CHORAL: a differential geometry approach to the prediction of the cores of protein structures
Motivation: Although the cores of homologous proteins are relatively well conserved, amino acid substitutions lead to significant differences in the structures of divergent superfamilies. Thus, the classification of amino acid sequence patterns and the selection of appropriate fragments of the prote...
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| Veröffentlicht in: | Bioinformatics 2005-10, Vol.21 (19), p.3719-3725 |
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| creator | Montalvão, Rinaldo W. Smith, Richard E. Lovell, Simon C. Blundell, Tom L. |
| description | Motivation: Although the cores of homologous proteins are relatively well conserved, amino acid substitutions lead to significant differences in the structures of divergent superfamilies. Thus, the classification of amino acid sequence patterns and the selection of appropriate fragments of the protein cores of homologues of known structure are important for accurate comparative modelling. Results: CHORAL utilizes a knowledge-based method comprising an amalgam of differential geometry and pattern recognition algorithms to identify conserved structural patterns in homologous protein families. Propensity tables are used to classify and to select patterns that most likely represent the structure of the core for a target protein. In our benchmark, CHORAL demonstrates a performance equivalent to that of MODELLER. Availability: The algorithm is available via internet on http://www-cryst.bioc.cam.ac.uk/servers.html Contact: rinaldo@cryst.bioc.cam.ac.uk |
| doi_str_mv | 10.1093/bioinformatics/bti595 |
| format | Article |
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Thus, the classification of amino acid sequence patterns and the selection of appropriate fragments of the protein cores of homologues of known structure are important for accurate comparative modelling. Results: CHORAL utilizes a knowledge-based method comprising an amalgam of differential geometry and pattern recognition algorithms to identify conserved structural patterns in homologous protein families. Propensity tables are used to classify and to select patterns that most likely represent the structure of the core for a target protein. In our benchmark, CHORAL demonstrates a performance equivalent to that of MODELLER. 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Thus, the classification of amino acid sequence patterns and the selection of appropriate fragments of the protein cores of homologues of known structure are important for accurate comparative modelling. Results: CHORAL utilizes a knowledge-based method comprising an amalgam of differential geometry and pattern recognition algorithms to identify conserved structural patterns in homologous protein families. Propensity tables are used to classify and to select patterns that most likely represent the structure of the core for a target protein. In our benchmark, CHORAL demonstrates a performance equivalent to that of MODELLER. Availability: The algorithm is available via internet on http://www-cryst.bioc.cam.ac.uk/servers.html Contact: rinaldo@cryst.bioc.cam.ac.uk</description><subject>Algorithms</subject><subject>Amino Acid Sequence</subject><subject>Biological and medical sciences</subject><subject>Computer Simulation</subject><subject>Fundamental and applied biological sciences. 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| subjects | Algorithms Amino Acid Sequence Biological and medical sciences Computer Simulation Fundamental and applied biological sciences. Psychology General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Chemical Models, Molecular Molecular Sequence Data Pattern Recognition, Automated - methods Protein Conformation Proteins - analysis Proteins - chemistry Sequence Alignment - methods Sequence Analysis, Protein - methods Sequence Homology, Amino Acid Software |
| title | CHORAL: a differential geometry approach to the prediction of the cores of protein structures |
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