supplementary information octobersupplementary information mapping the structure of amyloid...

Supplementary information

Mapping the structure of amyloid nucleation precursors by protein engineering

kinetic analysis

David Ruzafa1, Lorena Varela

1,‡, Ana I. Azuaga, Francisco Conejero-Lara

*, Bertrand

Morel*

Departamento de Química Física e Instituto de Biotecnología, Facultad de Ciencias,

Universidad de Granada, 18071 Granada, Spain

‡Present address: Department of Biochemistry, University of Oxford, South Parks Road,

Oxford, OX1 3QU, United Kingdom

1These authors contributed equally to this work.

*Correspondence should be addressed to F.C.-L. or B.M.; e-mail addresses:

[email protected], [email protected]; tel.: +34 958 242371; fax: +34 958 272879.

Electronic Supplementary Material (ESI) for Physical Chemistry Chemical PhysicsThis journal is © The Owner Societies 2013

Text S1: Determination of initial rates of growth

In this study, initial rates of growth of amyloid structure were calculated by fitting the initial region of the ThT kinetics curves using a double exponential function: y = y0 + A1 !e

"k1t + A2 !e"k2t

(1) The initial slope of the ThT curves was then accurately calculated from the resulting

fitting parameters as:

r0 =dydt!

"#

$

%&t'0

= A1 ( k1 + A2 ( k2 (2)

The fit of the initial ThT curve (Figure S1) is only used for extrapolation purposes and only the initial slope (extrapolated to time zero) is considered. This operational extrapolation procedure allowed us an accurate determination of the initial slopes of the ThT curves and avoided the assumption of any time interval in the kinetics to define an initial aggregation rate. Another alternative method of the initial slope was also tested by calculating the first derivative of the kinetic trace and extrapolated it to time zero. As shown in Table S1, the results obtained by the two procedures are identical.

Figure S1: (a) Aggregation kinetics curves of T32A-N47A obtained from ThT fluorescence experiments. Symbols represent the experimental data and the continuous lines correspond to the best fit to equation 1. (b) First derivative of the ThT kinetics curve obtained for T32A-N47A (9.1 mg.mL-1) is represented in black squares. The tangent of the maximum decay is shown as the red line. Concentration (mg.mL-1) Equation 2 First derivative

9.1 9.80 9.36 8.6 6.70 6.82 6.2 3.28 3.25 4.2 0.65 0.66

Table S1: Initial rates obtained from ThT aggregation kinetics performed at different protein concentrations using the two methods described.

0 20 40 60 80 1000

30

60

90

T32A 9.1 mg.mL-1

T32A 8.6 mg.mL-1

T32A 6.2 mg.mL-1

T32A 4.2 mg.mL-1

Th

T flu

ores

cenc

e in

tens

ity (a

.u)

Time (min)0 10 20 30 40 50

-2

0

2

4

6

8

10

Deriv

ativ

e Y1

Derivative X1

Equation y = a + b*x

Weight No Weighting

Residual Sum of Squares

0.04658

Pearson's r -0.99739Adj. R-Square 0.99347

Value Standard ErrorDerivative Y1 Intercept 9.36153 0.15089Derivative Y1 Slope -0.84436 0.03059

a b


Table S2. Thermodynamic parameters of the equilibrium thermal unfolding of N47A

Spc-SH3 and the double mutants measured by DSC in 0.1 M Glycine, 0.1 M NaCl pH

3.2.

Mutant Tm (°C) ∆Hm (kJ.mol-1)

N47Aa 51.2 ± 0.1 152.1 ± 0.4

N47A-L10A 30.3 ± 0.1 77.0 ± 0.5

N47A-R21D 53.5 ± 0.1 155.4 ± 0.3

N47A-K27A 46.7 ± 0.1 129.5± 0.6

N47A-T32A 41.6 ± 0.1 108.8 ± 0.2

N47A-N38A 38.6 ± 0.1 101.3 ± 0.3

N47A-K43A 43.9 ± 1.0 60.9 ± 4.2

N47A-V46A 42.3 ± 0.1 113.7 ± 0.4

N47A-D48G 55.7± 0.1 164.1 ± 0.4

N47A-V53A 54.5 ± 3.1 61.1 ± 6.7

N47A-A56G 46.1 ± 0.1 145.5 ± 0.4

N47A-V58A 56.1 ± 2.8 54.3 ± 4.8

Values of the unfolding temperature (Tm) and enthalpy change (∆Hm) have been obtained by

two-state analysis of the DSC unfolding transitions. The errors have been estimated from the

fittings as 95% confidence intervals for each parameter.

a : Data taken from Morel et al.,

1.


Figure S2. DSC experiments of N47A and the double mutants of Spc-SH3 domain.

Experiments were carried out at low protein concentration in 0.1 M Gly, 0.1 M NaCl

pH 3.2. Symbols represent the experimental data and the lines correspond to the best fit

using the two-state unfolding model. For sake of clarity, experimental data of N47A

mutant is shown in open squares and the best fit using the two-state unfolding model in

dashed lines.


Figure S3. Aggregation kinetics at 37 ºC of the double mutants and the N47A mutant

followed by scattering intensity.

0 50 100 150 200 2500

1

2

3

4

0 50 100 150 200 2500

1

2

3

4

0 50 100 150 200 2500

1

2

3

4

N47A N47A-T32A N47A-N38A N47A-V46A

Time (min)

N47A N47A-L10A N47A-R21D N47A-D48G

10-6·S

catte

ring

inte

nsity

(cn

ts s

-1)

Time (min)

N47A N47A-K27A N47A-V58A N47A-A56G N47A-V53A N47A-K43A

Time (min)


Figure S4. Time evolution of the apparent hydrodynamic radii (Rh) for the two smallest

peaks in the particle size distributions measured by DLS during the course of fibrillation

of the N47A and the double mutants of Spc-SH3 domain. The continuous lines

represent the best fit of the time dependence of the apparent Rh using a logistic

sigmoidal function.

0 50 100 150 200 250012345

10

20

30

40

50 N47A N47A-A56G N47A-R21D

Hyd

rody

nam

ic r

adiu

s (n

m)

Time (min)

0 50 100 150 200 250012345

10

20

30

40

50 N47A N47A-D48G N47A-L10A

Hyd

rody

nam

ic r

adiu

s (n

m)

Time (min)

0 50 100 150 200 250012345

10

20

30

40

50 N47A N47A-V46A N47A-T32A

Hyd

rody

nam

ic r

adiu

s (n

m)

Time (min)0 50 100 150 200 250

012345

10

20

30

40

50 N47A N47A-N38A N47A-K27A

Hyd

rody

nam

ic r

adiu

s (n

m)

Time (min)

0 50 100 150 200 250012345

10

20

30

40

50 N47A N47A-V53A N47A-V58A

Hyd

rody

nam

ic r

adiu

s (n

m)

Time (min)

0 50 100 150 200 250012345

10

20

30

40

50 N47A N47A-K43A

Hyd

rody

nam

ic r

adiu

s (n

m)

Time (min)


Figure S5. The energy changes produced by the different mutations in the native-state

stability do not correlate with the changes in the rates of nucleation. Plot of ∆GI versus

∆GN–U. a Determined from aggregation kinetics studied in the presence of 0.2 M NaCl.

-2 -1 0 1 2 3 4 5 6 7

8

9

10

11

12

13

14

15

16

17

18

V58A

V53A

V46A

K43AA56G

N47Aa

N47A

R21DK27AT32AN38A

L10Aa∆∆ ∆∆ GI (

kJ.m

ol-1)

∆∆∆∆GN-U

(kJ.mol-1)


Figure S6. The aggregation propensity of the primary sequence of N47A Spc-SH3

domain predicted by the algorithms (a) Aggrescan 2 (b) TANGO

3 and (c) Zyggregator

4.

0 10 20 30 40 50 600

1

2

3

4

5

Hot

spot

Are

a

Residue number

0 10 20 30 40 50 600

20

40

60

80

TA

NG

O S

core

Residue Number

0 10 20 30 40 50 60-4

-3

-2

-1

0

1

2

Zyg

greg

ator

sco

re

Residue Number

(a)

(c)

(b)


References

1. Morel, B., Varela, L., Azuaga, A.I. & Conejero-Lara, F. Environmental

Conditions Affect the Kinetics of Nucleation of Amyloid Fibrils and Determine

Their Morphology. Biophys. J. 99, 3801-3810 (2010).

2. Conchillo-Sole, O. et al. AGGRESCAN: a server for the prediction and

evaluation of "hot spots" of aggregation in polypeptides. BMC Bioinformatics 8,

65 (2007).

3. Fernandez-Escamilla, A.M., Rousseau, F., Schymkowitz, J. & Serrano, L.

Prediction of sequence-dependent and mutational effects on the aggregation of

peptides and proteins. Nat. Biotechnol. 22, 1302-1306 (2004).

4. Tartaglia, G.G. & Vendruscolo, M. The Zyggregator method for predicting

protein aggregation propensities. Chem. Soc. Rev. 37, 1395-1401 (2008).


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