Letter pubs.acs.org/JPCL

Mapping the Native Conformational Ensemble of Proteins from a Combination of Simulations and Experiments: New Insight into the src-SH3 Domain Fabio Pietrucci,*,† Luca Mollica,‡ and Martin Blackledge‡ Institute of Theoretical Physics, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland Protein Dynamics and Flexibility, Institut de Biologie Structurale, CEA, CNRS, UJF-Grenoble 1, 41 Rue Jules Horowitz, F-38027 Grenoble, France

† ‡

S Supporting Information *

ABSTRACT: The biological function of a protein is strongly tied to the ensemble of three-dimensional conformations populated at physiological temperature, and dynamically transforming into each other. Experimental techniques such as nuclear magnetic resonance spectroscopy (NMR) provide a wealth of structural and dynamical information, which, in combination with an accurate atomic-level computational modeling, can disclose the details of protein behavior. We here propose a fast and efficient protocol employing molecular dynamics (MD) simulations and NMR chemical shifts, which allows one to reconstruct the detailed conformational ensemble of small globular proteins. In the case of the well-studied src-SH3 domain, we are able to obtain new important insight including the existence of a helical state in the RT loop and a pathway for single-file water diffusion in and out of the core. SECTION: Biophysical Chemistry and Biomolecules

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dynamics (MD) technique has proven a useful complement of NMR measurements by providing atomic-resolution structural models, which can help to characterize the atomic level dynamics of proteins.8,9 To be predictive and reliable, simulations must fulfill the requirements of being based on an accurate description of interatomic forces and of being able to access time scales comparable to those experimentally probed. Nowadays the computational cost limits the maximum time scale of explicit solvent simulations to a few microseconds, setting a major obstacle to a reliable and systematic understanding of conformational ensembles. In this work we critically compare different simulation techniques (plain MD, replica exchange,10,11 and bias exchange12) in their ability to reproduce observed chemical shifts of the src-SH3 protein domain. This results in the proposal of a fast and efficient protocol which can be applied to characterize the near-native structural ensemble of any globular protein of similar size, including possible large conformational changes and the detailed role of water. src-Homology-3 (SH3) domains are ubiquitous as they mediate a myriad of protein−protein interactions.13,14 Despite a low sequence homology, these domains share a very welldefined three-dimensional structure characterized by a β-barrel

ignificant structural heterogeneity in the conformational ensembles of proteins can play an important role in fundamental processes of the cell life like molecular recognition,1 as well as in deleterious ones such as misfolding and amyloid aggregation.2 In the last two decades, many experimental techniques have clarified the importance and complexity of protein structure and dynamics: NMR and X-rays diffraction are the traditional techniques for determining the atomic level structure of proteins and complexes, whereas others such as circular dichroism, fluorescence, and small angle X-rays and neutron spectroscopy have been used to characterize average properties and molecular events, including the shape of complexes, structural transitions, and binding. NMR in particular has the advantage of being able to provide both structural and dynamic information at atomic level: chemical shifts,3 nuclear Overhauser effect (NOE) and residual dipolar couplings (RDCs)4 are the main experimental source of structural information, whereas spin relaxation and residual dipolar couplings4,5 reflect protein motions on time scales respectively in the picosecond−nanosecond and in the nanosecond−millisecond range. The chemical shift, the most easily accessible NMR observable, is an extremely powerful tool for the analysis of the secondary structure content of proteins3 and of local dynamics on a very broad range of time scales6,7 that can complement the information obtained by means of relaxation and RDCs. Computer simulations have an important role in combination with this kind of experiment: in particular, the molecular © 2013 American Chemical Society

Received: April 12, 2013 Accepted: May 23, 2013 Published: May 23, 2013 1943

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fold, with two-stranded and three-stranded β sheets packed orthogonally to form a single hydrophobic core, and a small 310 helix close to the C-terminus. A remarkable feature of SH3 domains is the long and largely unstructured RT loop, which has an important function in binding proline-rich peptides. Early kinetic experiments on src-SH3 were interpreted on the basis of simple two-state kinetics.15 Extensive φ-value analysis of point mutations provided the picture of a highly polarized transition state for folding:16 the distal β hairpin (residues 43− 57) appears as the most ordered part at the transition state, followed by the rest of the central three-standed β sheet (residues 29−42). On the contrary, the two terminal strands, the 310 helix and the RT loop, appear as mostly unstructured at the transition state, adopting the native conformation in the last stage of the folding process. The transition state ensemble seems to be conserved across different SH3 sequences.17,18 However, several recent experiments suggest the dynamics to be more rich and complex than previously expected: circular dichroism, fluorescence, and X-ray solution scattering showed that both src- and Fyn-SH3 have a compact α-helical intermediate on the folding pathway.19,20 NMR further indicates that src-SH3 presents transient helicity in the unfolded state,21 while acid denaturation can turn α-spectrin-22 and hNck2-SH3 into stable α-helical conformations.23 Hydrogen exchange and mass spectrometry indicate sequencedependent dynamical behavior in the SH3 family.24 Above all, the most prominent sequence-dependent feature is clearly the specificity in the binding to polyproline rich peptides.25,14 Aiming to understand the conformational ensemble of srcSH3 in detail, we performed extensive MD simulations26 in explicit solvent (employing the amber0327 force field for the protein and TIP3P28 for water) starting from the PDB structure 1SRL, obtained from NMR experiments at T = 298 K and pH = 6.29 We employed three different kinds of simulations, to assess their relative performance and accuracy. The first simulation is plain equilibrium MD (total length 560 ns). Under the ergodic hypothesis, a long enough trajectory of this kind should allow accessing all the relevant conformational space of the system. The second simulation is replica exchange MD (REMD)10,11 including 88 replicas (corresponding to 88 × 70 ns = 6160 ns). In this approach, the sampling is enhanced by the diffusion of trajectories in temperature space from the coldest (here T = 300 K) to the hottest (T = 500 K) replica and back. The third technique is bias exchange (BE)12,30 including eight replicas (for a total of 8 × 70 ns = 560 ns). This technique is a combination of replica exchange and metadynamics31 that enhances the sampling of rare events by exchanging different history-dependent bias potentials among replicas kept at the same temperature (T = 300 K). Each replica is biased along a different collective variable, including the root-mean-square deviation (RMSD) distance from the native state, the total amount of α or β (either parallel or antiparallel) secondary structure,32 the total number of contacts among hydrophobic side chain carbon atoms, and the number of water molecules in the protein core. These variables were chosen to capture the putative slow degrees of freedom associated with conformational changes. For computational efficiency, in REMD and BE simulations, we restricted the possible deformations from the native state to within 4 Å RMSD: this threshold has been chosen because the plain MD simulation almost never gets farther from the native state (Figure S1), and we further explicitly checked that more distorted configurations do not sizably improve the agreement of plain MD with measured

NMR chemical shifts (all details can be found in the Supporting Information (SI)). We assessed the degree of statistical convergence of both the plain MD and REMD simulations by performing a cluster analysis of the protein structures explored, and by comparing the population of each cluster in the first half of the trajectory with the population in the second half (similarity between the estimates would indicate that the populations are reproducible and hence reliable). Remarkably, in the case of plain MD the long simulation time of 560 ns is definitely insufficient to reliably sample the native conformational ensemble of the protein. In fact, few clusters are explored at all compared to REMD (see Figure S3), and even within 10 kJ/mol from the most stable state, several clusters have a large uncertainty on the free energy. The REMD simulation displays instead signs of a more extended and statistically reliable exploration, albeit at a computational cost 1 order of magnitude larger. Within 10 kJ/ mol from the minimum, the free energy of almost 20 clusters can be assigned with a moderate uncertainty (<2 kBT, Figure S3); however, there are still several structures whose population is not reliable. In fact, the average number of 1.7 round-trips per replica suggests a rather extensive but not exhaustive exploration (trajectories of shorter duration show a much poorer statistics). The convergence of the BE simulation, due to its out-ofequilibrium nature, cannot be estimated in the same way as above. Instead, the convergence must be evaluated by analyzing the time evolution of the one-dimensional bias profiles:12,33 for each replica in the last 40 ns of simulation, the bias profile fluctuates around a stable free energy estimator, therefore this time window contains a statistically converged sampling (see SI Figure S4). We employed a weighted-histogram approach34,35 to reconstruct the equilibrium free energy landscape of the system as a function of five collective variables, i.e., the RMSD distance from the native structure, the total α-helical or antiparallel β content, the total number of hydrophobic contacts, and the number of water molecules penetrating in the protein core. Two-dimensional projections are shown in Figure 1, and compared with plain MD and REMD (in the latter cases, directly estimated from populations: F = −kBT log P). Within 20 kJ/mol from the free energy minimum, rather large conformational changes occur, featuring either a significant increase of α-helical content or a significant decrease of β content (not both simultaneously). Inclusion of few water molecules in the hydrophobic core of the protein increases the free energy by more than 10 kJ/mol (see Figure 5). However, this rich palette of conformational changes is captured only by the BE simulation: plain MD and, remarkably, also the much longer REMD simulation, are able to sample only a reduced conformational space. The conformational ensemble reconstructed by each type of simulation is validated by assessing its ability to reproduce observed NMR chemical shifts.36 For each structure of the ensemble, we calculated the expected 13C, 15N, and 1H backbone chemical shifts using the program SHIFTX.37 For comparison, we also performed the calculation for the crystallographic structure (1SRL). Finally, for all the nuclei, we evaluated the RMSD between the chemical shifts estimated from the models and the experimental ones. The inclusion of dynamics improves the reproduction of experimental data with respect to the static crystallographic structure (Table 1): however, plain MD provides a significantly lower accuracy 1944

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Figure 1. Two-dimensional free energy surfaces as a function of different collective variables.

Table 1. Average Root Mean Square Deviations (in ppm) between Calculated (Using SHIFTX) and Experimental CSsa nucleus

1SRL

plain MD (560 ns)

REMD (6160 ns)

BE (560 ns)

C′ Cα Cβ HN N

1.272 1.683 1.736 0.784 4.411

1.274 1.335 1.169 0.541 3.475

1.135 1.246 1.270 0.535 3.272

1.131 1.192 1.161 0.490 3.251

a

Figure 2. Secondary chemical shifts (computed with reference to random coil values) for the BE simulation (black) and for the crystallographic structure 1SRL (blue) with respect to experimental data (red bars).

and n-src (residues 37−42) loops. Raising in free energy, conformation B in Figure 3 is similar to A, but lays 3.7 kJ/mol higher (corresponding to a population of about 5%) and is characterized by the helical secondary structure in the RT loop. To some extent, this conformational change is displayed also by the REMD simulation (Figure 1). It is interesting to note that a tendency to form helical structure in the RT loop region has been inferred from recent NMR experiments on unfolded srcSH3.21 Still higher in free energy, conformation C (6.3 kJ/mol) is characterized by the unfolding of the fourth β strand (residues 52−57). Remarkably, conformation D is very similar to A from the point of view of RMSD (1.1 Å excluding n-src and RT loops) and secondary structure, but it features 14% less hydrophobic contacts and about three/four water molecules close to the protein core, which explains the free energy 11.3 kJ/mol higher than A. Finally, substantial unfolding of the three-stranded β hairpin requires more than 20 kJ/mol (E in Figure 3). Overall, the C-terminus is very flexible, easily loosing β conformation as well as contacts with the N-terminus, in agreement with φ-value analysis, which suggested that it is unstructured in the transition state for folding.16 Figure 4 displays a statistical analysis of secondary structure (according to the dssp definition39) along the sequence within the equilibrium conformational ensemble. Explicit solvent simulations offer the possibility of precisely tracking the positions and diffusion pathways of individual water molecules: in the case of SH3, water can penetrate the

Values are reported for each of the simulations described in the text.

compared to REMD and BE. In particular, the RMSDs for all the nuclei reveal that BE is the enhanced sampling technique that better reproduces experimental data: REMD offers an inferior accuracy at a 10 times larger computational cost. The agreement between experimental data, crystallographic structure, and BE simulation is displayed in Figure 2 for the three most sensitive reporters of dynamics and secondary structure among the backbone nuclei (the absolute chemical shift is reported in Figure S5). We note that the agreement between experimental and computed C′ chemical shifts is the one less improved by the inclusion of dynamics (Table 1, Figure S5), even if the RMSD values are always lower than the intrinsic SHIFTX error for this nucleus.37 In order to check the dependency of the results on the engine used for the prediction of chemical shift, we also employed the software SPARTA+,38 obtaining qualitatively the same outcome (see SI). Due to a better agreement with experimental data, the analysis reported in the following will be focused on BE simulations. The minimum of the free energy landscape (A in Figure 3) and the geometry originally reconstructed in ref 29 look very similar, with an excellent reproduction of secondary structure elements: the RMSD over all the backbone plus Cβ is 3 Å, dropping to 1.8 Å excluding the flexible RT (residues 14−28) 1945

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Figure 5. Free energy profile for the penetration of water molecules in the protein core, with selected representative structures.

fundamental help for the process: the solvent molecules bridge segments of the protein backbone as well as side chains (i.e., Asp23, Ser25, and Gln52) through the formation of hydrogen bonds. Then, a single chain of water molecules progressively penetrates the hydrophobic core, traverses it, and finds its way out, opening a passage between the N- and C-terminus. Eventually, a seamless chain of at least 6−7 water molecules crosses the core through a tunnel, at a free energy penalty of about 20 kJ/mol (occasionally, non-chain-like configurations are also observed). Reversing the process, a corresponding desolvation of the core could be a relevant step in the final part of the protein folding mechanism,40 as also suggested from previous folding simulations.41 Noteworthy, the water-entry (and exit) region corresponds to the region of the SH3 domain involved in molecular recognition, by binding polyproline-rich peptides, where Asp23 acts as specificity element.25 This is suggestive of a possible involvement of small water clusters in the binding process of partner peptides. In conclusion, this work introduces a computational protocol for the rapid and accurate “in silico” reconstruction of the native conformational ensemble of globular proteins. NMR chemical shifts are interpreted based on the structures obtained from explicit solvent atomistic simulations. An enhanced sampling scheme is proposed, which presents a cheap computational cost (in the present case, 8 days on 32 Intel Xeon 2.66 GHz cores) and employs a general-purpose (thus transferable to any globular protein) set of collective variables encompassing changes in secondary structure, tertiary structure, and water localization. Our approach can be routinely employed in combination with NMR measurements to characterize the details of structure and dynamics, which are crucial to understand protein stability, function, and interaction with partner molecules.

Figure 3. Representative conformations from the BE simulation, and relative free energy values.

Figure 4. Probability of secondary structure elements along the sequence.

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protein hydrophobic core following a well-defined entry pathway (Figure 5) at the price of a steep increase in free energy (the most stable structures have a dry core). First, water penetrates in the groove between the fourth β strand (Gln52Pro57) and the proximal part of the RT loop (Tyr14, Asp15, Tyr16, Asp23, Leu24, Ser25, Phe26), whose flexibility is of

ASSOCIATED CONTENT

S Supporting Information *

Detailed description of all the simulations and of the comparison of predicted and experimental chemical shifts. This material is available free of charge via the Internet at http://pubs.acs.org. 1946

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AUTHOR INFORMATION

Corresponding Author

*E-mail: fabio.pietrucci@epfl.ch; tel. +41(0)216931964; fax +41(0)216935419. Notes

The authors declare no competing financial interest.

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