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Hydrophobic Residue Patterning in β -Strands and Implications for β -Sheet Nucleation. Brent Wathen Dept. of Biochemistry Queen’s University. Outline. Part I: Introduction Proteins Protein Folding Part II: Protein Structure Prediction Goals, Challenges Techniques State of the Art

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Hydrophobic residue patterning in strands and implications for sheet nucleation l.jpg

Hydrophobic ResiduePatterning in β-Strands and Implications for β-SheetNucleation

Brent Wathen

Dept. of Biochemistry

Queen’s University


Outline l.jpg
Outline

  • 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


  • Outline3 l.jpg
    Outline

    • 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


  • Proteins some basics l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • What Is a Protein?


    Proteins some basics5 l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • What Is a Protein?

      • Linear Sequence of Amino Acids...


    Proteins some basics6 l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • What Is a Protein?

      • Linear Sequence of Amino Acids...

    • What is an Amino Acid?


    Proteins some basics7 l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • What Is a Protein?

      • Linear Sequence of Amino Acids...

    • What is an Amino Acid?


    Proteins some basics8 l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • How many types of Amino Acids?


    Proteins some basics9 l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • How many types of Amino Acids?

      • 20 Naturally Occurring Amino Acids

      • Differ only in SIDE CHAINS

        IsoleucineArginineTyrosine


    Proteins some basics10 l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • Amino Acids connect via PEPTIDE BOND


    Proteins some basics11 l.jpg

    Part I: Introduction

    Proteins – Some Basics

    • Backbone can swivel:

      DIHEDRAL ANGLES

    • 2 per Amino Acid

    • Proteins can be 100’s of Amino Acids in length!

      • Lots of freedom of movement


    Protein functions l.jpg

    Part I: Introduction

    Protein Functions

    • What do proteins do?


    Protein functions13 l.jpg

    Part I: Introduction

    Protein Functions

    • What do proteins do?

      • Enzymes

      • Cellular Signaling

      • Antibodies


    Protein functions14 l.jpg

    Part I: Introduction

    Protein Functions

    • What do proteins do?

      • Enzymes

      • Cellular Signaling

      • Antibodies

      • WHAT DON’T THEY DO!


    Protein functions15 l.jpg

    Part I: Introduction

    Protein Functions

    • 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


    Protein functions16 l.jpg

    Part I: Introduction

    Protein Functions

    • How do proteins do so much?


    Protein functions17 l.jpg

    Part I: Introduction

    Protein Functions

    • 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


    Protein structure l.jpg

    Part I: Introduction

    Protein 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


    Protein structure19 l.jpg

    Part I: Introduction

    Protein 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


    Protein structure20 l.jpg

    Part I: Introduction

    Protein Structure


    Protein structure21 l.jpg

    Part I: Introduction

    Protein Structure


    Protein folding l.jpg

    Part I: Introduction

    Protein 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


    Protein folding23 l.jpg

    Part I: Introduction

    Protein Folding

    • What we DON’T know...

      • Mechanism...?

      • Forces...?

        • Relative contributions?

        • Hydrophobic Force thought to be critical


    Intro summary l.jpg

    Part I: Introduction

    Intro 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


    Outline25 l.jpg
    Outline

    • 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


  • The prediction problem l.jpg

    Part II: Structure Prediction

    The Prediction Problem

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


    The prediction problem27 l.jpg

    Part II: Structure Prediction

    The 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


    Psp goals l.jpg

    Part II: Structure Prediction

    PSP: 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.


  • Psp major hurdles l.jpg

    Part II: Structure Prediction

    PSP: 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


    Tertiary structure prediction l.jpg

    Part II: Structure Prediction

    Tertiary Structure Prediction

    • Major Techniques

      • Template Modeling

        • Homology Modeling

        • Threading

      • Template-Free Modeling

        • ab initio Methods

          • Physics-Based

          • Knowledge-Based


    Template modeling l.jpg

    Part II: Structure Prediction

    Template 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


    Template free modeling l.jpg

    Part II: Structure Prediction

    Template-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!


    Template free modeling33 l.jpg

    Part II: Structure Prediction

    Template-Free Modeling

    • 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


    Template free modeling34 l.jpg

    Part II: Structure Prediction

    Template-Free Modeling

    • 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


    Template free modeling35 l.jpg

    Part II: Structure Prediction

    Template-Free Modeling

    • Basic Approach:

      1. Begin with an unfolded conformation

      2. Make small conformational change

      3. Measure energy of new conformation

      Accept based on heuristic: SA, MC, etc.

      4. Repeat until ending criteria reached

    • Underlying Assumption:

      Correct Conformation has LOWEST ENERGY


    Diverse efforts l.jpg

    Part II: Structure Prediction

    Diverse 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!


    Rosetta l.jpg

    Part II: Structure Prediction

    ROSETTA

    • 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


    Rosetta38 l.jpg

    Part II: Structure Prediction

    ROSETTA

    • 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


    Diverse efforts39 l.jpg

    Part II: Structure Prediction

    Diverse 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!


    State of the art l.jpg

    Part II: Structure Prediction

    State 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


    State of the art41 l.jpg

    Part II: Structure Prediction

    State of the Art

    • Template Modeling

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

    Moult, J. (2005) Cur. Opin.

    Struct. Bio.15:285-289


    State of the art42 l.jpg

    Part II: Structure Prediction

    State of the Art

    • 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


    State of the art43 l.jpg

    Part II: Structure Prediction

    State of the Art

    • Template-Free Modeling

    CASP6 target 201, and best model.

    Vincent, J.J. et. al (2005)

    Proteins 7:67-83.


    State of the art44 l.jpg

    Part II: Structure Prediction

    State of the Art

    • Template-Free Modeling

    CASP6 target 241, and 3 best models.

    Vincent, J.J. et. al (2005)

    Proteins 7:67-83.


    State of the art45 l.jpg

    Part II: Structure Prediction

    State of the Art

    • 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.”

    Vincent, J.J. et. al (2005)

    Proteins 7:67-83.


    Psp summary l.jpg

    Part II: Structure Prediction

    PSP 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


    Outline47 l.jpg
    Outline

    • 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


  • Sheet basics l.jpg

    Part III: β-Strand Patterning

    β-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


    Beta sheet basics l.jpg

    Part III: β-Strand Patterning

    Beta Sheet Basics

    Internalin A Narbonin

    Polygalacturonase

    Galactose Oxidase


    Beta sheet basics50 l.jpg

    Part III: β-Strand Patterning

    Beta Sheet Basics

    • What do we know?

      •  Residues:

        • V, I, F, Y, W, T, C L

      • Found largely in Protein Cores

      • Amphipathic Nature


    Amphipathic l.jpg

    Part III: β-Strand Patterning

    Amphipathic


    Theory of sheet nucleation l.jpg

    Part III: β-Strand Patterning

    Theory of β-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”


    Theory of sheet nucleation53 l.jpg

    Part III: β-Strand Patterning

    Theory of β-Sheet Nucleation

    • Hydrophobic Zipper (HZ)

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

      Proc. Natl. Acad. Sci.

      USA 90: 1942-1946.


    Theory of nucleation l.jpg

    Part III: β-Strand Patterning

    Theory of Nucleation

    • Hydrophobic Zipper (HZ)

      • Once Hydrophobic “Seed” established, can grow out 2 directions


    Thought experiment l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • What would a Beta Seed look like?


    Thought experiment56 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • What would a Beta Seed look like?

      • Contain hydrophobics

        • On both strands


    Thought experiment57 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • What would a Beta Seed look like?

      • Contain hydrophobics

        • On both strands

      • How many?

        • Will single hydrophobic on each strand be sufficient?


    Thought experiment58 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • What would a Beta Seed look like?

      • Contain hydrophobics

        • On both strands

      • How many?

        • Will single hydrophobic on each strand be sufficient?

      • Single Unlikely:

        • 1 Hydrophobic Residue NOT SPECIFIC ENOUGH

        • Too many possible combinations


    Thought experiment59 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • What would a Beta Seed look like?

      • Contain hydrophobics

        • On both strands

      • How many?

        • Will single hydrophobic on each strand be sufficient?

      • Single Unlikely:

        • 1 Hydrophobic Residue NOT SPECIFIC ENOUGH

        • Too many possible combinations

          At least 1 strand must have >1 Hydrophobic


    Thought experiment60 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • What hydrophobic arrangement would lead to Beta Sheet Nucleation?

      • i,i+1?

      • i,i+2?

      • i,i+3?

      • i,i+4?


    Thought experiment61 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • 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?


    Thought experiment62 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • 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?


    Thought experiment63 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • 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...


    Thought experiment64 l.jpg

    Part III: β-Strand Patterning

    Thought Experiment...

    • 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?


    Hypothesis l.jpg

    Part III: β-Strand Patterning

    Hypothesis

    Assuming:

    • Beta Sheets Nucleate by Hydrophobics (HZ)

    • i,i+2 hydrophobic pairings on beta strands are necessary for nucleation


    Hypothesis66 l.jpg

    Part III: β-Strand Patterning

    Hypothesis

    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.


    Hypothesis67 l.jpg

    Part III: β-Strand Patterning

    Hypothesis


    Hypothesis68 l.jpg

    Part III: β-Strand Patterning

    Hypothesis


    Hypothesis69 l.jpg

    Part III: β-Strand Patterning

    Hypothesis


    Hypothesis70 l.jpg

    Part III: β-Strand Patterning

    Hypothesis


    Technique l.jpg

    Part III: β-Strand Patterning

    Technique

    • 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


    Technique72 l.jpg

    Part III: β-Strand Patterning

    Technique


    Technique73 l.jpg

    Part III: β-Strand Patterning

    Technique


    Technique74 l.jpg

    Part III: β-Strand Patterning

    Technique


    Technique75 l.jpg

    Part III: β-Strand Patterning

    Technique


    Technique76 l.jpg

    Part III: β-Strand Patterning

    Technique


    Technique77 l.jpg

    Part III: β-Strand Patterning

    Technique


    Technique78 l.jpg

    Part III: β-Strand Patterning

    Technique

    • 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


    Results l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+1


    Results80 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+1

      • Strongly Disfavoured: -20.5σ


    Results81 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+2


    Results82 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+2

      • Strongly Favoured: 13.0σ


    Results83 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+3


    Results84 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+3

      • Strongly Disfavoured: -6.1σ


    Results85 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+4


    Results86 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics

      • i,i+4

      • Strongly Favoured: 5.7σ


    Results87 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophilics: Summary

      • Demonstrate Amphipathic Separation

        • Suggests residues help guide tertiary formation

      • Moral Support: Technique seems sound


    Results88 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+1


    Results89 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+1

      • Strongly Disfavoured: -16.8σ


    Results90 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+3


    Results91 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+3

      • Strongly Disfavoured: -16.6σ


    Results92 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+2


    Results93 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+2

      • Barely Favoured!: 3.5σ


    Results94 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+4


    Results95 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics

      • i,i+4

      • Strongly Disfavoured: -19.6σ


    Results96 l.jpg

    Part III: β-Strand Patterning

    Results

    • Hydrophobics: Summary

      • Clearly amphipathic: i,i+1 i,i+3 Disfavoured

      • NOT particularly favoured at i,i+2 

      • Unexpectedly: i,i+4 strongly Disfavoured


    Results97 l.jpg

    Part III: β-Strand Patterning

    Results

    • 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


    Results98 l.jpg

    Part III: β-Strand Patterning

    Results


    Results99 l.jpg

    Part III: β-Strand Patterning

    Results


    Results100 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...


    Results101 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ NT


    Results102 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ NT

      • Only slightly favoured: 2.5σ


    Results103 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ NT+1


    Results104 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ NT+1

      • Strongly favoured!!: 9.3σ


    Results105 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ NT+2


    Results106 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ NT+2

      • Indifferent: 0.8σ


    Results107 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ CT


    Results108 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ CT

      • Favoured!: 5.7σ


    Results109 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ CT-1


    Results110 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ CT-1

      • Only slightly favoured: 3.4σ


    Results111 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ CT-2


    Results112 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ CT-2

      • Only slightly favoured: 3.9σ


    Results113 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ Interior Positions


    Results114 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • i,i+2 @ Interior Positions

      • Actually Disfavoured!!: -3.0σ


    Results115 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • Summary:

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

      • Disfavoured at interior positions


    Results116 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • Are these patterns sense-specific?

      • @ NT+1:

      • Favoured for Parallel, Antiparallel


    Results117 l.jpg

    Part III: β-Strand Patterning

    Results

    • Examine localized hydrophobic pairings...

      • Are these patterns sense-specific?

      • @ CT:

      • Favoured for Antiparallel, Mixed

        • NOT PARALLEL!


    Conclusions l.jpg

    Part III: β-Strand Patterning

    Conclusions

    • 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


    Implications l.jpg

    Part III: β-Strand Patterning

    Implications

    • How do beta sheets nucleate?

      • Parallel


    Implications120 l.jpg

    Part III: β-Strand Patterning

    Implications

    • How do beta sheets nucleate?

      • Parallel

      • Nucleate at NT

      • Growth is unidirectional: NTCT


    Implications121 l.jpg

    Part III: β-Strand Patterning

    Implications

    • How do beta sheets nucleate?

      • Antiparallel


    Implications122 l.jpg

    Part III: β-Strand Patterning

    Implications

    • How do beta sheets nucleate?

      • Antiparallel

      • Nucleate at edge

      • Growth is unidirectional


    Future work l.jpg

    Part III: β-Strand Patterning

    Future 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.


    Acknowledgements l.jpg

    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

    Acknowledgements