Predicting and Characterising Zinc Metal Binding Sites in Proteins

Zinc is one of the most important biologically active metals. Ten per cent of the human genome is thought to encode a zinc binding protein and its uses encompass catalysis, structural stability, gene expression and immunity. Knowing whether a protein binds to zinc can offer insights into its function, and knowing precisely where it binds zinc can show the mechanism by which it carries out its intended function, as well as provide suggestions as to how pharmaceutical molecules might disrupt or enhance this function where required for medical interventions. At present, there is no specific resource devoted to identifying and presenting all currently known zinc binding sites. This PhD has resulted in the creation of ZincBind — a database of zinc binding sites (ZincBindDB), predictive models of zinc binding at the family level (ZincBindPredict) and a user-friendly, modern website frontend (ZincBindWeb). Both ZincBindDB and ZincBindPredict are also available as GraphQL APIs. The database of zinc binding sites currently contains 38,141 sites, and is automatically updated every week. The predictive models, trained using the Random Forest Machine Learning algorithm, all achieve an MCC ≥ 0.88, recall ≥0.93 and precision ≥0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥0.80 and pre- cision ≥0.83 (mean MCC = 0.87), outperforming competing, previous predictive models

ZincBindPredict - prediction of zinc binding sites in proteins

Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechanism by which it carries out those functions. Computational means of doing so are faster than spectroscopic means, allowing for searching at much greater speeds and scales, and thereby guiding complimentary experimental approaches. Typically, computational models of zinc binding predict zinc binding for individual residues rather than as a single binding site, and typically do not distinguish between different classes of binding site—missing crucial properties indicative of zinc binding. Methods: Previously, we created ZincBindDB, a continuously updated database of known zinc binding sites, categorised by family (the set of liganding residues). Here, we use this dataset to create ZincBindPredict, a set of machine learning methods to predict the most common zinc binding site families for both structure and sequence. Results: The models all achieve an MCC ≥ 0.88, recall ≥ 0.93 and precision ≥ 0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥ 0.80 and precision ≥ 0.83 (mean MCC = 0.87), with the models for binding sites containing four liganding residues performing much better than this. Conclusions: The predictors outperform competing zinc binding site predictors and are available online via a web interface and a GraphQL API

Resolving the zinc binding capacity of honey bee vitellogenin and locating its putative binding sites

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Unravelling the structure of the tetrahedral metal-binding site in METP3 through an experimental and computational approach

Understanding the structural determinants for metal ion coordination in metalloproteins is a fundamental issue for designing metal binding sites with predetermined geometry and activity. In order to achieve this, we report in this paper the design, synthesis and metal binding properties of METP3, a homodimer made up of a small peptide, which self assembles in the presence of tetra-hedrally coordinating metal ions. METP3 was obtained through a redesign approach, starting from the previously developed METP molecule. The undecapeptide sequence of METP, which dimerizes to house a Cys4 tetrahedral binding site, was redesigned in order to accommodate a Cys2His2 site. The binding properties of METP3 were determined toward different metal ions. Successful assem-bly of METP3 with Co(II), Zn(II) and Cd(II), in the expected 2:1 stoichiometry and tetrahedral geometry was proven by UV-visible spectroscopy. CD measurements on both the free and metal-bound forms revealed that the metal coordination drives the peptide chain to fold into a turned conformation. Finally, NMR data of the Zn(II)-METP3 complex, together with a retrostructural analysis of the Cys-X-X-His motif in metalloproteins, allowed us to define the model structure. All the results establish the suitability of the short METP sequence for accommodating tetrahedral metal binding sites, regardless of the first coordination ligands

Sequence patterns and HMM profiles to predict proteome wide zinc finger motifs

Zinc finger (ZnF) is an important class of nucleic acid and protein recognition domain, wherein, zinc ion is the inorganic co-factor that forms a tetrahedral geometry with the cysteine and/or histidine residues. ZnF domains take up diverse architectures with different ZnF motifs and have a wide range of biological functions. Nonetheless, predicting the ZnF motif(s) from the sequence is quite challenging. To this end, 74 unique ZnF sequence patterns are collected from the literature and classified into 32 different classes. Since the shorter length of ZnF sequence patterns leads to inaccurate predictions, ZnF domain Pfam HMM profiles defined under 6 ZnF Pfam clans (215 HMM profiles) and a few undefined Pfam clans (74 HMM profiles) are used for the prediction. A web server, namely, ZnF-Prot (https://project.iith.ac.in/znprot/) is developed which can predict the presence of 31 ZnF domains in a protein/proteome sequence of any organism. The use of ZnF sequence patterns and Pfam HMM profiles resulted in an accurate prediction of 610 test cases (taken randomly from 249 organisms) considered here. Additionally, the application of ZnF-Prot is demonstrated by considering Arabidopsis thaliana, Homo sapiens, Saccharomyces cerevisiae, Caenorhabditis elegans and Ciona intestinalis proteomes as test cases, wherein, 87–96% of the predicted ZnF motifs are cross-validated

A comprehensive review of computation-based metal-binding prediction approaches at the residue level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods

Forståelse av forholdet mellom struktur og funksjon til Vitellogenin i honningbia

This thesis focuses on the structure and molecular function of Vitellogenin (Vg) from honey bees (Apis mellifera). Vg is an ancient protein found in animals. Most biological processes depend on proteins' activities, and the structural shape of proteins determines what they can do and how they work. It is important to understand the shape and associated functional properties of honey bee Vg, as honey bees are important pollinators in our natural environment and agricultural food system. A yolk-protein that transports nutrients like lipids and zinc, Vg is necessary for honey bee reproduction, and the protein also regulates social behavior and has immune-related functions. Paper I presents a full-length protein structure for honey bee Vg, generated using computational structure prediction. For the first time, we describe the complete structural fold of the protein, revealing previously unknown structural features. In Paper II, I use structural- and sequence-data analysis to identify seven potential zinc-binding sites at different protein regions. Element analysis of purified Vg shows that, on average, three zinc-sites are occupied per molecule – a ratio not reported before. Paper III explores the Vg structure from the perspective of allelic variation on the honey bee vg-gene. We used amplicon Nanopore sequencing with barcoded primers to identify 121 Vg variants. With these data, I found that the domains and subdomains of Vg are characterized by different levels of variation. While some of these patterns were expected, my results also provide new insights on possible structure-function relationships. I use findings from Papers I, II, and III in Paper IV to develop a novel explanatory model for how Vg holds its lipid load. In sum, this thesis presents a detailed structural study that contributes toward understanding the multifunctional role of honey bee Vg.Denne avhandlingen fokuserer på strukturen og funksjonen til Vitellogenin (Vg) hos honningbier (Apis mellifera). Vg er et gammelt protein som finnes i mange dyr. De fleste biologiske prosesser er avhengige av proteiners aktivitet, og den strukturelle formen til et protein bestemmer hva det kan gjøre og hvordan det fungerer. De er viktig å forstå formen og de assosierte funksjonelle egenskapene til Vg i honningbia, ettersom honningbier er viktige pollinatorer i vårt naturlige miljø og for matproduksjon i landbruk. Vg er nødvendig for reproduksjon i honningbier som et egg-protein, ved å transportere næringsstoffer som lipider og sink, men proteinet regulerer også sosial adferd og har immunrelaterte funksjoner. Paper I presenterer en full-lengde proteinstruktur av Vg i honningbia, generert ved å bruke beregningsmessig protein-prediksjon. Vi beskriver en fullstendig strukturell form av proteinet for første gang, som avdekker nye strukturelle egenskaper. I Paper II, bruker jeg struktur- og sekvensdata-analyser til å identifisere syv potensielle sink-bindingsseter på ulike områder i proteinet. Element-analyse av renset Vg viser at tre sink-seter, i snitt, er bundet per molekyl – en ratio som ikke har blitt rapportert tidligere. Paper III utforsker Vg strukturen fra et genetisk variasjonsperspektiv i vg-genet til honningbia. Vi bruker amplikon Nanoporesekvensering med seriekodede primere for å identifisere 121 Vg-varianter. Med disse data fant jeg ut at domener og subdomer i Vg karakteriseres av variasjonsnivå. Noen av disse mønstrene var forventet, men mine resultater bidrar også til ny innsikt i forholdet mellom Vgs struktur og funksjon. Jeg bruker funnene fra Paper I, II, og III i Paper IV for å utlede en ny forklaringsmodell for hvordan Vg bærer sin lipidlast. Min avhandling representerer en detaljert strukturell studie som tar viktige steg mot å forstå den flerfunksjonelle rollen til Vg i honningbia.Norges forskningsråd ; BioCa

Structural insight on the mechanism of an electron-bifurcating [FeFe] hydrogenase.

Electron-bifurcation is a fundamental energy conservation mechanism in nature in which two electrons from an intermediate potential electron donor are split so that one is sent along a high potential pathway to a high potential acceptor and the other is sent along a low potential pathway to a low potential acceptor. This process allows endergonic reactions to be driven by exergonic ones and is an alternative, less recognised, mechanism of energy coupling to the well-known chemiosmotic principle. The electron-bifurcating [FeFe] hydrogenase from Thermotoga maritima (HydABC) requires both NADH and ferredoxin to reduce protons generating hydrogen. The mechanism of electron-bifurcation in HydABC remains enigmatic in spite of intense research efforts over the last few years. Structural information may provide the basis for a better understanding of spectroscopic and functional information. Here, we present a 2.3 Å electron cryo-microscopy structure of HydABC. The structure shows a heterododecamer composed of two independent 'halves' each made of two strongly interacting HydABC heterotrimers connected via a [4Fe-4S] cluster. A central electron transfer pathway connects the active sites for NADH oxidation and for proton reduction. We identified two conformations of a flexible iron-sulfur cluster domain: a 'closed bridge' and an 'open bridge' conformation, where a Zn2+ site may act as a 'hinge' allowing domain movement. Based on these structural revelations, we propose a possible mechanism of electron-bifurcation in HydABC where the flavin mononucleotide serves a dual role as both the electron bifurcation center and as the NAD+ reduction/NADH oxidation site

Dangerous Proteins and Where to Find Them- Structural and functional studies of bacterial and viral proteins interacting with human immune receptors in health and disease

Bacteria and viruses are threats to human that evolved strategies to bypass the immune system and can cause massive damage. Understanding these strategies and elucidating pathogen interacting partners within the human immune system will pave the way for discovery of new medicines and increase human well-being. Superantigens (SAgs) are toxins that induce a massive immune response, causing sever diseases. The bacteria Staphylococcus aureus produces staphylococcal enterotoxins (SEs) that are the focus of this thesis. In human, SEs are presented by major histocompatibility complex II (MHCII) to T cell receptors (TCRs), located on T cells, leading to clonal expansion of respective T cells and an overactivation of the immune system. This T cell skewing, that is one of the hallmarks for superantigens, has also been seen for the corona virus. The spike protein that is on the surface of the corona virus, partly structurally resembles a superantigen and its superantigenic character must be analysed to further understand disease development.In this thesis, I will describe and discuss my structural and functional data of superantigens, and the superantigen-like spike protein interacting with human immune receptors and put them into context with the current knowledge of the immune system and try to highlight their implication in disease development in human.My work has resulted in new findings within the field of superantigen biology. Firstly, the SEs, SEA and SEH, were shown to interact with γδ T cells from human peripheral blood in an indirect mechanism utilizing monocytes and αβ T cells. Moreover, SEA was shown to bind γδ TCR (Vγ9δ2) directly in a protein interaction experiment. The biological outcome of this interaction is still unknown. Secondly, the interaction of SEA, SEE and SEH with the human cytokine receptor gp130 is further analysed. It was shown that their binding affinity differs and that they do not bind rodent gp130, suggesting a different mode of action in human. A computational model of SEA complexed with gp130 was generated. Taken together, our data supported by previous experiments indicates that the SEA-gp130 interaction might have implications in emesis. Finally, the spike glycoprotein in SARS-CoV-2 was shown to have superantigenic character, because of its sequential and structural similarity with SEB. Here, we show that specific TRBV of TCRs bind presumably the NTD/RBD domain of the spike glycoprotein