truncation of protein sequences for fast profile alignment with application to subcellular...

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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 KUNGPrinceton University

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Contents

1. Introduction– Cell Organelles and Proteins Subcellular Localization– Signal-Based vs. Homology-Based Methods

2. Speeding Up the Prediction Process– Predicting Cleaving Site Location– Truncating Profiles vs. Truncating Sequences– Perturbational Discriminant Analysis

3. Experiments and Results4. Conclusions

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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.

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Proteins and Their Subcellular Location

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Subcellular Localization Prediction

Two key methods:1. Signal-based2. Homology-based

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Signal-Based Method

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

Cleavage site

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Full-length Query Sequence

S(1)=KNKA···S(2)=KAKN···

··

S(N)=KGLL···

Full-length Training sequences

Align with each of the training

sequences ...

SVM classifier

N-dim alignment vector

Subcellular Location

1

N

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.

Homology-Based Method

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Sequences Length Distribution

• 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.

Length distribution of Seq.

Sequence Length

SP

820

Ext:

Mit:

Chl:

35

mTP

1050

18

cTP

760

Cleavage Site

Cleavage Site

Cleavage Site

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Proposed Method: Aligning the Segments that Contain the Most Relevant Info.

Signal-based Cleavage Site Predictor(e.g. TargetP)

N

truncate

Homology-based Method

SubcellularLocation

CAmino Acid Sequence…

Truncated sequence

Cleavage Site

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Aligning Profiles Vs. Aligning Sequences

shortprofilesequence

shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA

PairwiseAlignmentCut

SubcellularLocation

ScoreVector

Scheme I

Scheme II


shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA


SubcellularLocation

ScoreVector

Scheme I

Scheme II

QuerySequence

Scheme I : Truncate the profilesScheme II : Truncate the sequences

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Perturbational Discriminant Analysis

Input Space Hilbert Space

Input and Hilbert Spaces:

Empirical Space:

Nxk )(),( 1 xxK

Empirical Space

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• 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.

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3 classes of 2-dim data in the input space

RBF kernal matrix K

Projection onto the 2-dim PDA space

Decision boundaries in the input space

Example on 2-D Data

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Application to Sequence Classification

Training sequences

PSI-BLASTPairwise

AlignmentComputePDA Para

TrainingProfiles K

Test sequence

PSI-BLASTAlign withTraining Profiles

ComputePDA Score

TestProfile

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Perturbational Discriminant AnalysisApplication to Multi-Class Problems

1-vs-Rest PDA Classifier:

)(1 xf )(xfC

MAXNET

)(2 xf

x

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Application to Multi-Class Problems

Cascaded PDA-SVM Classifier:

Test sequence Project onto

(C–1)-dimPDA space

1-vs-restSVM

Classifier

Class label

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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)

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shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA


SubcellularLocation

ScoreVector

Scheme I

Scheme II


shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA


SubcellularLocation

ScoreVector

Scheme I

Scheme II

QuerySequence

Kernel matrix(Scheme I)

Kernel matrix(Scheme II)

Comparing Kernel Matrices

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Sensitivity Analysis

• 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.

19Cut-off Position

p-16 p-8 p-2 p p+2 p+16 p+32 p+64

Ground-truth cleavage site

Cyt/Nuc

Overall

Mit

Chl

Ext

Cut Seq. at p±xp: gournd-truth cleave site

Subcellular localization(PairProSVM)

Subcellular location

Seq

Sub

cellu

lar

Loca

liatio

n A

ccur

acy

(%)

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Performance of Cleavage Site Prediction

• 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.

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Targe

tP(P

lant)

Targe

tP(N

onPlan

t)

CRF

Category

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Findings: Profile creation time can be substantially reduced by truncating the protein sequences at the cleavage sites.

Comparing Profile Creation Time


shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA


SubcellularLocation

ScoreVector

Scheme I

Scheme II


shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA


SubcellularLocation

ScoreVector

Scheme I

Scheme II

QuerySequence


shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA


SubcellularLocation

ScoreVector

Scheme I

Scheme II


shortCut SVM or

KPDAPairwise

AlignmentPSI-

BLASTSubcellular

Location

ScoreVector

shortprofileprofile

LongPSI-BLAST

SVM or KPDA


SubcellularLocation

ScoreVector

Scheme I

Scheme II

QuerySequence

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Findings: The training time of 1-vs-rest PDA and Cascaded PDA-SVM are substantially shorter than that of SVM.

Training and Classification Time

)(1 xf )(xfC

MAXNET

)(2 xf

x

Project onto(C–1)-dim

PDA space

1-vs-restSVM

Classifier

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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.

Compare with State-of-the-Art Localization Predictors

ConditionalRandomFields

LocalizationAccuracy (%)

Subcellular localization(SubLoc/TargetP)

Cleavage site prediction(TargetP/CRF)


Subcellularlocation

Queryseq.




Subcellularlocation

Queryseq.

MCC

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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.

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Compare with State-of-the-Art Localization PredictorsSubcellular localization

(SubLoc/TargetP)



Subcellularlocation

Queryseq.




Subcellularlocation

Queryseq.

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Performance of Cascaded Fusion

• 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.

Full-length

Seq.

Seq. with Csite predicted by TargetP(P)

Seq. with Csite predicted by TargetP(N)

Seq. with Csite predicted by CRF

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Time

Subcellular localization accuracy

Acc(%)

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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.

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Fusion of Signal- and Homology-Based Methods

Signal-based Cleavage Site Predictor

N

truncate

Homology-based Method

SubcellularLocation

CAmino Acid Sequence

…

Pre-sequence

Cleavage Site

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Perturbational Discriminant AnalysisSpectral Space:

Define the kernel matrix

K can be factorized via spectral decomposition into

Empirical Space

Nx )(e),( 1 xxK

Spectral Space

truncation of protein sequences for fast profile alignment with application to subcellular...

Documents

sequence lengthsp820ext

functions of proteins

optimal discriminant

basedsignalbased methodsource

training set

signal segments

long computation time

cleavage sites