Protein dynamics at a single molecule level

Protein dynamics

• Proteins are in motion, sampling the ensemble of different conformations• Energy landscape: Conformational space that a protein can explore - Relative population of different conformational states - Rates of inter-conversion

Key biological processes occur on μs to ms scale - Recognition of cognate ligands - Signal transduction - Allosteric regulation of proteins - Enzyme catalysis Relatively long lifetimes - Individual states and kinetics of inter-conversion can be detected experimentally.

Henzler-Wildman and Kern, Nature (2007)

How do protein dynamics affect their functions?

Signal transduction

Ubiquitin

Signals such as ligand binding, mutation, PTMs alter energy landscape, affecting relative population of conformations

Molecular recognition

Forty-six crystal structures of ubiquitin: conformational dynamics control ubiquitin-protein interactions and influence in vivo signaling.

Enzyme catalysis and allosteric regulation

- Active (green) and inactive (blue, red, yellow, and gray) conformations. - Allosteric ligand binding (cyan star) stabilizes the inactive conformation

(red), inhibiting the enzyme activity.

Lee and Craik, Science (2009)

Molecular recognition

• The function of proteins depends on the recognition and binding of specific ligands Central to all biological processes• Precise molecular recognition mechanism: Crucial for understanding biology at

molecular level

• Two textbook mechanisms: The protein exists in a single, stable conformation.

- Lock-and-key model by Fischer (1894) - Induced-fit mechanism by Koshland (1958)

Conformational dynamics and ligand binding • Existence of conformational substates: Linkage between protein dynamics and molecular recognition• Conformational selection model : Dynamics-coupled recognition mechanism

• Induced-fit model Ligand binds a highly populated substate, inducing conformational change to a ligand-bound form.

• Conformational selection modelLigand selectively binds a weakly populated substate, but no further structural change occurs.

The role of conformational dynamics in molecular recognition remains controversial mainly owing to experimental limitations

Single molecule FRET measurements

Förster (or Fluorescence) Resonance Energy Transfer (FRET)

• Non-radiative energy transfer from an energy donor to an acceptor• Dipole – dipole interactions• Energy transfer efficiency between two probes: - Degree of spectral overlap between donor emission and acceptor absorption - ~ 1 / (separation distance)6

- Effective distance : ~ 10 nm

• Analysis of biomolecular interactions• Screening of inhibitors• Detection of a target analyte• Imaging (localization)

Ro : Förster distance at which the energy transfer is about 50%

E = Ro6/ (Ro

6 + R6)

1)

Single-molecule FRET (smFRET) measurements

Analysis of FRET at the level of single molecule which is labeled with a pair of dyes

Understanding the molecular mechanism

Kinetic analysis of biomolecular interactions and conformational changes at a single molecule level

Single-molecule FRET measurementsReal-time kinetic analysis of the conformational dynamics of a protein at a single-molecule level

Heterogeneous population Ensemble FRET(Bulk measurement)

Single-molecule FRET

Dynamic behavior

Time (s) Time (s)

FRET

effi

cien

cy

FRET

effi

cien

cy

FRET efficiency FRET efficiency

Coun

ts

Coun

ts

Active, real molecules

Kinetic rates in the absence and presence of increasing ligand concentration will provide crucial insight into the molecular recognition process

(Histogram of FRET)

Answers to many of fundamental biological questions

• Real dynamic characteristics of proteins /enzymes• Protein folding • Conformational change of protein• Replication• Recombination• Transcription• Translation• RNA folding and catalysis• Motor proteins • Signal transduction

Single molecule FRET analysis

Expected kinetic rates for a molecular recognition process

Induced-fit modelThe closing rate will be faster in the presence of a ligand than in its absence and will be accelerated by increasing ligand concentration.

Conformational selection modelThe closing rates in the presence of a ligand will be the same as those with no ligand because the population of a ligand-binding conformer is limited by the intrinsic conformational transition rate .

Kinetic rates by dwell time analysis

Real-time changes in FRET efficiency

Dwell time analysis

Time (s)

FRET

effi

cien

cy

Open state

Closed state

Closed lifetimeOpen lifetime

Time (s)

Inte

nsity

FRET

effi

cien

cy

Donor intensityAcceptor intensity

Acceptor bleachingDonor bleaching

IA + ID

EFRET =IA

The o The c

Time (s)

Coun

ts Single exponential decay curve

Estimation of average dwell time, τ

ID: Donor intensity IA: Acceptor intensity

Histogram of dwell times of each state

The opening and closing rates

Maltose binding protein (MBP)

• ATP-binding cassette (ABC) transporter in E. coli : active transport and chemotaxis• Large conformational change upon ligand binding: Rotation > 30° at hinge region• The highest binding affinity for maltotriose (Kd = ~ 1.6 X 10-7M)• Mr = 40 kDa

Maltose uptake in E. coli

Innermembrane

Outer membrane

Mal M

al

Mal

LamBLamBLamB

Scheme for single-molecule FRET measurements

+ Maltose

Cy3

Cy5

K34CR354C

5.92 nm 5.04 nmCy3

Cy5

K34CR354C

Labeling of MBP

Prism-type Total Internal Reflection Fluorescence (TIRF) Microscope

Intrinsic dynamics of wt-MBP

• The intrinsic transition was too fast to detect owing to a low time resolution (~ 2 ms)• Maltose induced a structural change of wt-MBP

0 µM

0.5 µM

2 µM

10 µM

MBP variants with hinge region mutations

Changes in domain closure angles and binding affinity of the MBP by hinge region mutations

Millet et al., PNAS (2003)

Introduction of bulky amino acids into the hinge region Slow down the intrinsic conformational dynamics

Allow the analysis of conformational dynamics using smFRET

35°

WT(0°) I329C(5.5°)

I329W(9.5°)

I329W/A96W(28.4°) WT(35°)

C-domain

N-domain

Labeling and evaluation of MBP wild-type

L F W1 EW2

Double Cys-MBP (K34C, R354C)

M

50

35

25

10075

L F W E

MBP (wild-type)

200 210 220 230 240 250 260-30000

-20000

-10000

0

10000

20000

30000

Wild-type MBP

Wavelength, nm

elli

pti

city

, m

deg

Amylose-binding assayCircular Dichroism (CD) analysis

Cysteine mutations and dye labeling do not affect the folding and the maltose-binding activity of MBP

Kim et al. Nature Chemical Biology (2013)

Single and double mutants of MBPMutant construction Real-time traces of MBP mutants

Single mutant

Double mutant

• Detection of intrinsic dynamics of the mutants in the absence of a ligand• Transitions between two distinct conformations (open and partially closed forms)

Kim et al. Nature Chemical Biology (2013)

High FRET: Partially closed state Low FRET : Open form

Representative time traces

Maltose-induced effect on the single mutant of MBP

• Maltose induced the clear conformational changes of MBP • The intrinsic transition rate could be measured in the absence of ligand

Kim et al. Nature Chemical Biology (2013)

Investigation of Single Mutant

Construction of single mutant Histogram of FRET efficiency

Maltose binding induces the population shift of the single mutant of MBP

Kim et al. Nature Chemical Biology (2013)

Ligand-binding kinetics of the single mutant

Dwell time analysis of maltose binding

The ligand-binding to MBP variant was governed by ligand-induced fit

The first experimental evidence showing molecular recognition mechanism based on con-formational dynamics analysis

Lifetime of the open state

Lifetime of open state

Lifetime of closed state

Investigation of double mutant similar to the partial closed form

Construction of double mutant

The ligand could bind to the partial closed form All conformers participate in the ligand binding.

* Partial closed form = 33.3° ± 6.7° Tang et al. (2007) Nature 449, 1078

Representative time traces

Histogram of FRET efficiency

maltose

maltose

maltose

maltose

Kim et al. Nature Chemical Biology (2013)

Intrinsic conformational dynamics of the mutants

Intr

ins

ic

clo

sin

g r

ate

(s-1

)

Intr

ins

ic

op

en

ing

ra

te (

s-1)

Relative population of the par-tially closed state

Intrinsic transition rates

• The partially closed states of the single and double mutants: 14% and 42% of the total population, respectively.

• Double mutant showed much slower closing and opening rates than the single mutant

Kim et al. Nature Chemical Biology (2013)

Effect of ligand concentration on the kinetic rates of the mutantsRepresentative time traces of the intensity and FRET efficiency for the single mutant

Kinetic rates in the absence and presence of a ligand

Single mutant

Double mutant

Wild- type

• The closing rate increased linearly with an increase in the ligand (maltose , maltotriose) concentration. • The opening rate remained almost constant over the range of tested ligand concentrations.

Closing rate Opening rate

• Ligands bind preferentially to the open state, inducing a structural change to a closed form• Direct evidence for the induced-fit mechanism.

Kim et al. Nature Chemical Biology (2013)

On state (closed form)

633 nm excitation532 nm excitation

Time (s)

E 12

Off state (open form)

On timeOff time

Maltose Binding

E 23E 13

Cy7-Maltose binding(Cy5 Cy7)

Cy7-Maltose binding(Cy3 Cy7)

Binding to a partially closed form

Binding to an open form

Lee et al. PLoS ONE (2010)

Three-color smFRET using Cy7-maltose

Direct quantification of the binding preference of a ligand to an open conformation

Monitoring of the opening and closing dynamics through Cy3-Cy5 FRET or Cy5 intensity

Tracking the binding of maltose through Cy7 intensity

Synthesis of Cy7-maltose

Synthesis of PEG-amine-linked maltose followed by conjugation with Cy7-mono NHS ester

Mass spectrometry analyses

Cy7-maltose

PEG-amine maltose

Cy7-maltose

Kim et al. Nature Chemical Biology (2013)

Three-color smFERT for the double mutant

Cy7-Maltose binding events : Jumps of Cy7 signal and concurrent drops of Cy5 signal

• Ligand binds both the open and partially closed conformations of the mutant• More than 80% of binding and dissociation events occurred in the open form,

indicating a strong preference for the open conformation

Relative distribution of the FRET efficiencies (Cy3-Cy5) before the binding and after the dissociation of Cy7-maltose

Kim et al. Nature Chemical Biology (2013)

Simultaneous analysis of Cy7-maltose binding and conformational dynamics

Partially closed state

Open state

Energetic and parameters : Energies of a ligand binding and conformational change

Protein Ligand Binding energy, Δε (kcal/mol)

Energy of conformational change, εc

(kcal/mol)

Wild-type MBPmaltose -7.45 ± 0.10

1.54maltotriose -8.56 ± 0.12

Single mutant(MBP-I329W)

maltose -9.25 ± 0.12

0.74maltotriose -9.72 ± 0.25

Double mutant(MBP-A96W/I329W)

maltose -10.43 ± 0.08

0.19maltotriose -10.20 ± 0.12

The existence of a partially closed form: critical role in the molecular recognition process

• Intrinsic dynamics facilitate a large conformational change that occurs upon sugar binding, with less energy expenditure • Conformational dynamics have a role in facilitating the transition to a holo structure or in influencing the chemical step during enzyme catalysis

Extended induced-fit model

Ligands bind both a highly populated and weakly populated substates, inducing a structural transition to a ligand-bound, closed form.

MBP mutants with varying binding affinity

MBP mutants Mutation Kd (nM)

Wild-type (WT) A96 I329 1102±65

AY A96 I329Y 22±6

AF A96 I329F 35±8

WI A96W I329 81±18

YI A96Y I329 218±23

FI A96F I329 492±50

WA A96W I329A 515±67

YA A96Y I329A 741±22

AR A96 I329R 905±50

AK A96 I329K 987±25

FA A96F I329A 989±37

• The dissociation constants (Kd) were determined through ITC• Two orders of magnitude variation in Kd

Interplay of conformational dynamics and binding affinity

Seo et al. Nature Communications (2014)

Analysis of conformational dynamics of MBP variants by smFRET

Calculation of transition rate by dwell time analysis

OpenPartially closed

kclosing

kopening

𝑶𝒑𝒆𝒏𝒊𝒏𝒈𝒓𝒂𝒕𝒆=𝟏

𝒄𝒍𝒐𝒔𝒆𝒅𝒅𝒘𝒆𝒍𝒍 𝒕𝒊𝒎𝒆(𝝉)

C

MBP mutants show different closed dwell times along with their Kd

Intrinsic dynamics of MBP mutants

Cy5Cy3

Seo et al. Nature Communications (2014)

OpenPartially closed

kclosing

kopening

Intrinsic opening rate directly dictates ligand-binding affinity

Intrinsic dynamics and binding affinity of MBP mutants

Seo et al. Nature Communications (2014)

Conformational energy and binding affinity of MBP mutants

• The energy barrier between an open state and a partially closed state (EOP.C., EP.C.O) determines the rate of inter-conversion (closing rate / opening rate)

• Mutant with an increased energy level in an open substate : - Lower structural energy cost (Ec) for transition to a partially closed state, - Higher energy barrier (E‡

P.C.O) for transition to an open state from a partially closed state, leading to a slower opening rate Higher binding affinity for a ligand

Seo et al. Nature Communications (2014)

Time traces and FRET histogram of a MBP mutant

Open Closed

kclosing

kopening

Characteristic, constant

Variable

Effect of a ligand on the transition rate

Ligand dissociation is solely determined by the intrinsic opening rate

Seo et al. Nature Communications (2014)

Conclusions

• A new platform based on FRET between QDs and AuNPs was well established

• The FRET-based system were found to be simple and effective for high throughput assays of protein glycosylation and protease activity

• Molecular beacon can be used for rapid detection of acquired drug resistance and poten-tial biomarker in lung cancer, offering a way of personalized medicine

• smFRET measurement enabled a kinetic analysis of conformational dynamics, providing direct evidence for the mechanism for ligand recognition and dissociation.

• Intrinsic dynamics (opening rate) directly dictate the binding affinity for a ligand

• The present approach will offer a new method to elucidate the molecular recognition and dissociation mechanisms of proteins with intrinsic dynamics, assisting in the design of more potent drugs and even proteins with new function.

Acknowledgements

KAIST Department of Biological Sciences Dr. Young-pil Kim Dr. Eunkeu Oh Dr. Young-Hee Oh

Sung-Min Cho Dr. Eunkyung Kim Dr. Moon-Hyeong Seo Dr. Jung Min Choi

Department of Chemistry Aram Jeon Prof. Hee-Seung Lee

Ministry of Science, ICT & Future Planning - Pioneer Research Program for Converging Technology - BK 21 program - Creative Research Initiatives (S.H.)

Pennsylvania State Univ., USA Prof. D. D. Boehr

The Uppsala Univ., Sweden Prof. S. Mowbray

Seoul National Univ. Prof. Sungchul Hohng Dr. Sanghwa Lee Jeongbin Park

SungKyunkwan Univ. School of Medicine Dr. Young-Uk Kim Prof. KeunChil Park

Biomolecular Engineering Lab.

Collaborative works

KAIST

바이오시스템 서울대 울리학과

고려대 화공과

Pacific National Laboratory

U.S.A

Pennsylvania State University.

U.S.A

KISTKRIBB

WeizmannInstitute of Science,

Israel.

아산 병원 삼성병원

고통 분담 : 1 N2

기쁨 : N2

고통 분담 : 1 N2

기쁨 : N2

Flow of ideasFlow of ideas

( N=1, 2, 3, …)( N=1, 2, 3, …)

( N=1, 2, 3, …)( N=1, 2, 3, …)

Biological role of intrinsic dynamics

• Correlation between the structural energy costs and the dissociation constants observed in the three MBP mutants

• The intrinsic dynamics of MBP facilitate a large conformational change that occurs upon sugar binding, which can be achieved with less energy expenditure

ProteinEnergy of

conformational change (εc)(kcal/mol)

Binding energy(Δε) (kcal/mol)

Dissociationconstants(Kd) (nM)a

DissociationConstants by ITC

(Kd) (nM)b

Wild-type 1.77 n.d.(maltose)- 8.91 (maltotriose)

n.d.(maltose)715 (maltotriose)

2180 (maltose)1950 (maltotriose)

Single mutant(I329W) 1.09 - 9.48 (maltose)

- 9.82 (maltotriose) 99 (maltose)

56 (maltotriose) 48 (maltose)

53 (maltotriose)

Double mutant(A96W/I329W) 0.19 - 10.39 (maltose)

- 10.19 (maltotriose) 7.2 (maltose)

10 (maltotriose) 4.7 (maltose)

2.3 (maltotriose)

Kim et al., Nature Chem. Biol. (2013)

a: The dissociation constants were determined from a hyperbolic Hill fit of the relative population of a closed state b: The dissociation constants were determined through ITC

FRET efficiency calculation

Lee S, Lee J, and Hohng S (2010) PLoS ONE 5(8): e12270

is the gamma-corrected fluorescence intensity of the ith dye af-ter whole correction steps when excited by the green laser

is the gamma-corrected fluorescence intensity of the ith dye af-ter whole correction steps when excited by the red laser

22.8 Å 48.1 Å

51.6 Å60.1 Å

23.0 Å 45.0 Å

Estimation of FRET efficiencies on binding of Cy7-maltose to dual labeled wt-MBP

Binding to an open form

Binding to a closed form

Distance (nm) FRET Efficiency

C34_Cy3C354_Cy5

closed formC34_Cy3-Cy7 4.81 0.20

C354_Cy5-Cy7 2.28 1.00

bound, open formC34_Cy3-Cy7 4.50 0.27

C354_Cy5-Cy7 2.30 1.00

C34_Cy5C354_Cy3

closed formC354_Cy3-Cy7 2.28 0.96

C34_Cy5-Cy7 4.81 0.82

bound, open formC354_Cy3-Cy7 2.30 0.95

C34_Cy5-Cy7 4.50 0.87

closed form Cy3-Cy5 5.16 0.57

bound, open form Cy3-Cy5 6.01 0.34

Cy3

Cy5

Cy7

Cy3

Cy5

Cy7

Dynamics-coupled molecular recognition mechanism

• Connection between the conformational dynamics and molecular recognition of proteins in the process of ligand binding

• Role of conformational dynamics in molecular recognition remains controversial and elusive mainly owing to experimental limitations

• Real-time kinetic analysis of the ligand binding and conformational dynamics

• Single-molecule FRET analysis : Detection of inter-conversion rate upon ligand binding at the single-molecule level

Regulation of ligand residence time

Our previous study reveals that the opening rate of MBP is independent of ligand existence.

Kim E. et al. (2013) Nature Chem. Biol. 9, 313

Single mutant Double mutant

We can modulate the protein dynamics by allosteric approach for optimizing ligand residence times to meet the desired pharmaceutical modulation of disease states(ligand residence times control the strength of regulatory process)

other reports,ligand binding affinity of the MBP variants is deter-mined primarily by differences in the off-rate

Marvin. et al. (2013) Nature Struc. Biol. 8(9), 795

𝑘𝑜𝑏𝑠=𝑘1 [𝐿 ]+𝑘−1

kobs = observed ratek1 = on-ratek-1 = off-rate

1. Interactions between a protein and a ligand are essential to all biological processes.

2. MBP mutants constructed through hinge mutations were shown to have a wide range of intrinsic opening rates and dissociation constants for maltose.

3. Intrinsic opening rate of the protein dictates the ligand dissociation, and consequently determines the binding affinity for a ligand.

4. The residence time of a ligand has a significant effect on the signal trans-duction, regulatory processes, and drug responses.

(1) The ligand residence time can be regulated by modulating the intrinsic dynamic characteristics.

(2) The changes in the dynamic personalities of a protein by allosteric approach can modulate the ligand-binding affinity and vice versa.

Conclusion

Interplay of conformational dynamics and ligand dissociation

A close correlation between the opening rate of MBPs and their binding affinity for a ligand

The binding affinity of the MBP variants for a ligand is determined primarily by differences in the off-rate

Intr

ins

ic

op

en

ing

ra

te (

s-1)

Single mutantKd = 48 nM

Double mutantKd = 4.7 nM

𝑘𝑜𝑏𝑠=𝑘1 [𝐿 ]+𝑘−1

Kd : 7 ~ 1800 nMk1 (on-rate) : 10.8 ~ 18.2 (s-1)k-1 (off-rate) : 0 ~ 100 (s-1)

Kim et al., Nature Chem. Biol. (2013) Marvin et al., Nature Struc. Mol. Biol. (2001)

Allosteric mutations of MBP

35°

WT(0°) I329C(5.5°) I329W(9.5°)

I329W/A96W(28.4°)

WT(35°)

C-domain

N-domain

Millet et al., PNAS (2003)

Hinge mutations of MBP are known to significantly alter the bind-ing affinity while maintaining the ligand-binding interfaces

Recognition mechanism of MBP

Structural difference between ligand-free and ligand-bound forms

Induced-fit model

Existence of the minor partially closed species (~5%) in the absence of a ligand by NMR relaxation experiment

Conformational selection model

Tang et al., Nature (2007)

Sharff et al., Biochem. (1992)

Ligand-freeLigand-bound

Induced-fit or conformational selection model ?