Protein Predicting Strategies
If a protein of known tertiary structure shares at least 30% of its sequence with a potential homolog of undetermined structure, comparative methods that overlay the putative unknown structure with the known can be utilized to predict the likely structure of the unknown. However, below this threshold three other classes of strategy are used to determine possible structure from an initial model: ab initio protein prediction, fold recognition, and threading. In ab initio methods, an initial effort to elucidate secondary structures (alpha helix, beta sheet, beta turn, etc.) from primary structure is made by utilization of physicochemical parameters and neural net algorithms. From that point, algorithms predict tertiary folding. One drawback to this strategy is that it is not yet capable of incorporating the locations and orientation of amino acid side chains. In fold recognition strategies, a prediction of secondary structure is first made and then compared to either a library of known protein folds, such as CATH or SCOP, or what is known as a "periodic table" of possible secondary structure forms. A confidence score is then assigned to likely matches. In threading strategies, the fold recognition technique is expanded further. In this process, empirically based energy functions for the interaction of residue pairs are used to place the unknown protein onto a putative backbone as a best fit, accommodating gaps where appropriate. The best interactions are then accentuated in order to discriminate amongst potential decoys and to predict the most likely conformation. The goal of both fold and threading strategies is to ascertain whether a fold in an unknown protein is similar to a domain in a known one deposited in a database, such as the protein databank (PDB). This is in contrast to de novo (ab initio) methods where structure is determined using a physics-base approach en lieu of comparing folds in the protein to structures in a data base.
Read more about this topic: De Novo Protein Structure Prediction
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