Characterization of Transmembrane Helices

Nov 07, 2005Nov 07, 2005 JKS-seminar JKS-seminar 11

Characterization of Characterization of Transmembrane Transmembrane HelicesHelices

Madhavi GanapathirajuMadhavi Ganapathiraju

Nov 07, 2005Nov 07, 2005 JKS-seminar JKS-seminar

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SummarySummary

Completion of classification procedures for TM prediction Completion of classification procedures for TM prediction using the LSA featuresusing the LSA features

Web-tool for the TM prediction has been designed; it is being Web-tool for the TM prediction has been designed; it is being developed by Christopher Jursadeveloped by Christopher Jursa

TMPDB, a set of 119 transmembrane proteins has also been TMPDB, a set of 119 transmembrane proteins has also been processed and included in evaluationsprocessed and included in evaluations

KchannelDB, the database of Kchannel proteins subdiviided KchannelDB, the database of Kchannel proteins subdiviided into families of 1, 2, 4 and 6 TMs each has been collected and into families of 1, 2, 4 and 6 TMs each has been collected and processed. First 2 have been evaluated.processed. First 2 have been evaluated.

Decision tree and support vector machine classifiers have Decision tree and support vector machine classifiers have been evaluated been evaluated

Paper summarizing the work has been writtenPaper summarizing the work has been written QQokok metric has been found to be incorrect in previous metric has been found to be incorrect in previous

evaluations – It has been corrected. evaluations – It has been corrected.


33Recap: TM prediction Recap: TM prediction methodmethod

Place a moving window at position i

Count Ci1, Ci2…Ci10

i = i + 1

(B) Window analysis from left to right

(A) Map amino acid sequence to 5 different property

sequences

Example:

MDPML…

Example:

-n----p---....RO..OOaDDad

Features(L-l+1) x 4

(D) Neural Network(4 input nodes, 1 output node)

(E) Hidden Markov Model

Prediction & confidenceLx1 & Lx1

PredictionLx1

(C) Singular Value

Decomposition(PCA)

Matrix of Counts(L-l+1) x 10

Place a moving window at position i

Count Ci1, Ci2…Ci10

i = i + 1

(B) Window analysis from left to right


sequences


sequences

Example:

MDPML…

Example:

-n----p---....RO..OOaDDad

Features(L-l+1) x 4

(D) Neural Network(4 input nodes, 1 output node)

(E) Hidden Markov Model

Prediction & confidenceLx1 & Lx1

PredictionLx1

(C) Singular Value

Decomposition(PCA)

Matrix of Counts(L-l+1) x 10


44

Neural Net ClassifierNeural Net Classifier

Inpu

t Lay

er

4 Dimensions of theVector obtained by LSA

form the input

Dimension 1

Dimension 2

Dimension 3

Dimension 4

Hid

den

Laye

r

Out

put L

ayer


55

Decision Tree & SVM Decision Tree & SVM ClassifiersClassifiers Used MATLABarsenal, the wrapper Used MATLABarsenal, the wrapper

tools developed by Rong (LTI) to see tools developed by Rong (LTI) to see the performance of classifiers on the the performance of classifiers on the feature setfeature set– Decision TreesDecision Trees– SVM (2SVM (2ndnd degree polynomial kernel) degree polynomial kernel)


66

Evaluation Data SetsEvaluation Data Sets

BenchmarkBenchmark– 36 proteins of high resolution TM 36 proteins of high resolution TM

informationinformation TMPDBTMPDB

– 119 proteins of known 3D structure119 proteins of known 3D structure KChannelDBKChannelDB

– Multiple sequence alignments of KChannel Multiple sequence alignments of KChannel proteins of 1 and 2 TM segments proteins of 1 and 2 TM segments


77

Results: 36 high resResults: 36 high res

Segment Residue level

Symbol Method

Qok F Qob

s

Qpred

Q2 F2T

F2N

Set 36 high resolution proteins

1 TMHMM* 71 90 90 90 80 74 77

2 TMpro (LC)* 61 94 94 94 76 ? ?

3 TMpro (HMM)* 66 95 97 92 77 76 76

4 TMpro (NN)* 75 95 95 94 73 70 75

Evaluations have been performed by submitting data on benchmark server


88

Results: TMPDBResults: TMPDB

Segment Residue level

FQobs

Qpred

Q2 F2T

F2N

TMHMM 90 89 90 89 80 90

NN 90 90 89 86 75 90

HMM 85 90 80 84 74 77

SVM 93 95 90 84 77 88

Decision Trees 92 97 86 83 75 87


99

Other thingsOther things

Processed KChannel DB proteins for Processed KChannel DB proteins for evaluationevaluation– Initial evaluations are done, but not ready Initial evaluations are done, but not ready

for discussion for discussion ……


1010

TMPro web serviceTMPro web service

TMPro website is being developed by Christopher, Dr. TMPro website is being developed by Christopher, Dr. Karimi’s studentKarimi’s student– Should be up in 2 weeks timeShould be up in 2 weeks time

Developed standalone versions of feature processing Developed standalone versions of feature processing required for the web-service for DT and SVMrequired for the web-service for DT and SVM


1111

Charge rich proteinsCharge rich proteins

I seem to have not mailed myself the latest figures I seem to have not mailed myself the latest figures here, I will show them separately here, I will show them separately


1212

Ongoing workOngoing work QQokok is not high for TMPDB data set is not high for TMPDB data set To overcome this, error analysis is being performedTo overcome this, error analysis is being performed

– Measure how far away from “truth” the prediction is (what Measure how far away from “truth” the prediction is (what threshold would have classified the segment correctly as TM or threshold would have classified the segment correctly as TM or non TM)non TM)

– Characteristics of the segments misclassifiedCharacteristics of the segments misclassified Are they traditional globular hydrophobic segments only, can aromatic Are they traditional globular hydrophobic segments only, can aromatic

and other properties be used to recover from error? and other properties be used to recover from error?

Combination with TMHMM prediction for improved Combination with TMHMM prediction for improved performance performance – Rule based combination on aromatic property has previously Rule based combination on aromatic property has previously

been shown to improve TMHMM predictions (March/June 2005?) been shown to improve TMHMM predictions (March/June 2005?) on high resolution proteinson high resolution proteins

– Do this on TMPDB set as wellDo this on TMPDB set as well Other architectures of NN to be studied? Error TM segments to Other architectures of NN to be studied? Error TM segments to

be studied further with DT rules that failbe studied further with DT rules that fail

Documents

Characterization of Transmembrane Helices