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The amount of data needed to effectively train modern deep neural architectures has grown significantly, leading to increased computational requirements. These intensive computations are tackled by the combination of last generation computing resources, such as accelerators, or classic processing units. Nevertheless, gradient communication remains as the major bottleneck, hindering the efficiency notwithstanding the improvements in runtimes obtained through data parallelism strategies. Data parallelism involves all processes in a global exchange of potentially high amount of data, which may impede the achievement of the desired speedup and the elimination of noticeable delays or bottlenecks. As a result, communication latency issues pose a significant challenge that profoundly impacts the performance on distributed platforms. This research presents node-based optimization steps to significantly reduce the gradient exchange between model replicas whilst ensuring model convergence. The proposal serves as a versatile communication scheme, suitable for integration into a wide range of general-purpose deep neural network (DNN) algorithms. The optimization takes into consideration the specific location of each replica within the platform. To demonstrate the effectiveness, different neural network approaches and datasets with disjoint properties are used. In addition, multiple types of applications are considered to demonstrate the robustness and versatility of our proposal. The experimental results show a global training time reduction whilst slightly improving accuracy. Code: https://github.com/mhaut/eDNNcomm.

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Morphological attribute profiles are multilevel decompositions of images obtained with a sequence of transformations performed by connected operators. They have been extensively employed in performing multi-scale and region-based analysis in a large number of applications. One main, still unresolved, issue is the selection of filter parameters able to provide representative and non-redundant threshold decomposition of the image. This paper presents a framework for the automatic selection of filter thresholds based on Granulometric Characteristic Functions (GCFs). GCFs describe the way that non-linear morphological filters simplify a scene according to a given measure. Since attribute filters rely on a hierarchical representation of an image (e.g., the Tree of Shapes) for their implementation, GCFs can be efficiently computed by taking advantage of the tree representation. Eventually, the study of the GCFs allows the identification of a meaningful set of thresholds. Therefore, a trial and error approach is not necessary for the threshold selection, automating the process and in turn decreasing the computational time. It is shown that the redundant information is reduced within the resulting profiles (a problem of high occurrence, as regards manual selection). The proposed approach is tested on two real remote sensing data sets, and the classification results are compared with strategies present in the literature.

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We have developed a database search tool to identify metal sites having structural similarity to a query metal site structure within the MetalPDB database of minimal functional sites (MFSs) contained in metal-binding biological macromolecules. MFSs describe the local environment around the metal(s) independently of the larger context of the macromolecular structure. Such a local environment has a determinant role in tuning the chemical reactivity of the metal, ultimately contributing to the functional properties of the whole system. The database search tool, which we called MetalS(3) (Metal Sites Similarity Search), can be accessed through a Web interface at http://metalweb.cerm.unifi.it/tools/metals3/ . MetalS(3) uses a suitably adapted version of an algorithm that we previously developed to systematically compare the structure of the query metal site with each MFS in MetalPDB. For each MFS, the best superposition is kept. All these superpositions are then ranked according to the MetalS(3) scoring function and are presented to the user in tabular form. The user can interact with the output Web page to visualize the structural alignment or the sequence alignment derived from it. Options to filter the results are available. Test calculations show that the MetalS(3) output correlates well with expectations from protein homology considerations. Furthermore, we describe some usage scenarios that highlight the usefulness of MetalS(3) to obtain mechanistic and functional hints regardless of homology.

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Approximately one-third of all proteins have been estimated to contain at least one metal cofactor, and these proteins are referred to as metalloproteins. These represent one of the most diverse classes of proteins, containing metal ions that bind to specific sites to perform catalytic, regulatory and structural functions. Bioinformatic tools have been developed to predict metalloproteins encoded by an organism based only on its genome sequence. Its function and the type of metal binder can also be predicted via a bioinformatics approach. Paracoccidioides complex includes termodimorphic pathogenic fungi that are found as saprobic mycelia in the environment and as yeast, the parasitic form, in host tissues. They are the etiologic agents of Paracoccidioidomycosis, a prevalent systemic mycosis in Latin America. Many metalloproteins are important for the virulence of several pathogenic microorganisms. Accordingly, the present work aimed to predict the copper, iron and zinc proteins encoded by the genomes of three phylogenetic species of Paracoccidioides (Pb01, Pb03, and Pb18). The metalloproteins were identified using bioinformatics approaches based on structure, annotation and domains. Cu-, Fe-, and Zn-binding proteins represent 7% of the total proteins encoded by Paracoccidioides spp. genomes. Zinc proteins were the most abundant metalloproteins, representing 5.7% of the fungus proteome, whereas copper and iron proteins represent 0.3 and 1.2%, respectively. Functional classification revealed that metalloproteins are related to many cellular processes. Furthermore, it was observed that many of these metalloproteins serve as virulence factors in the biology of the fungus. Thus, it is concluded that the Cu, Fe, and Zn metalloproteomes of the Paracoccidioides spp. are of the utmost importance for the biology and virulence of these particular human pathogens.

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We developed a new software tool, MetalS(2), for the structural alignment of Minimal Functional Sites (MFSs) in metal-binding biological macromolecules. MFSs are 3D templates that describe the local environment around the metal(s) independently of the larger context of the macromolecular structure. Such local environment has a determinant role in tuning the chemical reactivity of the metal, ultimately contributing to the functional properties of the whole system. On our example data sets, MetalS(2) unveiled structural similarities that other programs for protein structure comparison do not consistently point out and overall identified a larger number of structurally similar MFSs. MetalS(2) supports the comparison of MFSs harboring different metals and/or with different nuclearity and is available both as a stand-alone program and a Web tool ( http://metalweb.cerm.unifi.it/tools/metals2/).

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We present here MetalPDB (freely accessible at http://metalweb.cerm.unifi.it), a novel resource aimed at conveying the information available on the three-dimensional (3D) structures of metal-binding biological macromolecules in a consistent and effective manner. This is achieved through the systematic and automated representation of metal-binding sites in proteins and nucleic acids by way of Minimal Functional Sites (MFSs). MFSs are 3D templates that describe the local environment around the metal(s) independently of the larger context of the macromolecular structure embedding the site(s), and are the central objects of MetalPDB design. MFSs are grouped into equistructural (broadly defined as sites found in corresponding positions in similar structures) and equivalent sites (equistructural sites that contain the same metals), allowing users to easily analyse similarities and variations in metal-macromolecule interactions, and to link them to functional information. The web interface of MetalPDB allows access to a comprehensive overview of metal-containing biological structures, providing a basis to investigate the basic principles governing the properties of these systems. MetalPDB is updated monthly in an automated manner.

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UNLABELLED: Metals are essential for the structure and function of many proteins and nucleic acids. The geometrical arrangement of the atoms that coordinate a metal in a biological macromolecule is an important determinant of the specificity and role of that metal. At present, however, this information can be retrieved only from the literature, which sometimes contains an improper or incorrect description of the geometry, and often lacks it altogether. Thus, we developed FindGeo to quickly and easily determine the coordination geometry of selected, or all, metals in a given structure. FindGeo works by superimposing the metal-coordinating atoms in the input structure to a library of templates with alternative ideal geometries, which are ranked by RMSD to identify the best geometry assignment. AVAILABILITY: FindGeo is freely available as a web service and as a stand-alone program at http://metalweb.cerm.unifi.it/tools/findgeo/.

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Zinc is indispensable to all forms of life as it is an essential component of many different proteins involved in a wide range of biological processes. Not differently from other metals, zinc in proteins can play different roles that depend on the features of the metal-binding site. In this work, we describe zinc sites in proteins with known structure by means of three-dimensional templates that can be automatically extracted from PDB files and consist of the protein structure around the metal, including the zinc ligands and the residues in close spatial proximity to the ligands. This definition is devised to intrinsically capture the features of the local protein environment that can affect metal function, and corresponds to what we call a minimal functional site (MFS). We used MFSs to classify all zinc sites whose structures are available in the PDB and combined this classification with functional annotation as available in the literature. We classified 77% of zinc sites into ten clusters, each grouping zinc sites with structures that are highly similar, and an additional 16% into seven pseudo-clusters, each grouping zinc sites with structures that are only broadly similar. Sites where zinc plays a structural role are predominant in eight clusters and in two pseudo-clusters, while sites where zinc plays a catalytic role are predominant in two clusters and in five pseudo-clusters. We also analyzed the amino acid composition of the coordination sphere of zinc as a function of its role in the protein, highlighting trends and exceptions. In a period when the number of known zinc proteins is expected to grow further with the increasing awareness of the cellular mechanisms of zinc homeostasis, this classification represents a valuable basis for structure-function studies of zinc proteins, with broad applications in biochemistry, molecular pharmacology and de novo protein design.

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Sco proteins are present in all types of organisms, including the vast majority of eukaryotes and many prokaryotes. It is well established that Sco proteins in eukaryotes are involved in the assembly of the Cu(A) cofactor of mitochondrial cytochrome c oxidase; however their precise role in this process has not yet been elucidated at the molecular level. In particular, some but not all eukaryotes including humans possess two Sco proteins whose individual functions remain unclear. There is evidence that eukaryotic Sco proteins are also implicated in other cellular processes such as redox signalling and regulation of copper homeostasis. The range of physiological functions of Sco proteins appears to be even wider in prokaryotes, where Sco-encoding genes have been duplicated many times during evolution. While some prokaryotic Sco proteins are required for the biosynthesis of cytochrome c oxidase, others are most likely to take part in different processes such as copper delivery to other enzymes and protection against oxidative stress. The detailed understanding of the multiplicity of roles ascribed to Sco proteins requires the identification of the subtle determinants that modulate the two properties central to their known and potential functions, i.e. copper binding and redox properties. In this review, we provide a comprehensive summary of the current knowledge on Sco proteins gained by genetic, structural and functional studies on both eukaryotic and prokaryotic homologues, and propose some hints to unveil the elusive molecular mechanisms underlying their functions.

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A biochemical pathway can be viewed as a series of chemical reactions occurring within a cell, each of which is carried out by one or more biological macromolecules (protein, RNA, or complexes thereof). Computational methods can be applied to assess whether one organism is able to perform a biochemical process of interest by checking whether its genome encodes all the components that are known to be necessary for the task. Here we present a simple strategy for collecting the above data that is based on, but not limited to, our experience on processes involving metal ions and metal-binding cofactors. The strategy is fully implemented in a bioinformatics package, Retrieval of Domains and Genome Browsing (RDGB), which is available from http://www.cerm.unifi.it/home/research/genomebrowsing.html . The use of RDGB allows users to perform all the operations that are needed to implement the aforementioned strategy with minimal intervention and to gather all results in an ordered manner, with a tabular summary. This minimizes the (bio)informatics needed, thus facilitating its use by nonexperts. As examples, we analyzed the pathways for the degradation of organic compounds containing one or two aromatic rings as well as the distribution of some proteins involved in Cu(A) assembly in more than a thousand prokaryotes.

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Cytochromes c are very widespread proteins that play key roles in the electron transfer events associated to a wide variety of physiological redox processes. The function of cytochromes c is, at the broad level, to interact with different partners in order to allow electrons to flow from one protein to another. Here, we focused our attention on the protein-protein interactions that involve mono-heme cytochrome c domains in order to identify possible general vs. specific patterns of intermolecular interactions at the structural level. We observed that a number of physico-chemical properties are statistically different in transient vs. permanent and fused complexes. These include the extent of the protein interface area, the amino acid composition and the packing density at the interface. The understanding of the features of transient redox complexes is of particular importance because of the difficulty of obtaining co-crystals that preserve the physiologically relevant configuration. In addition, we identified three different structural modes of interaction that cover all the structurally characterized cytochrome c interactions except one. The mode of interaction does not correlate with the nature of the complex (transient, permanent, fused). Regardless of the mode of interaction, the distance between the heme iron and the partner metal center or organic cofactor center of mass is typically around 19-20 Å for complexes permitting direct electron transfer between the two sites.

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Bioinformatics is a central discipline in modern life sciences aimed at describing the complex properties of living organisms starting from large-scale data sets of cellular constituents such as genes and proteins. In order for this wealth of information to provide useful biological knowledge, databases and software tools for data collection, analysis and interpretation need to be developed. In this paper, we review recent advances in the design and implementation of bioinformatics resources devoted to the study of metals in biological systems, a research field traditionally at the heart of bioinorganic chemistry. We show how metalloproteomes can be extracted from genome sequences, how structural properties can be related to function, how databases can be implemented, and how hints on interactions can be obtained from bioinformatics.

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Twin CX(9)C proteins are eukaryotic proteins that derive their name from their characteristic motif, consisting of two pairs of cysteines that form two disulfide bonds stabilizing a coiled coil-helix-coiled coil-helix (CHCH) fold. The best characterized of these proteins are Cox17, a copper chaperone acting in cytochrome c oxidase biogenesis, and Mia40, the central component of a system for protein import into the mitochondrial inter-membrane space (IMS). However, the range of possible functions for these proteins is unclear. Here, we performed a systematic search of twin CX(9)C proteins in eukaryotic organisms, and classified them into groups of putative homologues, by combining bioinformatics methods with literature analysis. Our results suggest that the functions of most twin CX(9)C proteins vary around the common theme of playing a scaffolding role, which can tie their observed roles in mitochondrial structure and function. This study will enhance the present annotation of eukaryotic proteomes, and will provide a rational basis for future experimental work aimed at a deeper understanding of this remarkable class of proteins.

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Multiheme c-type cytochromes (MHCs) are metalloproteins that can play various biochemical roles, including enzymatic activity and electron transfer. As electron transfer proteins, the presence of multiple heme cofactors in the vicinity allows electrons to rapidly travel relatively long distances. MHCs are often characterized by relatively low structural complexity, with the heme cofactors being largely responsible for maintaining the structure in place, owing to the protein-heme covalent linkages. In this work, we analyzed an extensive ensemble of 594 complete prokaryotic proteomes, amounting to more than 1.9 million sequences, to characterize their content in MHCs. We identified 1,659 MHCs in 258 organisms. The presence of MHCs was found to correlate quite well with the capability of an organism to synthesize or take up heme. For two organisms, the presence of MHCs in the proteome could be taken as a hint to the presence of divergent heme uptake pathways. The most common numbers of heme-binding motifs in a sequence were four (25%) and two (23%), followed by five (13%) and ten (9.8%). The average protein-to-heme ratio was relatively similar for all MHCs, except diheme proteins, regardless of the number of motifs at around 60 +/- 30. The latter ratio could in favorable cases be a useful indicator for functional assignments of novel MHCs. Finally, we showed that the amount of structural information currently available for MHCs is limited with respect to the diversity of this broad class of metalloproteins. Experimental efforts in the structural investigation of MHCs are thus warranted.

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SUMMARY: Metal-MACiE is a new publicly available web-based database, held in MySQL, which aims to organize the available information on the properties and the roles of metals in the context of the catalytic mechanisms of metalloenzymes. Metal-MACiE, which currently covers 75% of metal-dependent enzyme commission (EC) sub-sub-classes and is continuously growing, exploits the existing MACiE database for the annotation of the reaction mechanisms. The two databases constitute complementary sources of information for enzymology, biochemistry and molecular pharmacology studies. AVAILABILITY: http://www.ebi.ac.uk/thornton-srv/databases/Metal_MACiE/home.html.

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In metalloproteins, the protein environment modulates metal properties to achieve the required goal, which can be protein stabilization or function. The analysis of metal sites at the atomic level of detail provided by protein structures can thus be of benefit in functional and evolutionary studies of proteins. In this work, we propose a structural bioinformatics approach to the study of metalloproteins based on structural templates of metal sites that include the PDB coordinates of protein residues forming the first and the second coordination sphere of the metal. We have applied this approach to non-heme iron sites, which have been analyzed at various levels. Templates of sites located in different protein domains have been compared, showing that similar sites can be found in unrelated proteins as the result of convergent evolution. Templates of sites located in proteins of a large superfamily have been compared, showing possible mechanisms of divergent evolution of proteins to achieve different functions. Furthermore, template comparisons have been used to predict the function of uncharacterized proteins, showing that similarity searches focused on metal sites can be advantageously combined with typical whole-domain comparisons. Structural templates of metal sites, finally, may constitute the basis for a systematic classification of metalloproteins in databases.

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Heme is the prosthetic group of many proteins that carry out a variety of key biological functions. In addition, for many pathogenic organisms, heme (acquired from the host) may constitute a very important source of iron. Organisms can meet their heme demands by taking it up from external sources, by producing the cofactor through a dedicated biosynthetic pathway, or both. Here we analyzed the distribution of proteins specifically involved in the processes of heme biosynthesis and heme uptake in 474 prokaryotic organisms. These data allowed us to identify which organisms are capable of performing none, one, or both processes, based on the similarity to known systems. Some specific instances where one or more proteins along the pathways had unusual modifications were singled out. For two key protein domains involved in heme uptake, we could build a series of structural models, which suggested possible alternative modes of heme binding. Future directions for experimental work are given.

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We analysed the roles and distribution of metal ions in enzymatic catalysis using available public databases and our new resource Metal-MACiE (http://www.ebi.ac.uk/thornton-srv/databases/Metal_MACiE/home.html). In Metal-MACiE, a database of metal-based reaction mechanisms, 116 entries covering 21% of the metal-dependent enzymes and 70% of the types of enzyme-catalysed chemical transformations are annotated according to metal function. We used Metal-MACiE to assess the functions performed by metals in biological catalysis and the relative frequencies of different metals in different roles, which can be related to their individual chemical properties and availability in the environment. The overall picture emerging from the overview of Metal-MACiE is that redox-inert metal ions are used in enzymes to stabilize negative charges and to activate substrates by virtue of their Lewis acid properties, whereas redox-active metal ions can be used both as Lewis acids and as redox centres. Magnesium and zinc are by far the most common ions of the first type, while calcium is relatively less used. Magnesium, however, is most often bound to phosphate groups of substrates and interacts with the enzyme only transiently, whereas the other metals are stably bound to the enzyme. The most common metal of the second type is iron, which is prevalent in the catalysis of redox reactions, followed by manganese, cobalt, molybdenum, copper and nickel. The control of the reactivity of redox-active metal ions may involve their association with organic cofactors to form stable units. This occurs sometimes for iron and nickel, and quite often for cobalt and molybdenum.

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System-level understanding of living organisms has been a long-standing goal of biological sciences. However, it was only recently that this possibility became concrete, by virtue of the development of technology platforms for the production of "omics" data from multiple experimental sources. Data sets such as those from genomics and proteomics are endowing researchers with an unprecedented view of the molecular constituents of cells and of their interactions, forming the basis to pursue the comprehension of how the concerted action of such components can determine biological functions. Within this challenge, bioinorganic chemistry is invested with a renewed significance, being called to place its distinctive subject matter, namely, the study of the interactions between inorganic and biological molecules, in a system-wide perspective. The first step to take in this direction is the construction of "omics" data sets for metalloproteins (metalloproteomics) that can be fruitfully integrated with other protein-centered "omics" data. While looking forward to the progress of high-throughput experimental techniques to accomplish this task, theoretical methods are yielding valuable predictions as to the number of metalloproteins encoded in various genomes. The integrated use of these and others "omics" data can be extremely useful to model complex cellular processes involving metals. Here, we review the current knowledge on copper homeostasis and the assembly of cytochrome c oxidase to exemplify the kind of important processes which need to be studied at the system level. The long-term goal of this approach is the overall description of how metals are framed as essential factors within living cells, which in fact is the ultimate purpose of bioinorganic chemistry.

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A novel ArsR-SmtB family transcriptional repressor, KmtR, has been characterized from mycobacteria. Mutants of Mycobacterium tuberculosis lacking kmtR show elevated expression of Rv2025c encoding a deduced CDF-family metal exporter. KmtR-dependent repression of the cdf and kmtR operator-promoters was alleviated by nickel and cobalt in minimal medium. Electrophoretic mobility shift assays and fluorescence anisotropy show binding of purified KmtR to nucleotide sequences containing a region of dyad symmetry from the cdf and kmtR operator-promoters. Incubation of KmtR with cobalt inhibits DNA complex assembly and metal-protein binding was confirmed. KmtR is the second, to NmtR, characterized ArsR-SmtB sensor of nickel and cobalt from M. tuberculosis suggesting special significance for these ions in this pathogen. KmtR-dependent expression is elevated in complete medium with no increase in response to metals, whereas NmtR retains a response to nickel and cobalt under these conditions. KmtR has tighter affinities for nickel and cobalt than NmtR consistent with basal levels of these metals being sensed by KmtR but not NmtR in complete medium. More than a thousand genes encoding ArsR-SmtB-related proteins are listed in databases. KmtR has none of the previously defined metal-sensing sites. Substitution of His88, Glu101, His102, His110, or His111 with Gln generated KmtR variants that repress the cdf and kmtR operator-promoters even in elevated nickel and cobalt, revealing a new sensory site. Importantly, ArsR-SmtB sequence groupings do not correspond with the different sensory motifs revealing that only the latter should be used to predict metal sensing.

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