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Search Results: 1 - 10 of 6684 matches for " Alex Bateman "
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The SGS3 protein involved in PTGS finds a family
Alex Bateman
BMC Bioinformatics , 2002, DOI: 10.1186/1471-2105-3-21
Abstract: The gene for the PTGS impaired Arabidopsis mutant sgs3 was recently cloned and was not found to have similarity to any other known protein. By a detailed analysis of the sequence of SGS3 we have defined three new protein domains: the XH domain, the XS domain and the zf-XS domain, that are shared with a large family of uncharacterised plant proteins. This work implicates these plant proteins in PTGS.The enigmatic SGS3 protein has been found to contain two predicted domains in common with a family of plant proteins. The other members of this family have been predicted to be transcription factors, however this function seems unlikely based on this analysis. A bioinformatics approach has implicated a new family of plant proteins related to SGS3 as potential candidates for PTGS related functions.Post transcriptional gene silencing (PTGS) is a recently discovered phenomenon [1]. The components of PTGS are being cloned and experiment combined with sequence analysis is helping to elucidate its mechanisms. Study of PTGS is providing links between diverse biological processes such as defence against viruses, RNA metabolism [2,3] and development [4]. The gene for the PTGS impaired Arabidopsis mutant sgs3 was recently cloned [5]. An initial analysis of the protein did not reveal any motifs, domains or similarity to any other protein. To help shed light on the function of SGS3 a more detailed analysis of the protein has been carried out.'After initial PSI-BLAST searches with the sequence of SGS3, weak matches were found to a number of plant proteins. Reciprocal matches can often verify the significance of weak matches. Using residues 85 to 225 of a weakly matching Sorghum bicolor protein (SWISSPROT accession O48878) as a PSI-BLAST [6] query at the NCBI site, using an inclusion E-value of 0.002, SGS3 was found in the second round with an E-value of 0.001. This search also found a number of other plant proteins including the rice gene X product (also known as gene X1) [7].I have t
The Characterisation of Three Types of Genes that Overlie Copy Number Variable Regions
Cara Woodwark,Alex Bateman
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0014814
Abstract: Due to the increased accuracy of Copy Number Variable region (CNV) break point mapping, it is now possible to say with a reasonable degree of confidence whether a gene (i) falls entirely within a CNV; (ii) overlaps the CNV or (iii) actually contains the CNV. We classify these as type I, II and III CNV genes respectively.
The YARHG Domain: An Extracellular Domain in Search of a Function
Penny Coggill, Alex Bateman
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0035575
Abstract: We have identified a new bacterial protein domain that we hypothesise binds to peptidoglycan. This domain is called the YARHG domain after the most highly conserved sequence-segment. The domain is found in the extracellular space and is likely to be composed of four alpha-helices. The domain is found associated with protein kinase domains, suggesting it is associated with signalling in some bacteria. The domain is also found associated with three different families of peptidases. The large number of different domains that are found associated with YARHG suggests that it is a useful functional module that nature has recombined multiple times.
Membrane-bound progesterone receptors contain a cytochrome b5-like ligand-binding domain
William Mifsud, Alex Bateman
Genome Biology , 2002, DOI: 10.1186/gb-2002-3-12-research0068
Abstract: We have identified MAPRs as distant homologs of cytochrome b5. We have also found regions homologous to cytochrome b5 in the mammalian HERC2 ubiquitin transferase proteins and a number of fungal chitin synthases.In view of these findings, we propose that the heme-binding cytochrome b5 domain served as a template for the evolution of membrane-associated binding pockets for non-heme ligands.There are two main kinds of cellular effect mediated by steroids. One involves the alteration of gene expression, and is therefore characterized by a latency period between steroid signal reception and cellular effects. The second is associated with a much more rapid onset of cellular effects, and does not involve gene expression. A rapid anesthetic effect, induced by progesterone, was the first identified example of this group of effects [1]. Several rapid effects have now been described for all classes of steroids [2].Several receptor types have been implicated in steroid action. The 'classical' receptors are members of the steroid/thyroid hormone receptor superfamily [3], and their ligand-binding domains have a characteristic helical sandwich structure around the steroid ligand [4]. They bind steroids in the nucleus or in the cytosol, dimerize, and migrate to the nuclear genome, where they act as transcription factors.Such a mechanism cannot account for the rapid cellular effects of steroids, and a number of different receptors may be involved. In the case of progesterone, such rapid effects include: depolarization of rat hepatocytes by decreasing cell-membrane potassium conductance [5], calcium influx and chloride efflux in sperm during the acrosome reaction [6,7], calcium influx in Xenopus oocytes [8], as well as the anesthetic effect in the CNS. The latter has been shown to be mediated by an effect of progesterone on GABAA receptors [9]. More recently, progesterone was found to inhibit the action of oxytocin through direct binding to uterine oxytocin receptors [10].A putative
The TROVE module: A common element in Telomerase, Ro and Vault ribonucleoproteins
Alex Bateman, Valerie Kickhoefer
BMC Bioinformatics , 2003, DOI: 10.1186/1471-2105-4-49
Abstract: The TROVE module – Telomerase, Ro and Vault module – is found in TEP1 and Ro60 the protein components of three ribonucleoprotein particles. This novel module, consisting of one or more domains, may be involved in binding the RNA components of the three RNPs, which are telomerase RNA, Y RNA and vault RNA. A second conserved region in these proteins is shown to be a member of the vWA domain family. The vWA domain in TEP1 is closely related to the previously recognised vWA domain in VPARP a second component of the vault particle. This vWA domain may mediate interactions between these vault components or bind as yet unidentified components of the RNPs.This work suggests that a number of ribonucleoprotein components use a common RNA-binding module. The TROVE module is also found in bacterial ribonucleoproteins suggesting an ancient origin for these ribonucleoproteins.Many important cellular components are ribonucleoprotein (RNP) complexes, such as the spliceosome and ribosome that have key roles in gene regulation and translation. The telomerase RNP is a reverse transcriptase that maintains the telomeric repeats of eukaryotic chromosomes. Telomerase is composed of two proteins, the functionally essential reverse transcriptase TERT and the non-essential TEP1 (also known as TP1 or TLP1) as well as the telomerase RNA. TEP1 [1,2] is also found to be a component of the enigmatic vault RNP [3]. The vault is a huge structure (13 Md) of unknown function. The vault RNP is mainly composed of the major vault protein MVP, but also contains smaller amounts of TEP1 and VPARP as well as the vault RNA. Although predominately cytoplasmic, a portion of vaults are found associated with nuclear pores [4]. Vaults have been suggested to be involved in multidrug resistance, nucleo-cytoplasmic transport, and formation of RNPs [5]. While investigating the components of these RNPs an interesting protein similarity was noticed.The complete sequence of the Tetrahymena thermophilus telomerase p80 co
Ten Simple Rules for Chairing a Scientific Session
Alex Bateman,Philip E. Bourne
PLOS Computational Biology , 2009, DOI: 10.1371/journal.pcbi.1000517
Abstract:
Pepsin homologues in bacteria
Neil D Rawlings, Alex Bateman
BMC Genomics , 2009, DOI: 10.1186/1471-2164-10-437
Abstract: Homologues of the aspartic peptidase pepsin have been found in the completed genomic sequences from seven species of bacteria. The bacterial homologues, unlike those from eukaryotes, do not possess signal peptides, and would therefore be intracellular acting at neutral pH. The bacterial homologues have Thr218 replaced by Asp, a change which in renin has been shown to confer activity at neutral pH. No pepsin homologues could be detected in any archaean genome.The peptidase family A1 is found in some species of bacteria as well as eukaryotes. The bacterial homologues fall into two groups, one from oceanic bacteria and one from plant symbionts. The bacterial homologues are all predicted to be intracellular proteins, unlike the eukaryotic enzymes. The bacterial homologues are bilobed like pepsin, implying that if no horizontal gene transfer has occurred the duplication and fusion event might be very ancient indeed, preceding the divergence of bacteria and eukaryotes. It is unclear whether all the bacterial homologues are derived from horizontal gene transfer, but those from the plant symbionts probably are. The homologues from oceanic bacteria are most closely related to memapsins (or BACE-1 and BACE-2), but are so divergent that they are close to the root of the phylogenetic tree and to the division of the A1 family into two subfamilies.Peptidases are widespread enzymes that catalyse the hydrolysis of peptide bonds. Not only do peptidases hydrolyse proteins to amino acids and peptides for nutrition and recycling, but they also perform some of the most important post-translational processing events leading to the activation (or inactivation) of many other proteins, including other enzymes and peptide hormones. Over 2% of the protein coding genes in a genome encode peptidases, and there are over 500 peptidase genes in the human genome. Peptidases exist in at least six catalytic types, depending on the nature of the nucleophile in the catalytic reaction (either the hydrox
The Hotdog fold: wrapping up a superfamily of thioesterases and dehydratases
Shane C Dillon, Alex Bateman
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-109
Abstract: Using sequence analysis we unify a large superfamily of HotDog domains. Membership includes numerous prokaryotic, archaeal and eukaryotic proteins involved in several related, but distinct, catalytic activities, from metabolic roles such as thioester hydrolysis in fatty acid metabolism, to degradation of phenylacetic acid and the environmental pollutant 4-chlorobenzoate. The superfamily also includes FapR, a non-catalytic bacterial homologue that is involved in transcriptional regulation of fatty acid biosynthesis.We have defined 17 subfamilies, with some characterisation. Operon analysis has revealed numerous HotDog domain-containing proteins to be fusion proteins, where two genes, once separate but adjacent open-reading frames, have been fused into one open-reading frame to give a protein with two functional domains. Finally we have generated a Hidden Markov Model library from our analysis, which can be used as a tool for predicting the occurrence of HotDog domains in any protein sequence.The HotDog domain is both an ancient and ubiquitous motif, with members found in the three branches of life.We have found the HotDog domain, as we suggest calling the Hotdog fold, to be widespread in eukaryotes, bacteria, and archaea and to be involved in a range of cellular processes, from thioester hydrolysis, to phenylacetic acid degradation and transcriptional regulation of fatty acid biosynthesis. We present the superfamily and its functional subfamilies here. The Hotdog fold was first observed in the structure of Escherichia coli β-hydroxydecanoyl thiol ester dehydratase (FabA), where Leesong et al. noticed that each subunit of this dimeric enzyme contained a mixed α + β 'hot dog' fold [1]. They described the seven-stranded antiparallel β-sheet as the 'bun', which wraps around a five-turn α-helical 'sausage', see Figure 1. This characteristic fold has been found in a number of other enzymes, including: 4-hydroxybenzoyl-CoA thioesterase (4HBT) from Pseudomonas sp. strain CBS
Quantifying the mechanisms of domain gain in animal proteins
Marija Buljan, Adam Frankish, Alex Bateman
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-7-r74
Abstract: Here we show that the major mechanism for gains of new domains in metazoan proteins is likely to be gene fusion through joining of exons from adjacent genes, possibly mediated by non-allelic homologous recombination. Retroposition and insertion of exons into ancestral introns through intronic recombination are, in contrast to previous expectations, only minor contributors to domain gains and have accounted for less than 1% and 10% of high confidence domain gain events, respectively. Additionally, exonization of previously non-coding regions appears to be an important mechanism for addition of disordered segments to proteins. We observe that gene duplication has preceded domain gain in at least 80% of the gain events.The interplay of gene duplication and domain gain demonstrates an important mechanism for fast neofunctionalization of genes.Protein domains are fundamental and largely independent units of protein structure and function that occur in a number of different combinations or domain architectures [1]. Most proteins have two or more domains [2] and, interestingly, more complex organisms have more complex domain architectures, as well as a greater variety of domain combinations [2-4]. A possible implication of this phenomenon is that new domain architectures have acted as drivers of the evolution of organismal complexity [3]. This is supported by a recent study that experimentally showed that recombination of domains encoded by genes that belong to the yeast mating pathway had a major influence on phenotype [5]. While there is evidence that in prokaryotes new domains are predominantly acquired through fusions of adjacent genes [6,7], determining the predominant molecular mechanisms that underlie gains of new domains in animals has been more challenging [3].The question of what mechanisms underlie domain gains is related to the question of what mechanisms underlie novel gene creation [3,8,9]. The recent increased availability of animal genome and transcriptome
Enhanced protein domain discovery using taxonomy
Lachlan Coin, Alex Bateman, Richard Durbin
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-56
Abstract: We show that by incorporating our understanding of the taxonomic distribution of specific protein domains, we can enhance domain recognition in protein sequences. We identify 4447 new instances of Pfam domains in the SP-TREMBL database using this technique, equivalent to the coverage increase given by the last 8.3% of Pfam families and to a 0.7% increase in the number of domain predictions. We use PSI-BLAST to cross-validate our new predictions. We also benchmark our approach using a SCOP test set of proteins of known structure, and demonstrate improvements relative to standard Hidden Markov model techniques.Explicitly including knowledge about the taxonomic distribution of protein domains can enhance protein domain recognition. Our method can also incorporate other context-specific domain distributions – such as domain co-occurrence and protein localisation.Protein domains are the structural, functional and evolutionary units of proteins. Several statistical techniques are currently used for detecting protein domains. In particular, Profile hidden Markov models (profile HMMs) have been successfully applied to this problem [1,2], and form the basis for databases such as Pfam [3]. Profile HMMs can be more sensitive than methods which look for pairwise homology [4]. Our ability to detect distant homology is limited by noise. This is due to the divergence of the amino acid sequence too far away from the profile to detect the similarity, despite the preservation of structure and function. We attempt to take into account extra information concerning the patterns of occurrence of domains in order to recognize distant homology. We have previously discovered that using the probabilities of domains occurring together in a sequence as contextual information significantly enhances domain detection [5]. In this paper we investigate using the species distribution of domains to enhance detection.Fig. 1 shows examples of domains which have biased taxonomic distribution. For exampl
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