SVMProt: webbased support vector machine software for functional classification from its primary seq

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SVM-Prot web-based support vector machine
software for functional classification from its
primary sequence

• Nucleic Acids Research, 2003
• speaker yfhuang_at_CSIE.NTU

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Outline

• Introduction
• Method
• Result Discussion
• Reference
• Extension Reading

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Introduction

• Briefing introduction
• Current developed approaches/methods
• Problem
• Goal

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Briefing Introduction

• Protein function prediction family prediction
• Based on protein primary sequence
  physicochemical property
• Using SVM to prediction functional family

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Various methods

• Sequence similarity
• Evolutionary analysis
• Structure-based approach
• Protein/gene fusion
• Protein interaction
• Family classification by sequence clustering

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Problems

• Not all homologous proteins have analogous
  functions
• Many proteins share promiscuous domains

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Goal

• Distantly-related proteins
• Closely-related proteins

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SVMProt

• SVMProt classification system
• Web-based software
• Target using SVM classification to classify
  protein into functional family form its primary
  sequence
• Representative proteins of a number of functional
  families Seed proteins of Pfam curated protein
  families
• URL http//jing.cz3.nus.edu.sg/cgi-bin/svmprot.cg
  i

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Method

• Database
• SVMProt processing
• Example of feature example
• Dataset
• Scoring function quality measurement

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Database

• Data
• 46 families Enzymes from BRENDA
• G-protein coupled receptors from GPCRDB
• Nuclear receptors from NucleaRDB
• 5 families of channels 1 family of transporters
  from TCDB LGICdb
• DNA- RNA-binding proteins derived from
  SWISS-PROT

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Processing (1)

• Feature vector - properity
• Amino acid composition
• Hydrophobicity
• Normalized Van der Waals volume
• Polarity
• Polarizability
• Charge
• Surface fusion
• Secondary structure
• Solvent accessibility

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(No Transcript)
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Dimension of feature vector
From Protein function classification via support
vector machine approach, Mathematical
Biosciences 185 (2003) 111-122
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Processing (2)

• 3 descriptors (21 elements)
• Composition (C) - 3
• C is the number of amino acids of a particular
  property divided by the total number of amino
  acids
• Transition (T) - 3
• T characterizes the percent frequency with which
  amino acids of particular property is followed by
  amino acids of a different property
• Distribution (D) - 15
• D means the chain length within which the first,
  25, 50, 75, 100 of the amino acids of a
  particular property is located respectively

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Example of feature vector
AA amino acid Sequence length (SL) 30 16
alanines ? n1 16 14 glutamic ? n2 14 C n1
100 / (n1n2) 53.33, n2 100 / (n1n2)
46.67 T (A ? E 15, E ? A 15) ? (15 / 29)
100 51.72 D (Index(AA) / SL) 100 As ?
3.33, 16.67, 40, 66.67, 96.67 Es ? 6.67,
26.67, 60, 76.67, 100
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Dataset

• Training set
• Positive all distinct protein members in each
  family
• Negative from seed proteins of the curated
  protein families in the Pfam database excluding
  those that belong to the family under study
• Testing set
• Positive all the remaining distinct proteins in
  each functional family
• Negative all the remaining representative seed
  protein in Pfam curated families
• Independent evaluation set (evaluate)
• Both positive and negative samples

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Scoring Function Quality Measurement

• Scoring for SVMProt
• Reliability index
• R-value
• P-value
• Quality measurement for experment
• TP (true positive)
• TN (true negative)
• FP (false positive)
• FN (false negative)
• Q (overall accuracy)

d the distance between the position of the
vector of classified protein and the optimal
separating hyperplane in hyperspace
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P-value Probability of correct classification
9932 positive samples 45,999 negative samples
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Result Discussion

• Q of protein classification ranges from 69.1 to
  99.6 ? provide more comprehensive sampling of
  proteins not in a functional class
• Prediction of distantly related proteins ? test
  on 24 randomly selected distantly related
  proteins in 7 families ? 14 proteins are
  correctly classified (58.3)