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Pcons5: combining consensus, structural evaluation and fold recognition scores
- Bioinformatics
, 2005
"... doi:10.1093/bioinformatics/bti702 ..."
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Automatic consensus-based fold recognition using pcons, proq, and pmodeller
- PROTEINS: Structure, Function, and Genetics
"... ABSTRACT CASP provides a unique opportu-nity to compare the performance of automatic fold recognition methods with the performance of manual experts who might use these methods. Here, we show that a novel automatic fold recognition server, Pmodeller, is getting close to the perfor-mance of manual ex ..."
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ABSTRACT CASP provides a unique opportu-nity to compare the performance of automatic fold recognition methods with the performance of manual experts who might use these methods. Here, we show that a novel automatic fold recognition server, Pmodeller, is getting close to the perfor-mance of manual experts. Although a small group of experts still perform better, most of the experts participating in CASP5 actually performed worse even though they had full access to all automatic predictions. Pmodeller is based on Pcons (Lund-ström et al., Protein Sci 2001; 10(11):2354–2365) the first “consensus ” predictor that uses predictions from many other servers. Therefore, the success of Pmodeller and other consensus servers should be seen as a tribute to the collective of all developers of fold recognition servers. Furthermore we show that the inclusion of another novel method, ProQ2, to evaluate the quality of the protein models improves the predictions. Proteins 2003;53:534–541. © 2003 Wiley-Liss, Inc. Key words: fold recognition; threading; LiveBench; CASP; CAFASP; protein structure pre-diction
doi:10.1093/nar/gkm319 Pcons.net: protein structure prediction meta server
, 2007
"... The Pcons.net Meta Server ..."
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alignment
"... FragQA: predicting local fragment quality of a sequence-structure ..."
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FragQA: predicting local fragment quality of a sequence-structure
ACE: Consensus Fold Recognition by Predicted Model Quality
, 2004
"... I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revision, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Protein structure prediction has been a fundamental cha ..."
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I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revision, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Protein structure prediction has been a fundamental challenge in the biological field. In this post-genomic era, the need for automated protein structure prediction has never been more evident and researchers are now focusing on developing computa-tional techniques to predict three-dimensional structures with high throughput. Consensus-based protein structure prediction methods are state-of-the-art in automatic protein structure prediction. A consensus-based server combines the outputs of several individual servers and tends to generate better predictions than any individual server. Consensus-based methods have proved to be successful in recent CASP (Critical Assessment of Structure Prediction). In this thesis, a Support Vector Machine (SVM) regression-based consensus method is proposed for protein fold recognition, a key component for high through-put protein structure prediction and protein function annotation. The SVM first extracts the features of a structural model by comparing the model to the other models produced by all the individual servers. Then, the SVM predicts the quality of each model. The experimental results from several LiveBench data sets confirm that our proposed consensus method, SVM regression, consistently performs better than any individual server. Based on this method, we developed a meta server, the Alignment by Consensus Estimation (ACE). iii
Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction
"... Abstract: The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the mo ..."
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Abstract: The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.
doi:10.1093/nar/gkp322 QMEAN server for protein model quality estimation
, 2009
"... Model quality estimation is an essential component of protein structure prediction, since ultimately the accuracy of a model determines its usefulness for specific applications. Usually, in the course of protein structure prediction a set of alternative models is produced, from which subsequently th ..."
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Model quality estimation is an essential component of protein structure prediction, since ultimately the accuracy of a model determines its usefulness for specific applications. Usually, in the course of protein structure prediction a set of alternative models is produced, from which subsequently the most accurate model has to be selected. The QMEAN server provides access to two scoring functions successfully tested at the eighth round of the community-wide blind test experiment CASP. The user can choose between the composite scoring function QMEAN, which derives a quality estimate on the basis of the geometrical analysis of single models, and the clustering-based scoring function QMEANclust which calculates a global and local quality estimate based on a weighted allagainst-all comparison of the models from the ensemble provided by the user. The web server performs a ranking of the input models and highlights potentially problematic regions for each model. The QMEAN server is available at
FragQA: predicting local fragment quality of a sequence-structure alignment
"... Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to id ..."
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Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well predicted regions of the model, so that the structure biology community can benefit from automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to. Results. We present a novel technique FragQA to accurately predict local quality of a sequence-structure (i.e., sequence-template) alignment generated by comparative modeling (i.e., homology modeling and threading). Different from previous local quality assessment methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure. FragQA uses an SVM (Support Vector Machines) regression method to perform prediction using information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local quality. More specifically, FragQA has prediction accuracy better than a top performer ProQres [18]. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e., sequence similarity) is the major factor in predicting local quality; and (3) structure information such as solvent accessibility and secondary structure helps improving prediction performance.
BIOINFORMATICS DUF283 Domain of Dicer Proteins Has a Double-Stranded RNA- Binding Fold
, 2006
"... Two RNases, Dicer and Argonaute, are at the heart of the RNA interference (RNAi) molecular machinery responsible for gene silencing. Both RNases contain multiple domains, most of which have been characterized or have functions that can be predicted based on sequence comparisons. However, Dicers of h ..."
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Two RNases, Dicer and Argonaute, are at the heart of the RNA interference (RNAi) molecular machinery responsible for gene silencing. Both RNases contain multiple domains, most of which have been characterized or have functions that can be predicted based on sequence comparisons. However, Dicers of higher eukaryotes contain the domain known as DUF283 which at present has no assigned role. Using sensitive profile-profile comparisons, we detected a divergent double-stranded RNA-binding domain coinciding with the DUF283 of Dicer. This finding has potential implications regarding the mechanistic role of Dicer in RNAi. Contact:
doi:10.1093/nar/gkn169 The YqfN protein of Bacillus subtilis is the tRNA: m 1 A22 methyltransferase (TrmK)
, 2007
"... N 1-methylation of adenosine to m 1 A occurs in several different positions in tRNAs from various organisms. A methyl group at position N 1 prevents Watson–Crick-type base pairing by adenosine and is therefore important for regulation of structure and stability of tRNA molecules. Thus far, only one ..."
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N 1-methylation of adenosine to m 1 A occurs in several different positions in tRNAs from various organisms. A methyl group at position N 1 prevents Watson–Crick-type base pairing by adenosine and is therefore important for regulation of structure and stability of tRNA molecules. Thus far, only one family of genes encoding enzymes responsible for m 1 A methylation at position 58 has been identified, while other m 1 A methyltransferases (MTases) remain elusive. Here, we show that Bacillus subtilis open reading frame yqfN is necessary and sufficient for N 1-adenosine methylation at position 22 of bacterial tRNA. Thus, we propose to rename YqfN as TrmK, according to the traditional nomenclature for bacterial
