Bioinformatics in bioinorganic chemistry

Unravelling neurological disorders through metallomics-based approaches

Understanding the biological process involving metals and biomolecules in the brain is essential for establishing the origin of neurological disorders, such as neurodegenerative and psychiatric diseases. From this perspective, this critical review presents recent advances in this topic, showing possible mechanisms involving the disruption of metal homeostasis and the pathogenesis of neurological disorders. We also discuss the main challenges observed in metallomics studies associated with neurological disorders, including those related to sample preparation and analyte quantification.

Bio-coordination of bismuth in Helicobacter pylori revealed by immobilized metal affinity chromatography

Over 300 Bi-binding peptides from 166 proteins in H. pylori were identified by Bi-IMAC. Bi(3+) exhibits high selectivity towards peptide enriched by cysteines and histidines with dominated motif patterns of CXnC, CXnH and HXnH. Structural rationalization and functional categorization on the identified Bi-binding peptides and proteins provide an insight into the inhibitory action of bismuth drugs.

Metallomics of two microorganisms relevant to heavy metal bioremediation reveal fundamental differences in metal assimilation and utilization

Although as many as half of all proteins are thought to require a metal cofactor, the metalloproteomes of microorganisms remain relatively unexplored. Microorganisms from different environments are likely to vary greatly in the metals that they assimilate, not just among the metals with well-characterized roles but also those lacking any known function. Herein we investigated the metal utilization of two microorganisms that were isolated from very similar environments and are of interest because of potential roles in the immobilization of heavy metals, such as uranium and chromium. The metals assimilated and their concentrations in the cytoplasm of Desulfovibrio vulgaris strain Hildenborough (DvH) and Enterobacter cloacae strain Hanford (EcH) varied dramatically, with a larger number of metals present in Enterobacter. For example, a total of 9 and 19 metals were assimilated into their cytoplasmic fractions, respectively, and DvH did not assimilate significant amounts of zinc or copper whereas EcH assimilated both. However, bioinformatic analysis of their genome sequences revealed a comparable number of predicted metalloproteins, 813 in DvH and 953 in EcH. These allowed some rationalization of the types of metal assimilated in some cases (Fe, Cu, Mo, W, V) but not in others (Zn, Nd, Ce, Pr, Dy, Hf and Th). It was also shown that U binds an unknown soluble protein in EcH but this incorporation was the result of extracellular U binding to cytoplasmic components after cell lysis.

Biomarkers to assess the efficiency of treatment with platinum-based drugs: what can metallomics add?

Since the approval of cisplatin as an antineoplastic drug, the medical and the scientific communities have been concerned about the side effects of platinum-based drugs, and this has been the dose-limiting factor that leads to reduced treatment efficiency. Another important issue is the intrinsic or acquired resistance of some patients to treatment. Identifying proper biomarkers is crucial in evaluating the efficiency of a treatment, assisting physicians in determining, at early stages, whether or not the patient presents resistance to the drug, minimizing severe side effects, and allowing them to redirect the established course of chemotherapy. A great effort is being made to identify biomarkers that can be used to predict the outcome of the treatment of cancer patients with platinum-based drugs. In this context, the metallomic approach has not yet been used to its full potential. Since the basis of these drugs is platinum, the monitoring of biomarkers containing this metal should be the natural approach to evaluate treatment progress. This review intends to show where the research in this field stands and points out some gaps that can be filled by metallomics.

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

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

Calciomics: integrative studies of Ca2+-binding proteins and their interactomes in biological systems

Calcium ion (Ca(2+)), the fifth most common chemical element in the earth's crust, represents the most abundant mineral in the human body. By binding to a myriad of proteins distributed in different cellular organelles, Ca(2+) impacts nearly every aspect of cellular life. In prokaryotes, Ca(2+) plays an important role in bacterial movement, chemotaxis, survival reactions and sporulation. In eukaryotes, Ca(2+) has been chosen through evolution to function as a universal and versatile intracellular signal. Viruses, as obligate intracellular parasites, also develop smart strategies to manipulate the host Ca(2+) signaling machinery to benefit their own life cycles. This review focuses on recent advances in applying both bioinformatic and experimental approaches to predict and validate Ca(2+)-binding proteins and their interactomes in biological systems on a genome-wide scale (termed "calciomics"). Calmodulin is used as an example of Ca(2+)-binding protein (CaBP) to demonstrate the role of CaBPs on the regulation of biological functions. This review is anticipated to rekindle interest in investigating Ca(2+)-binding proteins and Ca(2+)-modulated functions at the systems level in the post-genomic era.

Zinc biochemistry: from a single zinc enzyme to a key element of life

The nutritional essentiality of zinc for the growth of living organisms had been recognized long before zinc biochemistry began with the discovery of zinc in carbonic anhydrase in 1939. Painstaking analytical work then demonstrated the presence of zinc as a catalytic and structural cofactor in a few hundred enzymes. In the 1980s, the field again gained momentum with the new principle of "zinc finger" proteins, in which zinc has structural functions in domains that interact with other biomolecules. Advances in structural biology and a rapid increase in the availability of gene/protein databases now made it possible to predict zinc-binding sites from metal-binding motifs detected in sequences. This procedure resulted in the definition of zinc proteomes and the remarkable estimate that the human genome encodes ∼3000 zinc proteins. More recent developments focus on the regulatory functions of zinc(II) ions in intra- and intercellular information transfer and have tantalizing implications for yet additional functions of zinc in signal transduction and cellular control. At least three dozen proteins homeostatically control the vesicular storage and subcellular distribution of zinc and the concentrations of zinc(II) ions. Novel principles emerge from quantitative investigations on how strongly zinc interacts with proteins and how it is buffered to control the remarkably low cellular and subcellular concentrations of free zinc(II) ions. It is fair to conclude that the impact of zinc for health and disease will be at least as far-reaching as that of iron.

Zinc and the zinc proteome

Zinc(II) ions are catalytic, structural, and regulatory cofactors in proteins. In contrast to painstakingly collecting the pieces by isolating and characterizing zinc proteins, 'omics' approaches are now allowing us to tease out information about zinc proteins from genomes and to piece together the information to a broader knowledge and appreciation of the role of zinc in biology. Estimates for the number of zinc proteins in the human genome and in genomes of other organisms have been derived from a bioinformatics approach: mining sequence databases for homologies of known zinc-coordination motifs with characteristic ligand signatures for metal binding and combining this information with the knowledge about metal-binding domains of proteins. This approach resulted in an impressive number of almost 3000 human zinc proteins and made major contributions to our understanding of the composition of the zinc proteome and the functions of zinc proteins. However, the impact of zinc on protein science is even greater. Predictions do not include yet undiscovered ligand signatures, coordination environments that employ complex binding patterns with nonsequential binding of ligands and ligand bridges, zinc/protein interactions at protein interfaces, and transient interactions of zinc(II) ions with proteins that are not known to be zinc proteins. All this information and recent discoveries of how cellular zinc is controlled and how zinc(II) ions function as signaling ions add an hitherto unrecognized dimension to the zinc proteome of multicellular eukaryotic organisms. Zinc proteomics employs a combination of approaches from different disciplines, such as bioinformatics, biology, inorganic biochemistry, and significantly, analytical and structural chemistry. It provides crucial large-scale datasets for interpreting the roles of zinc in health and disease at both a molecular and a global, systems biology, level.

Predicting metal-binding sites from protein sequence

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

MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence

MetalDetector identifies CYS and HIS involved in transition metal protein binding sites, starting from sequence alone. A major new feature of release 2.0 is the ability to predict which residues are jointly involved in the coordination of the same metal ion. The server is available at http://metaldetector.dsi.unifi.it/v2.0/.