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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Dorn, Márcio Silva, Mariel Barbachan E. Buriol, Luciana S. Lamb, Luis C. |
| Description | Author Affiliation: Dorn M ( Federal University of Rio Grande do Sul, Institute of Informatics, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil. Electronic address: mdorn@inf.ufrgs.br.); E Silva MB ( Federal University of Rio Grande do Sul, Center of Biotechnology, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil. Electronic address: mariel.barbachan@ufrgs.br.); Buriol LS ( Federal University of Rio Grande do Sul, Institute of Informatics, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil. Electronic address: buriol@inf.ufrgs.br.); Lamb LC ( Federal University of Rio Grande do Sul, Institute of Informatics, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil. Electronic address: lamb@inf.ufrgs.br.) |
| Abstract | A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction. |
| File Format | HTM / HTML |
| ISSN | 14769271 |
| Journal | Computational Biology and Chemistry |
| Volume Number | 53PB |
| e-ISSN | 1476928X |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2014-10-12 |
| Publisher Place | Great Britain (UK) |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Discipline Medical Informatics Discipline Chemistry Discipline Computational Biology |
| Content Type | Text |
| Resource Type | Article |
| Subject | Organic Chemistry Structural Biology Biochemistry Computational Mathematics |
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