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Search Results: 1 - 10 of 2327 matches for " Todd Richmond "
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Global analysis of chloroplast proteins
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-2-reports0048
Abstract: Peltier et al. isolated pea chloroplasts and, through a series of purification steps, isolated lumenal and peripheral thylakoid proteins. A series of two-dimensional gels, using both low pH and high pH ranges, was used to improve the resolution of the resulting two-dimensional electrophoresis maps. A total of between about 820 and 920 protein spots can be detected using these methods, which, after adjusting for protein isoforms, post-translational modifications and proteolysis, represent approximately 200 proteins. The authors used three techniques to analyze protein spots: matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectroscopy, electrospray ionization tandem (ESI) mass spectroscopy and amino-terminal Edman sequencing. The initial analysis, conducted on 400 spots, consisted of an in-gel protease digestion of the proteins followed by MALDI-TOF mass spectroscopy. The list of measured peptide masses produced by this method was compared with the theoretical masses from predicted tryptic peptides for each entry in the public sequence databases. For proteins not unambiguously identified by this method, peptide sequence tags were obtained by ESI mass spectroscopy or Edman sequencing and used for homology-based searches. Peltier et al. successfully identified 61 proteins, and for 33 of these a clear function could be assigned. Of the remainder, 10 had no known function, and for the remaining 18 proteins, no expressed sequence tags or full-length genes were identified.The study also examined the predictive power of several programs designed to identify protein-sorting signals. The authors conclude that the PSORT and ChloroP programs are not ideal, the former being too conservative and the latter resulting in too many false positives. The lumenal transit peptides for 26 proteins were determined and found to be similar to those of signal peptides in bacteria. Peltier et al. point out several conserved features in these transit peptides, and su
Using chloroplasts to produce drugs
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-2-reports0049
Abstract: The production of xenogenic proteins in plants requires a number of optimization steps: choosing the right promoter, finding the correct codon usage, targeting the protein to the proper subcellular compartment, blocking potential degradation, and ensuring proper post-translational modification. Problems with any of these steps can lead to little or no protein production, or to synthesis of a biologically inactive protein. Staub et al. had to overcome several of these problems. Initial expression attempts using traditional transgenesis into the nuclear genome resulted in little or no hST production, so the authors instead used transformation of chloroplasts. Transformation of the plastid genome has several advantages. First, a plant cell can contain up to 10,000 plastid genomes, greatly increasing the possibility of high expression of the transgene. Staub et al. report that hST expression in one of the transgenic lines made up 7% of total soluble protein. Second, plastid transformation is via homologous recombination. The hST construct was cloned into a vector containing a selectable marker and portions of the plastid genome. This enabled target- specific insertion of the transgene between the trnV gene and the rps7/3'-rps12 operon in the plastid genome. This ensures that there are no positional effects for different constructs. Finally, the plastid genome is not transmitted through pollen, allowing greater biological containment of transgenic crops.Another difficulty that the authors had to face was how to produce recombinant hST identical to native hST, which contains an amino-terminal phenylalanine residue instead of the typical methionine. When producing therapeutic proteins, the recombinant proteins must be identical to previously characterized proteins, in order to avoid additional clinical trials and re-certification. To recreate the correct amino terminus, normally produced in mammalian systems by cleavage of a signal peptide, the authors created a ubiquitin-
Higher plant cellulose synthases
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-4-reviews3001
Abstract: A number of cellulose synthase (CesA) genes have been cloned from a variety of plant species, the first in 1996 [1]. The most information is known about the Arabidopsis thaliana CesA gene family, as the Arabidopsis genome sequence is nearly finished. CesA genes range in size from 3.5 to 5.5 kb, with 9-13 small introns (Figure 1). They produce transcripts ranging in size from 3.0 to 3.5 kb, encoding proteins 985 to 1,088 amino acids in length. The intron-exon boundaries are highly conserved, with differences in gene structure primarily due to the loss of introns.In Arabidopsis thaliana, there are at least ten cellulose synthase genes. These are scattered throughout the genome, with no apparent recent duplication events. Unlike bacterial cellulose synthase genes, there are no functionally linked genes in close proximity to one another. Sequence data indicate that the CesA gene family is as large, or larger, in other plant species.Plant cellulose synthases belong to family 2 of processive glycosyltransferases [2,3], a large family of enzymes with members from viruses, bacteria, fungi, and all other eukary-otes. The proteins in this family are inverting processive glycosyltransferases that make β linkages. Cellulose synthases synthesize β-1,4-glucans, homogeneous strands of glucose residues. In addition to higher plants, cellulose is synthesized by a number of bacterial species (i.e. Acetobacter, Agrobacterium, and Rhizobium), algae and lower eukaryotes (i.e. tunicates). While the end product is the same, there is little similarity at the amino-acid level between these genes and CesA genes from higher plants.In Arabidopsis thaliana, there are a total of six families of genes, designated 'cellulose synthase-like' (Csl), that appear to be related to the CesA family, on the basis of sequence similarity, conserved protein domains, and overall gene structure [4,5] (Figure 2). The function of these families is not yet known; it is possible that one or more of these families i
Gene identification software
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-3-reports2053
Abstract: The interface to GRAIL is somewhat confusing. Rather than start with a simple sequence-submission form, the user is presented with a mostly blank page with three buttons at the top: 'New Grail Analysis', 'Upload Grail Dataset', and 'New GenQuest Search'. As new users are unlikely to know what the last two buttons are for, there seems no need to have them here. In fact, if you click on 'New GenQuest Search', nothing happens. Clicking on 'New Grail Analysis' generates a page that allows you to choose an organism. As mentioned above, you only have a choice of five. It is not clear whether the server can be reliably used to analyze a sequence from another organism. After choosing the organism, a request form is generated. This form gives a variety of choices, depending on the organism, of what types of exons to find, and whether to identify polyadenylation sites, CpG islands, repetitive DNA or simple repeats. After submitting the request, a list of exons with positions, frames, and scores is presented. Currently this is all that you can do. Clicking on some of the optional buttons at the bottom of the page, such as 'Draw Results Image' or 'GenQuest Search', result in a message that says '!!! It's Coming Soon !!!'. Given that it has been four years since the last update, I suspect that these features will not be coming any time soon.Last updated 12 February 1996.The usefulness of GRAIL information is usually realized when integrated into a gene annotation program. So, there is an option to download the GRAIL results so that they can be utilized by another program. Unfortunately, most of these programs are UNIX-based and most casual users do not have access to them.Navigating the site is difficult and the output is useless without an external helper program of some kind.Too numerous to list. It appears, however, that the initial GRAIL server has been or will be supplanted by GrailEXP, a more sophisticated gene modeling program that builds on the exon-predicting power of G
Finding motifs in protein sequences
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-3-reports2052
Abstract: There is no difficulty in navigating the EMOTIF search site. The page has a box for pasting the protein sequence and two buttons: one to initiate the search, and another to clear the form. There are links in the upper corner of the page to all four of the related resources mentioned above. After submitting the search, the user is presented with a page of possible motif matches, in decreasing order of stringency. Each of the matches has a short description and a link to the complete motif description in the BLOCKS database. The location of the motif in the protein is specified and some of the sequence around it is displayed, and, when available, there is a link to the three-dimensional representation of the motif in 3MOTIF. You can also choose a link to find other proteins with the same motif, using EMOTIF scan.There is no indication of when the site was last updated, or what version of each of the sequence databases is being searched.The site is very simple to use, and the integration of the various resources is very useful. One can make a motif, search for proteins with the motif, and then determine if they, in turn, share any other motifs.Unfortunately, the results are of dubious use. Using one of my favorite proteins - a putative glycosyltransferase from Arabidopsis - one of the true conserved motifs was buried in a mess of false positives (though the page claims that no false positives are expected at that stringency). Worse, when I went to check on the description of the 'true hit' in the BLOCKS database using the supplied link, I received an error saying that no such BLOCK exists. When I used the link to initiate an EMOTIF scan, I was presented with a substantial list of matching proteins, from both SwissPROT and GenBank. But closer inspection revealed that a number of proteins that should have matched the same motif were not present. In fact, of the 22 known Arabidopsis proteins with this particular glycosyltransferase motif, not a single one was in the list
Arabidopsis chromosome 2 sequence
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-1-reports029
Abstract: The paper summarizes years of work by hundreds (if not thousands) of people in dozens of labs spread over three continents. The key features of chromosome 2 are as follows. The long arm of chromosome 2 is 16.0 Mb, the short arm 3.5 Mb. Nearly 50% of the sequence codes for protein, with a total of 4,037 predicted proteins. Each gene is about 4.4 kb in length, containing an average of 4.6 exons. The largest gene contains 52 predicted exons and is 50% identical to a human protein. The actual or potential cellular function for approximately 52% of the genes can be predicted on the basis of similarity to other characterized proteins. Only 33% of the predicted genes are represented among the 45,000 available Arabidopsis expressed sequence tags (ESTs). After classifying the predicted proteins into functional classes, the largest functional groups were genes involved in regulatory function and signal transduction (including DNA-binding proteins, transcription factors and protein kinases). The most frequent protein domains were leucine-rich repeats, protein kinases and zinc-finger domains. More than 60% of the predicted gene products (2,542) on chromosome 2 have significant similarity to another Arabidopsis protein. The products of most of the genes that have paralogs (83%) within the Arabidopsis genome are more similar to their paralog than to proteins from other completed sequences. Of these, 593 are found in tandem duplications that range in size from two to nine genes. The same phenomenon was seen in the analysis of chromosome 4. Lin et al. present a number of graphs that show the distribution of features along chromosome 2, summarizing predicted gene density, EST density, tandem duplications and repetitive elements. As expected, gene density decreases as you approach the centromere and the amount of repetitive DNA increases. There is a table of the transposable elements found on chromosome 2, broken down into class, subclass and family. Unfortunately, there is no equiva
Identification of complete gene structures in genomic DNA
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-1-reports222
Abstract: The GENSCAN program is designed to predict complete gene structures, including exons, introns, promoter and polyadenylation signals, in genomic sequences. The program was designed primarily to predict genes in human or vertebrate genomic sequences, although it works fairly well on Drosophila sequences and there are special versions for maize and Arabidopsis sequences. The server returns a table with the predicted exons, including position, length, reading frame, and some confidence scores. In addition, it returns the final predicted protein sequence, and the corresponding spliced DNA sequence (if desired). There is a graphical representation of the predicted coding regions, available in GIF or Postscript format.Although the submission form is easy to use, it is a bit difficult to find. You have to scroll down past some FAQ links, a list of GENSCAN-related websites and a cautionary note, before you get to the actual form. The form is followed up by a list of references and contact information.There is no indication of when the site was last updated. There are a number of dead or incorrect links on the FAQ pages, none of which affects the functionality of the server.The best feature of GENSCAN is the output of the final spliced-together protein. Often, prediction programs stop short and just give a list of potential exons, requiring the researcher to splice them together manually. Using the GENSCAN protein sequence output and a program like NCBI's Sequin, a researcher can quickly map the protein onto the genomic sequence and automatically annotate the predicted exon regions. Then one can start the manual 'tweaking' that is necessary with even the best prediction programs.The blue background and white lettering on all of the pages makes them hard to read. Plain black and white would be preferable.Two things. Change the color scheme, and reorganize the site so that the submission form is the very first thing on the page, and the rest of the information (related web site
Topology prediction of membrane proteins
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-1-reports224
Abstract: A simple introductory page links to a sequence submission page and to the source code if you wish to set up the program on your own machine. There is also a stand-alone program available for Macintosh computers (although the link to this program is currently broken on the site). Once on the sequence submission page, use of the server is simple. You paste your protein sequence into the window, check whether the protein sequence is eukaryotic or prokaryotic, select the desired confidence thresholds and the window size (length of possible transmembrane helices), and press submit.Last updated 5 November 1997.The graphic produced by TopPred 2 is one of the nicer images produced by transmembrane domain prediction servers.There is no documentation on the website. In order to understand what everything means, you have get the original journal article describing the software.Instead of just specifying the position of the putative transmembrane domains with numbers, it would be useful to have, in addition, the protein sequence of the predicted helix. This makes it easier for the researcher to annotate that region in the protein sequence.There are a number of sites that offer transmembrane domain prediction including TMHMM: predication of transmembrane helices in proteins; Tmpred: prediction of transmembrane regions and orientation; HMMTOP: predication of transmembrane helices and topology of proteins; SOSUI: Classification and secondary structure prediction of membrane proteins.TopPred 2TMHMM: predication of transmembrane helices in proteinsTmpred: prediction of transmembrane regions and orientationHMMTOP: predication of transmembrane helices and topology of proteinsSOSUI: Classification and secondary structure prediction of membrane proteins
Arabidopsis chromosome 4 sequence
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-1-reports030
Abstract: The paper summarizes years of work by hundreds (if not thousands) of people in dozens of labs spread over three continents. The key features of chromosome 4 are as follows. The long arm of chromosome 4 is 14.5 Mb, the short arm is 3.0 Mb (plus nearly 3.5 Mb of ribosomal DNA repeats). Nearly 50% of the sequence encodes for protein, for a total of 3,744 predicted proteins. Each gene is about 4.6 kb in length, containing an average of 5.2 exons. The actual or potential cellular function for approximately 60% of the genes can be predicted on the basis of similarity to other characterized proteins. Only 33% of the predicted genes are represented among the available 45,000 Arabidopsis expressed sequence tags (ESTs). Of these, 6% of the genes match 75% of the ESTs. Note that it is not clear if the authors are referring at this point only to the chromosome 4 sequence or to all Arabidopsis sequence available; it is clearly important to sequence normalized EST libraries in order to maximize the amount of non-redundant sequence gathered. Almost 8% of the predicted genes have no ESTs and no similarity to other proteins; these may represent spurious gene predictions or plant-specific genes expressed at low levels.The authors give some statistics on various motifs and structural topologies found in the predicted proteins. They also attempt to classify the proteins into major functional categories (such as metabolism and transcription). The only major surprise is the large number of genes involved in disease and defense responses. This is largely due to several large clusters of leucine-rich repeat genes, including one family of 15 contiguous genes. A surprisingly large number of genes are arranged in tandem copies. Of genes with products that have significant similarity to other proteins in Arabidopsis, 12% are arrayed in tandem clusters, ranging from pairs of genes to the 15 leucine-rich repeat genes. This hints at the underlying mechanism of how plants generate sequence diversi
Digital biochemical pathways database
Todd Richmond
Genome Biology , 2000, DOI: 10.1186/gb-2000-1-3-reports2051
Abstract: The initial page of the database has a single box for keyword entry. After entering a metabolite or enzyme, a page with the matching entries is presented. For enzymes, the Enzyme Commission (EC) number is given, along with a link to ExPASy's Enzyme nomenclature database. This contains a great deal of additional information, including alternative names, the reaction catalyzed by the enzyme, and links to a number of other databases where additional information can be found. In some cases, both the substrates and products of the reaction have links, so you can learn more about them (chemical composition, three-dimensional structure, and so on.). For metabolites, links are provided to GIF images of the Boehringer wallchart. Links are provided so that you can then follow a pathway from that particular section of the chart to other pathways, and all of the enzymes are linked to the Enzyme nomenclature database so that you can quickly access information about enzymes in the pathway of interest.Last updated 12 February 1999.The website provides a very simple and easy-to-use launch pad for metabolic pathway information. With the increasing use of DNA microarrays and proteomics, researchers are faced with the need quickly to locate information about enzymes that they may never have heard of before.Although the enzyme information is excellent, the information about the various metabolites is poor. For example, a search for glucose-6-phosphate brings up both glucose-6-phosphate dehydrogenase and glucose-6-phosphate isomerase, but nothing about the sugar itself. Using one of the enzymes above to get to the chart will give the structure of glucose-6-phosphate, but no other information is available.There are several things that would greatly improve the utility of this database. First, some way to highlight on the chart picture the enzyme or metabolite that you searched for would be useful. As it is, you are presented with a scanned image of the chart, and have to hunt for the obj
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