Study of Protein Prediction Related Problems Ph.D. candidate Le-Yi WEI 1

123 Background Methods Experiments Contents 2

Background 3

>Example PIVDTGSVAPLSAAEKTKIRSAWAPVYSTYETSGVDILVKFFTSTPAAQ EFFPKFKGLTTADELKKSADVRWHAERIINAVDDAVASMDDTEKMS MKLRNLSGKHAKSFQVDPEYFKVLAAVIADTVAAGDAGFEKLMSMI 4 Definition of protein 20 different amino acids … AC D V W Y

Protein prediction related problems 5 Protein Protein structural class prediction Protein fold prediction Multi-functional enzyme prediction Protein remote homology detection Other protein-related problems, etc. Protein subcellular localization prediction

6 Common points Treat the protein-related problems as classification tasks Query protein sequence Data presentation Classification algorithms Predicted results The framework of a classification task Two major components

Methods 7

Feature extraction methods 8  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 9 n-gram model Given a query protein sequence: Compute Obtain

10 A query protein sequence … … … Database sequence 1 Database sequence 2 Database sequence 3 Database sequence n-2 Database sequence n-1 Database sequence n … … … PSI-BLAST Functional protein database Feature vector Primary-sequence based Functional domain … … …

11 Position-Specific Score Matrix (PSSM) Protein database PSI-BLAST Primary-sequence based Evolution information

12 20-D features Primary-sequence based AAC features Compute Obtain

13 20*g-D features Primary-sequence based Auto-covariance (AC) transformation Compute Obtain

14 Primary-sequence based PSSM profileFrequency profile Consensus sequence Consensus sequence: A query sequence:

15 Secondary structure based Secondary structure sequence SLFEQLGGQAAVQAVTAQFYANIQAD A example of a query protein sequence : CCHEHEEEEECCCCHHHHHHEEEEECC Predicted secondary structure sequence, which has three states: PSI-PRED C (coil), H (Helix), E (strand)

16 Secondary structure based Structure state confidence matrix A example of a structure state confidence matrix: A query protein sequence Predicted structure sequence Predicted confidence

17 Secondary structure based Global structural features Compute Obtain Structure state confidence matrix:

18 Secondary structure based Local structural features ComputeObtain Structure state confidence matrix:

19 Sequence-structure based The framework of triple sequence-structure feature extraction method

20 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 21

22 The framework of RF_PSCP Webserver site :

23 Datasets Three benchmark datasets Three updated large-scale datasets Sequence similarity Protein structural class prediction

24 Results Comparison with existing methods on three benchmark datasets

25 Results Tests of the proposed method on three updated large-scale datasets

26 Results Comparison with different combinations of feature subsets on three benchmark datasets

27 Results Optimization of Random forest classifier

28

Q&A ！ 29