The Experts below are selected from a list of 42195 Experts worldwide ranked by ideXlab platform
Geoffrey J. Barton - One of the best experts on this subject based on the ideXlab platform.
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jpred4 a protein secondary Structure Prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary Structure Prediction server which provides Predictions by the JNet algorithm, one of the most accurate methods for secondary Structure Prediction. In addition to protein secondary Structure, JPred also makes Predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million Predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary Structure Prediction accuracy of 82.0% while solvent accessibility Prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials.
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JPred4: a protein secondary Structure Prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary Structure Prediction server which provides Predictions by the JNet algorithm, one of the most accurate methods for secondary Structure Prediction. In addition to protein secondary Structure, JPred also makes Predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million Predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary Structure Prediction accuracy of 82.0% while solvent accessibility Prediction accuracy has been raised to 90% for residues
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jpred a consensus secondary Structure Prediction server
Bioinformatics, 1998Co-Authors: James Cuff, Michele Clamp, A M Siddiqui, M Finlay, Geoffrey J. BartonAbstract:UNLABELLED An interactive protein secondary Structure Prediction Internet server is presented. The server allows a single sequence or multiple alignment to be submitted, and returns Predictions from six secondary Structure Prediction algorithms that exploit evolutionary information from multiple sequences. A consensus Prediction is also returned which improves the average Q3 accuracy of Prediction by 1% to 72.9%. The server simplifies the use of current Prediction algorithms and allows conservation patterns important to Structure and function to be identified. AVAILABILITY http://barton.ebi.ac.uk/servers/jpred.h tml CONTACT geoff@ebi.ac.uk
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Protein secondary Structure Prediction.
Current Opinion in Structural Biology, 1995Co-Authors: Geoffrey J. BartonAbstract:Abstract The past year has seen consolidation of protein secondary Structure Prediction methods. The advantages of Prediction from an aligned family of proteins have been highlighted by several accurate Predictions made ‘blind’, before any X-ray or NMR Structure was known for the family. New techniques that apply machine learning and discriminant analysis show promise as alternatives to neural networks.
David H. Mathews - One of the best experts on this subject based on the ideXlab platform.
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rnaStructure software for rna secondary Structure Prediction and analysis
BMC Bioinformatics, 2010Co-Authors: Jessica S Reuter, David H. MathewsAbstract:To understand an RNA sequence's mechanism of action, the Structure must be known. Furthermore, target RNA Structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary Structure Prediction, using thermodynamics, can be used to develop hypotheses about the Structure of an RNA sequence. RNAStructure is a software package for RNA secondary Structure Prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary Structure Prediction (using several algorithms), Prediction of base pair probabilities, bimolecular Structure Prediction, and Prediction of a Structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAStructure serve to make RNA secondary Structure Prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAStructure.html .
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Revolutions in RNA secondary Structure Prediction.
Journal of Molecular Biology, 2006Co-Authors: David H. MathewsAbstract:RNA Structure formation is hierarchical and, therefore, secondary Structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional Structure. Secondary Structure Prediction algorithms evolved from predicting a single, lowest free energy Structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary Structure Prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples Structures from the complete Boltzmann ensemble.
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RNA secondary Structure Prediction
Encyclopedia of Genetics Genomics Proteomics and Bioinformatics, 2005Co-Authors: David H. Mathews, Michael ZukerAbstract:The field of RNA secondary Structure Prediction matured in the last 10 years with the development of many readily available software packages. The thermodynamic parameters for predicting the free energy of an RNA secondary Structure are continuing to be revised on the basis of new experiments. This article reviews the available secondary Structure Prediction algorithms for both a single sequence and multiple sequences. Keywords: RNA thermodynamics; RNA secondary Structure; RNA statistical mechanics; folding free energy
David Baker - One of the best experts on this subject based on the ideXlab platform.
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improved protein Structure Prediction using predicted interresidue orientations
Proceedings of the National Academy of Sciences of the United States of America, 2020Co-Authors: Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, S G Ovchinnikov, David BakerAbstract:The Prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein Structure Prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating Structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described Structure-Prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the “ideality” of a protein Structure. The method promises to be useful for a broad range of protein Structure Prediction and design problems.
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Protein Structure Prediction and analysis using the Robetta server
Nucleic Acids Research, 2004Co-Authors: David E. Kim, Dylan Chivian, David BakerAbstract:The Robetta server (http://robetta.bakerlab.org) provides automated tools for protein Structure Prediction and analysis. For Structure Prediction, sequences submitted to the server are parsed into putative domains and structural models are generated using either comparative modeling or de novo Structure Prediction methods. If a confident match to a protein of known Structure is found using BLAST, PSI-BLAST, FFAS03 or 3D-Jury, it is used as a template for comparative modeling. If no match is found, Structure Predictions are made using the de novo Rosetta fragment insertion method. Experimental nuclear magnetic resonance (NMR) constraints data can also be submitted with a query sequence for RosettaNMR de novo Structure determination. Other current capabilities include the Prediction of the effects of mutations on protein-protein interactions using computational interface alanine scanning. The Rosetta protein design and protein-protein docking methodologies will soon be available through the server as well.
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protein Structure Prediction using rosetta
Methods in Enzymology, 2004Co-Authors: Carol A Rohl, Charlie E M Strauss, Kira M S Misura, David BakerAbstract:Publisher Summary This chapter elaborates protein Structure Prediction using Rosetta. Double-blind assessments of protein Structure Prediction methods have indicated that the Rosetta algorithm is perhaps the most successful current method for de novo protein Structure Prediction. In the Rosetta method, short fragments of known proteins are assembled by a Monte Carlo strategy to yield native-like protein conformations. Using only sequence information, successful Rosetta Predictions yield models with typical accuracies of 3–6 A˚ Cα root mean square deviation (RMSD) from the experimentally determined Structures for contiguous segments of 60 or more residues. For each Structure Prediction, many short simulations starting from different random seeds are carried out to generate an ensemble of decoy Structures that have both favorable local interactions and protein-like global properties. This set is then clustered by structural similarity to identify the broadest free energy minima. The effectiveness of conformation modification operators for energy function optimization is also described in this chapter.
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Protein Structure Prediction in 2002.
Current Opinion in Structural Biology, 2002Co-Authors: Jack Schonbrun, William J. Wedemeyer, David BakerAbstract:Central issues concerning protein Structure Prediction have been highlighted by the recently published summary of the fourth community-wide protein Structure Prediction experiment (CASP4). Although sequence/Structure alignment remains the bottleneck in comparative modeling, there has been substantial progress in fully automated remote homolog detection and in de novo Structure Prediction. Significant further progress will probably require improvements in high-resolution modeling.
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protein Structure Prediction and structural genomics
Science, 2001Co-Authors: David Baker, Andrej SaliAbstract:Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their Structure and function. Although experimental Structure determination methods are providing high-resolution Structure information about a subset of the proteins, computational Structure Prediction methods will provide valuable information for the large fraction of sequences whose Structures will not be determined experimentally. The first class of protein Structure Prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known Structure. The second class of methods, de novo or ab initio methods, predict the Structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known Structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the Prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein Structure Prediction methods play in the growing worldwide effort in structural genomics.
Alexey Drozdetskiy - One of the best experts on this subject based on the ideXlab platform.
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jpred4 a protein secondary Structure Prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary Structure Prediction server which provides Predictions by the JNet algorithm, one of the most accurate methods for secondary Structure Prediction. In addition to protein secondary Structure, JPred also makes Predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million Predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary Structure Prediction accuracy of 82.0% while solvent accessibility Prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials.
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JPred4: a protein secondary Structure Prediction server
Nucleic Acids Research, 2015Co-Authors: Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. BartonAbstract:JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary Structure Prediction server which provides Predictions by the JNet algorithm, one of the most accurate methods for secondary Structure Prediction. In addition to protein secondary Structure, JPred also makes Predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million Predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary Structure Prediction accuracy of 82.0% while solvent accessibility Prediction accuracy has been raised to 90% for residues
Janusz M. Bujnicki - One of the best experts on this subject based on the ideXlab platform.
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Protein-Structure Prediction by recombination of fragments.
ChemBioChem, 2005Co-Authors: Janusz M. BujnickiAbstract:The field of protein-Structure Prediction has been revolutionized by the application of "mix-and-match" methods both in template-based homology modeling and in template-free de novo folding. Consensus analysis and recombination of fragments copied from known protein Structures is currently the only approach that allows the building of models that are closer to the native Structure of the target protein than the Structure of its closest homologue. It is also the most successful approach in cases in which the target protein exhibits a novel three-dimensional fold. This review summarizes the recent developments in both template-based and template-free protein Structure modeling and compares the available methods for protein-Structure Prediction by recombination of fragments. A convergence between the "protein folding" and "protein evolution" schools of thought is postulated.
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GeneSilico protein Structure Prediction meta-server
Nucleic Acids Research, 2003Co-Authors: Michal A. Kurowski, Janusz M. BujnickiAbstract:Rigorous assessments of protein Structure Prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of Predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein Structure Predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein Structure Prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary Structure Prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein Structure Prediction meta-server is freely available for academic users at http://genesilico.pl/meta.
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Structure Prediction meta server
Bioinformatics, 2001Co-Authors: Janusz M. Bujnicki, Daniel Fischer, Arne Elofsson, Leszek RychlewskiAbstract:UNLABELLED The Structure Prediction Meta Server offers a convenient way for biologists to utilize various high quality Structure Prediction servers available worldwide. The meta server translates the results obtained from remote services into uniform format, which are consequently used to request a jury Prediction from a remote consensus server Pcons. AVAILABILITY The Structure Prediction meta server is freely available at http://BioInfo.PL/meta/, some remote servers have however restrictions for non-academic users, which are respected by the meta server. SUPPLEMENTARY INFORMATION Results of several sessions of the CAFASP and LiveBench programs for assessment of performance of fold-recognition servers carried out via the meta server are available at http://BioInfo.PL/services.html.
