1. Prediction of Protein Binding Sites in Protein Structures Using Hidden Markov Support Vector Machine

2. Slate:the target protein. Blue:the binding partner. Magenta:interface residues. SSSEIKIVRDEYGMPHIYANDTWHLFYGYG IIINIINNIINNNIIIIIIINIINIIINNN Input Output

3. Machine Learning Methods Applied Classification methods Sequential labelling methods ANNSVM CRF

4. FEATURES Neighboring residue profile feature Neighboring residue profile feature Hydrophobicity Hydrophobicity Sequence conservation Sequence conservation Secondary structure Secondary structure Solvent accessible surface area Solvent accessible surface area

5. Hidden Markov Support Vector Machine Emission feature function Transition feature function Corresponding weight

6. Hidden Markov Support Vector Machine  Spatially neighboring residue profile feature  Spatially neighboring residue accessible surface (ASA) feature Emission feature function

7. Hidden Markov Support Vector Machine Transition feature function

8. Hidden Markov Support Vector Machine Transition feature function

9. Hidden Markov Support Vector Machine Corresponding weight

10. Hidden Markov Support Vector Machine Source Code:http://www.cs.cornell.edu/People/tj/svm_light/svm_hmm.html ☆ The cutting-plane algorithm makes it linear

11. DATA SET

17. Influence of the number of training samples on the prediction performance and running time

19. The inter-relation information between neighboring residues is relevant for discrimination

21. The window size has not significant influence on the performance

23. Actual interface residues ANN SVMCRFHM-SVM Comparison with related methods

24. Actual interface residues ANN SVMCRFHM-SVM Comparison with related methods

25. SUMMARY Prediction of protein binding sites Prediction of protein binding sites Hidden Markov Support Vector Machine Hidden Markov Support Vector Machine Result Analysis Result Analysis Comparison with other methods Comparison with other methods Influence of the number of training samples Influence of the number of training samples The information between neighboring residues The information between neighboring residues Window size Window size Discussion Discussion