Remote Homology detection: A motif based approach

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Remote Homology detectionA motif based approach

• CS 6890 Bioinformatics - Dr. Yan
• Swati Adhau
• 04/14/06

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Outline of the Presentation

• Motivation
• Introduction
• Description (Remote Homology Detection)
• Methods
• Results Discussion
• Q and A

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Motivation

• Remote homology detection is the problem of
  detecting homology in case of low sequence
  similarity.
• A method based on presence of discrete sequence
  motifs for detecting remote homology.
• The motif content of a pair of sequences is used
  to define a similarity that is used as a kernel
  for support vector machine (SVM) classifier.

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• Testing of method is done upon two remote
  homology detection tasks
• 1) Prediction of previously unseen SCOP family
  (Structural classification of Proteins).
• 2) Prediction of an Enzyme class given other
  enzymes that have a similar function on other
  substrates.

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Introduction

• Protein Homology detection is one of the most
  important problems in computational biology.
• Homology is generally established by sequence
  similarity.
• Two established methods
• 1) Smith Waterman algorithm
• 2) Blast
• Protein sequence motifs are an alternative method
  of detecting sequence similarity

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Intro(continued)

• By focussing on limited highly conserved regions
  of proteins, motifs can often reveal important
  clues to a proteins role.
• Motifs often represent functionally important
  regions such as catalytic sites, binding sites
  and structural motifs.
• The Blocks database combines various databases
  such as pFAM, PRINTs, ProDom, DOMO and InterPro.
  eMotif database contains discrete sequence motifs
  constructed from blocks of BLOCKS.
• This paper uses discrete sequence motifs
  extracted from the eBLOCKS database using the
  eMOTIF method.

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Intro(Continued)

• Based upon the motif content of a pair of
  sequence we introduce sequence similarity
  measure.
• This paper uses an SVM method.
• SVM method is shown to perform better than
  methods for Fisher-Kernel method, SAM T-98 and
  PSI-BLAST.
• When a sequence similarity is shown to be a dot
  product in some space it is called the kernel.
• In this paper we use protein motifs to construct
  a kernel that can be computed efficiently which
  performs better than a kernel based on BLAST or
  Smith-Waterman scores.

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Remote Homology Detection

• This method was tested on the following two
  tasks-
• 1) Prediction of a SCOP family when trained on
  other families in that familys fold.
• 2) Prediction of the function of an enzyme when
  the training set contains enzyme that have same
  general functions but different substrates.

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BackGround of the first dataset

• The first dataset is composed of sequences of
  domains from the SCOP database.
• Objective- To detect homology at the SCOP
  superfamily level. Recognizing a SCOP family when
  the training set contains other families in the
  familys superfamily.

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• contd
• This specifies the ve examples in the test set
  and training set.
• The ve examples are taken from outside of the
  familys fold.
• A random family is chosen to belong to
• -ve test set rest of the families in its
  superfamily are added to negative training set.

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The second dataset

• We use the classification of Enzymes to simulate
  remote homology.
• The function of an enzyme is given by EC number
  given it to by Enzyme Commision.
• EC number is like n1.n2.n3.n4
• For eg 1.1.3.13 for alcohol oxidase.
• n1 1-6 - indicates the type of chemical
  reaction catalyzed by the enzyme.
• n2 specifies donor molecule.
• n3 specifies the acceptor.
• n4 specifies the substrate.

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• contd
• In this paper author concentrates on
  oxidoreductase (n1 1).
• A classifier is trained to predict
  oxidoreductases with a certain function
• (n2 n3).
• The classifier will be tested on oxidoreductases
  with adifferent substrate (n4) than those it was
  trained on.

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• For eg.
• EC class 1.14.13.8 ? Positive examples of
  training set.
• EC class 1.14.13.39 ? Positive examples of test
  set.
• So the similarity between the ve training
  test may not be very high.
• Negative test training set are defined
  analogusly.

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Methods?The Motif kernel

• When the similarity is a dot product it is called
  a kernel.
• The method is as follows- Each position in the
  motif represents the variability in the column in
  a block from multiple sequence alignment.
• For eg the motif
• as.dkffilmv..filmvlast.
• filmv is a substitution group.

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• .contd
• If the pattern of amino acids that appear in a
  column of a block does not match any substitution
  group, then the motif contains the wild card
  symbol . .
• A sequence will or match above motif if it has
  either an a an s in some position, then any
  character, then d, k, f so on, matching until
  the end of motif

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• A sequence x contains a motif m, if x contains m
  at some position.
• A sequence x can be represented in vector space
  indexed by a set of motifs M as follows-
• F(x) (fm(x))m?M
• where fm(x) is the number of occurences of
  the motif m in x.
• We can define motif kernel as
• K(x, x) F(x) F(x)

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• As in the most cases a motif appears only once in
  sequence, this kernel will count the number of
  motifs that are common to both sequence.
• Q Why are we using eBlocks database over other
  motif databases to define a motif kernel?
• Ans-
• Usage of databases like PROSITE the eMOTIF
  presents a problem in the evaluation of
  performance of the kernel.
• The eBLOCKS database are generated in an
  unsupervised way
• Increased coverage of eBLOCKS set of BLOCKS.

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Computing the Motif Kernel
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Computing the Motif kernel

• To compute the motif content of each sequence
  the subsequent computation of the kernel is
  simply a dot product between the vectors.
• To facilitate the efficient computation of the
  motif content of a sequence, the motif database
  is stored in TRIE which is defined as follows.
• ? Let m be a motif over the alphabet A U S U
  .
• ?Every prefix of m has a node.
• Let m1 and m2 be prefixes of m there is an
  edge from
• m1 to m2 if lm2l lm1l 1.
• ?To compute all the motifs that are contained
  in x at any position, this search is started at
  each position of x.

__
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The Blast kernel

• A query sequence by its BLAST scores against the
  training set is represented.
• This representation in conjuction with SVMs was
  used to address the problem of remote homology
  detection
• Results were better than Fisher-kernel method.

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Classification Methods

• We report results using two classification
  methods-
• 1) SVMs
• 2) K-Nearest-Neighbour.
• SVM
• f(x) w.x b
• w ? weight vector
• b ? constant bias
• Query is classified according to the sign of f.

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• As a consequence of optimization process, the
  weight vector can be expressed as a weighted sum
  of the Support Vectors(SV)-
• w S bixi
• The decision function is now written as
• f(x) S bixi x b
• In terms of kernel function, the decision is
  expressed as-
• f(x) S bi K(xi , x) b

iÎSV
iÎSV
iÎSV
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• KNN classifier
• We use a KNN classifier with a continuous valued
  decision functions.
• A score for class j is defined as
• fj(x) S K(xi , x)
• kNNj(x) is the set of k nearest neighbors of x
  
• in class j.

iÎkNNj(x)
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Metrics

• We consider two metrics for asessing the
  performance of a classifier
• ROC (area under receiver operator
  characteristic).
• RFP (the median rate of false positive)
• The ROC curve describes the tradeoff between
  sensitivity and specificity.
• More specifically we use ROC50 curve, which
  counts true positives only up to the first 50
  false positives.
• The RFP score of a positive test sequence x is
  the fraction of negative test sequences that have
  a value of the decision function that is at least
  as high as the value of the decision function of
  x.

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Results

• Use of astral database to obtain protein domain
  sequences of the SCOP database.
• Retained only superfamilies having atleast two
  families that have atleast 10 members in each
  family.
• A dataset with1639 domains in 23 superfamilies
  56 families was yielded.
• Protein sequences annotated with EC numbers were
  extracted from SwissProt database.
• The extracted dataset has 2187 enzymes in 65
  classes.
• To generate Blast kernel, authors ran an all vs
  all BLAST on two datasets using default
  parameters E value cut off 0.1.
• To generate motif kernel, datasets were computed
  with eBLOCKS sequence motifs using the TRIE
  method.

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Results contd

• A family by family comparison of classification
  performance of the motif-SVM BLAST-SVM methods
  is provided in figure in next slide.
• On the SCOP task the motif-SVM method performs
  significantly better than BLAST-SVM method with a
  p-value of 3.9 10-9. in a wilcoxon signed rank
  test for the ROC50 score.
• In enzyme classification task there is no
  significant difference in ROC50 scores.
• Similar behavior is observed in the median RFP
  and RFP50.
• The results were similar when Smith-Waterman
  algorithm was used instead of BLAST.

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Results contd
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Results contd
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Results contd

• The motif kernel in figure 4 shows the similarity
  between the families in superfamily whereas none
  is detected by the BLAST kernel.
• Increased sensitivity of motif kernel.

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Results contd
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Results contd

• Figure 5 shows the comparison of the SVM-based
  method to the one that uses KNN as a classifier.
• In both the motif and BLAST kernels, SVM based
  classifier performs significantly better than
  corresponding KNN classifier.

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Discussion

• This paper showed that an SVM classifier that
  uses motif kernel performs significantly better
  than SVM that uses a BLAST/Smith-Waterman kernel
  on a remote homology detection problem derived
  from SCOP database.
• Both methods performed equally well on the task
  of Enzyme detection.
• BLAST kernel motif kernel worked significantly
  better when used in conjunction with an SVM
  rather than a Nearest Neighbor classifier.
• Despite the relative success of motif method,
  there were many SCOP families EC classes that
  were not detected using this method.

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• Questions?? Comments!!

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• Thank you !!!