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Adrian Shehu - One of the best experts on this subject based on the ideXlab platform.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
Katharine Molloy - One of the best experts on this subject based on the ideXlab platform.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
M. Jennifer Van - One of the best experts on this subject based on the ideXlab platform.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
Daniel Barbara - One of the best experts on this subject based on the ideXlab platform.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
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Exploring representations of Protein Structure for automated remote homology detection and mapping of Protein Structure space
BMC Bioinformatics, 2014Co-Authors: Katharine Molloy, M. Jennifer Van, Daniel Barbara, Adrian ShehuAbstract:BACKGROUND: Due to rapid sequencing of genomes, there are now millions of deposited Protein sequences with no known function. Fast sequence-based comparisons allow detecting close homologs for a Protein of interest to transfer functional information from the homologs to the given Protein. Sequence-based comparison cannot detect remote homologs, in which evolution has adjusted the sequence while largely preserving Structure. Structure-based comparisons can detect remote homologs but most methods for doing so are too expensive to apply at a large scale over structural databases of Proteins. Recently, fragment-based structural representations have been proposed that allow fast detection of remote homologs with reasonable accuracy. These representations have also been used to obtain linearly-reducible maps of Protein Structure space. It has been shown, as additionally supported from analysis in this paper that such maps preserve functional co-localization of the Protein Structure space.\n\nMETHODS: Inspired by a recent application of the Latent Dirichlet Allocation (LDA) model for conducting structural comparisons of Proteins, we propose higher-order LDA-obtained topic-based representations of Protein Structures to provide an alternative route for remote homology detection and organization of the Protein Structure space in few dimensions. Various techniques based on natural language processing are proposed and employed to aid the analysis of topics in the Protein Structure domain.\n\nRESULTS: We show that a topic-based representation is just as effective as a fragment-based one at automated detection of remote homologs and organization of Protein Structure space. We conduct a detailed analysis of the information content in the topic-based representation, showing that topics have semantic meaning. The fragment-based and topic-based representations are also shown to allow prediction of superfamily membership.\n\nCONCLUSIONS: This work opens exciting venues in designing novel representations to extract information about Protein Structures, as well as organizing and mining Protein Structure space with mature text mining tools.
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 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.
