Self-Assembly of Human Profilin-1 Detected by CarrâPurcell

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Self-Assembly of Human Profilin-1 Detected by CPMG NMR Spectroscopy Enrico Rennella, Ashok Sekhar, and Lewis E. Kay Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.6b01263 • Publication Date (Web): 04 Jan 2017 Downloaded from http://pubs.acs.org on January 7, 2017

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Self-Assembly of Human Profilin-1 Detected by CPMG NMR Spectroscopy Enrico Rennella1, Ashok Sekhar1, and Lewis E. Kay1,2

1

Departments of Molecular Genetics, Biochemistry and Chemistry, The University of Toronto, Toronto,

Ontario, M5S 1A8, Canada 2

Hospital for Sick Children, Program in Molecular Structure and Function, 555 University Avenue,

Toronto, Ontario, M5G 1X8, Canada

Email: [email protected]

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Abstract Protein oligomerization in the cell has important implications in both health and disease and an understanding of the mechanisms by which proteins can self-associate is, therefore, of critical interest. Initial stages of the oligomerization process can be hard to detect, as they often involve the formation of sparsely populated and transient states that are difficult to characterize by standard biophysical approaches. Using relaxation dispersion NMR spectroscopy we study the oligomerization of human profilin-1, a protein that regulates the polymerization of actin. We show that in solution and at millimolar concentrations profilin-1 is predominantly monomeric. However, fits of concentration dependent relaxation data are consistent with the formation of a higher order oligomer that is generated via a multi-step process. Together with crystallographic data on profilin-2, a homologue of the protein studied here, our results suggest that profilin-1 forms a sparsely populated tetrameric conformer in solution.


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Introduction

Self-association is a ubiquitous feature of many proteins1 and it estimated that in E. coli approximately 65% of the total protein content forms homo-oligomers2. An evolutionary advantage of such complexes is that they can be produced from just a single small gene, reducing the impact of errors during translation relative to large proteins that involve only a single polypeptide chain. Moreover, homooligomeric receptors and enzymes can present multiple copies of identical binding sites. These can be arranged in specific topologies, with ligand binding occurring in a cooperative manner2 and with a strength that can vary depending on the oligomeric state, so that affinities can be modulated by protein concentrations3,4. In many cases, a shift in the equilibrium towards homo-oligomerization controls function, for example by activating/inhibiting enzymatic activity5,6 or by triggering/blocking binding to other proteins7. Polymerization of proteins can also lead to misfunction and to disease, especially in cases of neurodegeneration8–10 Several techniques are available for the characterization of protein self-association.11 These include, among others, small-angle X-ray scattering,12 mass spectrometry,13 electron cryomicroscopy14,15 and ultracentrifugation16. However, under conditions where self-association is weak, so that only a small fraction of an oligomeric state is present, the association process becomes difficult to study, and the sparsely populated oligomers so formed are challenging to characterize in detail. In many cases only average properties are extracted that essentially reflect those of the monomeric state (although scattering techniques are sensitive to small fractions of large complexes). Nuclear magnetic resonance (NMR) spectroscopy has emerged as a valuable method for the study of sparsely populated and transiently formed states of macromolecules. In particular, Carr-Purcell-Meiboom-Gill (CPMG) NMR experiments17,18 have been used to characterize conformationally excited states involved in protein folding19–21 in enzyme function,22–25 in allostery26,27 and in oligomerization21,28–31. Herein we apply CPMG methods to investigate the oligomeric states of human profilin-1, PFN1, ACS Paragon Plus Environment

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and the mechanism by which it oligomerizes. Profilins are evolutionary conserved proteins that bind monomeric

actin

with

sub-micromolar

dissociation

constants,32

and

thus

regulate

actin

polymerization.33–35 The profilin fold consists of a 5-stranded antiparallel β-sheet, with 2 helices on one face (N-terminal and C-terminal helices α1 and α4, respectively) and a mixture of a number of small α and β elements on the other face (helices α2 and α3, strands β3 and β4), Fig. 1. Profilins can inhibit growth of actin filaments by sequestering actin monomers,33,36 using an interface for actin binding comprising α3, β4, β5 and the C-terminal part of α4, or promote polymerization by catalyzing the exchange of ADP to ATP in actin37. Several signalling as well as actin-binding proteins associate with profilins through a specific pocket, located between α1 and α4, that binds proline-rich regions,38 Fig. 1. Profilins can also bind to poly-phosphoinositides,39 although the site of interaction remains to be defined. It has been shown that post-translational modifications of profilin can regulate binding in vivo, with the phosphorylation of Tyr129 and Ser138 or Tyr140 increasing affinities for actin40 and poly-Lproline41, respectively.

Figure 1. Ribbon representation of PFN1 shown as an isolated monomer (left, PDB ID 1PFL42) or in the ternary complex with actin (blue) and a proline-rich peptide (cyan) (right, PDB ID 2PAV43). The sidechains of Leu123 and His134 are shown as white spheres.


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In addition to their role in actin biology, profilins have also been found to localize to transcriptionally active loci,44 possibly regulating the activity of transcription factors containing proline-rich regions45 and/or playing a role in pre-mRNA processing46. They play an inhibitory role in multiple types of carcinomas47–49 and, recently, mutations in the PFN1 gene were identified in cases of familial amyotrophic lateral sclerosis (ALS)50 where PFN1 aggregates sequester TDP-4351, a key protein in the pathogenesis of ALS52. Profilins analyzed under non-reducing conditions have been found to exist as monomers, dimers and tetramers53–55 and a crystal structure of human profilin-2 (PFN2) shows it to be tetrameric in the crystal lattice56. Given the biological importance of PFN1 it is of considerable interest to elucidate the nature of its oligomeric states in solution and to obtain insight into the oligomerization process in general. Herein, we show that for the millimolar (mM) protein concentrations considered in this study PFN1 is largely monomeric. It does, however, form sparsely populated oligomers that are not observable in NMR spectra but that can be characterized by protein concentration dependent CPMG relaxation dispersion NMR experiments. Key to our analysis has been an approach that separates the effects of chemical exchange from contributions to transverse relaxation rates that arise from viscosity effects and potentially non-specific, transient protein aggregation that often also increase with protein concentration. Together with available structural data on PFN2 our NMR results strongly suggest the formation of a sparsely populated tetrameric PFN1 conformer in solution.


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Materials and Methods Protein production. BL21 E. Coli cells were transformed with a pET-29b plasmid containing the PFN1 gene linked to an N-terminal HisTag (GenScript). Cells were grown at 37 °C in M9 medium containing 15

NH4Cl and 12C-glucose (3 g/L) as the sole nitrogen and carbon sources until OD ~ 0.7, then induced

overnight at 25 °C. In the case where 2H,15N PFN1 was produced, the M9 medium was D2O based and 2

H-glucose (3 g/L) was used as the carbon source. Cells were harvested, resuspended in 25 mM

phosphate buffer, 150 mM NaCl, 25 mM imidazole, 2 mM DTT, pH 6.4, and sonicated. Both WT and L123R/H134R PFN1 mutant proteins localized to the soluble fraction. The protein was purified by nickel affinity chromatography, before removing the HisTag by TEV proteolysis overnight in 25 mM HEPES buffer, 150 mM NaCl, 1 mM EDTA, 2 mM DTT, pH 7. The final purification step consisted of gel-filtration using a Superdex 75 column (GE Healthcare Life Sciences) in 12 mM phosphate buffer, 150 mM NaCl, 2 mM DTT, pH 6.4. NMR samples (ranging in concentration from 0.5 – 3 mM) were dissolved in buffer comprised of 50 mM BisTris, 5 mM TCEP, 1 mM EDTA, pH 6.4, 10% D2O.

NMR experiments. All experiments were recorded at 10 °C, using either a 14.1 T Bruker AVANCE III HD spectrometer equipped with a cryogenically cooled x,y,z gradient probe (15N-PFN1, in-phase CPMG recorded at 5 protein concentrations) or both a 14.1 T spectrometer (Bruker) and an 18.8 T Varian spectrometer, the latter equipped with a room temperature probe (2H,15N-PFN1, in-phase, TROSY, anti-TROSY data sets at 2 protein concentrations). In-phase, TROSY (TR) and anti-TROSY (AT)

15

N CPMG experiments were acquired as previously described.58,63 Constant-time relaxation

intervals57, Trelax, varied between 20-40 ms, where shorter Trelax values (20 ms) were used for the most concentrated WT samples to compensate for the large R2 rates. 26 νCPMG values up to 1 kHz were used in all the experiments, with the CPMG refocusing pulses applied at a field strength of γB1/2π = 5.8 kHz. For the in-phase experiment, 1H continuous wave decoupling was applied during Trelax using a γB1/2π = 16.7 kHz field. To keep the amount of heating constant in these experiments the 1H continuous wave ACS Paragon Plus Environment

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element was applied during the recycle delay for reference experiments recorded with Trelax = 0 s. A recycle delay of 2.5 s was used in all CPMG experiments. Lorentz-to-Gauss transformation was applied to both direct and indirect acquisition dimensions before Fourier transform, and signal intensities were quantified using the multidimensional lineshape fitting routine nlinLS in the NMRPipe software package73. In total, relaxation dispersion data recorded on wild-type PFN1 required 13.5 days of acquisition time corresponding to (66 h, 33 h, 17 h, 17 h, 17 h) for in-phase 15N data (14.1 T) obtained at protein concentrations of (0.5 mM, 1 mM, 1.3 mM, 2 mM, 3 mM); (29 h, 57 h) for in-phase and TR/AT profiles, respectively, for both protein concentrations of 1 mM and 1.8 mM (18.8T). In addition, 6 in-phase

15

N dispersion profiles were recorded in total for the two mutants (L123R and H134R

PFN1), each of duration 17 hours, for a total of 4.2 days. Data fitting. CPMG data for 6 selected residues of wild-type PFN1 (those with ∆R2,eff > 2 s-1 at [PFN1] = 1 mM and free of spectral overlap, corresponding to Gly3, Gly121, Leu123, Ile124, Asn125 and Arg137) were fit to a model of chemical exchange via the Bloch-McConnell equations64, = −

⋅

where the explicit elements of the Liouvillian

[1]

⋅

depend on the model of exchange and the

subscripts K and J are the number of oligomeric states and the size of the Liouvillian (JxJ) that is

required for each exchanging state, l, respectively. When K = 2 or 3, ⋅ =

0 [2]

⋅ = 0 [3]

0

0 + − 0

0

0

0 +

− − −

⊗ , +

− −

(K=2)

+

− −

+

⊗ ,

(K=3)

where is the J-dimensional identity matrix. For the in-phase CPMG experiment (J=3) ACS Paragon Plus Environment

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= −

0 0

0

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0

[4]

so that the explicit Liouvillian describing exchange between two states (K=2) is given by

⋅

+ − 0 = − 0 0 [5]

where

,

0 0 + 0 0 −

+ 0 0 − 0

and

−

0 0 + − 0

−

0

0

+ 0

−

0 0

0 0 +

,

are longitudinal and transverse spin relaxation rates and the chemical shift

(rad/sec) for a nucleus in state l, and = [NAx, NAy, NAz, NBx, NBy, NBz]+ (see Eq [1]) where the

superscript ‘+’ denotes transpose.

For TROSY and anti-TROSY based CPMG experiments the equations are slightly more complicated because both in-phase and anti-phase operators are needed to describe the evolution of magnetization for each oligomeric state l (J=6). Thus Eq. [4] is replaced by74 , − 0 = − ⋅ 0

⋅

[6]

where

and

,

0

0

0 0

,

− ⋅ 0

0

0 0

−

,

⋅

0

,

0

0 0 , 0 0 ,

are longitudinal and transverse cross-correlated relaxation rates for an 15N spin of an

amide pair in state l, JHN is the one-bond 1H-15N scalar coupling constant and the i/a subscripts denote in-phase / anti-phase coherences. Thus, the appropriate Liouvillian for exchange between 2 and 3 states

is ⋅ (12x12 matrix) and ⋅ , (18x18 matrix) which can be constructed from Eqs. [2] and [3], respectively, with the sub-Liouvillian for each state l as defined by Eq. [6]. In the case of exchange


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between two states (J=6) the magnetization vector for each state l is given by l = [Nlx, Nly, Nlz, 2HzNlx,

2HzNly, 2HzNlz]+ and = [NAx, NAy, NAz, 2HzNAx, 2HzNAy, 2HzNAz, NBx, NBy, NBz, 2HzNBx, 2HzNBy, 2HzNBz ]+ where subscripts A, B denote the pair of exchanging states.

Because concentration dependent CPMG relation dispersion profiles are fit that depend on the overall tumbling of the protein (that, in turn, increases with protein concentration due to the increase of solvent viscosity and, possibly, due to non-specific protein-protein interactions) intrinsic transverses relaxation rate ,

!

1#$ =

,

in all states l were corrected using the relation 0

$⋅

,

[7]

are obtained from linear fits of the R2 vs [PFN1] profiles for each

where the per-residue factors

residue in the L123R mutant (Fig. S4). Values of

are equal to one at zero protein concentration

and range between 1.4-1.8, depending on internal protein dynamics, at [PFN1] = 3 mM.

rates for

the monomeric state were measured in a separate experiment and were not corrected for concentration in fits of CPMG data. Eq. [7] assumes that transverse relaxation rates are proportional to the overall tumbling time, which is a good approximation for a 15 kDa protein at 10 oC. In simultaneous fits of TROSY, anti-TROSY and in-phase

perdeuterated samples, all transverse relaxation terms, calculated from the product,

,

⋅

,

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N CPMG profiles recorded on

, , and

for

each state, l, were

$, which was a floating variable in the fits (S2,l is the square of

the backbone order parameter in state l and

is the correlation time in that state). Additional

contributions to relaxation from 1H spin flips RHl z ,ext were included for anti-phase relaxation terms, ,

to

and ,

⋅

,

,

=

$ + RHl z ,ext where RHl z ,ext was assumed to be linearly proportional

$. As in the analysis of 15N in-phase CPMG data sets, the change in sample viscosity with

protein concentration was included using equations analogous to Eq. [7].

Different models of chemical exchange were considered, as indicated in Table 1. Exchange data


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were fit to pseudo 2-state or 3-state models,

⇌

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⇌

or [

,

⇌

⇌

,

#, respectively,

and the extracted rates kij, i,j ∈ {A,B,C} converted into k±1,k±2 via relations in Table 1 (see Supporting Information for further details). It is, of course, not surprising that fits to multi-step models would be more time consuming than for those based on two-state exchange. In Fig. S6 it is shown that robust estimates of exchange parameters and chemical shift differences can be readily obtained from fits of simulated data generated k1   → A model. In fits to multi-step models, such as 2 using the 4 A ←  4 k −1

⇌ '

,2

('

⇌

)

()

*,

we have

initially used a grid-search focusing on dispersion data for G121 that has the largest of the experimental profiles examined. A 3-dimensional space (K1,k-1,k-2) was explored, with K1=k1/k-1; k-1 and k-2 were sampled logarithmically from 10 to 108 s-1, while K1 was varied linearly between 0.4 and 20.4 M-1 (that 2 gives p A values ranging from 0.2% to 10% for a monomer-dimer model). All fits for which χ red < 1.5 2

2 were clustered. Parameters corresponding to the lowest χ red were, therefore, used as starting values for

a final optimization where the four microscopic rates and chemical shift values were allowed to float.

Table 1: Chemical exchange models considered in the analysis of PFN1 CPMG profiles. Model

[2]

=

[2]

= 2⋅

⋅ ! #,

*

[2]

= 4⋅

⋅ ! # ,

Eqn.

⇌ '

('

(k1 and k-1 are allowed to ‘float’ with concentration) 2

⇌ '

('

3 2

⇌

'

('

4

⇌ '

('

⇌ '

,

('

+

[2]

⇌ )

()

[3]

Rates

=

,

= 3⋅ = 2⋅

⋅ ! # , ⋅ ! #,

+

=

= =

=

+

+ +

+ ,

=

⋅


10

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2

⇌

'

('

,2

⇌ )

()

! #, *

[3]

+

= ⋅

+ ,

= 2 ⋅ ⋅ ! #, ! #, = +,

=

= = 0,

⋅!

+ ,

#,

=0

= ⋅

=2⋅

⋅

All fits were carried out using the chemex package (https://github.com/gbouvignies/chemex), minimizing a χ2 target function, as described previously.75 Specific chemex modules for the various oligomerization models considered in this paper are available upon request.

Proline-rich peptide. A peptide, GAGGGPPPAPPLPAAQ (MW = 1354.54), corresponding to the proline-rich PFN1 binding site of human VASP43, was purchased from ThermoFisher. A complex of 2.0 mM WT PFN1 was generated with 3.7 mM peptide; based on the published affinity constant (≤ 84 1kHz µM43), the estimated amount of PFN1 bound to the peptide is over 95%. Expected R2,eff values for the

1:1 complex have been extrapolated from rates measured for the L123R mutant, that take into account viscosity effects, Eq. [7]. Values obtained in this manner were further multiplied by a factor of 1.09 = ( MWPFN1 + MWpeptide ) / MWPFN1, to account for the size increase upon binding.

Results Experimental Approach In what follows we have used CPMG-based experiments to study the oligomerization process of PFN1. In this approach signal intensities derived from the ground state of the protein are measured as a function of the repetition rate of chemical shift refocusing pulses, νCPMG, that are applied during a delay of constant duration, Trelax. These intensities, I(νCPMG), are subsequently converted to effective transverse relaxation rates, R2,eff = (1/Trelax) ln[I0/I(νCPMG)], where I0 is the reference signal intensity when Trelax = 0 s57. Random fluctuations between exchanging states on the millisecond (ms) timescale lead to a stochastic modulation of chemical shifts and for small νCPMG values (i.e., only a few refocusing pulses are applied) dephasing of the magnetization occurs, resulting in a reduction of the net ACS Paragon Plus Environment

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signal and an increase in R2,eff values. In contrast, when refocusing pulses are applied more frequently (large νCPMG) the fluctuations lead to less dephasing, the signal is thus affected less by the exchange process and R2,eff values become smaller. The resulting dispersion profile, R2,eff vs νCPMG, can be fit to appropriate models to extract the kinetics and thermodynamics of the exchange process as well as the chemical shifts of the interconverting low populated state(s), so long as the effective two-site exchange rate lies between several hundred to several thousand per second and the population of the rare state is 0.5% or larger.17,18 A large variety of CPMG experiments have been developed to measure additional structural parameters in rare states such as bond-vector orientations58–61 as well as amplitudes of bond vector motions62. Taken together the data so obtained facilitates the calculation of atomic-resolution structural models of excited state conformers, such as protein folding and misfolding intermediates19,20,28. To date relatively few studies have, however, focused on oligomerization processes more complex than the monomer-dimer variety, that forms the basis of the present work.

PFN1 forms oligomers on the ms timescale 15

N CPMG relaxation dispersion experiments58,63 have been performed on samples of PFN1 at different

protein concentrations (Fig. 2). An initial analysis of the raw CPMG data can be performed simply by calculating the sizes of the dispersion profiles, ∆R2,eff = R2,eff(νCPMG,low) - R2,eff(νCPMG,high) where

νCPMG,low and νCPMG,high are apparent R2 values recorded at the lowest and highest CPMG frequencies, respectively (~ 30 Hz and 1 kHz in this study). Non-zero values of ∆R2,eff delineate regions of the protein where exchange processes are occurring.

15

N ∆R2,eff values for PFN1 acquired at different

protein concentrations indicate the presence of at least two distinct conformational exchange processes occurring on the ms timescale, Fig. 2A. One of them, involving the loop between strands β4 and β5, corresponding to residues Asp76-Met86, does not depend on protein concentration in a significant way and hence is not related to protein oligomerization. It will not be discussed further. The second process is localized mainly to the C-terminal helix α4, but also includes the N-terminal helix α1 and Glu83 in the ACS Paragon Plus Environment

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loop between strands β4 and β5. 15N ∆R2,eff values for this process depend on protein concentration, Fig. 2A, consistent with self-assembly of PFN1.

Figure 2. Analysis of in-phase concentrations, 10oC.

15

N CPMG data acquired on a sample of

15

N WT PFN1 at several protein

30 Hz 1kHz 1kHz ∆R2,eff = R2,eff − R2,eff and R2,eff are plotted at two different concentrations in panels A

and B, respectively. The position of Glu83, the only residue in loop β4-β5 that clearly displays concentrationdependent ∆R2,eff values, is indicated by the dashed vertical line in panel A. (C) 15N-CPMG profiles for Leu123 are shown at three different protein concentrations, clearly establishing that both the baseline and the sizes of the dispersion profiles increase with concentration. (D) Crystal structure of the tetrameric state of human profilin-2 (PDB ID: 1D1J56). Regions colored in red in the yellow chain indicate residues with ∆R2,eff > 1 s-1 at 1 mM ACS Paragon Plus Environment

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protein concentration, 600 MHz; the backbone 15N atoms of Leu123 and His134 are shown as white spheres.

Fig. 2B shows a plot of apparent R2 values measured at the highest νCPMG frequency of 1 kHz, 1kHz 1kHz , where the exchange process of interest is quenched (i.e., flat dispersions, see below). R2,eff rates R2,eff

are sensitive to the rotational diffusion of the protein and their increase with concentration is thus consistent with an oligomerization process. However, the effective solution viscosity also grows with 1kHz protein concentration, leading to increased R2,eff values, and it is not possible to separate these two

effects from this data alone. A representative

15

N CPMG profile acquired as a function of protein

concentration is shown for Leu123, Fig. 2C, clearly establishing that both the size of the dispersions and the apparent R2 values at 1 kHz increase as a function of protein concentration. Residues with concentration dependent CPMG profiles (red) are localized to regions defined as points of contact between monomers in a previously published tetrameric crystal structure of human PFN256, Fig. 2D, a protein that is a close homologue to PFN1 (sequence identity of 62%). Notably, helix 1 and the Cterminus of helix 4 (yellow molecule, Fig. 2D) form one of the interfaces with an adjacent molecule (blue), while the N-terminus of helix 4 and the loop between strands β4 and β5 contact a second molecule (green). The fact that both isoforms of the same protein share similar oligomerization properties may reflect a functional role for the self-assembly of profilins.

Mutation of either L123 or H134 inhibits oligomerization of PFN1 In order to gain insight into the nature of the concentration dependent exchange process that is quantified by our CPMG relaxation dispersion experiments we exploited the available X-ray structure of the PFN2 tetramer showing that Leu123 and His134 sit in two distinct dimer-dimer interfaces of the tetramer56 (Fig, 2D, Fig. S1). These residues were modified (one at a time) to Arg since the burial of positively charged Arg residues in the mutant structures was predicted to be unfavourable, potentially stabilizing the monomeric structure. Both L123R and H134R mutations have little effect on the tertiary


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structure of the protein as judged by 1H,15N HSQC spectra (Fig. S2). 15N CPMG experiments acquired for L123R-PFN1 (H134R-PFN1) at a number of protein concentrations have been analyzed and, in contrast to the wild-type (WT) protein, ∆R2,eff rates in the terminal helices are close to zero and independent of concentration (Fig. 3 and Fig. S3). Values of ∆R2,eff ~ 0 s-1 for both mutants very likely reflect an impaired ability to oligomerize or, less likely, a shift of the exchange timescale outside the CPMG window (i.e., exchange lifetime not within the ms range). If a decrease in the kinetics of exchange is the reason for the lack of concentration dependent dispersions then it might be expected that additional resonances would be present in spectra, yet these are not observed. Conversely, an increase in kinetics that would place the exchange rate in the fast regime would lead to small shifts in peak positions, that again is not consistent with experiment. Although we cannot unequivocally exclude either of these two possibilities, we can estimate upper limits for the residual populations of the oligomers, pB, in each of the two cases. Assuming that peaks at a threshold of three times the noise can be detected in high sensitivity 1H,15N HSQC spectra an upper bound for pB of 1.5% is calculated if slow exchange between oligomeric states is operative. Conversely, assuming that a shift of 0.05 ppm (15N) can be detected then pB is estimated to be less than 5% in the fast exchange regime. These bounds hold for both mutants at a protein concentration of 2.5 mM.


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Figure 3. Analysis of in-phase

15

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N CPMG dispersion profiles for U-15N L123R and H134R PFN1 at several

1kHz protein concentrations, 10oC, as for WT PFN1 in Figure 2. (A,B) ∆R2,eff and R2,eff for L123R PFN1, plotted at

two protein concentrations, with 15N CPMG profiles for Arg123 of L123R PFN1 at three concentrations shown in 1kHz panel C. (D) Effect of protein concentration on average R2 values ( R2,eff ) for the most rigid regions of the

protein (85 residues); the standard errors are less than 0.25 s-1 for wild-type PFN1 and less than 0.15 s-1 for both mutants.

1kHz Notably, and in a manner analogous to the WT protein, R2,eff values for amide 15N nuclei from


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either of the two mutants increase with protein concentration (Fig. 3B), as do the CPMG profiles that remain flat over the entire νCPMG range, illustrated for Arg123 of L123R-PFN1 in Fig. 3C. However, the 1kHz increase in R2,eff as a function of protein concentration for both L123R and H134R mutants is less than

what is observed for WT PFN1, Fig. 3D, reflecting the fact that the oligomerization process observed for the wild-type protein is inhibited in both mutants. As shown in Fig. 3D, the relationship between 1kHz and [PFN1] is linear for both variants of PFN1 considered, where rates have been averaged for R2,eff

approximately 80 residues that are associated with rigid portions of the protein. For these residues 1kHz rates report primarily on the overall tumbling of the molecule that is affected by solvent viscosity R2,eff

and, possibly, by non-specific protein-protein interactions. As expected, the slopes are smaller in the case of residues that reside in flexible regions where the amplitudes of amide bond vector motions increase (Fig. S4). Oligomerization of PFN1 proceeds through multiple steps The concentration dependent CPMG profiles from 6 residues of WT PFN1 (largest dispersions, see Materials and Methods), were globally fit to the Bloch-McConnell equations that describe the exchange of magnetization during the CPMG interval64. In addition to the ‘traditional’

15

N-based experiments

where in-phase magnetization profiles are measured63 (5 protein concentrations ranging from 0.5 to 3.0 mM), we also recorded both TROSY- and anti-TROSY CPMG experiments58 (2 protein concentrations, 1.0 and 1.8 mM) where the coherences of interest are slowly and rapidly relaxing

15

N elements,

respectively, that correspond to nitrogen transverse magnetization coupled to β and α amide proton spin states. Unlike the in-phase data set, where protonated samples were used for measurements, a perdeuterated sample was employed in the TROSY-/anti-TROSY-based experiments to minimize 1H spin flips. Initially, we considered a simple kinetic model,

⇌

, where the populated and sparse

states, A and B, respectively, interconvert with an effective exchange rate kex = kAB+kBA. We recognize


17

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that this simplest of models is in some sense a ‘virtual scheme’ because it does not explicitly take into account oligomerization, yet as we show below, it provides a starting framework by which to begin to make choices about different oligomerization models. The interconversion process could be well fit by this pseudo first-order exchange model (reduced χ2red ~ 1), Figs. 4A-C, that also included the contribution to the intrinsic R2 rates of the exchanging states from the viscosity vs protein concentration dependence (see Materials and Methods). Values of kAB and kBA were allowed to ‘float’ with concentration. Notably, kAB and pB, increase with [PFN1] in a reasonably linear manner, while kBA is essentially independent of protein concentration. Such a dependence would be expected for a dimerization reaction, yet in that case kAB(pB) would extrapolate to 0 s-1(0%) for [PFN1]=0 mM and this is not what is observed. Indeed, the utility of the pseudo first-order model considered here is that it shows clearly that a dimerization model cannot explain the concentration dependence of the dispersion profiles. However, it provides no physical insight into what the oligomerization process might be. This can only be obtained by considering more complex models, as described below.

Figure 4. Fits of concentration dependent 15N CPMG datasets for WT PFN1 (10oC) using a pseudo first-order


18

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Biochemistry

model of exchange. The CPMG profiles for three residues are shown in panels (A-C), with experimental data (best global fit) indicated by points (solid lines). In (A) and (B) dispersion profiles are shown for Gly3 and Leu123 at [PFN1] = 3, 2, 1.3, 1 and 0.5 mM, from the top to bottom (light gray to black), while in (C) TROSY- (TR, light-gray at the bottom), in-phase (CW, dark-gray in the middle) and anti-TROSY (AT, black at the top) 15N CPMG profiles are illustrated for Asn125, recorded on a U-[2H,15N]-PFN1 sample at [PFN1] = 1 mM (closed circles) and 1.8 mM (open diamonds). Fitted rates and populations are plotted in panels (D-F) as a function [PFN1]. The solid lines in (D-F) connect the fitted parameters as a function of concentration and are intended to guide the eye.

Having established that a model with higher order kinetics is necessary to describe the CPMG data, we initially considered several simple oligomerization schemes involving the interconversion between two states. These include a monomer-dimer (Fig. 5A) scheme where the relatively poor fit (χ2reduced = 3.3) confirms the results of the simple

⇌

model, discussed above. We have also

examined more complex models such as those involving monomer-trimer (Fig. S5) and monomertetramer (Fig. 5B) interconversions. These models were, however, not able to explain the recorded CPMG profiles, even in cases where the effect of viscosity on the microscopic rates was included (not shown). It is worth emphasizing that the poor fits are not a reflection of lack of convergence. Figure S6 shows correlations of extracted exchange parameters from fits of simulated data generated from the two k1   → A model, where excellent agreement is obtained with the input parameters. In contrast state 4 A ←  4 k −1

to fits involving two-state models, a more complex model involving consecutive oligomerization steps, !2

⇌

'

('

,2

⇌ )

()

* #,

Fig. 5C, was able to fit the data reasonably well (χ2reduced = 1.6) with the

fractional populations of A2 and A4, pA and pA , decreasing to 0% in the limit that [PFN1] = 0 mM, as 2

4

would be expected. In these fits the value of k-1 was reasonably well defined (~ 500 s-1, Fig. S7A) as were the equilibrium constants and hence populations (Fig. S7B), but other rate constants were found to be correlated and thus not well determined. Other models also gave reasonable fits, such as a scheme


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where dimers combine with monomers to form trimers (Fig. S5). Thus, while it is not possible to unambiguously determine the underlying kinetic model, it is clear that schemes with more than a single reaction step are required to fit the data, strongly supporting the notion that oligomerization of PFN1 proceeds through a series of consecutive binding events.

Figure 5. (A-C) Fits of CPMG data for WT PFN1, 10oC, using several models of oligomerization, as indicated. The models used to fit the data are shown on the left side, along with the reduced χ2. Shown also are the populations of states A2 and A4 as a function of [PFN1] (defined as p A = n[ An ] / [PT ] where PT is the total

protein concentration) derived from the fit in (C), !2

⇌ '

('

n

,2

⇌

)

()

*# ,

along with the errors in the

populations (shaded region), as obtained via a boot-strap analysis65,66. Concentrations of PFN1 used in the CPMG analysis are as in the legend to Figure 4.

Binding of poly-proline substrate shifts equilibrium to monomer The X-ray structure of PFN2 shows that the surface sequestered upon oligomerization overlaps with the ACS Paragon Plus Environment

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binding pockets for both actin and poly-proline and the µM binding affinities that have been reported previously67 indicate that binding of either partner would thus prevent oligomer formation. If a similar oligomeric structure to that observed in the solid state for PFN2 is adopted by PFN1 in solution then binding of a proline rich peptide to PFN1 would be expected to shift the equilibrium to the monomeric state, that would lead to quenching of exchange contributions to transverse relaxation rates. Fig. 6A plots ∆R2,eff values for WT PFN1 (2.0 mM) measured in the absence (black) and in the presence (grey) of a two-fold excess of proline-rich peptide. Binding of the peptide reduces ∆R2,eff values in the N- and C-terminal helices, that contain reporter residues of oligomerization, to close to 0 s-1, consistent with 1kHz inhibition of PFN1 oligomerization. In addition, R2,eff values for residues in the sample with peptide

1kHz are consistent with PFN1 binding to peptide as a monomer (Fig. 6B). The offset between R2,eff rates for

PFN1 with or without peptide in Fig. 6B reflects the fact that the oligomerization process observed for the wild-type protein is inhibited in the presence of the proline-rich substrate.


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1kHz Figure 6. Addition of a proline-rich peptide eliminates chemical exchange in WT PFN1. ∆R2,eff (A) and R2,eff (B)

are plotted vs residue based on measurements recorded on a 2 mM PFN1 sample with (grey) or without (black) 1kHz 3.7 mM peptide (10oC). Calculated R2,eff values for the 1:1 complex from extrapolation of rates measured for

the L123R mutant to 2 mM protein concentration and taking into account the increased molecular weight of the complex are shown with a solid grey line in (B) (see Materials and Methods). Dashed lines in (A) and (B) are meant to guide the eye.

Discussion Protein oligomerization plays a critically important role in both cellular function and misfunction.1,2,9 For example, many of the vital processes in the cell as related to homeostasis are carried out by oligomeric molecular machines, where the multi-subunit nature of the structure is clearly important functionally.68 Oligomeric conformers are also implicated in disease, in particular in neurodegeneration, where excursions from a populated monomeric state to higher order structures can lead to the formation


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Biochemistry

of toxic species.10 The continued development of NMR spin-relaxation experiments provides an avenue for investigating oligomerization at atomic resolution, generating mechanistic and physical chemical insights into this important process. Herein we have characterized homo-oligomerization of the actin binding protein PFN1 by analyzing

15

N CPMG relaxation dispersion profiles of the protein as a function of concentration. An

assumption in most analyses of CPMG relaxation dispersion data is that the intrinsic relaxation rates of spins in the interconverting states are identical. This is clearly incorrect in general, and in the present case pathologic. It is also important that the concentration dependence of the intrinsic relaxation rates be taken into account properly. Here we have kept intrinsic rates as fitting parameters, subject to the constraint that they vary with protein concentration on a per residue basis as measured for L123RPFN1 that does not show evidence of chemical exchange (i.e., flat dispersion profiles) but whose tumbling is sensitive to the increased viscosity and potentially to non-specific protein-protein interactions that accompany higher PFN1 protein concentrations (Fig. S4, see Materials and Methods). Figures 3C and D indicate that these effects can be considerable. In the present study we show that PFN1 undergoes a conformational exchange process involving the interconversion between an abundant monomer species and a sparsely populated oligomeric structure in solution. Notably, the dispersion data cannot be well fit to a simple oligomerization model,

⇌

, but rather must involve at least a pair of association steps. 15N in-

phase, TROSY, and anti-TROSY CPMG data can be reasonably well fit to a monomer-dimer-tetramer model although this is not the only scheme that is consistent with the dispersion profiles. We have attempted to use our concentration dependent NMR spin relaxation measurements to provide an estimate of the effective increase in particle size upon oligomerization in a model independent manner. Using a simple

⇌

scheme, average values of R2 for state B can be obtained

(A is the monomer) that, in principle, might provide insight into the size of B. Detailed simulations


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establish that such an approach works well for simple interconversion processes of the form,

⇌

,

but not for more complex, multi-process schemes. For example, consider a 3-step process such as ⇌

⇌

, where B and C are sparse states. Since dispersion profiles are critically sensitive to

chemical shift differences between spins in each of the states and if the majority of these happen to be between A and B then effective R2 values obtained for the rare state

′ from fits to the simple ′ ⇌

′

scheme are skewed to those in B. Thus, the extracted relaxation rates depend on chemical shift differences, so that, in general, reliable rates cannot be obtained. We have further attempted to use a ‘generic’ 3 state model to obtain information about molecular size but the rates so obtained are not well defined. Despite the lack of ‘proof’ from our experimental data there are, nevertheless, several lines of evidence to suggest that the sparse PFN1 state characterized in the present study is predominantly tetrameric. First, an X-ray structure of a related profilin, PFN2, that shares a high degree of sequence homology to PFN1, is tetrameric in the crystal56 and significant dispersion profiles are observed precisely in regions that form contacts between adjacent monomers in the solid state, including terminal helices α1 and α4 and Glu83 in the loop connecting β4 with β5. The fact that other residues within this loop do not show significant concentration dependent dispersion profiles may reflect the fact that only small chemical shift differences arise from the oligomerization process in this region and/or that the concentration independent exchange event in this region (Fig. 3) masks the one of interest in this study. We have shown that in solution the oligomeric structures can be disrupted by either of two mutants, L123R and H134R, which are at the interface between the monomers in the Xray model, supporting the notion that the oligomeric states characterized by NMR and X-ray methods are structurally similar. Second, the results from the present study establish that binding of ligands, such as poly-proline rich peptides, compete with PFN1 oligomerization. Dispersion profiles recorded in the presence of saturating amounts of a poly-proline peptide were flat, consistent with a significant


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Biochemistry

decrease in the population of the sparse oligomeric state. This is, again, in keeping with X-ray studies that establish that binding of such peptides would be expected to disrupt the interface between protomers in the reported tetrameric X-ray structure of PFN2 by preventing contacts between the Nterminal helix of one protomer and the C-terminus of the C-terminal helix from an adjacent molecule (Figure S1C). The X-ray structure also predicts that actin binding would lead to a similar result, although we have not tested this here due to the difficulties in working with monomeric actin. The fact that the PFN1 oligomer is only sparsely populated at mM concentrations ( p A ~7% for 4

[PFN1]=3mM) implies low association constants (Fig. S7); assuming a monomer-dimer-tetramer model a value of pA + pA ~ 50% is predicted when [PFN1] = 12 mM. Given the weak association it is 2

4

reasonable to ask whether the tetramer observed in solution is physiologically relevant. Profilins are amongst the most highly abundant proteins in the cytoplasim with an estimated concentration of 20 µM - 100 µM,69 and it is known that the local concentration of this protein is significantly enriched in proximity to proline-rich binding partners70 or in highly dynamic microfilament structures associated with cellular membranes in motile cells71,72. Notably, one calculation suggests that profilins can be concentrated to approximately 1 mM at polyproline sites inside cells.70 In addition, rapid modulation of microfilament architecture in response to external signals is thought to require elevated concentrations of profilins.72 It may be that at sites of high profilin concentration the protein is stored, at least partially, as homo-oligomers. Notably, the potential to oligomerize is not restricted to PFN1 or PFN2. Structures of a pair of additional profilins from plants showed dimerization contacts involving one of the oligomerization interfaces that has been identified in this work, involving helices α1 and α473. In summary, we have demonstrated that relaxation dispersion NMR approaches can be applied to study protein oligomerization and have the potential to discriminate between alternative models for the initial steps of the oligomerization pathway. In combination with results from other studies it is possible to establish the likely oligomerization state of sparse conformers that are formed. This has


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important implications in characterizing the energy landscapes of other aggregation prone protein systems, where protein association can lead to the formation of toxic species that have been implicated in a number of debilitating neurological diseases10.

Acknowledgements This work was supported by grants from the CIHR. L.E.K holds a Canada Research Chair in Biochemistry.

Supporting Information Figures of (i) putative PFN1 tetramer and stabilizing interfaces, (ii) HSQC spectra of WT, L123R and 1kHz 1kHz rates for H134R, (iv) R2,eff values as a function of protein H134R PFN1, (iii) ∆R2,eff and R2,eff

concentration, (v) Fits of CPMG data to additional exchange models, (vi) Demonstration of robust fitting of two-state exchange data, (vii) Reduced χ2 surface for k-1 from monomer-dimer-trimer model as well as contour plot of k-1/k1 vs k-2/k2,. A derivation of effective 3-site exchange rates for the monomer-dimer-tetramer exchange model is provided as well. This information is available free of charge via the internet at http://pubs.acs.org.


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