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TYPED OF PROTEIN MODELLING
by prasanth Mon Jan 14, 2008 9:48 pm
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 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.
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.
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 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.
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.
prasanth- Number of posts : 4
Registration date : 2008-01-11
Re: TYPED OF PROTEIN MODELLING
by lizz Tue Sep 21, 2010 2:04 pm
hell this is lizz I am bio tech student and i am interested in bioinformatics
lizz- Number of posts : 2
Registration date : 2010-09-21
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