains a structured N-terminal domain (CosmcΔ256), and biochemical characterization of CosmcΔ256 reveals wild type chaperone activity. Interestingly, CosmcE152K, which shows loss of function in vivo, exhibits wild type-like activity in vitro. Cosmc and CosmcE152K heterogeneously oligomerize and form monomeric, dimeric, trimeric, and tetrameric species, while CosmcΔ256 is predominantly monomeric as characterized by chemical crosslinking and blue native page electrophoresis. Additionally, Cosmc selectively binds divalent cations in thermal shift assays and metal binding is abrogated by the CosmcΔ256 truncation, and perturbed by the E152K mutation. Therefore, the N-terminal domain of Cosmc mediates T-synthase binding and chaperone function, whereas the C-terminal domain is necessary for oligomerization and metal binding. Our results provide new structure-function insight to Cosmc, indicate that Cosmc behaves as a modular protein and suggests points of modulation or regulation of in vivo chaperone function.

Normal Modes Expose Active Sites in Enzymes

Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes.

In this paper, we present an improved technique to predict active sites in enzymes. Our technique is based on changes of solvent accessibility that accompany normal mode dynamics. We assert the technique strength using several enzyme datasets with known catalytic residues. We show the technique successfully locates the active site in most cases, and consistently surpasses the accuracy of other techniques. We show how the technique is advantageous and paves the way for high precision prediction of active sites.

A Crystallographic Examination of Predisposition versus Preorganization in de Novo Designed Metalloproteins

Preorganization and predisposition are important molecular recognition concepts exploited by nature to obtain site-specific and selective metal binding to proteins. While native structures containing an MS3 core are often unavailable in both apo- and holo-forms, one can use designed three-stranded coiled coils (3SCCs) containing tris-thiolate sites to evaluate these concepts. We show that the preferred metal geometry dictates the degree to which the cysteine rotamers change upon metal complexation. The Cys ligands in the apo-form are preorganized for binding trigonal pyramidal species (Pb(II)S3 and As(III)S3) in an endo conformation oriented toward the 3SCC C-termini, whereas the cysteines are predisposed for trigonal planar Hg(II)S3 and 4-coordinate Zn(II)S3O structures, requiring significant thiol rotation for metal binding. This study allows assessment of the importance of protein fold and side-chain reorientation for achieving metal selectivity in human retrotransposons and metalloregulatory proteins.

Mechanism of Iron-Dependent Repressor (IdeR) Activation and DNA Binding: A Molecular Dynamics and Protein Structure Network Study

Metalloproteins form a major class of enzymes in the living system that are involved in crucial biological functions such as catalysis, redox reactions and as 'switches' in signal transductions. Iron dependent repressor (IdeR) is a metal-sensing transcription factor that regulates free iron concentration in Mycobacterium tuberculosis. IdeR is also known to promote bacterial virulence, making it an important target in the field of therapeutics. Mechanistic details of how iron ions modulate IdeR such that it dimerizes and binds to DNA is not understood clearly. In this study, we have performed molecular dynamic simulations and integrated it with protein structure networks to study the influence of iron on IdeR structure and function. A significant structural variation between the metallated and the non-metallated system is observed. Our simulations clearly indicate the importance of iron in stabilizing its monomeric subunit, which in turn promotes dimerization. However, the most striking results are obtained from the simulations of IdeR-DNA complex in the absence of metals, where at the end of 100ns simulations, the protein subunits are seen to rapidly dissociate away from the DNA, thereby forming an excellent resource to investigate the mechanism of DNA binding. We have also investigated the role of iron as an allosteric regulator of IdeR that positively induces IdeR-DNA complex formation. Based on this study, a mechanistic model of IdeR activation and DNA binding has been proposed.

Structural characterization of metal binding to a cold-adapted frataxin

Frataxin is an evolutionary conserved protein that participates in iron metabolism. Deficiency of this small protein in humans causes a severe neurodegenerative disease known as Friedreich's ataxia. A number of studies indicate that frataxin binds iron and regulates Fe-S cluster biosynthesis. Previous structural studies showed that metal binding occurs mainly in a region of high density of negative charge. However, a comprehensive characterization of the binding sites is required to gain further insights into the mechanistic details of frataxin function. In this work, we have solved the X-ray crystal structures of a cold-adapted frataxin from a psychrophilic bacterium in the presence of cobalt or europium ions. We have identified a number of metal-binding sites, mainly solvent exposed, several of which had not been observed in previous studies on mesophilic homologues. No major structural changes were detected upon metal binding, although the structures exhibit significant changes in crystallographic B-factors. The analysis of these B-factors, in combination with crystal packing and RMSD among structures, suggests the existence of localized changes in the internal motions. Based on these results, we propose that bacterial frataxins possess binding sites of moderate affinity for a quick capture and transfer of iron to other proteins and for the regulation of Fe-S cluster biosynthesis, modulating interactions with partner proteins.

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.

Recombinant production and characterization of a highly active alkaline phosphatase from marine bacterium Cobetia marina

The psychrophilic marine bacterium, Cobetia marina, recovered from the mantle tissue of the marine mussel, Crenomytilus grayanus, which contained a gene encoding alkaline phosphatase (AP) with apparent biotechnology advantages. The enzyme was found to be more efficient than its counterparts and showed k cat value 10- to 100-fold higher than those of all known commercial APs. The enzyme did not require the presence of exogenous divalent cations and dimeric state of its molecule for activity. The recombinant enzyme (CmAP) production and purification were optimized with a final recovery of 2 mg of the homogenous protein from 1 L of the transgenic Escherichia coli Rosetta(DE3)/Pho40 cells culture. CmAP displayed a half-life of 16 min at 45 °C and 27 min at 40 °C in the presence of 2 mM EDTA, thus suggesting its relative thermostability in comparison with the known cold-adapted analogues. A high concentration of EDTA in the incubation mixture did not appreciably inhibit CmAP. The enzyme was stable in a wide range of pH (6.0-11.0). CmAP exhibited its highest activity at the reaction temperature of 40-50 °C and pH 9.5-10.3. The structural features of CmAP could be the reason for the increase in its stability and catalytic turnover. We have modeled the CmAP 3D structure on the base of the high-quality experimental structure of the close homologue Vibrio sp. AP (VAP) and mutated essential residues predicted to break Mg(2+) bonds in CmAP. It seems probable that the intrinsically tight binding of catalytic and structural metal ions together with the flexibility of intermolecular and intramolecular links in CmAP could be attributed to the adapted mutualistic lifestyle in oceanic waters.

The bioinorganic chemistry of apoptosis: potential inhibitory zinc binding sites in caspase-3

Zn(2+) inhibits the action of several of the caspases and thus may act as a regulator of apoptosis. Reversal of this inhibition is one possible approach for the development of apoptosis-based therapies. Few studies describe the molecular details of the Zn(2+)-caspase interaction, the understanding of which is essential for the success of any therapeutic strategies. Enzyme kinetics and biophysical studies have shown that the inhibition is of mixed type with prominent (ca. 60 % of inhibition) uncompetitive characteristics and an IC50 of 0.8 μM under the conditions used. Fluorescence-based techniques confirmed that, during inhibition in the sub-micromolar range, substrate binding remains unaffected. A new zinc binding site composed of the catalytic histidine and a nearby methionine residue, rather than the catalytic histidine and cysteine dyad, is proposed based on the experimental observations. DFT models were used to demonstrate that the proposed site could be the preferred inhibitory zinc binding site.

Predicting Ca2+ -binding sites using refined carbon clusters

Identifying Ca(2+) -binding sites in proteins is the first step toward understanding the molecular basis of diseases related to Ca(2+) -binding proteins. Currently, these sites are identified in structures either through X-ray crystallography or NMR analysis. However, Ca(2+) -binding sites are not always visible in X-ray structures due to flexibility in the binding region or low occupancy in a Ca(2+) -binding site. Similarly, both Ca(2+) and its ligand oxygens are not directly observed in NMR structures. To improve our ability to predict Ca(2+) -binding sites in both X-ray and NMR structures, we report a new graph theory algorithm (MUG(C) ) to predict Ca(2+) -binding sites. Using carbon atoms covalently bonded to the chelating oxygen atoms, and without explicit reference to side-chain oxygen ligand co-ordinates, MUG(C) is able to achieve 94% sensitivity with 76% selectivity on a dataset of X-ray structures composed of 43 Ca(2+) -binding proteins. Additionally, prediction of Ca(2+) -binding sites in NMR structures was obtained by MUG(C) using a different set of parameters, which were determined by the analysis of both Ca(2+) -constrained and unconstrained Ca(2+) -loaded structures derived from NMR data. MUG(C) identified 20 of 21 Ca(2+) -binding sites in NMR structures inferred without the use of Ca(2+) constraints. MUG(C) predictions are also highly selective for Ca(2+) -binding sites as analyses of binding sites for Mg(2+) , Zn(2+) , and Pb(2+) were not identified as Ca(2+) -binding sites. These results indicate that the geometric arrangement of the second-shell carbon cluster is sufficient not only for accurate identification of Ca(2+) -binding sites in NMR and X-ray structures but also for selective differentiation between Ca(2+) and other relevant divalent cations.

Dissecting protein loops with a statistical scalpel suggests a functional implication of some structural motifs

Background

One of the strategies for protein function annotation is to search particular structural motifs that are known to be shared by proteins with a given function.

Results

Here, we present a systematic extraction of structural motifs of seven residues from protein loops and we explore their correspondence with functional sites. Our approach is based on the structural alphabet HMM-SA (Hidden Markov Model - Structural Alphabet), which allows simplification of protein structures into uni-dimensional sequences, and advanced pattern statistics adapted to short sequences. Structural motifs of interest are selected by looking for structural motifs significantly over-represented in SCOP superfamilies in protein loops. We discovered two types of structural motifs significantly over-represented in SCOP superfamilies: (i) ubiquitous motifs, shared by several superfamilies and (ii) superfamily-specific motifs, over-represented in few superfamilies. A comparison of ubiquitous words with known small structural motifs shows that they contain well-described motifs as turn, niche or nest motifs. A comparison between superfamily-specific motifs and biological annotations of Swiss-Prot reveals that some of them actually correspond to functional sites involved in the binding sites of small ligands, such as ATP/GTP, NAD(P) and SAH/SAM.

Conclusions

Our findings show that statistical over-representation in SCOP superfamilies is linked to functional features. The detection of over-represented motifs within structures simplified by HMM-SA is therefore a promising approach for prediction of functional sites and annotation of uncharacterized proteins.

A Transferable Non-bonded Pairwise Force Field to Model Zinc Interactions in Metalloproteins

Herein we introduce a novel practical strategy to overcome the well-known challenge of modeling the divalent zinc cation in metalloproteins. The main idea is to design short-long effective functions (SLEF) to describe charge interactions between the zinc ion and all other atoms. This SLEF approach has the following desired features: (1). It is pairwise, additive and compatible with widely used atomic pair-wise force fields for modeling biomolecules; (2). It only changes interactions between the zinc ion and other atoms, and does not affect force field parameters that model other interactions in the system; (3). It is a non-bonded model that is inherently capable to describe different zinc ligands and coordination modes. By optimizing two SLEF parameters as well as zinc vdW parameters through force matching based on Born-Oppenheimer ab initio QM/MM molecular dynamics simulations, we have successfully developed the first SLEF force field (SLEF1) to describe zinc interactions. Extensive molecular dynamics simulations of seven zinc enzyme systems with different coordination ligands and distinct chelation modes (4-,5-,6-fold), including the binuclear zinc active site, yielded zinc coordination numbers and binding distances in good agreement with the corresponding crystal structures as well as ab initio QM/MM MD results. This not only demonstrates the transferability and adequacy of the new SLEF1 force field in describing a variety of zinc proteins, but also indicates that this novel SLEF approach is a promising direction to explore for improving force field description of metal ion interactions.

Predicting the disruption by UO2(2+) of a protein-ligand interaction

The uranyl cation (UO(2) (2+)) can be suspected to interfere with the binding of essential metal cations to proteins, underlying some mechanisms of toxicity. A dedicated computational screen was used to identify UO(2) (2+) binding sites within a set of nonredundant protein structures. The list of potential targets was compared to data from a small molecules interaction database to pinpoint specific examples where UO(2) (2+) should be able to bind in the vicinity of an essential cation, and would be likely to affect the function of the corresponding protein. The C-reactive protein appeared as an interesting hit since its structure involves critical calcium ions in the binding of phosphorylcholine. Biochemical experiments confirmed the predicted binding site for UO(2) (2+) and it was demonstrated by surface plasmon resonance assays that UO(2) (2+) binding to CRP prevents the calcium-mediated binding of phosphorylcholine. Strikingly, the apparent affinity of UO(2) (2+) for native CRP was almost 100-fold higher than that of Ca(2+). This result exemplifies in the case of CRP the capability of our computational tool to predict effective binding sites for UO(2) (2+) in proteins and is a first evidence of calcium substitution by the uranyl cation in a native protein.

Conformational changes associated with the binding of zinc acetate at the putative active site of XcTcmJ, a cupin from Xanthomonas campestris pv. campestris

In the plant pathogen Xanthomonas campestris pv. campestris, the product of the tcmJ gene, XcTcmJ, encodes a protein belonging to the RmlC family of cupins. XcTcmJ was crystallized in a monoclinic space group (C2) in the presence of zinc acetate and the structure was determined to 1.6 Å resolution. Previously, the apo structure has been reported in the absence of any bound metal ion [Chin et al. (2006), Proteins, 65, 1046-1050]. The most significant difference between the apo structure and the structure of XcTcmJ described here is a reorganization of the binding site for zinc acetate, which was most likely acquired from the crystallization solution. This site is located in the conserved metal ion-binding domain at the putative active site of XcTcmJ. In addition, an acetate was also bound within coordination distance of the zinc. In order to accommodate this binding, rearrangement of a conserved histidine ligand is required as well as several nearby residues within and around the putative active site. These observations indicate that binding of zinc serves a functional role in this cupin protein.

Analysis and prediction of calcium-binding pockets from apo-protein structures exhibiting calcium-induced localized conformational changes

Calcium binding in proteins exhibits a wide range of polygonal geometries that relate directly to an equally diverse set of biological functions. The binding process stabilizes protein structures and typically results in local conformational change and/or global restructuring of the backbone. Previously, we established the MUG program, which utilized multiple geometries in the Ca(2+)-binding pockets of holoproteins to identify such pockets, ignoring possible Ca(2+)-induced conformational change. In this article, we first report our progress in the analysis of Ca(2+)-induced conformational changes followed by improved prediction of Ca(2+)-binding sites in the large group of Ca(2+)-binding proteins that exhibit only localized conformational changes. The MUG(SR) algorithm was devised to incorporate side chain torsional rotation as a predictor. The output from MUG(SR) presents groups of residues where each group, typically containing two to five residues, is a potential binding pocket. MUG(SR) was applied to both X-ray apo structures and NMR holo structures, which did not use calcium distance constraints in structure calculations. Predicted pockets were validated by comparison with homologous holo structures. Defining a "correct hit" as a group of residues containing at least two true ligand residues, the sensitivity was at least 90%; whereas for a "correct hit" defined as a group of residues containing at least three true ligand residues, the sensitivity was at least 78%. These data suggest that Ca(2+)-binding pockets are at least partially prepositioned to chelate the ion in the apo form of the protein.

Integration of Diverse Research Methods to Analyze and Engineer Ca-Binding Proteins: From Prediction to Production

In recent years, increasingly sophisticated computational and bioinformatics tools have evolved for the analyses of protein structure, function, ligand interactions, modeling and energetics. This includes the development of algorithms to recursively evaluate side-chain rotamer permutations, identify regions in a 3D structure that meet some set of search parameters, calculate and minimize energy values, and provide high-resolution visual tools for theoretical modeling. Here we discuss the interdependency between different areas of bioinformatics, the evolution of different algorithm design approaches, and finally the transition from theoretical models to real-world design and application as they relate to Ca(2+)-binding proteins. Within this context, it has become evident that significant pre-experimental design and calculations can be modeled through computational methods, thus eliminating potentially unproductive research and increasing our confidence in the correlation between real and theoretical models. Moving from prediction to production, it is anticipated that bioinformatics tools will play an increasingly significant role in research and development, improving our ability to both understand the physiological roles of Ca(2+) and other metals and to extend that knowledge to the design of function-specific synthetic proteins capable of fulfilling different roles in medical diagnostics and therapeutics.

Prediction of 3D metal binding sites from translated gene sequences based on remote-homology templates

Database-scale analysis was performed to determine whether structural models, based on remote homologues, are effective in predicting 3D transition metal binding sites in proteins directly from translated gene sequences. The extent by which side chain modeling alone reduces sensitivity and selectivity is shown to be <10%. Surprisingly, selectivity was not dependent on the level of sequence homology between template and target, or on the presence of a metal ion in the structural template. Applying a modification of the CHED algorithm (Babor et al., Proteins 2008;70:208-217) and machine learning filters, a selectivity of approximately 90% was achieved for protein sequences using unrelated structural templates over a sequence identity range of 18-100%. Below approximately 18% identity, the number of analyzable target-template pairs and predictability of metal binding sites falls off sharply. A full third of structural templates were found to have target partners only in the remote homology range of 18-30%. In this range, nonmetal-binding templates are calculated to be the majority and serve to predict with 50% sensitivity at the geometric level. Overall, sensitivity at the geometric level for targets having templates in the 18-30% sequence identity range is 73%, with an average of one false positive site per true site. Protein sequences described as "unknown" in the UniProt database and composed largely of unidentified genome project sequences were studied and metal binding sites predicted. A web server for prediction of metal binding sites from protein sequence is provided.

Prediction of calcium-binding sites by combining loop-modeling with machine learning

Background

Protein ligand-binding sites in the apo state exhibit structural flexibility. This flexibility often frustrates methods for structure-based recognition of these sites because it leads to the absence of electron density for these critical regions, particularly when they are in surface loops. Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information.

Results

We report a hybrid approach for recognizing calcium-binding sites in disordered regions. Our approach combines loop modeling with a machine learning method (FEATURE) for structure-based site recognition. For validation, we compared the performance of our method on known calcium-binding sites for which there are both holo and apo structures. When loops in the apo structures are rebuilt using modeling methods, FEATURE identifies 14 out of 20 crystallographically proven calcium-binding sites. It only recognizes 7 out of 20 calcium-binding sites in the initial apo crystal structures.

We applied our method to unstructured loops in proteins from SCOP families known to bind calcium in order to discover potential cryptic calcium binding sites. We built 2745 missing loops and evaluated them for potential calcium binding. We made 102 predictions of calcium-binding sites. Ten predictions are consistent with independent experimental verifications. We found indirect experimental evidence for 14 other predictions. The remaining 78 predictions are novel predictions, some with intriguing potential biological significance. In particular, we see an enrichment of beta-sheet folds with predicted calcium binding sites in the connecting loops on the surface that may be important for calcium-mediated function switches.

Conclusion

Protein crystal structures are a potentially rich source of functional information. When loops are missing in these structures, we may be losing important information about binding sites and active sites. We have shown that limited loop modeling (e.g. loops less than 17 residues) combined with pattern matching algorithms can recover functions and propose putative conformations associated with these functions.

Efficient algorithms to explore conformation spaces of flexible protein loops

Several applications in biology - e.g., incorporation of protein flexibility in ligand docking algorithms, interpretation of fuzzy X-ray crystallographic data, and homology modeling - require computing the internal parameters of a flexible fragment (usually, a loop) of a protein in order to connect its termini to the rest of the protein without causing any steric clash. One must often sample many such conformations in order to explore and adequately represent the conformational range of the studied loop. While sampling must be fast, it is made difficult by the fact that two conflicting constraints - kinematic closure and clash avoidance - must be satisfied concurrently. This paper describes two efficient and complementary sampling algorithms to explore the space of closed clash-free conformations of a flexible protein loop. The "seed sampling" algorithm samples broadly from this space, while the "deformation sampling" algorithm uses seed conformations as starting points to explore the conformation space around them at a finer grain. Computational results are presented for various loops ranging from 5 to 25 residues. More specific results also show that the combination of the sampling algorithms with a functional site prediction software (FEATURE) makes it possible to compute and recognize calcium-binding loop conformations. The sampling algorithms are implemented in a toolkit (LoopTK), which is available at https://simtk.org/home/looptk.

Structural differences between Pb2+- and Ca2+-binding sites in proteins: implications with respect to toxicity

Pb(2+) is known to displace physiologically-relevant metal ions in proteins. To investigate potential relationships between Pb(2+)/protein complexes and toxicity, data from the protein data bank were analyzed to compare structural properties of Pb(2+)- and Ca(2+)-binding sites. Results of this analysis reveal that the majority of Pb(2+) sites (77.1%) involve 2-5 binding ligands, compared with 6+/-2 for non-EF-Hand and 7+/-1 for EF-Hand Ca(2+)-binding sites. The mean net negative charge by site (1.7) fell between values noted for non-EF-Hand (1+/-1) and EF-Hand (3+/-1). Oxygen is the dominant ligand for both Pb(2+) and Ca(2+), but Pb(2+) binds predominantly with sidechain Glu (38.4%), which is less prevalent in both non-EF-Hand (10.4%) and EF-Hand (26.6%) Ca(2+)-binding sites. A comparison of binding geometries where Pb(2+) has replaced Ca(2+) in calmodulin (CaM) and Zn(2+) in 5-aminolaevulinic acid dehydratase (ALAD) revealed protein structural changes that appear to be unrelated to ionic displacement. Structural changes observed with CaM may be related to opportunistic binding of Pb(2+) in regions of high electrostatic charge, whereas ALAD may bind multiple Pb(2+) ions in the active site. These results suggest that Pb(2+) adapts to structurally-diverse binding geometries and that opportunistic binding may play an active role in molecular metal toxicity.

Exploiting 3D structural templates for detection of metal-binding sites in protein structures

High throughput structural genomics efforts have been making the structures of proteins available even before their function has been fully characterized. Therefore, methods that exploit the structural knowledge to provide evidence about the functions of proteins would be useful. Such methods would be needed to complement the sequence-based function annotation approaches. The current study describes generation of 3D-structural motifs for metal-binding sites from the known metalloproteins. It then scans all the available protein structures in the PDB database for putative metal-binding sites. Our analysis predicted more than 1000 novel metal-binding sites in proteins using three-residue templates, and more than 150 novel metal-binding sites using four-residue templates. Prediction of metal-binding site in a yeast protein YDR533c led to the hypothesis that it might function as metal-dependent amidopeptidase. The structural motifs identified by our method present novel metal-binding sites that reveal newer mechanisms for a few well-known proteins.

Prediction of transition metal-binding sites from apo protein structures

Metal ions are crucial for protein function. They participate in enzyme catalysis, play regulatory roles, and help maintain protein structure. Current tools for predicting metal-protein interactions are based on proteins crystallized with their metal ions present (holo forms). However, a majority of resolved structures are free of metal ions (apo forms). Moreover, metal binding is a dynamic process, often involving conformational rearrangement of the binding pocket. Thus, effective predictions need to be based on the structure of the apo state. Here, we report an approach that identifies transition metal-binding sites in apo forms with a resulting selectivity >95%. Applying the approach to apo forms in the Protein Data Bank and structural genomics initiative identifies a large number of previously unknown, putative metal-binding sites, and their amino acid residues, in some cases providing a first clue to the function of the protein.

Conservation of metal-coordinating residues

As a result of rapid advances in genome sequencing, the pace of discovery of new protein sequences has surpassed that of structure and function determination by orders of magnitude. This is also true for metal-binding proteins, that is, proteins that bind one or more metal atoms necessary for their biological function. While metal binding site geometry and composition have been extensively studied, no large scale investigation of metal-coordinating residue conservation has been pursued so far. In pursuing this analysis, we were able to corroborate anecdotal evidence that certain residues are preferred to others for binding to certain metals. The conservation of most metal-coordinating residues is correlated with residue preference in a statistically significant manner. Additionally, we also established a statistically significant difference in conservation between metal-coordinating and noncoordinating residues. These results could be useful for providing better insight to functional importance of metal-coordinating residues, possibly aiding metal binding site prediction and design, metal-protein complex structure prediction, drug discovery, as well as model fitting to electron-density maps produced by X-ray crystallography.

Dynamic alpha-helices: conformations that do not conform

Structural transitions are important for the stability and function of proteins, but these phenomena are poorly understood. An extensive analysis of Protein Data Bank entries reveals 103 regions in proteins with a tendency to transform from helical to nonhelical conformation and vice versa. We find that these dynamic helices, unlike other helices, are depleted in hydrophobic residues. Furthermore, the dynamic helices have higher surface accessibility and conformational mobility (P-value = 3.35e-07) than the rigid helices. Contact analyses show that these transitions result from protein-ligand, protein-nucleic acid, and crystal-contacts. The immediate structural environment differs quantitatively (P-value = 0.003) as well as qualitatively in the two alternate conformations. Often, dynamic helix experiences more contacts in its helical conformation than in the nonhelical counterpart (P-value = 0.001). There is differential preference for the type of short contacts observed in two conformational states. We also demonstrate that the regions in protein that can undergo such large conformational transitions can be predicted with a reasonable accuracy using logistic regression model of supervised learning. Our findings have implications in understanding the molecular basis of structural transitions that are coupled with binding and are important for the function and stability of the protein. Based on our observations, we propose that several functionally relevant regions on the protein surface can switch over their conformation from coil to helix and vice-versa, to regulate the recognition and binding of their partner and hence these may work as "molecular switches" in the proteins to regulate certain biological process. Our results supports the idea that protein structure-function paradigm should transform from static to a highly dynamic one.

Robust recognition of zinc binding sites in proteins

Metals play a variety of roles in biological processes, and hence their presence in a protein structure can yield vital functional information. Because the residues that coordinate a metal often undergo conformational changes upon binding, detection of binding sites based on simple geometric criteria in proteins without bound metal is difficult. However, aspects of the physicochemical environment around a metal binding site are often conserved even when this structural rearrangement occurs. We have developed a Bayesian classifier using known zinc binding sites as positive training examples and nonmetal binding regions that nonetheless contain residues frequently observed in zinc sites as negative training examples. In order to allow variation in the exact positions of atoms, we average a variety of biochemical and biophysical properties in six concentric spherical shells around the site of interest. At a specificity of 99.8%, this method achieves 75.5% sensitivity in unbound proteins at a positive predictive value of 73.6%. We also test its accuracy on predicted protein structures obtained by homology modeling using templates with 30%-50% sequence identity to the target sequences. At a specificity of 99.8%, we correctly identify at least one zinc binding site in 65.5% of modeled proteins. Thus, in many cases, our model is accurate enough to identify metal binding sites in proteins of unknown structure for which no high sequence identity homologs of known structure exist. Both the source code and a Web interface are available to the public at http://feature.stanford.edu/metals.

Structural insights into protein–uranyl interaction: towards an in silico detection method

Documenting the modes of interaction of uranyl (UO(2)2+) with large biomolecules, and particularly with proteins, is instrumental for the interpretation of its behavior in vitro and in vivo. The gathering of three-dimensional information concerning uranyl-first shell atoms from two structural databases, the Cambridge Structural Databank and the Protein Data Bank (PDB) allowed a screening of corresponding topologies in proteins of known structure. In the computer-aided procedure, all potentially bound residues from the template structure were granted full flexibility using a rotamer library. The Amber force-field was used to loosen constraints and score each predicted site. Our algorithm was validated as a first stage through the recognition of existing experimental data in the PDB. The coherent localization of missing atoms in the density map of an ambiguous uranium/uranyl-protein complex exemplified the efficiency of our approach, which is currently suggesting the experimental investigation of uranyl-protein binding site.

Role of Na+and K+in Enzyme Function

Metal complexation is a key mediator or modifier of enzyme structure and function. In addition to divalent and polyvalent metals, group IA metals Na+and K+play important and specific roles that assist function of biological macromolecules. We examine the diversity of monovalent cation (M+)-activated enzymes by first comparing coordination in small molecules followed by a discussion of theoretical and practical aspects. Select examples of enzymes that utilize M+as a cofactor (type I) or allosteric effector (type II) illustrate the structural basis of activation by Na+and K+, along with unexpected connections with ion transporters. Kinetic expressions are derived for the analysis of type I and type II activation. In conclusion, we address evolutionary implications of Na+binding in the trypsin-like proteases of vertebrate blood coagulation. From this analysis, M+complexation has the potential to be an efficient regulator of enzyme catalysis and stability and offers novel strategies for protein engineering to improve enzyme function.

Computational tools for the analysis of heteroatom groups and their neighbours in protein tertiary structure

A number of Protein Data Bank (PDB) entries contain heteroatoms defined as HETATM. These include the atomic co-ordinates mainly for heteroatom groups, such as cofactors, coenzymes, prosthetic groups, metal ions, sugars, drugs, peptides, heavy-atom derivatives, non-standard amino acid residues/nucleotides, water molecules and so on. In order to evaluate the different heteroatom (Het) groups and their distribution in protein tertiary structure, we have extracted these from all proteins in the PDB and provided the data in an easily accessible format at the following website. The data can be queried on the PDB code, protein name/description, Het Group code or Het Group name. Further, we have also developed a web-based software application that reports neighbouring atoms evaluated by a "user-defined" distance cut-off value (in Angstrom units), either between a specific Het Group or all Het Groups in a given PDB with amino acid residues and water molecules in the corresponding protein, or neighbours for only all the amino acid residues in the given PDB with respect to Het Groups and water molecules. Together, the database and software applications are useful to gather information that can be further analyzed in order to obtain insights into the preferred interactions of heteroatom groups in proteins, study their binding mode, design novel molecules or to annotate protein function.