Academic literature on the topic 'Bioinformatics, metalloproteins, metal-binding proteins'

Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life.

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.

Abstract Motivation Molecular docking is a widely used technique for large-scale virtual screening of the interactions between small-molecule ligands and their target proteins. However, docking methods often perform poorly for metalloproteins due to additional complexity from the three-way interactions among amino-acid residues, metal ions and ligands. This is a significant problem because zinc proteins alone comprise about 10% of all available protein structures in the protein databank. Here, we developed GM-DockZn that is dedicated for ligand docking to zinc proteins. Unlike the existing docking methods developed specifically for zinc proteins, GM-DockZn samples ligand conformations directly using a geometric grid around the ideal zinc-coordination positions of seven discovered coordination motifs, which were found from the survey of known zinc proteins complexed with a single ligand. Results GM-DockZn has the best performance in sampling near-native poses with correct coordination atoms and numbers within the top 50 and top 10 predictions when compared to several state-of-the-art techniques. This is true not only for a non-redundant dataset of zinc proteins but also for a homolog set of different ligand and zinc-coordination systems for the same zinc proteins. Similar superior performance of GM-DockZn for near-native-pose sampling was also observed for docking to apo-structures and cross-docking between different ligand complex structures of the same protein. The highest success rate for sampling nearest near-native poses within top 5 and top 1 was achieved by combining GM-DockZn for conformational sampling with GOLD for ranking. The proposed geometry-based sampling technique will be useful for ligand docking to other metalloproteins. Availability and implementation GM-DockZn is freely available at www.qmclab.com/ for academic users. Supplementary information Supplementary data are available at Bioinformatics online.

Liquid-liquid phase separation (LLPS) is a rapidly growing research focus due to numerous demonstrations that many cellular proteins phase-separate to form biomolecular condensates (BMCs) that nucleate membraneless organelles (MLOs). A growing repertoire of mechanisms supporting BMC formation, composition, dynamics, and functions are becoming elucidated. BMCs are now appreciated as required for several steps of gene regulation, while their deregulation promotes pathological aggregates, such as stress granules (SGs) and insoluble irreversible plaques that are hallmarks of neurodegenerative diseases. Treatment of BMC-related diseases will greatly benefit from identification of therapeutics preventing pathological aggregates while sparing BMCs required for cellular functions. Numerous viruses that block SG assembly also utilize or engineer BMCs for their replication. While BMC formation first depends on prion-like disordered protein domains (PrLDs), metal ion-controlled RNA-binding domains (RBDs) also orchestrate their formation. Virus replication and viral genomic RNA (vRNA) packaging dynamics involving nucleocapsid (NC) proteins and their orthologs rely on Zinc (Zn) availability, while virus morphology and infectivity are negatively influenced by excess Copper (Cu). While virus infections modify physiological metal homeostasis towards an increased copper to zinc ratio (Cu/Zn), how and why they do this remains elusive. Following our recent finding that pan-retroviruses employ Zn for NC-mediated LLPS for virus assembly, we present a pan-virus bioinformatics and literature meta-analysis study identifying metal-based mechanisms linking virus-induced BMCs to neurodegenerative disease processes. We discover that conserved degree and placement of PrLDs juxtaposing metal-regulated RBDs are associated with disease-causing prion-like proteins and are common features of viral proteins responsible for virus capsid assembly and structure. Virus infections both modulate gene expression of metalloproteins and interfere with metal homeostasis, representing an additional virus strategy impeding physiological and cellular antiviral responses. Our analyses reveal that metal-coordinated virus NC protein PrLDs initiate LLPS that nucleate pan-virus assembly and contribute to their persistence as cell-free infectious aerosol droplets. Virus aerosol droplets and insoluble neurological disease aggregates should be eliminated by physiological or environmental metals that outcompete PrLD-bound metals. While environmental metals can control virus spreading via aerosol droplets, therapeutic interference with metals or metalloproteins represent additional attractive avenues against pan-virus infection and virus-exacerbated neurological diseases.

We present a summary of the quadrupolar metal ion NMR studies of metalloproteins conducted in our laboratory in recent years. The approaches we employ can be subdivided into two categories: (i) the use of low-frequency metal nuclei to probe metal ion binding sites in small proteins, exemplified by 43Ca NMR studies of alpha-lactalbumins and calcium-binding lysozymes, and (ii) the novel detection of the central transition of half-integer quadrupolar nuclei of moderate frequency bound to large metalloproteins, typified by 27Al, 45Sc, 69,71Ga, and 51V NMR studies of the transferrins. We highlight the chemical information regarding the nature of metal ion binding sites that can be obtained from this technique and emphasize the salient parameters that an investigator must consider to successfully apply quadrupolar NMR to the study of biological macromolecules.Key words: quadrupolar NMR, metalloproteins, transferrins.

1. Introduction 1681.1 Genomic annotation of metalloproteins 1681.2 Why NMR structures? 1681.3 Why paramagnetic metalloproteins? 1692. General theory 1702.1 Nuclear and electron spins 1702.2 Hyperfine coupling 1712.3 The effect of the hyperfine coupling on the NMR shift: the hyperfine shift 1732.4 The effect of the hyperfine coupling on nuclear relaxation 1742.5 Interplay between electron spin properties and features of the NMR spectra 1783. Paramagnetism-based structural restraints 1803.1 Contact shifts and relaxation rates as restraints 1813.2 Locating the metal ion within the protein frame: pseudocontact shifts 1843.3 Cross-correlation rates 1863.4 Residual dipolar couplings 1883.5 Interplay between different restraints 1904. NMR without1H detection 1914.1 The protocol for 13C-detected protonless assignment of backbone and side-chains 1944.2 Protonless heteronuclear NMR experiments tailored to paramagnetic systems 1965. The use of lanthanides as paramagnetic probes 1986. The case of Cu(II) proteins 2027. Perspectives 2088. Acknowledgments 2099. References 209Metalloproteins represent a large share of the proteome and many of them contain paramagnetic metal ions. The knowledge, at atomic resolution, of their structure in solution is important to understand processes in which they are involved, such as electron transfer mechanisms, enzymatic reactions, metal homeostasis and metal trafficking, as well as interactions with their partners. Formerly considered as unfeasible, the first structure in solution by nuclear magnetic resonance (NMR) of a paramagnetic protein was obtained in 1994. Methodological and instrumental advancements pursued over the last decade are such that NMR structure of paramagnetic proteins may be now routinely obtained. We focus here on approaches and problems related to the structure determination of paramagnetic proteins in solution through NMR spectroscopy. After a survey of the background theory, we show how the effects produced by the presence of a paramagnetic metal ion on the NMR parameters, which are in many cases deleterious for the detection of NMR spectra, can be overcome and turned into an additional source of structural restraints. We also briefly address features and perspectives given by the use of 13C-detected protonless NMR spectroscopy for proteins in solution. The structural information obtained through the exploitation of a paramagnetic center are discussed for some Cu2+-binding proteins and for Ca2+-binding proteins, where the replacement of a diamagnetic metal ion with suitable paramagnetic metal ions suggests novel approaches to the structural characterization of proteins containing diamagnetic and NMR-silent metal ions.

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.

A protein sequence is often insufficient for knowledge of the chemical formula and the properties of the mature molecule that perform its function. Post-translational modifications are very common and most of them cannot be predicted on the basis of the protein sequence alone. A very common chemical modification of proteins that is not directly encoded by a single gene is the complexation with metal cations. Here it is shown that the uptake of metal ions (calcium, cobalt, copper, iron, magnesium, manganese, nickel or zinc) by proteins can be predicted on the basis of the amino acid composition, by using a mixture of several simplified amino acid alphabets and by employing machine learning methods, with 70–90% accuracy, depending on the type of metal. Not only is it possible to predict if a protein requires a certain metal ion but it is also possible to discriminate amongst various metal species. These results are likely to be useful in structural proteomics, by improving the experiment success rate, and in comparative genomics, where it is interesting to compare metal-ion contents in different organisms. It is particularly important that these predictions can be made when homology-based annotations are impossible.

This article reviews the use of nuclear magnetic resonance methods of spin 1/2 metal nuclei to probe the metal binding site(s) in a variety of metalloproteins. The majority of the studies have involved native Zn(II) and Ca(II) metalloproteins where there has been isostructural substitution of these metal ions with the I = 1/2 111/113Cd(II) ion. Also included are recent studies that have utilized the 109Ag(I) ion to probe Cu(I) sites in yeast metallothionein and 199Hg(II) as a probe of the metal binding sites in mercury resistance proteins. Pertinent aspects for the optimal execution of these experiments along with the procedures for the metal substitution reactions are discussed together with the presentation of a 113Cd chemical shift correlation map with ligand type and coordination number. Specific examples of protein systems studied using the 111/113Cd and 109Ag nuclei include the metallothionein superfamily of Zn(II)- and Cu(I)-binding proteins from mammalian, invertebrate, and yeast systems. In addition to the structural features revealed by these metal ion nuclear magnetic resonance studies, important new information is frequently provided about the dynamics at the active-site metal ion. In an effort for completeness, other less frequently used spin 1/2 metal nuclei are mentioned.Key words: metallothionein, 111/113Cd, 199Hg, 109Ag, 57Fe, 205Tl, 195Pt, 207Pb, 119Sn, nuclear magnetic resonance.

Szeto Tsz Kwan Leo.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.<br>Includes bibliographical references (leaves 112-120).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>摘要 --- p.iii<br>Acknowledgements --- p.v<br>Table of Contents --- p.vi<br>List of Tables --- p.ix<br>List of Figures --- p.x<br>Abbreviations --- p.xii<br>Chapter Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Heavy metals contaminations in Shing Mun River --- p.1<br>Chapter 1.1 --- Importance of copper regulation and role of liverin copper metabolism --- p.6<br>Chapter 1.1.1 --- Role of copper --- p.6<br>Chapter 1.1.2 --- Toxicity due to unbalanced copper regulation --- p.7<br>Chapter 1.1.3 --- Function of liver in copper detoxification --- p.9<br>Chapter 1.2 --- Aims and rationale of this research --- p.11<br>Chapter Chapter 2 --- Heavy metal concentrations of tilapia samples collected from Shing Mun River --- p.12<br>Chapter 2.1 --- Introduction --- p.12<br>Chapter 2.1.1 --- Sampling sites - Fo Tan and Siu Lek Yuen Nullah --- p.12<br>Chapter 2.1.2 --- Tilapia samples collected from the sites --- p.16<br>Chapter 2.1.3 --- Tilapia as a study model --- p.18<br>Chapter 2.1.4 --- Bioavailability of heavy metals in water --- p.19<br>Chapter 2.1.5 --- Metal content in liver --- p.20<br>Chapter 2.1.6 --- Aim of this chapter --- p.20<br>Chapter 2.2 --- Materials and Methods --- p.22<br>Chapter 2.2.1 --- Collection of control and field samples --- p.22<br>Chapter 2.2.2 --- Heavy metal concentrations determination --- p.23<br>Chapter 2.2.3 --- Homogenization of liver cells --- p.24<br>Chapter 2.2.4 --- Subcellular fractionation --- p.24<br>Chapter 2.2.5 --- Determination of copper and zinc content in each subcellular fraction --- p.253<br>Chapter 2.3 --- Results --- p.27<br>Chapter 2.3.1 --- Physical data --- p.27<br>Chapter 2.3.2 --- Metal concentrations in liver and muscle --- p.27<br>Chapter 2.3.3 --- Copper and zinc subcellular distribution in liver cell --- p.33<br>Chapter 2.4 --- Discussion --- p.36<br>Chapter 2.4.1 --- Difference in metal concentration between sites --- p.36<br>Chapter 2.4.2 --- Copper contamination in water and fish organ (muscle and liver) from the Shing Mun River --- p.36<br>Chapter 2.4.3 --- Comparison of metal content in muscle and liver at Fo Tan site with previous studies --- p.39<br>Chapter 2.4.4 --- Copper and zinc concentrations in the liver of tilapia --- p.42<br>Chapter 2.4.5 --- Copper and zinc sebcellular distribution in the liver of tilapia --- p.43<br>Chapter Chapter 3 --- Column chromatography of hepatic proteins from tilapias --- p.44<br>Chapter 3.1 --- Transport of metals from circulatory system to liver --- p.44<br>Chapter 3.1.1 --- Copper transporting plasma proteins in vertebrates --- p.44<br>Chapter 3.1.2 --- Copper uptake into hepatocytes --- p.45<br>Chapter 3.1.3 --- Intracellular metabolism of copper --- p.48<br>Chapter 3.1.4 --- Mechanism of copper toxicity following excess accumulation --- p.49<br>Chapter 3.1.5 --- Aim of this chapter --- p.50<br>Chapter 3.2 --- Materials and Methods --- p.51<br>Chapter 3.2.1 --- Purification of liver cytosolic proteins by gel-filtration column chromatography --- p.51<br>Chapter 3.2.2 --- Copper content detection in elution --- p.52<br>Chapter 3.2.3 --- Analysis of peaks from elution profile using tricine gel SDS PAGE --- p.53<br>Chapter 3.3 --- Results --- p.55<br>Chapter 3.3.1 --- Gel-filtration liquid chromatography elution profiles --- p.55<br>Chapter 3.3.2 --- SDS PAGE analysis of peaks in elution profiles --- p.51<br>Chapter 3.4 --- Discussion --- p.54<br>Chapter 3.4.1 --- Comparison of gel filtration profiles of sample liver cytosol between sites and sexes --- p.64<br>Chapter 3.4.2 --- Possible proteins in peaks found in the gel filtration profiles --- p.64<br>Chapter 3.4.3 --- Common copper-indeced proteins --- p.67<br>Chapter 3.5 --- Conclusion --- p.70<br>Chapter Chapter 4 --- Two-dimensional electrophoresis of hepatic cutosol of tilapias caught from Shing Mun River and copper-treated HEPA T1 cell --- p.72<br>Chapter 4.1 --- Introduction --- p.72<br>Chapter 4.1.1 --- The need of ´بin vitro' experiment --- p.72<br>Chapter 4.1.2 --- Choice of cell line --- p.73<br>Chapter 4.1.3 --- Aim of this chapter --- p.74<br>Chapter 4.2 --- Materials and Methods --- p.76<br>Chapter 4.2.1 --- HEPA T1 cell cultivation --- p.76<br>Chapter 4.2.2 --- Copper exposure of HEPA T1 cell --- p.77<br>Chapter 4.2.3 --- Subcellular protein extraction of the copper-treated HEPA T1 cells --- p.77<br>Chapter 4.2.4 --- Bicinchoninic Acidic (BCA) Protein Assay --- p.79<br>Chapter 4.2.5 --- Two-dimensional gel electrophoresis --- p.79<br>Chapter 4.3 --- Results --- p.83<br>Chapter 4.3.1 --- Graphical presentation of spots observed on 2-dimensional gel of field samples and copper-injected samples --- p.33<br>Chapter 4.3.2 --- Graphical presentation of spots detected on 2-dimensional gel of HEPAT1 cells --- p.84<br>Chapter 4.3.3 --- Comparison of matched spots on 2-dimensional gels among control and copper-treated HEPAT1 cells --- p.97<br>Chapter 4.4 --- Discussion --- p.105<br>Chapter 4.4.1 --- Comparison of the spot patterns between field sample and copperOtreated HEPA T1 cells --- p.105<br>Chapter 4.5 --- Conclusion --- p.107<br>Chapter Chapter 5 --- General Discussions --- p.108<br>Chapter 5.2 --- Research Overview --- p.108<br>Chapter 5.2 --- Characterization of metal binding proteins from the cytosol of liver of tilapia --- p.109<br>REFERENCES --- p.112