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Sequential structure alignment program

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The SSAP (Sequential Structure Alignment Program) method uses double dynamic programming to produce a structural alignment based on atom-to-atom vectors in structure space. Instead of the alpha carbons typically used in structural alignment, SSAP constructs its vectors from the beta carbons for all residues except glycine, a method which thus takes into account the rotameric state of each residue as well as its location along the backbone. SSAP works by first constructing a series of inter-residue distance vectors between each residue and its nearest non-contiguous neighbors on each protein. A series of matrices are then constructed containing the vector differences between neighbors for each pair of residues for which vectors were constructed. Dynamic programming applied to each resulting matrix determines a series of optimal local alignments which are then summed into a "summary" matrix to which dynamic programming is applied again to determine the overall structural alignment.

SSAP originally produced only pairwise alignments but has since been extended to multiple alignments as well.[1] It has been applied in an all-to-all fashion to produce a hierarchical fold classification scheme known as CATH (Class, Architecture, Topology, Homology),[2] which has been used to construct the CATH Protein Structure Classification database.

Generally, SSAP scores above 80 are associated with highly similar structures. Scores between 70 and 80 indicate a similar fold with minor variations. Structures yielding a score between 60 and 70 do not generally contain the same fold, but usually belong to the same protein class with common structural motifs[3].

See also

References

  1. ^ Taylor WR, Flores TP, Orengo CA. (1994). Multiple protein structure alignment. Protein Sci 3(10):1858-70.
  2. ^ Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. (1997) CATH: A hierarchical classification of protein domain structures. Structure 5(8): 1093-1108.
  3. ^ Porwal G, Jain S, Babu SD, Singh D, Nanavati H, Noronha S. (2007) Protein Structure Prediction Aided by Geometrical and Probabilistic Constraints. J. Comput. Chem. 28: 1943-1952.