Download Mika-ProteinFoldingClassification

Protein Folding recognition with
Committee Machine
Mika Takata
Outline
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
Background
System Outline
Experiment
Experimental result
Reference
2
Background
SCOP
class
All alpha
All beta
a/b
Fold
Globinlike
Cytochrome c
Cupredoxins
a+b
(TIM)barrel
・・・・・
βgrasp
・・・・
・
・
・
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Computation + biology + chemical + medicine + ・・・・
= significantly important
Structure Classification Of Protein database
Fold level class : remote homology
Better recognition, better Tertiary structure prediction
1. Chemical approaching parameter ( i )
i.
6 types of Chemical features
ii.
String windows N-grams
Protein molecular weight value
Protein sequential length value
iii.
iv.
4
1. Chemical approaching parameter ( ii ):
Global parameter
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Symbol C
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Frequencies of 20 amino acid symbols in a protein sequence
Symbol S, H, V, P, Z
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(3-dim: composition, 3-dim: transition, 3×5-dim: Distribution)
1. Chemical approaching parameter ( iii )
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Protein molecular weight value
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Sum of Amino acids molecular weight
Utilize of molecular weight
yi  y
yi 
SD
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Protein sequential length value
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Utilize of sequential length
li
li  l

SD
2. Feature parameter based on Sliding window
N-Gram
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Proteomic fragment similarity
c( , x)  ( number of occurrence of  in x ) /( length of x)
…… NSDWTNNETRHAIVILIIIIIMLRHGKIPYWCMIPFAA…
（＊）string length =2
3: Feature parameter based on HMM
Fig 1： feature parameter flow based on HMM
Step 1
Model Ⅰ
Training
data
C
V
S
P
H
Z
Mol-Weight
Seq-Length
Model Ⅱ
Spectrum Kernel
Test data
Model Ⅲ
HMM
Step 2
Committe
e SVM_1
decision_1
・・
・
Committe
e SVM_i
decision_ i
・
・
・
・
Committe
e SVM_27
decision_ 27
Evaluation measurement：”Accuracy Q”
C i shows how correctly recognized in class i
Ci 

The numbers of
data in each class
are various
Q 

jclassi
27
TPj
TPj  FPj
 i Qi 
i 1
27
 i
i 1
ci
ni

C
N
n 

 i  i 
N 

Experiment

Parameter
Chemical approaching parameter
Feature parameter based on Sliding window kernel (string
length = 2 & 3)
Feature parameter based on HMM
i.
ii.
iii.
i.
Classification Methods
i.
independent SVM
ii.
Committee SVM Array
 Multi-class
recognition approaches
One-vs-others
All-vs-All method
i.
ii.
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Data set
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Training data： 341, test data： 353 (total: 694)
 http://www.nersc.gov/~cding/protein
Cross Validation：10 times
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Result (1)： Independent SVM- Model I
Result (2)： CM- Model I
Result (3)： CM- Model II
Result (3)： Model I & II
Result (4)： Model I & III
Result (5) : Model I & II & III
Conlusion
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Improvement by using all models of Committee
Machine
Spectrum kernel was works if used with string
length of 2
advantage
 Take advantage of sporadic data ( ex. chemical
base and hmm)
 Reduce of computational cost
Reference ( i )
1.
Takata, M., Matsuyama, Y.: Protein Folding Classification by Committee
SVM Array, Lecture Notes in Computer Science, No.5507, pp. 369-377,
2009.
2.
Matsuyama, Y., Kawasaki, K., Hotta, T, mizutani, Takata, M., Ishida, A.:
Eukaryotic transcription start site recognition involving non-promoter model.
Intelligent Systems for Molecular Biology, Toronto (2008) L05
3.
Matsuyama, Y., Ishihara, Y., Ito, Y., Hotta, T., Kawasaki, K., Hasegawa, T.,
Takata, M.: Promoter recognition involving motif detection: Studies on E. coli
and human genes. Intelligent Systems for Molecular Biology, Vienna (2007)
H06.
4.
Dubchak, I., Muchunik, I., Holbrook, S.R., Kim, S-H.: Prediction of protein
folding class using global description of amino acid sequence. Proc. Natl.
Acad. Sci. USA 92 (1995) 8700–8704
5.
Dubchak, I., Muchnik, I., Mayor, C., Dralyyuk, I., Kim, S-H.: Recognition of a
Protein Fold in the Context of the SCOP Classification. Proteins: Structure,
Function, and Genetics 35 (1999) 401–407
Reference ( ii )
1.
Ding, C.H.Q, Dubchak, I.: Multi-class protein fold recognition using support
vector machines and neural networks. Bioinfo. 17 (2001) 349–358
2.
Mount,. D.W.: Bioinformatics. Cold Spring Harbor Laboratory Press (2001)
3.
Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C.: SCOP: a structural
classification of proteins database for the investigation of sequences and
structures. J. Mol. Biol., 247 (1995) 536–540.
4.
Leslie, C., Eskin, E., Noble, W.S.: The Spectrum kernel: A string kernel for
SVM protein classification. Pacific Symposium on Biocomputing 7 (2002)
566–575
5.
Tabrez, M., Shamim, A., Anwaruddin, M., Nagarajaram, H.A.: Support vector
machine-based classification of protein folds using the structural properties
of amino acid residues and amino acid residue pairs. Bioinfo. 23 (2007)
3320–3327
6.
Lodhi, H,., Saunders, C., Shawe-Taylor, J., Watkins, C.: Text classification
using string kernels. J. of Machine Learning Research 2 (2002) 419–444.