Frequent-Subsequence-Based Prediction of Outer Membrane Proteins

1. Frequent-Subsequence-Based Prediction of Outer Membrane Proteins R. She, F. Chen, K. Wang, M. Ester, School of Computing Science J. L. Gardy, F. S. L. Brinkman Dept. of Mol. Biology & Biochemistry Simon Fraser University, BC Canada

2. 1. Problem Introduction • Gram-negative bacteria • Medically important disease-causing bacteria • 5 sub-cellular localizations (2 layers of membranes) 1. Cytoplasmic 5. Extra-cellular 2. Inner Membrane 4. Outer Membrane 3. Periplasmic

3. Outer Membrane Proteins • Predicting outer membrane proteins (OMPs) of Gram-negative bacteria • attached to the “outer membrane” of Gram-negative bacterial cell • Particularly useful as drug target

4. Outer loop Inner turn -strand Outer Membrane Protein (Cont.) • structure: • -strands, form central barrel shape • Inner turns, shorter stretches • Outer loops, longer stretches Extracellular side Outer membrane Periplasmic side

5. Challenges • Identifying OMPs from sequence information alone • Discriminative sequence patterns of OMPs would be helpful

6. Challenges (Cont.) • favor precision over recall • lengthy time and laborious effort to study targeted drug in lab Confusion Matrix • Overall accuracy = (TP+TN) / E • Precision = TP / A • Recall = TP / C

7. 2. Dataset • OMP sequence dataset • Excellent quality (http://www.psort.org/dataset) • Protein sequences (strings over alphabet of 20 letters) e.g. MNQIHK… • Two classes with imbalanced distributions

8. Evaluation • Majority of data is non-OMP, overall accuracy is determined mainly by non-OMP prediction; • Precision is our main concern (90%); • Recall should be maintained at reasonable level (50%).

9. 3. Related Work • Existing sub-cellular localization predictors • Inner membrane proteins have -helix structures – prediction is highly accurate • Prediction of cytoplasmic, periplasmic and extracellular proteins • neural networks, covariant discriminate algorithm, Markov chain models, support vector machines (highest accuracy: 91%) • Do not apply to OMPs

10. Existing work on OMP prediction • Neural networks, Hydrophobicity analysis, Combination of methods (homology analysis, amino acid abundance) • Current state-of-the-art • Hidden Markov Models by Martelli et al. [1] • Use HMM to model OMPs according to their 3D structures • Training set is small (12 proteins with known 3D structures) • Overall accuracy: 89%; Recall: 84%; Precision: 46%.

11. 4. Algorithms • Motivations • Frequent subsequence mining is helpful • frequent subsequence: consecutive amino acids that occur frequently in OMPs • OMP characteristics • Common structure in OMPs • Different regions have different characteristic sequence residues • Model local similarities by frequent subsequences and highly variable regions by wild cards (*X*X*…) => Association-Rule-based classification

12. Algorithm 1: Rule-Based Classification • Mine frequent subsequences X (consecutive amino acids) only from OMP class (support(X)  MinSup). • Remove trivial similarities by restricting minimum length (MinLgh) of frequent subsequences • Find frequent patterns (*X*X*…) • Build classifier using frequent pattern rules (*X*X*… OMP).

13. Algorithm 1: Refined • The previous classifier performs good in precision, but poor in recall • A second level of classifier is built on top of the existing classifier • New training data: cases covered by the default rule in the first classifier • Apply same pattern-mining and classifier-building process • Future case is first matched against the 1st classifier; if it is classified as OMP, we accept it; otherwise the 2nd classifier is used.

14. Algorithm 2: SVM-based Classification • Support Vector Machines (SVM) [5] • Excellent performer in previous biological sequence classification • Data needs to be transformed for SVM to be used (sequences => vectors) • Frequent subsequences of OMPs are used as features. • Protein sequences are mapped into binary vectors.

15. 5. Empirical Studies • 5 Classification methods • Single-level Rule-Based Classification (SRB) • Refined Rule-Based Classification (RRB) • SVM-based Classification (SVM-light [6]) • Martelli’s HMM • See5 (latest version of C4.5) • 5-fold cross validation (same folding for all algorithms)

16. Summary of Classifier Comparison • SVM outperforms all methods • RRB is the 2nd best performer • Both SVM and RRB outperform HMM • Improvement from SRB to RRB shows that refinement works

17. Other Biological Benefits (Rule-Based Classifiers) • Sequential rules (obtained by SRB/RRB) lead to biological insights • Mapped to both β-strands and periplasmic turn regions • Assist in developing 3D models for proteins • Identification of primary drug target regions • conserved sequences in the surface-exposed regions are ideal targets for new diagnostics and drugs

18. 6. Conclusions and Future Work • Contributions • Provide excellent predictors for OMP prediction; • Obtained interpretable sequential patterns for further biological benefits; • Proposed the use of frequent subsequences for SVM feature extraction; • Demonstrated the usefulness of data mining techniques in biological sequence analysis.

19. Future Work • Include more features of sequences, e.g., secondary structure, additional properties of proteins, barrel and turn sizes, polarity of amino acides, etc. • Explore ways to extract symbolic information from SVMs

20. References • Martelli P., Fariselli P., Krogh A. and Casadio R., A sequence-profile-based HMM for predicting and discrimating  barrel membrane proteins, Bioinformatics, 18(1) 2002, S46-S53, 2002. • Wang J., Chirn G., Marr T., Shapiro B., Shasha D. and Zhang K., Combinatorial Pattern Discovery for Scientific Data: Some Preliminary Results, SIGMOD-94, Minnesota, USA, 1994. • Wang K., Zhou S. and He Y., Growing Decision Tree on Support-less Association Rules, KDD’00, Boston, MA, USA, 2000. • Quinlan J., C4.5: programs for machine learning, Morgan Kaufmann Publishers, 1993. • Vapnik V., The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995. • Joachims T., Learning to Classify Text Using Support Vector Machines. Dissertation, Kluwer, 2002. software downloadable at http://svmlight.joachims.org/ • Rulequest Research, Information on See5/C5.0, at http://www.rulequest.com/see5-info.html