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Chapter 7 Protein and RNA Structure Prediction

This chapter explores the prediction of protein and RNA structures using computational methods such as neural networks, hidden Markov models, and evolutionary computation. It covers topics like amino acids, secondary structure prediction, tertiary and quaternary structure, and algorithms for protein folding.

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Chapter 7 Protein and RNA Structure Prediction

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  1. Chapter 7Protein and RNA Structure Prediction 暨南大學資訊工程學系 黃光璿 2004/05/24

  2. Proteins • Built from a repertoire of 20 amino acids

  3. 7.1 Amino Acids

  4. 胺基酸 • 中心碳 • 胺基（NH2） • COOH • 氫（H） • 側鏈（side chain, R）

  5. 同分異構物

  6. Fig. 7.2

  7. pH, pKa, and pI • pH • -log [H+] • pKa • = pH ~ half of the amino acid residues will dissociate (釋放出H+). • pI • = pH, isoelectric point for protein

  8. 7.2 Polypeptide Composition

  9. 7.3 Secondary Structure

  10. 7.3.1 Backbone Flexibility

  11. Conformation of Polypeptide Chain

  12. Ramachandran Plot N:藍 C:黑 O:紅 H:白

  13. 二級結構（Secondary Structure） • Alpha helix

  14. Beta sheet

  15. Beta turn

  16. Loop

  17. 7.3.2 Accuracy of Prediction • Computational methods • neural network • discrete-state models • hidden Markov models • nearest neighbor classification • evolutionary computation

  18. PHD, Predator • structure prediction algorithms • accuracies in the range 70% ~ 75%

  19. 7.3.3 Chou-Fasman Method

  20. Identifying Alpha Helices • Find all regions where four out of six have P(a)>100. • Extend the regions until four with P(a) < 100 in both directions. • If ΣP(a) > ΣP(b) and the stretch >5, then it is identified as a helix.

  21. Identifying Beta Sheets • Find all regions where four out of six have P(b)>100. • Extend the regions until four with P(b) < 100 in both directions. • If ΣP(b) > ΣP(a) and the average value of P(b) over the stretch >100, then it is identified as a helix.

  22. Resolving Overlapping Regions • Identified as helix if ΣP(a) > ΣP(b), as sheet if ΣP(b) > ΣP(a) over the overlapping regions.

  23. Identifying Turns • Let P(t) = f(i)xf(i+1)xf(i+2)xf(i+3) for each position i. • Identify as a turn if • P(t) > 0.000075; • The average of P(turn) over the four residues > 100; • ΣP(a) < ΣP(turn) > ΣP(b) over the four residues.

  24. 7.3.4 GOR Method • on a window of 17 residues

  25. 7.4 Tertiary and Quaternary Structure

  26. 折疊成立體的形狀 三級結構（Tertiary Structure）

  27. 四級結構（Quaternary Structure） • 數個三級結構結合成具有功能的大分子 人類的血球蛋白

  28. Driving Forces for Folding • electrostatic forces • hydrogen bonds • van der Waals forces • disulfide bonds • solvent interactions

  29. 7.4.1 Hydrophobicity (疏水性) • hydrophobic collapse • Tend to keep polar, charged residues on the surface. • The class of membrane-integral proteins is an exception.

  30. sickle-cell anemia (鐮狀細胞性貧血) • human hemoglobin: 2 alpha & 2 beta globins • charged glutamic acid residue  hydrophobic valine residues

  31. 7.4.2 Disulfide Bonds

  32. 7.4.3 Active Structures vs Most Stable Structures • Natural selection favors proteins that are both active and robust.

  33. Levinthal Paradox • in 1968 • 100 residues, each assume 3 different conformations • 3100 ~ 5x1047 possibilities • Suppose it takes 10-13 s for one trial. • Proteins fold by progressive stabilization of intermediates rather than by random search.

  34. 7.5 Algorithms for Modeling Protein Folding • Lattice Models • Off-Lattice Models

  35. 7.5.1 Lattice Models • Reduce the search space and make computing tractable.  Minimize free energy conformation

  36. HP-model • hydrophobic-polar model • Scoring is based on hydrophobic contacts. • Maximize the H-to-H contacts. • Fig. 7.8

  37. 7.5.2 Off-Lattice Models • Use RMSD (root mean square deviation) to measure the accuracy. • Determine Φ and Ψin the allowable region of the Ramachandran plot.

  38. 7.5.3 Energy Functions and Optimization • Problems • The exact forces that drive the folding process are not well understood. • It is too computationally expensive.

  39. Summary • model • representation • scoring function • search (optimization) • Folding@Home (V. Pande, Stanford)

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