01811nas a2200145   4500008004100000245010300041210006900144260001600213300000800229490000700237520125000244100001801494700002401512856012901536       2010                        eng d00aQuantifying the relationship between sequence and three-dimensional structure conservation in RNA.0 aQuantifying the relationship between sequence and threedimension  c2010 Jun 15  a3220 v113 a<p>ABSTRACT: BACKGROUND: In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA. RESULTS: Here we introduce an all-against-all sequence- and three-dimensional (3D) structure-based comparison of a representative set of RNA structures, which have allowed us to quantitatively confirm that: (i) there is a measurable relationship between sequence and structure conservation that weakens for alignments resulting in below 60% sequence identity, (ii) evolution tends to conserve more RNA structure than sequence, and (iii) there is a twilight zone for RNA homology detection. DISCUSSION: The computational analysis here presented quantitatively describes the relationship between sequence and structure for RNA molecules and defines a twilight zone region for detecting RNA homology. Our work could represent the theoretical basis and limitations for future developments in comparative RNA 3D structure prediction.</p>1 aCapriotti, E.1 aMarti-Renom, M., A.  uhttp://clinbioinfosspa.es/content/quantifying-relationship-between-sequence-and-three-dimensional-structure-conservation-rna00479nas a2200157   4500008004100000245004000041210004000081250000800121260003700129653003000166100002400196700001800220700001900238700001500257856004900272       2009                        eng d00aStructural Comparison and Alignment0 aStructural Comparison and Alignment  a2nd  aNew Jersey. USAbWiley-Blackwell10aStructural Bioinformatics1 aMarti-Renom, M., A.1 aCapriotti, E.1 aShindyalov, I.1 aBourne, P.  uhttp://www.amazon.com/gp/product/0470181052/00387nas a2200109   4500008004100000245004800041210004800089260005700137100001800194700002400212856004100236       2008                        eng d00aAssessment of protein structure predictions0 aAssessment of protein structure predictions  aNew Jersey, USAbWorld Scientific Publishing Company1 aCapriotti, E.1 aMarti-Renom, M., A.  uhttp://www.amazon.com/dp/9812778772/02322nas a2200181   4500008004100000245005400041210005300095300001100148490000700159520158000166653005701746653002101803653014101824653002701965100001801992700002402010856010602034       2008                        eng d00aRNA structure alignment by a unit-vector approach0 aRNA structure alignment by a unitvector approach  ai112-80 v243 aMOTIVATION: 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 >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.10aAlgorithms Base Sequence Computer Simulation *Models10aChemical *Models10aMolecular Molecular Sequence Data Nucleic Acid Conformation RNA/*chemistry/*ultrastructure Sequence Alignment/*methods Sequence Analysis10aRNA/*methods *Software1 aCapriotti, E.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1868981102909nas a2200241   4500008004100000245013300041210006900174300001200243490000700255520182100262653009602083653009302179653007402272653002302346653008902369100001802458700001502476700001602491700001502507700001502522700002402537856010602561       2008                        eng d00aUse of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans0 aUse of estimated evolutionary strength at the codon level improv  a198-2040 v293 aPredicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.10aAlgorithms Codon/genetics Computational Biology/*methods *DNA Mutational Analysis Databases10aHuman Humans Iduronic Acid/analogs & derivatives/metabolism *Point Mutation Polymorphism10aMolecular *Genetic Predisposition to Disease Genetic Variation Genome10aProtein *Evolution10aSingle Nucleotide Proteins/chemistry/*genetics Tumor Suppressor Protein p53/genetics1 aCapriotti, E.1 aArbiza, L.1 aCasadio, R.1 aDopazo, J.1 aDopazo, H.1 aMarti-Renom, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17935148