| Protein Structure Prediction |
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Every two years, the performance of current methods is assessed in the CASP experiment. OVERVIEW The practical role of protein structure prediction is now more important than ever. Massive amounts of protein sequence data may be derived from 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. A number of factors exist that make protein structure prediction a very difficult task, including:
Due to exponentially improving computer power, and new algorithms, much progress is being made to overcome these factors by the many research groups that are interested in the task. Prediction of structures for small proteins is now a perfectly realistic goal. A wide range of approaches are routinely applied for such predictions. These approaches may be classified into two broad classes; '' Ab Initio '' modelling and comparative modelling. ''AB INITIO'' PROTEIN MODELLING ''Ab initio''- or ''de novo''- protein modelling methods seek to build three-dimensional protein models "from scratch", i.e., based on physical principles rather than (directly) on previously solved structures. There are many possible procedures that either attempt to mimic Protein Folding or apply some Stochastic method to search possible solutions (i.e., Global Optimization of a suitable energy function). These procedures tend to require vast computational resources, and have thus only been carried out for tiny proteins. To predict protein structure ''de novo'' for larger proteins will require better algorithms and larger computational resources like those afforded by either powerful supercomputers (such as Blue Gene or MDGRAPE-3 ) or distributed computing (such as Folding@home , the Human Proteome Folding Project and Rosetta@Home ). Although these computational barriers are vast, the potential benefits of structural genomics (by predicted or experimental methods) make ''ab initio'' structure prediction an active research field. COMPARATIVE PROTEIN MODELLING Comparative protein modelling uses 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 Tertiary Structural Motif s 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. These methods may also be split into two groups:
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 Chain s 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 Angle s that determine a side chain's orientation relative to the backbone into a set of Rotamer s 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.3}} SOFTWARE MODELLER is a popular software tool for producing homology models using methodology derived from NMR Spectroscopy data processing. SwissModel provides an automated web server for basic homology modeling. A common software tool for protein threading is 3D-PSSM . The basic algorithm for threading is described in and is fairly straightforward to implement. TIP is a knowledgebase of STRUCTFAST4}} models and precomputed similarity relationships between sequences, structures, and binding sites. A very recent review of currently popular software for structure prediction can be found at.5}} A partial list of web servers and available tools is maintained here . Several Distributed Computing projects concerning protein structure prediction have also been implemented, such as the Folding@home , Rosetta@home , Human Proteome Folding Project , Predictor@home and TANPAKU . PROTEIN-PROTEIN COMPLEXES In the case of Complexes Of Two Or More Proteins , where the structures of the proteins are known or can be predicted with high accuracy, Protein-protein Docking methods can be used to predict the structure of the complex. Information of the effect of mutations at specific sites on the affinity of the complex helps to understand the complex structure and to guide docking methods. SEE ALSO
REFERENCES Further reading
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