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

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

OutlineOutline

Proteins – Some BasicsProteins – Some Basics

• What Is a Protein?

Part I: Introduction

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

• What is an Amino Acid?

• How many types of Amino Acids?

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

Isoleucine Arginine Tyrosine

• Amino Acids connect via PEPTIDE BOND

• 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 FunctionsProtein Functions

• What do proteins do?

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

• 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

• How do proteins do so much?

• 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 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

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

Protein StructureProtein StructurePart I: Introduction

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

Protein FoldingProtein Folding

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

• Relative contributions?• Hydrophobic Force thought to be critical

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

OutlineOutline

The Prediction ProblemThe Prediction Problem

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

Part II: Structure Prediction

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

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.

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

Tertiary Structure PredictionTertiary Structure Prediction

• Major Techniques• Template Modeling

• Homology Modeling• Threading

• Template-Free Modeling• ab initio Methods

• Physics-Based• Knowledge-Based

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

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!

• 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

• 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

• 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

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!

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

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

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!

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

• Template Modeling

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

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

• 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

• Template-Free Modeling

CASP6 target 201, and best model.

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

CASP6 target 241, and 3 best models.

• Template-Free Modeling

• 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.”

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

OutlineOutline

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

Beta Sheet BasicsBeta Sheet Basics

Internalin A Narbonin

Polygalacturonase

Galactose Oxidase

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

AmphipathicAmphipathicPart III: β-Strand Patterning

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”

• 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

Theory of NucleationTheory of Nucleation

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

grow out 2 directions

Thought Experiment...Thought Experiment...

• What would a Beta Seed look like?

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

• On both strands

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

sufficient?

• On both strands

sufficient?

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

• On both strands

sufficient?

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

At least 1 strand must have >1 Hydrophobic

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

• 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?

• 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?

• 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...

• 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?

HypothesisHypothesis

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

necessary for nucleation

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.

HypothesisHypothesisPart III: β-Strand Patterning

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

TechniqueTechniquePart III: β-Strand Patterning

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

ResultsResults

• Hydrophilics• i,i+1

ResultsResults

• Strongly Disfavoured: -20.5σ

ResultsResults

• Strongly Favoured: 13.0σ

ResultsResults

• Strongly Favoured: 5.7σ

ResultsResults

• Hydrophilics: Summary

• Demonstrate Amphipathic Separation• Suggests residues help guide tertiary formation

• Moral Support: Technique seems sound

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

Pattern

z-Score

ResultsResults

• Hydrophobics• i,i+1

ResultsResults

• Barely Favoured!: 3.5σ

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

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

Pattern

z-Score

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

ResultsResultsPart III: β-Strand Patterning

ResultsResults

• Examine localized hydrophobic pairings...

ResultsResults

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

ResultsResults

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

• Only slightly favoured: 2.5σ

ResultsResults

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

ResultsResults

• Strongly favoured!!: 9.3σ

ResultsResults

• Indifferent: 0.8σ

ResultsResults

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

ResultsResults

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

• Favoured!: 5.7σ

ResultsResults

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

ResultsResults

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

ResultsResults

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

• Actually Disfavoured!!: -3.0σ

ResultsResults

• Examine localized hydrophobic pairings...• Summary:

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

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

Pattern Location

z-Score

ResultsResults

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

• Favoured for Parallel, Antiparallel

Parallel Antiparallel Mixed Edge

Strand Type

z-Score

ResultsResults

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

• Favoured for Antiparallel, Mixed• NOT PARALLEL!

Parallel Antiparallel Mixed Edge

Strand Type

z-Score

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

ImplicationsImplications

• How do beta sheets nucleate?• Parallel

• Nucleate at NT• Growth is unidirectional: NTCT

• How do beta sheets nucleate?• Antiparallel

• Nucleate at edge• Growth is unidirectional

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.

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...

Documents

Transcript of Hydrophobic Residue Patterning in β-Strands and Implications for β-Sheet Nucleation Brent Wathen...

Elliptic integrals, the arithmetic-geometric mean and …Elliptic integrals, the arithmetic-geometric mean and the Brent-Salamin algorithm for ˇ Notes by G.J.O. Jameson Contents 1.

The Queen · 2021. 2. 12. · The Queen • The Queen’s name is Elizabeth Alexandra Mary. • Her royal title is Queen Elizabeth ΙΙ. • She is the head of the Royal Family. •

Wnt and TGF-β Expression in the Sponge Amphimedon queenslandica and the Origin of Metazoan Embryonic Patterning Daprès Adamska et al., 2007 UE 106 : Diversité.

Chapter 10: Radians Rotational Motion s = rθ ω θ testrada.cune.edu/facweb/brent.royuk/phys111/docs/Chapter... · 2016-11-10 · 1 Chapter 10: Rotational Motion Δ Brent Royuk Phys-111

Large-Diameter Patterned Sapphire SubstratesPatterned Sapphire Substrates—PSS—in 4" through 8" diameters. We offer fully customizable sub-micron patterning capability with tight

Chapter 28: Relativityestrada.cune.edu/facweb/brent.royuk/phys111/docs/Chapter...1 Chapter 28: Relativity Brent Royuk Phys-111 Concordia University Classical Mechanics Translational

Spatially Precise Transfer of Patterned Monolayer WS2 and ...nano.eecs.berkeley.edu/publications/ACS AEM_2019_2D...the substrate post-transfer would not overcome this limitation. Patterning

Precise error estimate of the Brent-McMillan algorithm for ...demailly/... · Jean-Pierre Demailly, Institut Fourier Grenoble 3 The reﬁned version (BM0) of the Brent-McMillan algorithm

Chapter 6: Uniform Circular Motion and Gravityestrada.cune.edu/facweb/brent.royuk/phys111/docs/Chapter...1 Chapter 6: Uniform Circular Motion and Gravity Brent Royuk Phys-111 Concordia

Photo-patterning micro-mirror devices using azo …lcd.creol.ucf.edu/people/yingzhou/publications/Opt Express Lin CLC.pdfPhoto-patterning micro-mirror devices using azo ... and bandwidth

Η ΜΕΛΕΤΗ ΤΟΥ ΠΙΑΝΟΥ · Brent Hugh Assistant Professor of Piano Missouri Western State College Department of Music Η ΜΕΛΕΤΗ ΤΟΥ ΠΙΑΝΟΥ Αρχές και

EUV Lithography - eng.kuleuven.be · 2D multi patterning LE = Litho –Etch Critical overlay = accuracy of placement on existing pattern. 1D self-aligned multi patterning Every different

Aleksandr A. Gerasimenko 1, Brent Millare 1, Duoduo Bao 1, M. Khalid Ashraf 2, Roger Lake 2 and Valentine I. Vullev 1 1 Department of Bioengineering, University.

Effects of Poly (ε-caprolactone) Coating on the Properties ... · School of Pharmacy, Queen’s University Belfast, Lisburn Road, Belfast BT9 7BL, UK 4. School of Mechanical and

Astrochemistry University of Helsinki, December 2006 Lecture 3 T J Millar, School of Mathematics and Physics Queen’s University Belfast,Belfast BT7 1NN,

Tony Noble Queen’s University Development of the SNOLAB Scientific Program.

A demonstration of clustering in protein contact maps for α-helix pairs Robert Fraser, University of Waterloo Janice Glasgow, Queen’s University.

Protein patterning by microcontact printing using ... · Protein patterning by microcontact printing using pyramidal PDMS stamps Luisa Filipponi1 & Peter Livingston1 & Ondřej Kašpar2

Oral Agents in Management of Type 2 Diabetes Nemanja Stojanović Consultant Endocrinologist Queen’s Hospital, Romford.

14 – 16 Curriculum Seminar The impact of recent DfE changes on curriculum planning and outcomes for schools Rajmund Brent Skills and Employability Team.