Protein Structure Prediction

1. Protein Structure Prediction (Lecture for CS397-CXZ Algorithms in Bioinformatics) April 23, 2004 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign

2. Topics in Bioinformatics Function (Protein) Gene (DNA) Gene expression & regulation Microarray data (Matrix) > DNA sequence AATTCATGAAAATCGTATACTGGTCTGGTACCGGC TGAGAAAATGGCAGAGCTCATCGCTAAAGGTA TCTGGTAAAGACGTCAACACCATCAACGTGTC ACATCGATGAACTGCTGAACGAAGATATCCTG TTGCTCTGCCATGGGCGATGAAGTTCTCGAGG > Protein sequence MKIVYWSGTGNTEKMAELIAKGIIESGKDV DELLNEDILILGCSAMGDEVLEESEFEPFIE KVALFGSYGWGDGKWMRDFEERMNGYG PDEAEQDCIEFGKKIANI transcriptomics Proteomics Genomics

3. Proteomics: Protein Sequence Analysis • Determine protein sequences (primary structure) • Indirect: Find genes and then translate them to proteins • Direct: Mass spectrometry data • Determine 3-D protein structures (secondary, tertiary, quaternary) • Computational: Sequence matching, energy minimization etc. • Experimental: X-ray Crystallography, Nuclear Magnetic Resonance spectroscopy (NMR), Electron Microscopy/Diffraction • Determine protein functions • Computational: Profile HMMs, protein classification, motif analysis • Experimental: Web lab experiments • Determine protein-protein interactions • Gene network finding (time series microarray data) • Metabolic engineering

4. Basics of Protein Structures…

5. The Building Blocks (Amino Acids)

6. The 20 Amino Acids

7. SECONDARY STRUCTURE (helices, strands) PRIMARY STRUCTURE (amino acid sequence) VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH TERTIARY STRUCTURE (fold) QUATERNARY STRUCTURE (oligomers) Protein structure hierarchical levels -helix -sheet loop/coil (Adapted from Jaap Heringa’s slide)

8. Domain and Folds • A discrete portion of a protein assumed to fold independently of the rest of the protein and possessing its own function. • Most proteins have multi-domains. • The core 3D structure of a domain is called a fold. There are only a few thousand possible folds.

9. Examples of fold classes (CATH architectures)

10. Protein Structure & Function structure medicine Most functions depend on structures sequence function

11. Structure Prediction Methods Homology modeling High sequence similarity (> 30% identity) Exploit known whole structure Fold Recognition Medium sequence similarity (generally < 30% identity) Exploit known partial structures (e.g., known folds, secondary structures) Ab Initio Low sequence similarity Use “first principles” (e.g., energy minimization) (Adapted from a slide by P. Johansson, E. Jakobsson)

12. First, suppose we have high similarity…

13. Homology Modeling • Simplest, reliable approach • Basis: proteins with similar sequences tend to fold into similar structures • Has been observed that even proteins with 30% sequence identity fold into similar structures • Does not work for remote homologs (< 30% pairwise identity)

14. Homology Modeling (cont.) • Given: • A query sequence Q • A database of known protein structures • Find protein P such that P has high sequence similarity to Q • Based on sequence alignment (tuned for protein structure matching, less penalty for gaps) • HMMs, BLAST, etc. • Return P’s structure as an approximation to Q’s structure

15. Now, if we don’t have high similarity, but we have medium similarity…

16. Threading (Fold Recognition) • Given: • Sequence of protein P with unknown structure • Database of known folds (overall structures) • Find: • Most plausible fold for P • Evaluate quality of such arrangement • Places the residues of unknown P along the backbone of a known structure and determines stability of side chains in that arrangement

17. What if we have really low similarity?

18. Secondary Structure Prediction • Given an amino acid sequence • Predict a secondary structure state (, , coil) for each residue in the sequence • Secondary structures can help • Determine 3D structures (e.g., help threading) • Provide insights about functions • Evaluation: Q3 = percentage of correct assignments • Accuracy • 64% -75% based on primary sequence only (recent methods perform better) • Higher accuracy for a-helices than b- strands • Accuracy is dependent on protein family

19. Typical Secondary Structure Prediction Results

20. Secondary Structure Prediction Methods • Early approaches (Chou and Fasman 1978) • Make prediction for a given residue by considering a window of n (13 – 21) neighboring residues • Learn model that performs mapping from window of residues to secondary structure state • Later methods utilize evolutionary information (e.g., PHD system (Rost & Sander, 1993) ) and consider related sequences when making prediction • Most recent approaches: Neural networks (PSIPRED, 77%) (Altschul et al., 1997)

21. Chou-Fasman Method • Developed by Chou & Fasman in 1974 & 1978 • Based on frequencies of residues in a-helices, b-sheets and turns • Assumptions: • The entire information for forming secondary structure is contained in the primary sequence • Side groups of residues will determine structure • Examining windows of 13 - 17 residues is sufficient to predict structure • Basis for window size selection: • a-helices 5 – 40 residues long • b-strands 5 – 10 residues long • Accuracy ~50 - 60% Q3

22. Chou-Fasman Pij-values Values indicate how likely an amino acid occurs in one secondary structure as opposed to others

23. Improved Chou-Fasman 1. Assign all of the residues the appropriate set of parameters 2. Identify a-helix and b-sheet regions. Extend the regions in both directions. 3. If structures overlap compare average values for P(H) and P(E) and assign secondary structure based on best scores. 4. Turns are modeled as tetrapeptides using 2 different probability values.

24. Assign Pij values 1. Assign all of the residues the appropriate set of parameters

25. Scan peptide for a-helix regions 2. Identify regions where 4/6 have a P(H) >100 “alpha-helix nucleus”

26. Extend a-helix nucleus 3. Extend helix in both directions until a set of four residues have an average P(H) <100. Repeat steps 1 – 3 for entire peptide

27. Scan peptide for b-sheet regions 4. Identify regions where 3/5 have a P(E) >100 “b-sheet nucleus” 5. Extend b-sheet until 4 continuous residues an have an average P(E) < 100 6. If region average > 105 and the average P(E) > average P(H) then “b-sheet”

28. Visit http://fasta.bioch.virginia.edu/fasta_www/chofas.htm

29. Neural Network Predictors • All current state of the art methods for secondary structure prediction (except consensus methods) employ neural network classifiers. • (Large) data sets are used to train the neural net • A sequence window centered on the amino acid to predict is presented to the classifier • Homologous sequences (e.g. Y-Blast profile) are used to augment prediction capability

30. What about exploit physical principles?

31. Ab Initio Prediction Solve a complex optimization Problem: - Measure “goodness” based on energy etc - Randomly start with some conformation - Heuristically propose a next conformation - Search for the best conformation

32. Best so far… Using Rosetta for Ab Initio Structure Prediction in the Fourth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP4) Group of David Baker, Univ. of Washington Visit their website and read the paper if you are interested… http://depts.washington.edu/bakerpg/