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      CYSTM, a novel cysteine-rich transmembrane module with a role in stress tolerance across eukaryotes

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      Author(s): , *
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Using sensitive sequence profile analysis, we identify a hitherto uncharacterized cysteine-rich, transmembrane (TM) module, CYSTM, found in a wide range of tail-anchored membrane proteins across eukaryotes. This superfamily includes Schizosaccharomyces Uvi15, Arabidopsis PCC1, Digtaria CDT1 and Saccharomyces proteins YDL012C and YDR210W, which have all been implicated in resistance/response to stress or pathogens. Based on the pattern of conserved cysteines and data from different chemical genetics studies, we suggest that CYSTM proteins might have critical role in responding to deleterious compounds at the plasma membrane via chelation or redox-based mechanisms. Thus, CYSTM proteins are likely to be part of a novel cellular protective mechanism that is widely active in eukaryotes, including humans.

          Contact: aravind@ 123456ncbi.nih.gov

          Supplementary Information: Supplementary data are available at Bioinformatics online.

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          Kalign – an accurate and fast multiple sequence alignment algorithm

          Timo Lassmann, Erik Sonnhammer (2005)
          Background The alignment of multiple protein sequences is a fundamental step in the analysis of biological data. It has traditionally been applied to analyzing protein families for conserved motifs, phylogeny, structural properties, and to improve sensitivity in homology searching. The availability of complete genome sequences has increased the demands on multiple sequence alignment (MSA) programs. Current MSA methods suffer from being either too inaccurate or too computationally expensive to be applied effectively in large-scale comparative genomics. Results We developed Kalign, a method employing the Wu-Manber string-matching algorithm, to improve both the accuracy and speed of multiple sequence alignment. We compared the speed and accuracy of Kalign to other popular methods using Balibase, Prefab, and a new large test set. Kalign was as accurate as the best other methods on small alignments, but significantly more accurate when aligning large and distantly related sets of sequences. In our comparisons, Kalign was about 10 times faster than ClustalW and, depending on the alignment size, up to 50 times faster than popular iterative methods. Conclusion Kalign is a fast and robust alignment method. It is especially well suited for the increasingly important task of aligning large numbers of sequences.
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            Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways.

            Ainslie B Parsons, Renee L. Brost, Huiming Ding … (2004)
            Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H(+)-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.
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              Application of multiple sequence alignment profiles to improve protein secondary structure prediction.

              J Cuff, G Barton (2000)
              The effect of training a neural network secondary structure prediction algorithm with different types of multiple sequence alignment profiles derived from the same sequences, is shown to provide a range of accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard deviation 8.4%), is 3.1% (Q(3)) and 4.4% (SOV2) better than the PHD algorithm run on the same set of 406 sequence non-redundant proteins that were not used to train either method. Residues predicted by the new method with a confidence value of 5 or greater, have an average Q(3) accuracy of 84%, and cover 68% of the residues. Relative solvent accessibility based on a two state model, for 25, 5, and 0% accessibility are predicted at 76.2, 79.8, and 86. 6% accuracy respectively. The source of the improvements obtained from training with different representations of the same alignment data are described in detail. The new Jnet prediction method resulting from this study is available in the Jpred secondary structure prediction server, and as a stand-alone computer program from: http://barton.ebi.ac.uk/. Proteins 2000;40:502-511. Copyright 2000 Wiley-Liss, Inc.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 January 2010
                Publication date (Electronic): 17 November 2009
                Publication date PMC-release: 17 November 2009
                Volume: 26
                Issue: 2
                Pages: 149-152
                Affiliations
                National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Alex Bateman

                Article
                Publisher ID: btp647
                DOI: 10.1093/bioinformatics/btp647
                PMC ID: 2804304
                PubMed ID: 19933165
                SO-VID: cc130653-21ef-4119-85da-b606a6ad4603
                Copyright © © The Author(s) 2009. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 29 September 2009
                Date revision received : 11 November 2009
                Date accepted : 12 November 2009
                Categories
                Subject: Discovery Note
                Subject: Sequence Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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