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Study of Protein Prediction Related
Problems
Ph.D. candidate
2013.10.16
Le-Yi WEI
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Background
Methods
Experiments
Contents
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Background
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>Example PIVDTGSVAPLSAAEKTKIRSAWAPVYSTYETSGVDILVKFFTSTPAAQEFFPKFKGLTTADELKKSADVRWHAERIINAVDDAVASMDDTEKMSMKLRNLSGKHAKSFQVDPEYFKVLAAVIADTVAAGDAGFEKLMSMI
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Definition of protein
20 different amino acids
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A C D … … V W Y
Protein prediction related problems
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Protein Protein structural class prediction
Protein foldprediction
Multi-functional enzyme predictionProtein remote
homology detection
Other protein-related problems, etc.
Protein subcellular localization prediction
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Common points
Treat the protein-related problems as classification tasks
Query protein sequence
Data presentation
Classificationalgorithms
Predictedresults
The framework of a classification task
Two major components
Methods
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Feature extraction methods
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Primary sequence based
Secondary structure based
Sequence-structure based
e.g. Physicochemical features, N-gram, Functional Domain, PSSM-profile (auto-covariance), etc.
e.g. Secondary sequence based, and probability matrix based
e.g. Triple-sequence-structure features
Primary-sequence based
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• n-gram model
Given a query protein sequence:
Compute
Obtain
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A query protein sequence
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Database sequence 1
Database sequence 2
Database sequence 3
Database sequence n-2
Database sequence n-1
Database sequence n
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PSI-BLAST
Functional protein database
Featurevector
Primary-sequence based
• Functional domain
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Position-Specific Score Matrix (PSSM)
Protein database
PSI-BLAST
Primary-sequence based
• Evolution information
1220-D features
Primary-sequence based
• AAC features
Compute
Obtain
1320*g-D features
Primary-sequence based
• Auto-covariance (AC) transformation
Compute
Obtain
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Primary-sequence based
PSSM profile Frequency profile
• Consensus sequence
Consensus sequence:
A query sequence:
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Secondary structure based
• Secondary structure sequence
SLFEQLGGQAAVQAVTAQFYANIQADA example of a query protein sequence :
CCHEHEEEEECCCCHHHHHHEEEEECC
Predicted secondary structure sequence , which has three
states:
PSI-PRED
C (coil), H (Helix), E (strand)
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Secondary structure based
• Structure state confidence matrix
A example of a structure state confidence matrix:
A query protein sequencePredicted structure sequence
Predicted confidence
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Secondary structure based
• Global structural features
Compute Obtain
Structure state confidence matrix:
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Secondary structure based
• Local structural features
Compute Obtain
Structure state confidence matrix:
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Sequence-structure based
The framework of triple sequence-structure feature extraction method
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Classification algorithms
Commonly used classification algorithms
e.g. Support Vector Machine (SVM), Random Forest (RF), SMO, Naive Bayes, etc.
Ensemble classification algorithms
e.g. Majority Vote, Average Probability, Selective Ensemble, etc.
Experiments
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The framework of RF_PSCP
Webserver site : http://59.77.16.70:8080/RF_PSCP/Index.html
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Datasets
Three benchmark datasets
Three updated large-scale datasets
Sequence similarity
• Protein structural class prediction
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Results
Comparison with existing methods on three benchmark datasets
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Results
Tests of the proposed method on three updated large-scale datasets
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Results
Comparison with different combinations of feature subsets on three benchmark datasets
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Results
Optimization of Random forest classifier
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Q&A！
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