Threading works by using statistical knowledge of the relationship between the structures deposited in the PDB and the sequence of the protein which one wishes to model.
Many levels exist in the hierarchy, but the principal levels are family, superfamily, and fold: A general paradigm of protein threading consists of the following four steps: Homology modeling and protein threading are both template-based methods and there is no rigorous boundary between them in terms of prediction techniques.
A simple example of a profile representation would be to take each amino acid in the structure and simply label it according to whether it is buried in the core of the protein or exposed on the surface.
[1] The term threading was first coined by David Jones, William R. Taylor and Janet Thornton in 1992,[2] and originally referred specifically to the use of a full 3-D structure atomic representation of the protein template in fold recognition.
Many different algorithms have been proposed for finding the correct threading of a sequence onto a structure, though many make use of dynamic programming in some form.
[citation needed] Researchers have made use of many combinatorial optimization methods such as conditional random fields, simulated annealing, branch and bound, and linear programming, searching to arrive at heuristic solutions.