Download Support vector machines for protein function prediction

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Immunoprecipitation wikipedia , lookup

Ubiquitin wikipedia , lookup

Implicit solvation wikipedia , lookup

Structural alignment wikipedia , lookup

Rosetta@home wikipedia , lookup

Protein design wikipedia , lookup

Circular dichroism wikipedia , lookup

List of types of proteins wikipedia , lookup

Protein folding wikipedia , lookup

Protein domain wikipedia , lookup

Bimolecular fluorescence complementation wikipedia , lookup

Protein wikipedia , lookup

Cyclol wikipedia , lookup

Homology modeling wikipedia , lookup

Proteomics wikipedia , lookup

Intrinsically disordered proteins wikipedia , lookup

Nuclear magnetic resonance spectroscopy of proteins wikipedia , lookup

Protein moonlighting wikipedia , lookup

Protein purification wikipedia , lookup

Protein mass spectrometry wikipedia , lookup

Western blot wikipedia , lookup

Protein structure prediction wikipedia , lookup

Protein–protein interaction wikipedia , lookup

Transcript
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
2
Protein Function and Functional Family
Proteins of similar functional characteristics can be grouped into a family
3
Protein Function and Functional Family
Proteins of similar functional characteristics can be grouped into a family
4
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
5
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).
6
Machine Learning Method
Inductive learning:
Example-based learning
Descriptor
Positive
examples
Negative
examples
7
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
8
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
9
SVM Method
Protein family
members
Border
New border
Protein family
members
Nonmembers
Nonmembers
Project to a higher dimensional space
10
SVM method
New border
Support vector
Support vector
Protein family
members
Nonmembers
11
SVM Method
Support vector
Protein family
members
Nonmembers
New border
Support vector
12
SVM Method
Border line is nonlinear
13
SVM method
Non-linear transformation: use of kernel function
14
SVM method
Non-linear transformation
15
SVM Method
16
SVM Method
17
SVM Method
18
SVM Method
19
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?
21
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
22
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
27