1. Protein Classification Using Averaged Perceptron SVM
CS6772 Project Presentation
12/03/2003
Protein Classification Using Averaged Perceptron SVM
Eugene Ie

2. Protein Sequence Classification
Protein = ()* |  | = 20 amino acids
Easy to sequence proteins, difficult to obtain structure
3D Structure
Sequence
VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR ?
Class
Globin family
Globin-like superfamily
Function
Oxygen transport

3. Sequence Alignment vs. Classification
Sequence similarity through alignment
distant
homology
SGFIEEDELKLFL
SGFIEEEELKFVL
close homology
Sequence classification for remote homology
Classifier

4. Structural Hierarchy of Proteins
SCOP
Fold
Superfamily
Negative
Test Set
Negative
Training Set
Family
Positive
Test Set
Positive
Training Set
Remote homologs:
Structure and function conserved
Sequence similarity - low

5. Remote Homology Detection
Discriminative supervised learning approach to protein classification
Approach: Support Vector Machines with String Kernels
C. Leslie, E. Eskin, J. Weston, and W. Noble, Mismatch String Kernels for SVM Protein Classification.
C. Leslie and R. Kuang, Fast Kernels for Inexact String Matching.

6. QP SVM Training QP Solver Sequence Training Data
>VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR …
>TYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR
Total: n sequences + n labels
Learned Weights and Bias
QP Solver
(slow)
From KKT

7. Averaged Perceptron SVM Training
Training Algorithm:
Y. Freund and R. Schapire, Large Margin Classification Using the Perceptron Algorithm.

8. Averaged Perceptron SVM Training
Iterate
t Epochs
Sequence Training Data
Run Perceptron
Algorithm
>VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR …
>TYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR
Total: n sequences + n labels
Generalized Bound for k
Final Weight Vector, Voting Weights
s = no. of dimensions in feature space
k = no. of mistakes made during perceptron run
SCOP experiments show:
For average n ~ 1000
Average k ~ 50-60

9. Averaged Perceptron SVM Classification
Testing Algorithm:
Note: Only k kernel products with unknown sequence x need to be computed.
Recurrence relation:
M is the set of “mistake indices”

10. Implementation Details
Built on top of protclass (Protein Classification) platform
Java Platform
Classification Task
Hash table scan instead of Mismatch Trie
Generate mismatch mappings once using shifts
Dynamic kernel matrix storage
Still needs debugging
Speed/Space Performance
~80% reduction in space requirement
~50% reduction in training time
~50% reduction in testing time
Mainly from simple online algorithm