Protein modeling

1. Protein modeling

2. Protein structure Prediction AKA protein Modeling: • Protein structure prediction is the prediction of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of a protein's tertiary structure from its primary structure • Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry • Protein structure prediction is of high importance in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes)

3. Protein structure Prediction AKA protein Modeling (Cont’d): • Massive amounts of protein sequence data are produced by modern large-scale DNA sequencing efforts such as the Human Genome Project. Despite community-wide efforts in structural genomics, the output of experimentally determined protein structures—typically by time-consuming and relatively expensive X-ray crystallography or NMR spectroscopy—is lagging far behind the output of protein sequences.

4. Difficulties : • A number of factors exist that make protein structure prediction a very difficult task. The two main problems are: • The number of possible protein structures is extremely large • The physical basis of protein structural stability is not fully understood • As a result, any protein structure prediction method needs a way to explore the space of possible structures efficiently (a search strategy), and a way to identify the most plausible structure

5. Methods of Modelling : • Ab initio Protein Modeling • Comparative Protein Modeling 2 Methods: • Homology Modeling • Threading Modeling

6. Comparativeproteinmodeling: • Using previously solved structures as starting points, or templates. This is effective because it appears that although the number of actual proteins is vast, there is a limited set of tertiarystructural motifs to which most proteins belong. It has been suggested that there are only around 2000 distinct protein folds in nature, though there are many millions of different proteins.

7. Comparativeproteinmodeling (cont’d): • These methods may also be split into two groups : • Homology modeling is based on the reasonable assumption that two homologous proteins will share very similar structures. • Protein threadingscansthe amino acid sequence of an unknown structure against a database of solved structures. In each case, a scoring function is used to assess the compatibility of the sequence to the structure, thus yielding possible three-dimensional models

8. Side chain Geometry Prediction • Even structure prediction methods that are reasonably accurate for the peptide backbone often get the orientation and packing of the amino acid side chains wrong. Methods that specifically address the problem of predicting side chain geometry include dead-end elimination and the self-consistent mean field method. Both discretize the continuously varying dihedral angles that determine a side chain's orientation relative to the backbone into a set of rotamers with fixed dihedral angles. The methods then attempt to identify the set of rotamers that minimize the model's overall energy. Rotamers are the side chain conformations with low energy. Such methods are most useful for analyzing the protein's hydrophobic core, where side chains are more closely packed; they have more difficulty addressing the looser constraints and higher flexibility of surface residues.

9. Software : • MODELLER is a popular software tool for producing homology models using methodology derived from NMR spectroscopy data processing. Swiss Modelprovides an automated web server for basic homology modeling

10. Automatic Structure Prediction: • CASP, which stands for Critical Assessment of Techniques for Protein Structure Prediction, is a community-wide experiment for protein structure prediction taking place every two years since 1994. CASP provides users and research groups with an opportunity to assess the quality of available methods and automatic servers for protein structure prediction

11. References : • Zhang Y (2008). "Progress and challenges in protein structure prediction". CurrOpinStructBiol18 (3): 342–348. • Zhang Y and Skolnick J (2005). "The protein structure prediction problem could be solved using the current PDB library". Proc NatlAcadSci USA102 (4): 1029–1034 • Bowie JU, Luthy R, Eisenberg D (1991). "A method to identify protein sequences that fold into a known three-dimensional structure". Science253 (5016): 164–170 • Voigt CA, Gordon DB, Mayo SL (2000). "Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design • Sivasubramanian A, Sircar A, Chaudhury S, Gray J J (2009). "Toward high-resolution homology modeling of antibody Fv regions and application to antibody–antigen docking". Proteins • Nayeem A, Sitkoff D, Krystek S Jr (2006). "A comparative study of available software for high-accuracy homology modeling: From sequence alignments to structural models". • Battey JN, Kopp J, Bordoli L, Read RJ, Clarke ND, Schwede T (2007). "Automated server predictions in CASP7". Proteins69 (Suppl 8): 68–82

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