Nimisha Mittal (4019684), Rohini Muthuswami (56262), Rentala Madhubala (85260) · Figshare · 2017
<p>Multiple sequence alignment of <i>Leishmania</i> sirtuins with <i>Hs</i>SIRT1, <i>Hs</i>SIRT2, <i>Hs</i>SIRT3, <i>Hs</i>SIRT4 and <i>Hs</i>SIRT5 using Clustal Omega. The conserved GAG, TQNID, and HG motifs are highlighted in light grey, The Cys residues from the Zn<sup>2+</sup>-binding domain are shown in dark grey (arrowheads).</p>
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Xu Zhang, Tamer Kahveci · 2013
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Xuehui Li, Tamer Kahveci, Dmitrij Frishman · 2006
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Erik Arner, Yoshihide Hayashizaki, Carsten O. Daub et al. · 2009
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East Zhao, Hsuan-liang Liu, Chi-hung Tsai et al. · 2004
Motivation: Disulfide bonds play an important role in protein folding. A precise prediction of disulfide connectivity can strongly reduce the conformational search space and increase the accuracy in protein structure prediction. Conventional disulfide connectivity predictions use sequence information, and prediction accuracy is limited. Here, by using an alternative scheme with global information for disulfide connectivity prediction, higher performance is obtained with respect to other approaches. Result: Cysteine separation profiles have been used to predict the disulfide connectivity of proteins. The separations among oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Through com-parisons of their CSPs, the disulfide connectivity of a test protein is inferred from a non-redundant template set. For non-redundant pro-teins in SwissProt 39 (SP39) sharing less than 30 % sequence identity, the prediction accuracy of a fourfold cross-validation is 49%. The prediction accuracy of disulfide connectivity for proteins in SwissProt 43 (SP43) is even higher (53%). The relationship between the sim-ilarity of CSPs and the prediction accuracy is also discussed. The method proposed in this work is relatively simple and can generate higher accuracies compared to conventional methods. It may be also combined with other algorithms for further improvements in protein structure prediction. Availability: The program and datasets are available from the authors upon request. Contact:
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Bogumił Kaczkowski, Elfar Torarinsson, Kristin Reiche et al. · 2008
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Simon G. Coetzee, Gerhard A. Coetzee, Dennis J. Hazelett · 2016
motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites
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David C. Walley, Young C. Song, Keith R. Walley et al. · 2015
MACGT: multi-dimensional automated clustering genotyping tool for analysis of microarray-based mini-sequencing data
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Janne Korhonen, Petri Martinmäki, Cinzia Pizzi et al. · 2013
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Yuting He, Fan Zhang, Patrick Flaherty · 2016
RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data
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Springer texts in statistics · 2008
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Yann Ponty, Michel Termier, Alain Denise · 2016
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Massimo Andreatta, Morten Nielsen · 2016
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Tuck Seng Wong, Kang Lan Tee · Learning materials in biosciences · 2020
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Holger Jahn (199288), Stefan Wittke (199290), Petra Zürbig (199292) et al. · Figshare · 2015
<p>Identified sequences of 57 out of the initial set of 131 putative AD biomarkers are listed. Shown are unique internal protein ID (Pr ID), molecular mass (in Dalton), CE-migration time (in minutes), sequence, originating protein name, and calculated mass (in Dalton). Peptides that are utilized in the final AD pattern are highlighted in bold letters.</p>
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Thiago M. Venancio, L. Aravind · 2013
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Robel Y. Kahsay, Guang Gao, Li Liao · 2005
An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes
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Tetsuya Sato, Yoshihiro Yamanishi, Minoru Kanehisa et al. · 2005
The inference of protein–protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships
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John A. Capra, Mona Singh, Burkhard Rost · 2008
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Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams et al. · 2006
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