Functionally specified protein signatures distinctive for each of the different blue copper proteins

BACKGROUND: Proteins having similar functions from different sources can be identified by the occurrence in their sequences, a conserved cluster of amino acids referred to as pattern, motif, signature or fingerprint. The wide usage of protein sequence analysis in par with the growth of databases signifies the importance of using patterns or signatures to retrieve out related sequences. Blue copper proteins are found in the electron transport chain of prokaryotes and eukaryotes. The signatures already existing in the databases like the type 1 copper blue, multiple copper oxidase, cyt b/b6, photosystem 1 psaA&B, psaG&K, and reiske iron sulphur protein are not specified signatures for blue copper proteins as the name itself suggests. Most profile and motif databases strive to classify protein sequences into a broad spectrum of protein families. This work describes the signatures designed based on the copper metal binding motifs in blue copper proteins. The common feature in all blue copper proteins is a trigonal planar arrangement of two nitrogen ligands [each from histidine] and one sulphur containing thiolate ligand [from cysteine], with strong interactions between the copper center and these ligands. RESULTS: Sequences that share such conserved motifs are crucial to the structure or function of the protein and this could provide a signature of family membership. The blue copper proteins chosen for the study were plantacyanin, plastocyanin, cucumber basic protein, stellacyanin, dicyanin, umecyanin, uclacyanin, cusacyanin, rusticyanin, sulfocyanin, halocyanin, azurin, pseudoazurin, amicyanin and nitrite reductase which were identified in both eukaryotes and prokaryotes. ClustalW analysis of the protein sequences of each of the blue copper proteins was the basis for designing protein signatures or peptides. The protein signatures and peptides identified in this study were designed involving the active site region involving the amino acids bound to the copper atom. It was highly specific for each kind of blue copper protein and the false picks were minimized. The set of signatures designed specifically for the BCP's was entirely different from the existing broad spectrum signatures as mentioned in the background section. CONCLUSIONS: These signatures can be very useful for the annotation of uncharacterized proteins and highly specific to retrieve blue copper protein sequences of interest from the non redundant databases containing a large deposition of protein sequences

Predicting zinc binding at the proteome level

BACKGROUND: Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for structural purposes. Metal-binding properties remain difficult to predict as well as to investigate experimentally at the whole-proteome level. Consequently, the current knowledge about metalloproteins is only partial. RESULTS: The present work reports on the development of a machine learning method for the prediction of the zinc-binding state of pairs of nearby amino-acids, using predictors based on support vector machines. The predictor was trained using chains containing zinc-binding sites and non-metalloproteins in order to provide positive and negative examples. Results based on strong non-redundancy tests prove that (1) zinc-binding residues can be predicted and (2) modelling the correlation between the binding state of nearby residues significantly improves performance. The trained predictor was then applied to the human proteome. The present results were in good agreement with the outcomes of previous, highly manually curated, efforts for the identification of human zinc-binding proteins. Some unprecedented zinc-binding sites could be identified, and were further validated through structural modelling. The software implementing the predictor is freely available at: CONCLUSION: The proposed approach constitutes a highly automated tool for the identification of metalloproteins, which provides results of comparable quality with respect to highly manually refined predictions. The ability to model correlations between pairwise residues allows it to obtain a significant improvement over standard 1D based approaches. In addition, the method permits the identification of unprecedented metal sites, providing important hints for the work of experimentalists

A Less-Biased Analysis of Metalloproteins Reveals Novel Zinc Coordination Geometries

Zinc metalloproteins are involved in many biological processes and play crucial biochemical roles across all domains of life. Local structure around the zinc ion, especially the coordination geometry (CG), is dictated by the protein sequence and is often directly related to the function of the protein. Current methodologies in characterizing zinc metalloproteins\u27 CG consider only previously reported CG models based mainly on nonbiological chemical context. Exceptions to these canonical CG models are either misclassified or discarded as outliers. Thus, we developed a less-biased method that directly handles potential exceptions without pre-assuming any CG model. Our study shows that numerous exceptions could actually be further classified and that new CG models are needed to characterize them. Also, these new CG models are cross-validated by strong correlation between independent structural and functional annotation distance metrics, which is partially lost if these new CGs models are ignored. Furthermore, these new CG models exhibit functional propensities distinct from the canonical CG models

Prediction of Metal Ion Binding Sites in Proteins from Amino Acid Sequences by Using Simplified Amino Acid Alphabets and Random Forest Model

Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression’s studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc

Promises and Pitfalls of Metal Imaging in Biology

A picture may speak a thousand words, but if those words fail to form a coherent sentence there is little to be learned. As cutting-edge imaging technology now provides us the tools to decipher the multitude of roles played by metals and metalloids in molecular, cellular and developmental biology, as well as health and disease, it is time to reflect on the advances made in imaging, the limitations discovered, and the future of a burgeoning field. In this Perspective, the current state-of-the-art is discussed from a self-imposed contrarian position, as we not only highlight the major advances made of the years but use them as teachable moments to zoom in on challenges that remain to be overcome. We also describe the steps being taken towards being able to paint a completely undisturbed picture of cellular metal metabolism, which is, metaphorically speaking, the Holy Grail of the discipline