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=1868981102791nas a2200193   4500008004100000245009800041210006900139300001200208490000800220520199800228653005602226653012802282100001302410700001802423700002402441700001302465700001302478856010602491       2006                        eng d00aRefinement of protein structures by iterative comparative modeling and CryoEM density fitting0 aRefinement of protein structures by iterative comparative modeli  a1655-680 v3573 aWe developed a method for structure characterization of assembly components by iterative comparative protein structure modeling and fitting into cryo-electron microscopy (cryoEM) density maps. Specifically, we calculate a comparative model of a given component by considering many alternative alignments between the target sequence and a related template structure while optimizing the fit of a model into the corresponding density map. The method relies on the previously developed Moulder protocol that iterates over alignment, model building, and model assessment. The protocol was benchmarked using 20 varied target-template pairs of known structures with less than 30% sequence identity and corresponding simulated density maps at resolutions from 5A to 25A. Relative to the models based on the best existing sequence profile alignment methods, the percentage of C(alpha) atoms that are within 5A of the corresponding C(alpha) atoms in the superposed native structure increases on average from 52% to 66%, which is half-way between the starting models and the models from the best possible alignments (82%). The test also reveals that despite the improvements in the accuracy of the fitness function, this function is still the bottleneck in reducing the remaining errors. To demonstrate the usefulness of the protocol, we applied it to the upper domain of the P8 capsid protein of rice dwarf virus that has been studied by cryoEM at 6.8A. The C(alpha) root-mean-square deviation of the model based on the remotely related template, bluetongue virus VP7, improved from 8.7A to 6.0A, while the best possible model has a C(alpha) RMSD value of 5.3A. Moreover, the resulting model fits better into the cryoEM density map than the initial template structure. The method is being implemented in our program MODELLER for protein structure modeling by satisfaction of spatial restraints and will be applicable to the rapidly increasing number of cryoEM density maps of macromolecular assemblies.10aAmino Acid Sequence Cryoelectron Microscopy *Models10aMolecular Molecular Sequence Data Plant Viruses/chemistry *Protein Conformation Software Viral Proteins/*chemistry/genetics1 aTopf, M.1 aBaker, M., L.1 aMarti-Renom, M., A.1 aChiu, W.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1649020701632nas a2200193   4500008004100000245007000041210006900111300001100180490000700191520092300198653004001121653008301161100002401244700002401268700001401292700001301306700001301319856010601332       2002                        eng d00aReliability of assessment of protein structure prediction methods0 aReliability of assessment of protein structure prediction method  a435-400 v103 a<p>The reliability of ranking of protein structure modeling methods is assessed. The assessment is based on the parametric Student’s t test and the nonparametric Wilcox signed rank test of statistical significance of the difference between paired samples. The approach is applied to the ranking of the comparative modeling methods tested at the fourth meeting on Critical Assessment of Techniques for Protein Structure Prediction (CASP). It is shown that the 14 CASP4 test sequences may not be sufficient to reliably distinguish between the top eight methods, given the model quality differences and their standard deviations. We suggest that CASP needs to be supplemented by an assessment of protein structure prediction methods that is automated, continuous in time, based on several criteria applied to a large number of models, and with quantitative statistical reliability assigned to each characterization.</p>10a*Computer Simulation Humans *Models10aMolecular *Protein Conformation Proteins/*chemistry Reproducibility of Results1 aMarti-Renom, M., A.1 aMadhusudhan, M., S.1 aFiser, A.1 aRost, B.1 aSali, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12005441