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

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2 Proteins Built from a repertoire of 20 amino acids

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4 7.1 Amino Acids

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5 胺基酸 中心碳 胺基（ NH 2 ） COOH 氫（ H ） 側鏈（ side chain, R ）

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6 同分異構物

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9 Fig. 7.2

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

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Polypeptide Composition

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Secondary Structure

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Backbone Flexibility

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18 Conformation of Polypeptide Chain

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19 Ramachandran Plot N: 藍 C: 黑 O: 紅 H: 白

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20 二級結構（ Secondary Structure ） Alpha helix

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21 Beta sheet

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23 Beta turn

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24 Loop

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

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26 PHD, Predator structure prediction algorithms accuracies in the range 70% ~ 75%

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Chou-Fasman Method

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28 Identifying Alpha Helices 1. Find all regions where four out of six have P(a)> Extend the regions until four with P(a) < 100 in both directions. 3. If ΣP(a) > ΣP(b) and the stretch >5, then it is identified as a helix.

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29 Identifying Beta Sheets 1. Find all regions where four out of six have P(b)> Extend the regions until four with P(b) < 100 in both directions. 3. If ΣP(b) > ΣP(a) and the average value of P(b) over the stretch >100, then it is identified as a helix.

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30 Resolving Overlapping Regions 1. Identified as helix if ΣP(a) > ΣP(b), as sheet if ΣP(b) > ΣP(a) over the overlapping regions.

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

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GOR Method on a window of 17 residues

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Tertiary and Quaternary Structure

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

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35 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

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36 Driving Forces for Folding electrostatic forces hydrogen bonds van der Waals forces disulfide bonds solvent interactions

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

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

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Disulfide Bonds

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

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43 Levinthal Paradox in residues, each assume 3 different conformations ~ 5x10 47 possibilities Suppose it takes s for one trial. Proteins fold by progressive stabilization of intermediates rather than by random search.

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Algorithms for Modeling Protein Folding Lattice Models Off-Lattice Models

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Lattice Models Reduce the search space and make computing tractable. Minimize free energy conformation

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

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

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

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50 Summary model representation scoring function search (optimization) (V. Pande, Stanford)

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Structure Prediction very high accuracy < 3.0 Å

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Comparative Modeling Also called homology modeling Rely on the robustness of the folding code

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53 1. Identify a set of protein structures related to the target protein. 2. Align the sequence of the target with the sequence of the template. 3. Construct the model. 4. Model the loop. 5. Model the side chains. 6. Evaluate the model.

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Threading Given a conformation and a protein sequence, measure its favorability.

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Predicting RNA Secondary Structures

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56 Nearest Neighbor Energy Rules Zuker’s Mfold program

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57 Why study RNA secondary structures? For understanding of gene regulation expression of protein products

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58 參考資料及圖片出處 1. Fundamental Concepts of Bioinformatics Dan E. Krane and Michael L. Raymer, Benjamin/Cummings, Fundamental Concepts of Bioinformatics 2. Biochemistry, by J. M. Berg, J. L. Tymoczko, and L. Stryer, Fith Edition, Biochemistry

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