Protein tertiary structure prediction with new machine learning approaches

Transcription

1 Protein tertiary structure prediction with new machine learning approaches Rui Kuang Department of Computer Science Columbia University Supervisor: Jason Weston(NEC) and Christina Leslie(Columbia) NEC summer internship talk, August 30th, 2005

2 Agenda 1. Introduction to protein structure 2. Protein backbone angle prediction with structured output learning 3. Protein domain detection based on protein structural classification 4. Discussion

3 1. Protein structure 2. Backbone angle prediction 3. Domain detection 4. Discussion Part 1: Protein structure Protein Derived from Greek word proteios meaning of the first rank in 1838 by Jöns J. Berzelius Crucial in all biological processes Function depends on structure (structure can help us to understand function) Determination of protein structures is time consuming and expensive

4 How to describe protein structure Primary structure: amino acid sequence Secondary structure: local structure elements Tertiary structure: packing and arrangement of secondary structure, also called domain Quaternary structure: arrangement of several polypeptide chains

5 Describe protein tertiary structure by protein backbone angles Phi-Psi Angles 3-D structure (Φ1,Ψ1) (Too complicated to predict!) (Φ2,Ψ2) (Φ3,Ψ3) (Φ4,Ψ4) Simplify (Φ5,Ψ5) (Φ6,Ψ6) (Φ7,Ψ7) (Φ8,Ψ8)

6 Discretization of Phi-Psi Psi angles: conformational states Oliver et al.(journal of Molecular Biology, 1997)

7 Protein blocks 16 small prototypes (a-p) of local protein structures of 5 residue length, clustered from Phi-Psi angles Phi-Psi Angles (Φ1,Ψ1) (Φ2,Ψ2) (Φ3,Ψ3) (Φ4,Ψ4) (Φ5,Ψ5) (Φ6,Ψ6) (Φ7,Ψ7) (Φ8,Ψ8) Protein Blocks g l p d b b m De Brevern et al.(protein Science, 2002)

8 Summary: representations of 3-D 3 D protein structure 3-D structure Phi-Psi Angles (Φ1,Ψ1) (Φ2,Ψ2) (Φ3,Ψ3) (Φ4,Ψ4) (Φ5,Ψ5) (Φ6,Ψ6) (Φ7,Ψ7) (Φ8,Ψ8) Conformational States: AAAGBBBBBBGEBBBB Protein Blocks: ammmalpppmmlmlbb

9 Protein domains A polypeptide chain or a part of a polypeptide chain that can fold independently into a stable tertiary structure.

10 1. Protein structure 2. Backbone angle prediction 3. Domain detection 4. Discussion Part 2: Prediction of protein backbone angle with structured output learning

11 Naïve window-based approach Encode each position independently with sequence information within a length-k window. Kuang, Leslie and Yang et al.(bioinformatics, 2004) Conformational States A A A B B B B G We are neighbors! G We have E dependency! B B B B B A: A: B: B: B: To SVM Predictions are independent.

12 2-Stage window-based approach Take the prediction of the naïve window-based approach as input to a second sets of SVMs. Ideally this smoothing step can correct some wrong predictions. A second stage of smoothing Sequence profiles SVM Prediction profiles A B G E SVM Window-based mapping Predictions Window-based mapping Naïve window-based approach Final Predictions AABGBEGE

13 Topographic SVM Training with profiles+true labels iteratively update the predictions in the testing phase. Sequence profiles Sequence profiles True Label profiles A B G E Window-based mapping Training SVM Window-based mapping Update predictions Mohr and Obermayer et al. (NIPS,2004) Testing + Prediction profiles A B G E From a base approach for the first round

14 Struct-SVM SVM Training: make joint feature mapping and apply large margin principle for the difference between the feature mapping of correct label and of wrong label. This is equivalent to the following optimization problem Testing: a pre-image problem Tsochantaridis(ICML, 2004)

15 Pre-image for Labeling Sequences Hidden-Markov kernel Pre-image is equivalent to Viterbidecoding of a HMM built from support vectors Altun et al. (ICML 2003)

16 Preliminary Results Prediction of Conformational States: 697 sequences of 97,365 amino acids with sequence identity < 25 % Prediction of Protein Blocks: 675 sequences of 146,978 amino acids with sequence identity < 30 % Methods State of art Naïve window-based approach 2-Stage window-based approach Topographic SVM SVM for structured output Accuracy (Conformational States) 75.0% 75.0% 76.0%> 75.3% 70.0%> Accuracy (Protein Blocks) 40.3% 57.7% 59.5%> 58.4% 50%>

17 1. Introduction 2. Backbone angle prediction 3. Domain detection 4. Discussion Part 3: Protein domain detection based on protein structural classification

18 Protein structural classification SCOP Fold Superfamily Family Positive Training Set Murzin et al. (Journal of Molecular Biology, 1995) Negative Training Set Positive Test Set Negative Test Set Family : Sequence identity > 30% or functions and structures are very similar Superfamily : low sequence similarity but functional features suggest probable common evolutionary origin Common fold : same major secondary structures in the same arrangement with the same topological connections

19 Spectrum kernel Feature map indexed by all possible k-length subsequences ( k-mers ) from alphabet Σ of amino acids, Σ = 20 Q1:AKQDYYYYE AKQ KQD QDY DYY YYY YYY YYE Feature Space (AAA-YYY) 1 AKQ 1 1 DYY 1 0 EIA 1 0 IAK 1 1 KQD 0 0 KQY 1 1 QDY 0 0 YEI 1 1 YYE 1 2 YYY 0 Leslie et al. (PSB, 2002) Q2:DYYEIAKQY DYY YYE YEI EIA IAK AKQ KQY K(Q1,Q2)=<( ),( )>=3

20 Profile kernel { } P(x) = p j (b),b Σ, j =1K x Use profile to define position-dependent mutation neighborhoods: E.g. k=3, σ=5 and a profile of negative log probabilities M ( k,σ P( x[ j+ 1: j + k] ))= A K Q ( ) A { C b 1 b 2 Lb k : log( p j + i ( b i )) < σ} i D AKQ YKQ K YKC (2+1+1<σ) AKQ AKC (2+1+1<σ) Q Y (1+1+1<σ) (1+1+2<σ) AKQ ( 0,, 1,, 1,, 1,, 1,, 0 ) AKC AKQ YKC YKQ Kuang and Leslie et al.(jbcb, 2005)

21 Positional classification scores PDB ID: 1f2e PDB ID:1hnf A simple probabilistic model to detect domains: P( S, E F) = P( s 2, e 2 P( s 0 F)* P( e 2, s 1 1 F)* P( s + 1, S 3 1, e 1 F)* P( e + 1, s 1 F)* 1 F)*...* P( sn, en F)* P( en + 1, F 1 2 F)

22 1.Dataset Experiments 7,329 sequences from SCOP Sequence identity less than 95%. 2.Preliminary Results (with a simplified model) Criteria Domain positions Domain start Domain end Accuracy 73.2% 51.1% 31.1% Coverage 73.1% 36.0% 21.9%

23 Part 4: Discussion 1. Introduction 2. Backbone angle prediction 3. Domain detection 4. Discussion Dependency between conformational states or protein blocks does not help much in the 2-stage window-based approach. Struct-SVM does not scale very well for large problems. Perceptron training may speed up the training stage. A proper probabilistic model is needed for detecting domain boundaries from positional classification scores

24 Acknowledgement William Stafford Noble Genome Science Department, University of Washington Asa Ben-Hur Genome Science Department, University of Washington An-Suei Yang Genome Research Center, Academia Sinica of Taiwan Yasemin Altun Toyota Technological Institute at Chicago Thorsten Joachims Computer Science Department, Cornell University