Protein secondary structure Prediction

1. Protein secondary structure Prediction • The problem Seq: RPLQGLVLDTQLYGFPGAFDDWERFMRE Pred:CCCCCHHHHHCCCCEEEECCHHHHHHCC • Why 2nd Structure prediction?

2. Some historical landmarks • 1st generation – 70’s (~50-60% accuracy) • single residue statistics, explicit rules • Chou & Fasman 1974, GOR1 1978 • 2nd generation – 80’s (~60-70% accuracy) • single residue statistics, nearest-neighbors, neural network (more with local interaction) • GOR3 1987, Levin et al. 1986, Qian & Sejnowski 1988, Holly & Karplus, 1989 • 3rd generation – 90’s (~78% accuracy) • neural network with homologous sequence information • PHD 1993, PSIPRED 1999, SSPRO 2000

3. Chou-Fasman method • Straight statistical approach • Conformational propensity e.g. helical propensity • Categorize each amino acid • e.g. helix former, helix breaker, helix indifferent • Find nucleation sites • short sequence with high concentration of a category • Extend the nucleation sites till a threshold • Handle overlaps

4. Chou-Fasman method Conformational parameters (Table from Krane and Raymer’s book) • What is the drawback of the method?

5. Introduction to neural network • A self learning system – using a training data set • A perceptron • An analogy – apple and orange sorter • Threshold unit – classify a vector of inputs • Weight ! How to get it?

6. Basics in neural network (1 unit only) • Modify threshold unit a little bit • Step function vs. continuous threshold function (a) • Problem about weight • Do not fit examples exactly - minimize an error function

7. Basics in neural network (1 unit only) • Squared error function E(w) • Minimize error E(w) - using gradient descent method • Weight update in each step • Learning rate 

8. Basic neural network in secondary structure prediction (Figure from Kneller et. al. JMB 1990) Activation a1= Output y1= Error E1= E1 E2 E3 y1 y2 y3 w11 w12 w13 w14 x1 x2 x3 x4

9. Multi-layer neural network • Complete neural network • - a set of continuous threshold units interconnected in a topology • - output of some unit is input of other units Output units (z) Hidden units (y) Input units (x) x1 x2 x3 x4

10. PHD method (Rost B. & Sander C, JMB 1993) • Use profile of multiple sequence alignment • Multiple layers • Accuracy >70%

11. Protein Folding Problem • A protein folds into a unique 3D structure in physiological condition • What is the protein folding problem? • 3D structure is a key to understand function mechanism • Rational drug design • 3D structure prediction

12. Protein Folding Problem • Hard? • Can it be done? • Sampling conformational space • SS structures offer simplicity • Side chain filling the space • May not be random search • Free energy ( G) = • Interaction energy – Entropic energy

13. Protein Folding Problem • Experimental finding • Protein does not start folding from the end • SS seem to fold early • Hydrophobic aa in the core • Hydrophilic aa on surface • Energy function approximation • Physics based (bond length, bond angle, pair interactions) • Statistics based

14. Scope of the problem • Majority of the newly solved protein structure share certain level of similarity with a known structure • Certain families of proteins have no or few structures solved • Human genes ~20k • Structure genomics initiative

15. Protein structure prediction • Comparative modeling • >30% sequence identify • Fold recognition – formally known as threading • twilight zone <25% sequence identity • Ab initio • new fold

16. CASP Compare and rank Experimentally solved structure Predicted structure • CASP – • e.g. Skolnick (2003) Proteins: 53:p469-79 • Ginalski (2003) Proteins: 53: p410-17 • Zhang, Y. “Template-based modeling and free modeling by I-TASSER in CASP7 (pages 108–117)” Proteins, 69, S8, P108-17 (2007).

17. Search for structures Select templates Align target sequence with structures Build model Evaluate model Comparative Modeling http://www.salilab.org/~andras/watanabe/main.html • Sequence identity vs. structure overlap (Fig)

18. Comparative Modeling • Search for structures: • pair-wise sequence alignment with database • multiple sequence alignment -> profile • fold assignment / threading – use structure information in comparison • Select template: • sequence similarity, evolutionary relationship, environment, resolution • Sequence alignment (target and template) • standard method with tune

19. Ab Inito Prediction • Challenge: • Search space • Energy function • Reduction in search space • use lattice • use simplified amino acids • use building blocks available in nature • Energy function: • physics • statistics - empirical

20. Ab inito 3D Structure prediction An example - ROSETTA Simons KT, Kooperberg C, Huang E, Baker D; J Mol Biol. (1997) 268, 209-225 Schonbrun J, Wedemeyer W, Baker D; Current Opinion in Structure biology, (2002), 12:348-54 ROSETTA narrow search - use local structure available statistical based energy function one of the top few ab initio methods in CASP4.

21. ROSETTA – segment matching Observations: Analysis of 9-a.a. segments in structure database distribution of the conformations of 9-mers Main idea of the method build segment conformational library (fragment library for 3mer and 9mer) put pieces together better (energy function and search space)

22. Model Building • Assembly of rigid bodies • dissecting structure into core, loops and side- chains • Satisfy spatial constraints (Fig.) • derive spatial constraints, find a structure that optimize all the constraints • spatial constraints generated from • input alignment; • general spatial preferences found in known structures; • molecular force field;