hydrophobic residue patterning in β-strands and implications for β-sheet nucleation brent wathen...

Hydrophobic ResidueHydrophobic ResiduePatterning in Patterning in ββ-Strands and -Strands and

Implications for Implications for ββ-Sheet-SheetNucleationNucleation

Brent Wathen

Dept. of Biochemistry

Queen’s University

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OutlineOutline

• Part I: Introduction• Proteins• Protein Folding

• Part II: Protein Structure Prediction• Goals, Challenges• Techniques• State of the Art

• Part III: Residue Patterning on β-Strands• β-Sheet Nucleation• Hydrophobic/Hydrophilic Patterning

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OutlineOutline




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Proteins – Some BasicsProteins – Some Basics

• What Is a Protein?

Part I: Introduction

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• What Is a Protein?• Linear Sequence of Amino Acids...


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• What is an Amino Acid?


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• What is an Amino Acid?


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• How many types of Amino Acids?


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• How many types of Amino Acids?• 20 Naturally Occurring Amino Acids• Differ only in SIDE CHAINS

Isoleucine Arginine Tyrosine


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• Amino Acids connect via PEPTIDE BOND


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• Backbone can swivel:

DIHEDRAL ANGLES

• 2 per Amino Acid• Proteins can be 100’s of

Amino Acids in length!• Lots of freedom of

movement


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Protein FunctionsProtein Functions

• What do proteins do?


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• What do proteins do?• Enzymes• Cellular Signaling• Antibodies


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• What do proteins do?• Enzymes• Cellular Signaling• Antibodies• WHAT DON’T THEY DO!


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• What do proteins do?• Enzymes• Cellular Signaling• Antibodies• WHAT DON’T THEY DO!

• Comes from Greek Work Proteios – PRIMARY• Fundamental to virtually all cellular processes


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• How do proteins do so much?


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• How do proteins do so much?• Proteins FOLD spontaneously• Assume a characteristic 3D SHAPE• Shape depends on particular Amino Acid

Sequence• Shape gives SPECIFIC function


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Protein StructureProtein Structure

• STRUCTURE FUNCTION relationship• Determining structure is often critical in

understanding what a protein does• 2 main techniques

• X-ray crystallography• NMR• 0.5Å RMSD accuracy

• Both are very challenging• Months to years of work• Many proteins don’t yield to these methods


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Protein StructureProtein Structure

• Levels of organization• Primary Sequence• Secondary Structure (Modular building blocks)

• α-helices• β-sheets

• Tertiary Structure• Quartenary Structure

• Hydrophobic/Hydrophilic Organization• Hydrophobics ON INSIDE

• Hydrophobic Cores


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Protein StructureProtein StructurePart I: Introduction

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Protein StructureProtein StructurePart I: Introduction

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Protein FoldingProtein Folding

• What we DO know...• Protein folding is FAST!!

• Typically a couple of seconds

• Folding is CONSISTENT!!• Involves weak forces – Non-Covalent

• Hydrogen Bonding, van der Waals, Salt Bridges

• Mostly, 2-STATE systems• VERY FEW INTERMEDIATES• Makes it hard to study – BLACK BOX


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Protein FoldingProtein Folding

• What we DON’T know...• Mechanism...?• Forces...?

• Relative contributions?• Hydrophobic Force thought to be critical


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Intro SummaryIntro Summary

• Proteins are central to all living things• Critical to all biological studies

• Folding process is largely unknown• Sequence Structure Mapping• Structure Function relationship• Determining Protein Structure Experimentally is

HARD WORK


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OutlineOutline




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The Prediction ProblemThe Prediction Problem

Can we predict the final 3D protein structure knowing only its amino acid sequence?

Part II: Structure Prediction

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The Prediction ProblemThe Prediction Problem

Can we predict the final 3D protein structure knowing only its amino acid sequence?

• Studied for 4 Decades• “Holy Grail” in Biological Sciences• Primary Motivation for Bioinformatics• Based on this 1-to-1 Mapping of Sequence to

Structure• Still very much an OPEN PROBLEM


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PSP: GoalsPSP: Goals

• Accurate 3D structures. But not there yet.• Good “guesses”

• Working models for researchers

• Understand the FOLDING PROCESS• Get into the Black Box

• Only hope for some proteins• 25% won’t crystallize, too big for NMR

• Best hope for novel protein engineering• Drug design, etc.


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PSP: Major HurdlesPSP: Major Hurdles

• Energetics• We don’t know all the forces involved in detail

• Too computationally expensive BY FAR!

• Conformational search impossibly large• 100 a.a. protein, 2 moving dihedrals, 2 possible positions

for each diheral: 2200 conformations!

• Levinthal’s Paradox

• Longer than time of universe to search

• Proteins fold in a couple of seconds??

• Multiple-minima problem


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Tertiary Structure PredictionTertiary Structure Prediction

• Major Techniques• Template Modeling

• Homology Modeling• Threading

• Template-Free Modeling• ab initio Methods

• Physics-Based• Knowledge-Based


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Template ModelingTemplate Modeling

• Homology Modeling• Works with HOMOLOGS

• ~ 50% of new sequences have HOMOLOGS

• BLAST or PSI-BLAST search to find good models• Refine:

• Molecular Dynamics• Energy Minimization


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Template-Free ModelingTemplate-Free Modeling

• Modeling based primarily from sequence• May also use: Secondary Structure Prediction,

analysis of residue contacts in PDB, etc.

• Advantages:• Can give insights into FOLDING MECHANISMS• Adaptable: Prions, Membrane, Natively Unfolded• Doesn’t require homologs• Only way to model NEW FOLDS• Useful for de novo protein design

• Disadvantages: HARD!


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• Physics-Based• Use ONLY the PRIMARY SEQUENCE• Try to model ALL FORCES• EXTREMELY EXPENSIVE computationally

• Knowledge-Based• Include other knowledge: SSP, PDB Analysis

• Statistical Energy Potentials• Not so interested in folding process• “Hot” area of research


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• All methods SIMPLIFY problem• Reduced Atomic Representations

• C-α’s only; C-α + C-β; etc.

• Simplify Force Fields• Only van der Waals; only 2-body interactions

• Reduced Conformational Searches• Lattice Models• Dihedral Angle Restrictions


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• Basic Approach:

1. Begin with an unfolded conformation

2. Make small conformational change

3. Measure energy of new conformationAccept based on heuristic: SA, MC, etc.

4. Repeat until ending criteria reached

• Underlying Assumption:

Correct Conformation has LOWEST ENERGY


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Diverse EffortsDiverse Efforts

• Data Mining• Pattern Classification

• Neural Networks, HMMs, Nearest Neighbour, etc.

• Packing Algorithms• Search Optimization

• Traveling Salesman Problem

• Contact Maps, Contact Order• Constraint Logic, etc.

• Combinations of the above!


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ROSETTAROSETTA

• Pioneered by Baker Group (U. of Washington)• Fragment Based Method• Guiding Assumption:

• Fragment Conformations in PDB approximate their structural preferences

• Pre-build fragment library• Alleviates need to do local energy calculations• Lowest energy conformations should already be in

library


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ROSETTAROSETTA

• Pre-build fragment library• 3-mers and 9-mers• 200 structural possibilities for each

• Build conformations from the library• Randomly assign 3-mers, 9-mers along chain• During conformational search, reassign a 3-mer or a

9-mer to a new conformation at random

• Score using energy function• Adaptive: Coarse grain at first, detailed at end• Accept changes based on Monte Carlo method


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Diverse EffortsDiverse Efforts

• Data Mining• Pattern Classification

• Neural Networks, HMMs, Nearest Neighbour, etc.

• Packing Algorithms• Search Optimization

• Traveling Salesman Problem

• Contact Maps, Contact Order• Constraint Logic, etc.

• Combinations of the above!


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State of the ArtState of the Art

• CASP Competition• Critical Assessment of Structure Prediction• Blind Competition Every 2 years• CASP6 in 2004 - CASP7 just completed• ~75 proteins whose structures have not been

published as yet• Easy homologs examples• Distant homologs available• De novo structures: no homologs known


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• Template Modeling

CASP6 Target 266 (green), and best model (blue)

Moult, J. (2005) Cur. Opin. Struct. Bio. 15:285-289


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• Template Modeling• Alignment still not easy, and often requires multiple

templates• Accurate core models (within 2-3Å RMSD)• Still not good at modeling regions missing from

template• Side-chain modeling not too good• Molecular dynamics not able to improve models as

hoped


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• Template-Free Modeling

CASP6 target 201, and best model.

Vincent, J.J. et. al (2005) Proteins 7:67-83.


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CASP6 target 241, and 3 best models.

• Template-Free Modeling



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• How Good are Current Techniques?• CASP6 Summary:

“The disappointing results for [hard new fold] targets suggest that the prediction community as a whole has learned to copy well but has not really learned how proteins fold.”



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PSP SummaryPSP Summary

• Many diverse, creative efforts• Progress IS being made in finding final 3D

structures• Less so with regards to understanding folding

mechanisms• NEEDED:

• Marriage of Creative Ideas and Increased Resources


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OutlineOutline




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ββ-Sheet Basics-Sheet Basics

• Made up of β-Strands• Diverse:

• Parallel/Antiparallel• Edge/Interior Strands• Typically Twisted• Many Forms

• β-sandwiches, β-barrels, β-helices, β-propellers, etc.

• 2D? 3D?• Less studied than helices

Part III: β-Strand Patterning

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Beta Sheet BasicsBeta Sheet Basics

Internalin A Narbonin

Polygalacturonase

Galactose Oxidase


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Beta Sheet BasicsBeta Sheet Basics

• What do we know? Residues:

• V, I, F, Y, W, T, C L

• Found largely in Protein Cores• Amphipathic Nature


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AmphipathicAmphipathicPart III: β-Strand Patterning

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Theory of Theory of ββ-Sheet Nucleation-Sheet Nucleation

• Hydrophobic Zipper (HZ)• Dill et. al. (1993)• Hydrophobic residues from different parts of

chain make initial contact• Correct alignment of backbones

• Hydrogen bonding

• Subsequent growth via “Zipping Up”


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• Hydrophobic Zipper (HZ)

Dill, K.A. et al., (1993)

Proc. Natl. Acad. Sci.

USA 90: 1942-1946.


Theory of Theory of ββ-Sheet Nucleation-Sheet Nucleation

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Theory of NucleationTheory of Nucleation

• Hydrophobic Zipper (HZ)• Once Hydrophobic “Seed” established, can

grow out 2 directions


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Thought Experiment...Thought Experiment...

• What would a Beta Seed look like?


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• What would a Beta Seed look like?• Contain hydrophobics

• On both strands


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• On both strands

• How many?• Will single hydrophobic on each strand be

sufficient?


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• On both strands


sufficient?

• Single Unlikely:• 1 Hydrophobic Residue NOT SPECIFIC ENOUGH• Too many possible combinations


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• On both strands


sufficient?

• Single Unlikely:• 1 Hydrophobic Residue NOT SPECIFIC ENOUGH• Too many possible combinations

At least 1 strand must have >1 Hydrophobic


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• What hydrophobic arrangement would lead to Beta Sheet Nucleation?• i,i+1? • i,i+2?• i,i+3?• i,i+4?


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• What hydrophobic arrangement would lead to Beta Sheet Nucleation?• i,i+1? No, not likely: Amphipathic.• i,i+2?• i,i+3?• i,i+4?


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• What hydrophobic arrangement would lead to Beta Sheet Nucleation?• i,i+1? No, not likely: Amphipathic.• i,i+2?• i,i+3? No... Amphipathic.• i,i+4?


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• What hydrophobic arrangement would lead to Beta Sheet Nucleation?• i,i+1? No, not likely: Amphipathic.• i,i+2?• i,i+3? No... Amphipathic.• i,i+4? Seems too far apart...


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• What hydrophobic arrangement would lead to Beta Sheet Nucleation?• i,i+1? No, not likely: Amphipathic.• i,i+2? Most likely.• i,i+3? No... Amphipathic.• i,i+4? Seems too far apart... Chain loop?


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HypothesisHypothesis

Assuming:• Beta Sheets Nucleate by Hydrophobics (HZ)• i,i+2 hydrophobic pairings on beta strands are

necessary for nucleation


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HypothesisHypothesis

Assuming:• Sec. structures contain their nucleating residues• Beta Sheets Nucleate by Hydrophobics (HZ)• i,i+2 hydrophobic pairings on beta strands are

necessary for nucleation

Beta Strands contain an increased frequency of i,i+2 hydrophobic residue pairings.


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HypothesisHypothesisPart III: β-Strand Patterning

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TechniqueTechnique

• Looking for statistically significant patterns• For any particular pattern:

1. Count how often it occurs in database

2. Randomly shuffle residues in sheets

3. Re-count how often pattern occurs

4. Repeat random shuffle and counting x1000

5. Compare initial count, avg random count

Calculate the Std Dev σ

If σ > 3.0, statistically significant


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TechniqueTechniquePart III: β-Strand Patterning

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TechniqueTechnique

• Patterns of Interest:• Hydrophobic patterning (V L I F M)• Hydrophilic patterning (K R E D S T N Q)• Positions:

• i,i+1• i,i+2• i,i+3• i,i+4

• Consider only strands of length >= 5 residues


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ResultsResults

• Hydrophilics• i,i+1


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ResultsResults


• Strongly Disfavoured: -20.5σ


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ResultsResults



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ResultsResults


• Strongly Favoured: 13.0σ


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ResultsResults



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ResultsResults




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ResultsResults



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ResultsResults


• Strongly Favoured: 5.7σ


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ResultsResults

• Hydrophilics: Summary

• Demonstrate Amphipathic Separation• Suggests residues help guide tertiary formation

• Moral Support: Technique seems sound

-25

-20

-15

-10

-5

0

5

10

15

(i,i+1) (i,i+2) (i,i+3) (i,i+4)

Pattern

z-Score


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ResultsResults

• Hydrophobics• i,i+1


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ResultsResults




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ResultsResults



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ResultsResults




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ResultsResults



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ResultsResults


• Barely Favoured!: 3.5σ


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ResultsResults



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ResultsResults




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ResultsResults

• Hydrophobics: Summary

• Clearly amphipathic: i,i+1 i,i+3 Disfavoured• NOT particularly favoured at i,i+2 • Unexpectedly: i,i+4 strongly Disfavoured

-25

-20

-15

-10

-5

0

5

(i,i+1) (i,i+2) (i,i+3) (i,i+4)

Pattern

z-Score


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ResultsResults

• Hydrophobics: Summary• Where are the hydrophobic pairings??

• Not at i,i+1 or i,i+3 or i,i+4• Barely at i,i+2

• Note:• Moderate i,i+2 pairing: No strong aggregation• Low low i,i+4 pairing: Not Dispersed! Isolated


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ResultsResultsPart III: β-Strand Patterning

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ResultsResultsPart III: β-Strand Patterning

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ResultsResults

• Examine localized hydrophobic pairings...


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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ NT


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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ NT

• Only slightly favoured: 2.5σ


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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ NT+1


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ResultsResults


• Strongly favoured!!: 9.3σ


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ResultsResults



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ResultsResults


• Indifferent: 0.8σ


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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ CT


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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ CT

• Favoured!: 5.7σ


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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ CT-1


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ResultsResults




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ResultsResults



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ResultsResults




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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ Interior Positions


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ResultsResults

• Examine localized hydrophobic pairings...• i,i+2 @ Interior Positions

• Actually Disfavoured!!: -3.0σ


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ResultsResults

• Examine localized hydrophobic pairings...• Summary:

• Localized i,i+2 hydrophobic pairing at NT and CT• Disfavoured at interior positions

-4

-2

0

2

4

6

8

10

NT NT+1 NT+2 Central CT-2 CT-1 CT Avg

Pattern Location

z-Score


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ResultsResults

• Examine localized hydrophobic pairings...• Are these patterns sense-specific?• @ NT+1:

• Favoured for Parallel, Antiparallel

-4

-2

0

2

4

6

8

10

Parallel Antiparallel Mixed Edge

Strand Type

z-Score


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ResultsResults

• Examine localized hydrophobic pairings...• Are these patterns sense-specific?• @ CT:

• Favoured for Antiparallel, Mixed• NOT PARALLEL!

-1

0

1

2

3

4

5

Parallel Antiparallel Mixed Edge

Strand Type

z-Score


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ConclusionsConclusions

• Hydrophobic patterning suggests:• Hydrophobics are located on one side of beta

sheets AMPHIPATHIC

• Hydrophobics are CLUSTERED• Hydrophobics aggregate at NT, CT

• Parallel Strands: @ NT only• Antiparallel Strands: @ NT & CT

• Supports HYDROPHOBIC ZIPPER theory for sheet nucleation


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ImplicationsImplications

• How do beta sheets nucleate?• Parallel


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• How do beta sheets nucleate?• Parallel

• Nucleate at NT• Growth is unidirectional: NTCT


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• How do beta sheets nucleate?• Antiparallel


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• How do beta sheets nucleate?• Antiparallel

• Nucleate at edge• Growth is unidirectional


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Future WorkFuture Work

1. Extend this work to 2D

Both intra- and inter-strand patterning

2. Consider more complex patterning

3 residues on one strand? NT Position?

Specific residue combinations?

3. Consider patterning by beta-sheet type

Beta Helices, Barrels, Sandwiches, etc.


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AcknowledgementsAcknowledgements

• Dr. Jia

• Lab Members• Dr. Qilu Ye• Dr. Vinay Singh• Dr. Susan Yates • Daniel Lee• Jimmy Zheng• Neilin Jaffer

• NSERC

• Andrew Wong• Michael Suits• Laura van Staalduinen• Mark Currie• Kateryna Podzelinska

hydrophobic residue patterning in β-strands and implications for β-sheet nucleation brent wathen...

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