1. Protein Structural Classification

2. Structural Classification Databases Sequence pairwise comparison
SCOP, CATH, FSSP
Sequence pairwise comparison
Smith-waterman, BLAST, PSI-BLAST, rank-propagation, SAM-T98
Discriminative classification
SVM pairwise, mismatch kernel, EMOTIF kernel, I-Site kernel, semi-supervised kernel

3. SCOP Fold Superfamily Family SCOP Negative Test Set Positive
Training Set
Test Set
Negative
Family : Sequence identity > 30% or functions and structures are very similar
Superfamily : low sequence similarity but functional features suggest probable common evolutionary origin
Common fold : same major secondary structures in same arrangement with the same topological connections

4. CATH
Class
Architecture
Topology
Homologous
Sequence family

5. Local alignment: Smith-Waterman algorithm
For two string x and y, a local alignment with gaps is:
The score is:
Smith-Waterman score:
Thanks to Jean Philippe

6. BLAST: a heuristic algorithm for matching DNA/Protein sequences
Idea: True match are likely to contain a short stretch of identity
A list of ‘neighborhood words” of the query sequence
Search database with the list, whenever there is a match do a ‘hit extension’, stopping at the maximum scoring extension
Altschul, Madden, Schaffer, Zhang etc., 1997

7. PSI-BLAST: Position-specific iterated BLAST
Only extend those double hit within a certain range.
A gapped alignment uses dynamic programming to extend a central pair of aligned residues in both directions.
PSI-BLAST can takes PSSM as input to search database
Altschul, Madden, Schaffer, Zhang etc., 1997

8. Local and Global Consistency
Affinity matrix
D is a diagonal matrix
Iterate
F* is the limit of seuqnce {F(t)}
Zhou, Bousquet, Lal, Weston, and Scholkopf, 2003

9. Weston, Elisseeff, Zhou, Leslie and Noble, 2004
Rank propagation
Protein similarity network:
Graph nodes: protein sequences in the database
Directed edges: a exponential function of the PSI-BLAST e-value (destination node as query)
Activation value at each node: the similarity to the query sequnce
Exploit the structure of the protein similarity network
Weston, Elisseeff, Zhou, Leslie and Noble, 2004

10. Karplus, Barrett and Hughey, 1999
SAM-T98
The first iteration: query sequence to search NR database using WU-BLASTP and build alignment for the found homologs
2nd-4th iterations: take the alignment from the previous iterations to find more homologs with WU-BLASTP and update the alignment with the new homologs found.
Build a HMM from the final alignment. The HMM of query sequence is used to search database, or we can use query sequence to search against HMM database
Karplus, Barrett and Hughey, 1999

11. To do it in a discriminative manner with SVM…

12. Jaakkola, Diekhans and Haussler, 2000
Fisher Kernel
A HMM (or more than one) is built for each family
Derive kernel function from the fisher scores of each sequence given a HMM H1:
Jaakkola, Diekhans and Haussler, 2000