Transcript Support vector machines for protein function prediction

LSM3241: Bioinformatics and Biocomputing
Lecture 3: Machine learning method for
protein function prediction
Prof. Chen Yu Zong
Tel: 6516-6877
Email: [email protected]
http://bidd.nus.edu.sg
Room 07-24, level 7, SOC1,
National University of Singapore
Protein Function and Functional Family
Proteins of similar functional characteristics can be grouped into a family
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Protein Function and Functional Family
Proteins of similar functional characteristics can be grouped into a family
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Protein Function and Functional Family
Proteins of similar functional characteristics can be grouped into a family
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Functional Classification of Proteins by SVM
• A protein is classified as either belong (+) or not belong (-) to a
functional family
• By screening against all families, the function of this protein can be
identified (example: SVMProt)
Protein
Family-1
SVM
-
Family-2
SVM
-
Family-3
SVM
+
Protein belongs to
Family-3
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Functional Classification of Proteins by SVM
What is SVM?
• Support vector machines, a machine learning method,
learning by examples, statistical learning, classify objects
into one of the two classes.
Advantages of SVM:
• Diversity of class members (no racial discrimination).
• Use of sequence-derived physico-chemical features as
basis for classification.
• Suitable for functional classification of novel proteins
(distantly-related proteins, homologous proteins of
different functions).
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Machine Learning Method
Inductive learning:
Example-based learning
Descriptor
Positive
examples
Negative
examples
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Machine Learning Method
Feature vectors:
A=(1, 1, 1)
B=(0, 1, 1)
C=(1, 1, 1)
D=(0, 1, 1)
E=(0, 0, 0)
F=(1, 0, 1)
Descriptor
Feature vector
Positive
examples
Negative
examples
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SVM Method
Feature vectors in input space:
Z
Input space
Feature vector
A=(1, 1, 1)
B=(0, 1, 1)
C=(1, 1, 1)
D=(0, 1, 1)
E=(0, 0, 0)
F=(1, 0, 1)
F
E A
B
Y
X
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SVM Method
Protein family
members
Border
New border
Protein family
members
Nonmembers
Nonmembers
Project to a higher dimensional space
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SVM method
New border
Support vector
Support vector
Protein family
members
Nonmembers
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SVM Method
Support vector
Protein family
members
Nonmembers
New border
Support vector
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SVM Method
Border line is nonlinear
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SVM method
Non-linear transformation: use of kernel function
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SVM method
Non-linear transformation
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SVM Method
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SVM Method
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SVM Method
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SVM Method
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SVM for Classification of Proteins
How to represent a protein?
• Each sequence represented by specific feature vector assembled
from encoded representations of tabulated residue properties:
– amino acid composition
– Hydrophobicity
– normalized Van der Waals volume
– polarity,
– Polarizability
– Charge
– surface tension
– secondary structure
– solvent accessibility
• Three descriptors, composition (C), transition (T), and distribution
(D), are used to describe global composition of each of these
properties.
Nucleic Acids Res., 31: 3692-369720
SVM for Classification of Proteins
How to represent a protein?
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SVM for Classification of Proteins
How to represent a protein?
From protein sequence:
To Feature vector :
(C_amino acid composition, T_ amino acid composition, D_ amino acid composition,
C_hydrophobicity, T_hydrophobicity, D_hydrophobicity, … )
Nucleic Acids Res., 31: 3692-3697
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Protein function prediction software SVMProt
Useful for functional prediction of novel proteins, distantly-related proteins,
homologous proteins of different functions
Your protein sequence
Option 1
Your protein
sequence
Option 2
http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Which functional
families your protein
belong to?
Send sequence to classifier
Input sequence
through internet
Computer loaded
with SVMProt
Input sequence
on local machine
Protein functional
indications
Support vector machines
classifier for every
protein functional family
Identified
Functional families
Nucl. Acids Res. 31, 3692-3697 (2003)
Protein function prediction software SVMProt
Useful for functional prediction of
novel proteins, distantly-related
proteins, homologous proteins of
different functions.
Protein families covered:
46 enzyme families, 3 receptor families,
4 transporter and channel families,
6 DNA- and RNA-binding families,
8 structural families, 2 regulator/factor
families.
SVMProt web-version at:
http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi
Nucl. Acids Res. 31, 3692-3697 (2003)
Protein function prediction software SVMProt
Check covered
protein families here
Check format here
Input sequence here
Nucl. Acids Res. 31, 3692-3697 (2003)
Protein function prediction software SVMProt
Prediction Probability of
score
correct prediction
Nucl. Acids Res. 31, 3692-3697 (2003)
Summary of Today’s lecture
• Machine learning method for protein function
prediction.
• Use of SVMProt for probing protein function
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