Disulfide bonds
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Disulfide Bonds. Two cyteines in close proximity will form a covalent bond Disulfide bond, disulfide bridge, or dicysteine bond. Significantly stabilizes tertiary structure. Determining Protein Structure. There are O(100,000) distinct proteins in the human proteome.

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Disulfide bonds l.jpg
Disulfide Bonds

  • Two cyteines in close proximity will form a covalent bond

  • Disulfide bond, disulfide bridge, or dicysteine bond.

  • Significantly stabilizes tertiary structure.

Protein Folding


Determining protein structure l.jpg
Determining Protein Structure

  • There are O(100,000) distinct proteins in the human proteome.

  • 3D structures have been determined for 14,000 proteins, from all organisms

    • Includes duplicates with different ligands bound, etc.

  • Coordinates are determined by X-ray crystallography

Protein Folding


X ray crystallography l.jpg

~0.5mm

X-Ray Crystallography

  • The crystal is a mosaic of millions of copies of the protein.

  • As much as 70% is solvent (water)!

  • May take months (and a “green” thumb) to grow.

Protein Folding


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X-Ray diffraction

  • Image is averagedover:

    • Space (many copies)

    • Time (of the diffractionexperiment)

Protein Folding


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Electron Density Maps

  • Resolution is dependent on the quality/regularity of the crystal

  • R-factor is a measure of “leftover” electron density

  • Solvent fitting

  • Refinement

Protein Folding


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The Protein Data Bank

  • http://www.rcsb.org/pdb/

ATOM 1 N ALA E 1 22.382 47.782 112.975 1.00 24.09 3APR 213

ATOM 2 CA ALA E 1 22.957 47.648 111.613 1.00 22.40 3APR 214

ATOM 3 C ALA E 1 23.572 46.251 111.545 1.00 21.32 3APR 215

ATOM 4 O ALA E 1 23.948 45.688 112.603 1.00 21.54 3APR 216

ATOM 5 CB ALA E 1 23.932 48.787 111.380 1.00 22.79 3APR 217

ATOM 6 N GLY E 2 23.656 45.723 110.336 1.00 19.17 3APR 218

ATOM 7 CA GLY E 2 24.216 44.393 110.087 1.00 17.35 3APR 219

ATOM 8 C GLY E 2 25.653 44.308 110.579 1.00 16.49 3APR 220

ATOM 9 O GLY E 2 26.258 45.296 110.994 1.00 15.35 3APR 221

ATOM 10 N VAL E 3 26.213 43.110 110.521 1.00 16.21 3APR 222

ATOM 11 CA VAL E 3 27.594 42.879 110.975 1.00 16.02 3APR 223

ATOM 12 C VAL E 3 28.569 43.613 110.055 1.00 15.69 3APR 224

ATOM 13 O VAL E 3 28.429 43.444 108.822 1.00 16.43 3APR 225

ATOM 14 CB VAL E 3 27.834 41.363 110.979 1.00 16.66 3APR 226

ATOM 15 CG1 VAL E 3 29.259 41.013 111.404 1.00 17.35 3APR 227

ATOM 16 CG2 VAL E 3 26.811 40.649 111.850 1.00 17.03 3APR 228

Protein Folding


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A Peek at Protein Function

  • Serine proteases – cleave other proteins

    • Catalytic Triad: ASP, HIS, SER

Protein Folding


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Cleaving the peptide bond

Protein Folding


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Three Serine Proteases

  • Chymotrypsin – Cleaves the peptide bond on the carboxyl side of aromatic (ring) residues: Trp, Phe, Tyr; and large hydrophobic residues: Met.

  • Trypsin – Cleaves after Lys (K) or Arg (R)

    • Positive charge

  • Elastase – Cleaves after small residues: Gly, Ala, Ser, Cys

Protein Folding



The protein folding problem l.jpg
The Protein Folding Problem

  • Central question of molecular biology:“Given a particular sequence of amino acid residues (primary structure), what will the tertiary/quaternary structure of the resulting protein be?”

  • Input: AAVIKYGCAL…Output: 11, 22…= backbone conformation:(no side chains yet)

Protein Folding


Protein folding biological perspective l.jpg
Protein Folding – Biological perspective

  • “Central dogma”: Sequence specifies structure

  • Denature – to “unfold” a protein back to random coil configuration

    • -mercaptoethanol – breaks disulfide bonds

    • Urea or guanidine hydrochloride – denaturant

    • Also heat or pH

  • Anfinsen’s experiments

    • Denatured ribonuclease

    • Spontaneously regained enzymatic activity

    • Evidence that it re-folded to native conformation

Protein Folding


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Folding intermediates

  • Levinthal’s paradox – Consider a 100 residue protein. If each residue can take only 3 positions, there are 3100 = 5  1047 possible conformations.

    • If it takes 10-13s to convert from 1 structure to another, exhaustive search would take 1.6  1027 years!

  • Folding must proceed by progressive stabilization of intermediates

    • Molten globules – most secondary structure formed, but much less compact than “native” conformation.

Protein Folding


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Forces driving protein folding

  • It is believed that hydrophobic collapse is a key driving force for protein folding

    • Hydrophobic core

    • Polar surface interacting with solvent

  • Minimum volume (no cavities)

  • Disulfide bond formation stabilizes

  • Hydrogen bonds

  • Polar and electrostatic interactions

Protein Folding


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Folding help

  • Proteins are, in fact, only marginally stable

    • Native state is typically only 5 to 10 kcal/mole more stable than the unfolded form

  • Many proteins help in folding

    • Protein disulfide isomerase – catalyzes shuffling of disulfide bonds

    • Chaperones – break up aggregates and (in theory) unfold misfolded proteins

Protein Folding


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The Hydrophobic Core

  • Hemoglobin A is the protein in red blood cells (erythrocytes) responsible for binding oxygen.

  • The mutation E6V in the  chain places a hydrophobic Val on the surface of hemoglobin

  • The resulting “sticky patch” causes hemoglobin S to agglutinate (stick together) and form fibers which deform the red blood cell and do not carry oxygen efficiently

  • Sickle cell anemia was the first identified molecular disease

Protein Folding


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Sickle Cell Anemia

Sequestering hydrophobic residues in the protein core protects proteins from hydrophobic agglutination.

Protein Folding


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Computational Problems in Protein Folding

  • Two key questions:

    • Evaluation – how can we tell a correctly-folded protein from an incorrectly folded protein?

      • H-bonds, electrostatics, hydrophobic effect, etc.

      • Derive a function, see how well it does on “real” proteins

    • Optimization – once we get an evaluation function, can we optimize it?

      • Simulated annealing/monte carlo

      • EC

      • Heuristics

      • We’ll talk more about these methods later…

Protein Folding


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Fold Optimization

  • Simple lattice models (HP-models)

    • Two types of residues: hydrophobic and polar

    • 2-D or 3-D lattice

    • The only force is hydrophobic collapse

    • Score = number of HH contacts

Protein Folding


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Scoring Lattice Models

  • H/P model scoring: count noncovalent hydrophobic interactions.

  • Sometimes:

    • Penalize for buried polar or surface hydrophobic residues

Protein Folding


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What can we do with lattice models?

  • For smaller polypeptides, exhaustive search can be used

    • Looking at the “best” fold, even in such a simple model, can teach us interesting things about the protein folding process

  • For larger chains, other optimization and search methods must be used

    • Greedy, branch and bound

    • Evolutionary computing, simulated annealing

    • Graph theoretical methods

Protein Folding


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Learning from Lattice Models

  • The “hydrophobic zipper” effect:

Ken Dill ~ 1997

Protein Folding


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Representing a lattice model

  • Absolute directions

    • UURRDLDRRU

  • Relative directions

    • LFRFRRLLFFL

    • Advantage, we can’t have UD or RL in absolute

    • Only three directions: LRF

  • What about bumps? LFRRR

    • Bad score

    • Use a better representation

Protein Folding


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Preference-order representation

  • Each position has two “preferences”

    • If it can’t have either of the two, it will take the “least favorite” path if possible

  • Example: {LR},{FL},{RL},{FR},{RL},{RL},{FR},{RF}

  • Can still cause bumps:{LF},{FR},{RL},{FL},{RL},{FL},{RF},{RL},{FL}

Protein Folding


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“Decoding” the representation

  • The optimizer works on the representation, but to score, we have to “decode” into a structure that lets us check for bumps and score.

  • Example: How many bumps in: URDDLLDRURU?

  • We can do it on graph paper

    • Start at 0,0

    • Fill in the graph

  • In PERL we use a two-dimensional array

Protein Folding


A two dimensional array in perl l.jpg
A two-dimensional array in PERL

$configuration = “URDDLLDRURU”;

$sequence = “HPPHHPHPHHH”;

foreach $i (1..100) {

foreach $j (1..100) {

$grid[$i][$j] = “empty”;

}

}

$x = 0;

$y = 0;

@moves = split(//,$configuration);

@residues = split(//,$sequence);

Protein Folding


Setting up the grid l.jpg
Setting up the grid

foreach $move (@moves) {

$residue = shift(@residues);

if ($move = “U”) {

$y_position++;

}

if ($move = “R”) {

$x_position++;

}

etc…

if ($grid[$x][$y] ne “empty”) {

BUMP!

} else {

$grid[$x][$y] = $residue;

}

Protein Folding


More realistic models l.jpg
More realistic models

  • Higher resolution lattices (45° lattice, etc.)

  • Off-lattice models

    • Local moves

    • Optimization/search methods and / representations

      • Greedy search

      • Branch and bound

      • EC, Monte Carlo, simulated annealing, etc.

Protein Folding


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The Other Half of the Picture

  • Now that we have a more realistic off-lattice model, we need a better energy function to evaluate a conformation (fold).

  • Theoretical force field:

    • G = Gvan der Waals + Gh-bonds + Gsolvent + Gcoulomb

  • Empirical force fields

    • Start with a database

    • Look at neighboring residues – similar to known protein folds?

Protein Folding


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Threading: Fold recognition

  • Given:

    • Sequence: IVACIVSTEYDVMKAAR…

    • A database of molecular coordinates

  • Map the sequence onto each fold

  • Evaluate

    • Objective 1: improve scoring function

    • Objective 2: folding

Protein Folding


Secondary structure prediction l.jpg
Secondary Structure Prediction

AGVGTVPMTAYGNDIQYYGQVT…

A-VGIVPM-AYGQDIQY-GQVT…

AG-GIIP--AYGNELQ--GQVT…

AGVCTVPMTA---ELQYYG--T…

AGVGTVPMTAYGNDIQYYGQVT…

----hhhHHHHHHhhh--eeEE…

Protein Folding


Secondary structure prediction32 l.jpg
Secondary Structure Prediction

  • Easier than folding

    • Current algorithms can prediction secondary structure with 70-80% accuracy

  • Chou, P.Y. & Fasman, G.D. (1974). Biochemistry, 13, 211-222.

    • Based on frequencies of occurrence of residues in helices and sheets

  • PhD – Neural network based

    • Uses a multiple sequence alignment

    • Rost & Sander, Proteins, 1994 , 19, 55-72

Protein Folding


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Chou-Fasman Parameters

Protein Folding


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Chou-Fasman Algorithm

  • Identify -helices

    • 4 out of 6 contiguous amino acids that have P(a) > 100

    • Extend the region until 4 amino acids with P(a) < 100 found

    • Compute P(a) and P(b); If the region is >5 residues and P(a) > P(b) identify as a helix

  • Repeat for -sheets [use P(b)]

  • If an  and a  region overlap, the overlapping region is predicted according to P(a) and P(b)

Protein Folding


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Chou-Fasman, cont’d

  • Identify hairpin turns:

    • P(t) = f(i) of the residue  f(i+1) of the next residue  f(i+2) of the following residue  f(i+3) of the residue at position (i+3)

    • Predict a hairpin turn starting at positions where:

      • P(t) > 0.000075

      • The average P(turn) for the four residues > 100

      • P(a) < P(turn) > P(b) for the four residues

  • Accuracy  60-65%

Protein Folding


Chou fasman example l.jpg
Chou-Fasman Example

  • CAENKLDHVRGPTCILFMTWYNDGP

  • CAENKL – Potential helix (!C and !N)

    • Residues with P(a) < 100: RNCGPSTY

  • Extend: When we reach RGPT, we must stop

  • CAENKLDHV: P(a) = 972, P(b) = 843

  • Declare alpha helix

  • Identifying a hairpin turn

    • VRGP: P(t) = 0.000085

    • Average P(turn) = 113.25

      • Avg P(a) = 79.5, Avg P(b) = 98.25

  • Protein Folding


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