<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">E. Capriotti</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">RNA structure alignment by a unit-vector approach</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms Base Sequence Computer Simulation *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemical *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Molecular Sequence Data Nucleic Acid Conformation RNA/*chemistry/*ultrastructure Sequence Alignment/*methods Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA/*methods *Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18689811</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">16</style></number><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">i112-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: The recent discovery of tiny RNA molecules such as microRNAs and small interfering RNA are transforming the view of RNA as a simple information transfer molecule. Similar to proteins, the native three-dimensional structure of RNA determines its biological activity. Therefore, classifying the current structural space is paramount for functionally annotating RNA molecules. The increasing numbers of RNA structures deposited in the PDB requires more accurate, automatic and benchmarked methods for RNA structure comparison. In this article, we introduce a new algorithm for RNA structure alignment based on a unit-vector approach. The algorithm has been implemented in the SARA program, which results in RNA structure pairwise alignments and their statistical significance. RESULTS: The SARA program has been implemented to be of general applicability even when no secondary structure can be calculated from the RNA structures. A benchmark against the ARTS program using a set of 1275 non-redundant pairwise structure alignments results in inverted approximately 6% extra alignments with at least 50% structurally superposed nucleotides and base pairs. A first attempt to perform RNA automatic functional annotation based on structure alignments indicates that SARA can correctly assign the deepest SCOR classification to &gt;60% of the query structures. AVAILABILITY: The SARA program is freely available through a World Wide Web server http://sgu.bioinfo.cipf.es/services/SARA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</style></abstract><notes><style face="normal" font="default" size="100%">Capriotti, Emidio Marti-Renom, Marc A Research Support, Non-U.S. Gov’t England Bioinformatics (Oxford, England) Bioinformatics. 2008 Aug 15;24(16):i112-8.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aragues, R.</style></author><author><style face="normal" font="default" size="100%">Sali, A.</style></author><author><style face="normal" font="default" size="100%">Bonet, J.</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author><author><style face="normal" font="default" size="100%">Oliva, B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterization of protein hubs by inferring interacting motifs from protein interactions</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Motifs Amino Acid Sequence Binding Sites Computer Simulation *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemical *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Molecular Sequence Data Protein Binding Protein Interaction Mapping/*methods Proteins/*chemistry Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein/*methods</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=17941705</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">1761-71</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.</style></abstract><notes><style face="normal" font="default" size="100%">Aragues, Ramon Sali, Andrej Bonet, Jaume Marti-Renom, Marc A Oliva, Baldo PN2 EY016525,/EY/NEI NIH HHS/United States U54 RR022220/RR/NCRR NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t United States PLoS computational biology PLoS Comput Biol. 2007 Sep;3(9):1761-71. Epub 2007 Jul 30.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eswar, N.</style></author><author><style face="normal" font="default" size="100%">Webb, B.</style></author><author><style face="normal" font="default" size="100%">M. A. Marti-Renom</style></author><author><style face="normal" font="default" size="100%">Madhusudhan, M. S.</style></author><author><style face="normal" font="default" size="100%">Eramian, D.</style></author><author><style face="normal" font="default" size="100%">Shen, M. Y.</style></author><author><style face="normal" font="default" size="100%">Pieper, U.</style></author><author><style face="normal" font="default" size="100%">Sali, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparative protein structure modeling using Modeller</style></title><secondary-title><style face="normal" font="default" size="100%">Curr Protoc Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms Amino Acid Sequence Computer Simulation Crystallography/*methods *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemical *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Molecular Sequence Data Protein Conformation Protein Folding Proteins/*chemistry/*ultrastructure Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein/*methods *Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=18428767</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">Chapter 5</style></volume><pages><style face="normal" font="default" size="100%">Unit 5 6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.</style></abstract><notes><style face="normal" font="default" size="100%">Eswar, Narayanan Webb, Ben Marti-Renom, Marc A Madhusudhan, M S Eramian, David Shen, Min-Yi Pieper, Ursula Sali, Andrej P01 A135707/PHS HHS/United States P01 GM71790/GM/NIGMS NIH HHS/United States R01 GM54762/GM/NIGMS NIH HHS/United States U54 GM62529/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t United States Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.] Curr Protoc Bioinformatics. 2006 Oct;Chapter 5:Unit 5.6.</style></notes></record></records></xml>