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Man-Wai MAK and Wei WANG The Hong Kong Polytechnic University Sun-Yuan KUNG Princeton University

Truncation of Protein Sequences for Fast Profile Alignment with Application to Subcellular Localization. Man-Wai MAK and Wei WANG The Hong Kong Polytechnic University Sun-Yuan KUNG Princeton University. Contents. Introduction Cell Organelles and Proteins Subcellular Localization

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Man-Wai MAK and Wei WANG The Hong Kong Polytechnic University Sun-Yuan KUNG Princeton University

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  1. Truncation of Protein Sequences for Fast Profile Alignment with Application to Subcellular Localization Man-Wai MAK and Wei WANG The Hong Kong Polytechnic University Sun-Yuan KUNG Princeton University

  2. Contents • Introduction • Cell Organelles and Proteins Subcellular Localization • Signal-Based vs. Homology-Based Methods • Speeding Up the Prediction Process • Predicting Cleaving Site Location • Truncating Profiles vs. Truncating Sequences • Perturbational Discriminant Analysis • Experiments and Results • Conclusions

  3. Organelles • Cells have a set of organelles that are specialized for carrying out one or more vital functions. • Proteins must be transported to the correct organelles of a cell to properly perform their functions. • Therefore, knowing the subcellular localization is one step towards understanding the functions of proteins.

  4. Proteins and Their Subcellular Location

  5. Subcellular Localization Prediction • Two key methods: • Signal-based • Homology-based

  6. Signal-Based Method Cleavage site Source: S. R. Goodman, Medical Cell Biology, Elsevier, 2008. • The amino acid sequence of a protein contains information about its organelle destination. • Typically, the information can be found within a short segment of 20 to 100 amino acids preceding the cleavage site. • Signal-based methods (e.g. TargetP) can determine the cleavage site location

  7. Homology-Based Method N-dim alignment vector 1 Align with each of the training sequences Full-length Query Sequence SVM classifier Subcellular Location . . . N S(1)=KNKA··· S(2)=KAKN··· · · S(N)=KGLL··· Full-length Training sequences • Advantage: • Can predict sequences that do not have cleavage sites. • Drawback: • Given a query sequence, we need to align it with every training sequence in the training set, causing long computation time.

  8. Sequences Length Distribution Cleavage Site Length distribution of Seq. SP Ext: Occurrences of Seq. 21 820 Cleavage Site mTP Mit: 1050 35 cTP Cleavage Site Chl: 760 Sequence Length 18 • Many sequences are fairly long, thus, aligning the whole sequence will take long computation time. • cTP, mTP and SP are under 100 AAs only and contain the most relevant segment. • Computation saving can be achieved by aligning the signal segments only. 8

  9. Proposed Method: Aligning the Segments that Contain the Most Relevant Info. N Amino Acid Sequence C Signal-based Cleavage Site Predictor (e.g. TargetP) … Cleavage Site truncate Subcellular Location Homology-based Method Truncated sequence

  10. Aligning Profiles Vs. Aligning Sequences Scheme I : Truncate the profiles Scheme II : Truncate the sequences Query Sequence

  11. Perturbational Discriminant Analysis Input and Hilbert Spaces: Hilbert Space Input Space Empirical Space: Empirical Space

  12. Perturbational Discriminant Analysis • The objective of PDA is to find an optimal discriminant function in the Hilbert space or empirical space: • The optimal solution (see derivation in paper) in the empirical space is • ρ represents the noise (uncertainty) level in the measurement. It also ensures numerical stability of the matrix inverse. • Ρ = 1 in this work.

  13. Perturbational Discriminant Analysis Example on 2-D Data 3 classes of 2-dim data in the input space RBF kernal matrix K Decision boundaries in the input space Projection onto the 2-dim PDA space

  14. Perturbational Discriminant Analysis Application to Sequence Classification Training sequences Training Profiles K PSI-BLAST Pairwise Alignment Compute PDA Para Test sequence Test Profile Align with Training Profiles PSI-BLAST Compute PDA Score

  15. Perturbational Discriminant Analysis Application to Multi-Class Problems 1-vs-Rest PDA Classifier: MAXNET

  16. Perturbational Discriminant Analysis Application to Multi-Class Problems Cascaded PDA-SVM Classifier: Test sequence Project onto (C–1)-dim PDA space 1-vs-rest SVM Classifier Class label

  17. Experiments Materials: • Eukaryotic sequences extracted from Swiss-Prot 57.5 • Ext, Mit, and Chl contain experimentally determined cleavage sites • 25% Sequence identity (based on BLASTclust) Performance Evaluation: • 5-Fold cross validation • Prediction accuracy and Matthew’s correlation coefficient (MCC)

  18. Comparing Kernel Matrices Kernel matrix (Scheme I) Query Sequence Kernel matrix (Scheme II)

  19. Sensitivity Analysis Seq Subcellular localization (PairProSVM) Cut Seq. at p±x p: gournd-truth cleave site Subcellular location Subcellular Localiation Accuracy (%) • The localization performance degrades when the cut-off position drifts away from the ground-truth cleavage site. • mTP and cTP are more sensitive to the error of cleavage site prediction than Ext. Cyt/Nuc Ext Overall Mit Chl Ground-truth cleavage site p p+32 p+64 p+2 p+16 p-2 p-16 p-8 Cut-off Position 19

  20. Performance of Cleavage Site Prediction TargetP(NonPlant) • Conditional Random Field (CRF) is better than TargetP(Plant) in terms of predicting the cleavage sites of signal peptide (Ext) but is worse than TargetP(Nonplant). • CRF is slightly inferior to TargetP in predicting the cleavage sites of mitochondria, but it is significantly better than TargetP in predicting the cleavage site of chloroplasts. TargetP(Plant) CRF Csite Prediction ACC(%) Category 20

  21. Scheme I Scheme I Score Score short short Subcellular Subcellular Long Long PSI PSI - - Pairwise Pairwise SVM or SVM or Cut Cut Vector Vector Location Location BLAST BLAST profile profile Alignment Alignment KPDA KPDA profile profile short short short short Subcellular Subcellular PSI PSI - - Pairwise Pairwise Score Score SVM or SVM or Cut Cut Location Location sequence sequence BLAST BLAST Alignment Alignment Vector Vector KPDA KPDA profile profile Scheme II Scheme II Comparing Profile Creation Time Scheme I Scheme I Score Score short short Subcellular Subcellular Long Long PSI PSI - - Pairwise Pairwise SVM or SVM or Cut Cut Vector Vector Location Location BLAST BLAST profile profile Alignment Alignment KPDA KPDA profile profile Query Query Sequence Sequence short short short short Subcellular Subcellular PSI PSI - - Pairwise Pairwise Score Score SVM or SVM or Cut Cut Location Location sequence sequence BLAST BLAST Alignment Alignment Vector Vector KPDA KPDA profile profile Scheme II Scheme II Findings: Profile creation time can be substantially reduced by truncating the protein sequences at the cleavage sites.

  22. Training and Classification Time 1-vs-rest SVM Classifier Project onto (C–1)-dim PDA space Findings: The training time of 1-vs-rest PDA and Cascaded PDA-SVM are substantially shorter than that of SVM.

  23. Compare with State-of-the-Art Localization Predictors MCC Localization Accuracy (%) Conditional Random Fields Findings: In terms of localization accuracy, the proposed “Signal+Homology” method performs slightly better than the signal-based TargetP and is substantially better than the homology-based SubLoc.

  24. Conclusion • Fast subcellular-localization-prediction can be achieved by a cascaded fusion of signal-based and homology-based methods. • As far as localization accuracy is concerned, it does not matter whether we truncate the sequences or truncate the profiles. However, truncating the sequence can save the profile creation time by 6 folds. 24

  25. Compare with State-of-the-Art Localization Predictors

  26. Performance of Cascaded Fusion Time • The computation time for full-length profile alignment is a striking 116 hours • Our method not only leads to nearly a 20 folds reduction in computation time but also boosts the prediction performance. Time (hr.) Acc (%) Subcellular localization accuracy Full-length Seq. Seq. with Csite predicted by TargetP(P) Seq. with Csite predicted by TargetP(N) Seq. with Csite predicted by CRF 26

  27. Fusion of Signal- and Homology-Based Methods 1) Cleavage site detection. The cleavage site (if any) of a query sequence is determined by a signal-based method. 2) Pre-sequence selection. The pre-sequence of the query is obtained by selecting from the N-terminal up to the cleavage site. 3) Pairwise alignment. The pre-sequence is aligned with each of the training pre-sequences to form an N-dim vector, which is fed to a one-vs-rest SVM classifier for prediction. 27

  28. Perturbational Discriminant Analysis Spectral Space: Define the kernel matrix Kcan be factorized via spectral decomposition into Empirical Space Spectral Space

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