Protein structure prediction
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Protein structure prediction is one of the most significant technologies pursued by computational structural biology and theoretical chemistry. It has the aim of determining the three-dimensional structure of proteins from their amino acid sequences. In more formal terms, this is expressed as the prediction of protein tertiary structure from primary structure. Given the usefulness of known protein structures in such valuable tasks as rational drug design this is a highly active field of research.
Every two years, the performance of current methods is assessed in the CASP experiment.
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[edit] 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:
- The number of possible structures that proteins may possess is extremely large, as highlighted by the Levinthal paradox.
- The physical basis of protein structural stability is not fully understood.
- The tertiary structure of a native protein may not be readily formed without the aid of trans-acting factors. For example, proteins known as chaperones are required for some proteins to properly fold; other proteins cannot fold properly without modifications such as glycosylation.
- A particular sequence may be able to assume multiple conformations depending on its environment, and the biologically active conformation may not be the most thermodynamically favorable.
- Direct simulation of protein folding via methods such as molecular dynamics is not tractable for both practical and theoretical reasons except in very small proteins, despite the efforts of distributed computing projects such as Folding@home.
Despite the above hindrances, much progress is being made 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.
[edit] 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 attempt to predict protein structure de novo for larger proteins, we will need better algorithms and larger computational resources like those afforded by either powerful supercomputers (such as Blue Gene or MDGRAPE-3) or distributed computing (see Human Proteome Folding Project). 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.
[edit] 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 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.
These methods may also be split into two groups:
- Homology modelling is based on the reasonable assumption that two homologous proteins will share very similar structures. Because a protein's fold is more evolutionarily conserved than its amino acid sequence, a target sequence can be modeled with reasonable accuracy on a very distantly related template, provided that the relationship between target and template can be discerned through sequence alignment. It has been suggested that the primary bottleneck in comparative modelling arises from difficulties in alignment rather than from errors in structure prediction given a known-good alignment.[1] Unsurprisingly, homology modelling is most accurate when the target and template have similar sequences.
- Protein threading[2] scans the 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. This type of method is also known as 3D-1D fold recognition due to its compatibility analysis between three-dimensional structures and linear protein sequences. This method has also given rise to methods performing an inverse folding search by evaluating the compatibility of a given structure with a large database of sequences, thus predicting which sequences have the potential to produce a given fold.
[edit] 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.[3]
[edit] 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[2] and is fairly straightforward to implement.
TIP is a knowledgebase of STRUCTFAST[4] 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 Rosetta@home, Human Proteome Folding Project, Predictor@home and TANPAKU.
[edit] 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.
[edit] See also
[edit] References
- ^ Zhang Y and Skolnick J. (2005). The protein structure prediction problem could be solved using the current PDB library. Proc. Natl. Acad. Sci. USA 102(4):1029-34. Entrez PubMed 15653774
- ^ a b Bowie JU, Luthy R, Eisenberg D. (1991). A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164-70. Entrez PubMed 1853201
- ^ Voigt CA, Gordon DB, Mayo SL. (2000). Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design. J Mol Biol 299(3):789-803.Entrez PubMed 10835284
- ^ Debe DA, Danzer JF, Goddard WA, Poleksic A. STRUCTFAST:Protein Sequence Remote Homology Detection and Alignment Using Novel Dynamic Programming and Profile-Profile Scoring. Proteins. 2006 64:960-967. Entrez PubMed 16786595
- ^ 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. Protein Sci 15:808-824.Entrez PubMed 16600967
Bonneau R, Baliga NS, Deutsch EW, Shannon P, Hood L. (2004) Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1.Genome Biology. 5(8):R52-68
| Protein structure determination methods | ||
|---|---|---|
| High resolution: | X-ray crystallography | NMR | Electron crystallography | |
| Medium resolution: | Cryo-electron microscopy | Fiber diffraction | Mass spectrometry | |
| Spectroscopic: | NMR | Circular dichroism | Absorbance | Fluorescence | Fluorescence anisotropy | |
| Translational Diffusion: | Analytical ultracentrifugation | Size exclusion chromatography | Light scattering | NMR | |
| Rotational Diffusion: | Fluorescence anisotropy | Flow birefringence | Dielectric relaxation | NMR | |
| Chemical: | Hydrogen-deuterium exchange | Site-directed mutagenesis | Chemical modification | |
| Thermodynamic: | Equilibrium unfolding | |
| Computational: | Protein structure prediction | Molecular docking | |
| ←Tertiary structure | Quaternary structure→ | |
Experimental : Protein purification | Green fluorescent protein | Western blot | Protein immunostaining | Protein sequencing | Gel electrophoresis/Protein electrophoresis | Protein immunoprecipitation | Peptide mass fingerprinting
Bioinformatics : Protein structure prediction | Protein-protein docking | Protein structural alignment | Protein ontology | Protein-protein interaction prediction
