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COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science McGill University
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Page 1: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

COMP 564:Introduction to protein structure

predictionJérôme Waldispühl

School of Computer ScienceMcGill University

Page 2: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Folding problemKLHGGPMLDSDQKFWRTPAALHQNEGFT

Nétats

~ 10n

n = 100-300

Levinthal paradox

Page 3: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Amino acids: The simple ones

Page 4: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Amino acids: Aliphatics

Page 5: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Amino acids: Cyclic and Sulfhydryl

Page 6: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Amino acids: Aromatics

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Amino acids: Aliphatic hydroxyl

Page 8: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Amino acids: Carboxamides & Carboxylates

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Amino acids: Basics

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Histidine ionisation

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Primary structureA peptide bond assemble two amino acids together:

A chain is obtained through the concatenation of several amino acids:

Page 12: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Peptide bond is pH dependent

Page 13: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Peptide bonds lies on a plane

Bond lengths

Peptide bond features (1)

Page 14: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Peptide bond features (2)

The chain has 2 degrees of liberty given by the dihedral angles F and Y.The geometry of the chain can be characterized though F and Y.

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Peptide bond features (3)

Cis/trans isomers of the peptide group

Trans configuration ispreferred versus Cis(ratio ~1000:1)

An exception is theProline with a preferenceratio of ~3:1

Page 16: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Ramachandran diagram gives the values which canbe adopted by F and Y

Page 17: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

CαH

N C

H O

ψ

φ

CH2

NH3

CH2

CH2

CH2

+

Lysine

χ1

χ2

χ3

The side chains also have flexible torsion angles

Page 18: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

-2.5

-4.3

The preferred side-chains conformationsare called “rotamers”

Example: Asparagine-3.3

Typical conformations experimentally observedconformations observed by simulation

Energy (chi1,chi2)

Cα NCCβ

OδN

δ

χ1

χ2

-4.5

chi2

chi1

1 kcal/mole betw

een levels

Page 19: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

α helix β−sheet

In helices and sheets, polar groups are involved intohydrogen bonds

3.6 residuesper turn

Pseudo-periodicity of 2

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a-helix

3.6 residues per turn, H-bond between residue n and n+4Although other (rare) helices are observed: p-helices, 3.10-helices...

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b-sheets

b-strand (elementary blocks) :

b-strands are assembled into(parallel, anti-parallel)b-sheets.

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b-sheets

Anti-parallel b-sheets

Parallel b-sheets

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b-sheetsVarious shapes of b structures

Twisted b-sheets b-barrel

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b-sheets

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Loops

turn

~ 1/3 of amino acids

Loops

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Super-secondary & Tertiary structure

The tertiary structure is the set of3D coordinates of atoms of a singleamino acid chain

Secondary structure elementscan be assembled intosuper-secondary motifs.

Page 27: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Quaternary structure

A protein can be composedof multiple chains withinteracting subunits.

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Protein can interact with moleculesExample: Hemoglobin

An Heme (iron + organic ring) binds to the protein, andallow the capture of oxygen atoms.

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Disulfide bond

Two cysteines can interactand create a disulfide bond.

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Cytochrom cHemoglobine

water

The tertiary structure is globular, with a preferencefor polar residues on its surface but rather apolar in

its interior

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Membrane proteins are an exception

~ 30% of human genome, ~ 50% of antibiotics

Cytochrom oxidase

lipidProtein

Lipid bilayer

Hydrophobiccore

Hydrophilicregion

Page 32: COMP 564:Introduction to protein structure predictionjeromew/teaching/564/W... · COMP 564:Introduction to protein structure prediction Jérôme Waldispühl School of Computer Science

Proteins folds into a native structure

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Overview of the methods used to predictthe protein structure

● Which degree of definition?● What's the length of the sequence?● Which representation/modeling suits the best?● Should we simulate the folding or predict the structure?● Do we want a single prediction or a set of candidates?● Machine learning approach or physical model?

Several issue must be addressed first:

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Molecular Dynamics

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HP lattice model

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Hidden Markov models(and other machine learning approaches)

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Structural template methods


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