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Characterization of the Free-Energy Landscapes of Proteins by NMR-Guided Metadynamics

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Characterization of the Free-Energy Landscapes of Proteins by NMR-Guided Metadynamics Characterization of the free-energy landscapes of proteins by NMR-guided metadynamics Daniele Granataa, Carlo Camillonib, Michele Vendruscolob,1, and Alessandro Laioa,1 aInternational School for Advanced Studies (SISSA), Trieste 34136, Italy; and bDepartment of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom Edited by José N. Onuchic, Rice University, Houston, TX, and approved February 21, 2013 (received for review October 21, 2012) The use of free-energy landscapes rationalizes a wide range of aspects experimental information is exploited to create an additional term of protein behavior by providing a clear illustration of the different in the force field that penalizes the deviations from the measured states accessible to these molecules, as well as of their populations and values, thus restraining the sampling of the conformational space to pathways of interconversion. The determination of the free-energy regions close to those observed experimentally (25). landscapes of proteins by computational methods is, however, very Here, we propose an alternative strategy to use experimental challenging as it requires an extensive sampling of their conforma- information to aid molecular dynamics simulations. In this tional spaces. We describe here a technique to achieve this goal with approach, the measured parameters are not used as structural relatively limited computational resources by incorporating nuclear restraints in the simulations but rather to build collective varia- magnetic resonance (NMR) chemical shifts as collective variables in bles (CVs) within metadynamics calculations. In metadynamics (18, 19), the conformational sampling is enhanced by construct- metadynamics simulations. As in this approach the chemical shifts are ing a time-dependent potential that discourages the explorations not used as structural restraints, the resulting free-energy landscapes fi fi of regions already visited in terms of speci c functions of the correspond to the force elds used in the simulations. We illustrate this atomic coordinates called collective variables. In this work, we approach in the case of the third Ig-binding domain of protein G from show that NMR chemical shifts may be used as collective varia- streptococcal bacteria (GB3). Our calculations reveal the existence of bles to guide the sampling of conformational space in molecular a folding intermediate of GB3 with nonnative structural elements. dynamics simulations. Furthermore, the availability of the free-energy landscape enables Because the method that we discuss here enables the confor- the folding mechanism of GB3 to be elucidated by analyzing the mational sampling to be enhanced without modifying the force BIOPHYSICS AND conformational ensembles corresponding to the native, intermedi- field through the introduction of structural restraints, it provides COMPUTATIONAL BIOLOGY ate, and unfolded states, as well as the transition states between the statistical weights corresponding to the force field used in the them. Taken together, these results show that, by incorporating molecular dynamics simulations. In the present implementation, experimental data as collective variables in metadynamics simula- we used the bias-exchange metadynamics (BE-META) method tions, it is possible to enhance the sampling efficiency by two or (33), an enhanced sampling technique that allows the recon- more orders of magnitude with respect to standard molecular struction of free energy as a simultaneous function of several dynamics simulations, and thus to estimate free-energy differences variables. By using this approach, we computed the free-energy among the different states of a protein with a kBT accuracy by gen- landscape in explicit solvent of the third Ig-binding domain of erating trajectories of just a few microseconds. streptococcal protein G (GB3). Our calculations predict the native fold as the lowest free-energy minimum, also identifying the NMR spectroscopy | protein folding | protein structure determination | presence of an on-pathway compact intermediate with nonnative bias-exchange metadynamics | enhanced sampling structural elements. In addition, we provide a detailed atomistic picture of the structure at the folding barrier, which shares with the native state a fraction of the secondary structure elements. n the past two decades, a series of experimental and theoretical These results have been obtained using relatively limited com- Iadvances has made it possible to obtain a detailed un- putational resources. Through the advanced sampling method derstanding of the molecular mechanisms underlying the folding that we discuss, the total simulation time required to reach con- process (1–6). With the increasing power of computers (7), as fi vergence in the free energy estimates was 380 ns on seven repli- well as the improvements in force elds (8, 9), atomistic simu- cas, which is about three orders of magnitude less than the typical lations are also becoming increasingly important because they timescale required to fold similar proteins (34). We thus antici- can generate highly detailed descriptions of the motions of pate that the technique introduced here will allow the determi- proteins (10–12). A supercomputer specifically designed to in- ’ nation of the free-energy landscapes of a wide range of proteins tegrate Newton s equations of motion of proteins (7) recently in cases in which NMR chemical shifts are available. broke the millisecond time barrier. This achievement has allowed the direct calculation of repeated folding events for several fast- Results and Discussion folding proteins (13) and the characterization of molecular We performed molecular dynamics simulations of GB3 at 330 K, mechanisms underlying protein dynamics and function (14). using the Gromacs 4.5.3 package (35) and the AMBER99SB- Reliable descriptions of the folding process have also been ILDN force field (8). To enhance conformational sampling, we obtained by exploiting enhanced sampling techniques (15, 16), used the BE-META scheme (33) with seven replicas. We started including replica-exchange molecular dynamics (17), metady- the simulations from a structure at 5.7 Å from the reference namics (18, 19), and distributed computing (20). structure [Protein Data Bank (PDB) ID code 2OED (36)] and It has also been realized that by bringing together experimental ran them for a total of 380 × 7 ns. For each replica, we used measurements and computational methods, it is possible to expand the range of problems that may be addressed (4, 21–24). For ex- ample, by incorporating structural information relative to transition states (TSs; ϕ values) as structural restraints in molecular dynamics Author contributions: D.G., C.C., M.V., and A.L. designed research, performed research, simulations, it is possible to obtain structural models of these contributed new reagents/analytic tools, analyzed data, and wrote the paper. transiently populated states (25, 26), as well as of native (27) and The authors declare no conflict of interest. nonnative intermediates (28) explored during the folding process. This article is a PNAS Direct Submission. By applying this strategy to structural parameters measured by 1To whom correspondence may be addressed. E-mail: [email protected] or [email protected]. NMR spectroscopy, one can determine the atomic-level structures This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. and dynamics of proteins (29–32). In these approaches, the 1073/pnas.1218350110/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1218350110 PNAS Early Edition | 1of6 Downloaded by guest on September 24, 2021 a different metadynamics (18) history-dependent potential acting the reference structure has an rmsd of 2.7 Å (Fig. 2, Inset B). After on a different CV (Methods and SI Text). Three CVs act at the 50 ns, the rmsd starts increasing progressively (red line) and the secondary structure level by quantifying, respectively, the frac- folded state is not explored at all. By contrast, the simulation with tion of α-helical, antiparallel, and parallel β-sheet content of the the CamShift CV visits the folded state several times, with several protein. Three other CVs act at the tertiary structure level by unfolding–refolding events. During the first 50 ns, the latter sim- biasing the number of hydrophobic contacts and the orientation ulation not only performed better, reaching an rmsd of 2.5 Å, but it of the side-chain dihedral angles χ1 and χ2 for hydrophobic and also formed the correct secondary and tertiary contacts, particu- polar side chains. The seventh CV, called “CamShift,” measures larly the ones involved in forming the first β-hairpin (Fig. 2, Inset the difference between the experimental and calculated chemical A), which is critical for the folding of this protein (38, 39). The shifts, which were obtained using the CamShift method (37) fraction of native contacts also was systematically higher in the (Methods and SI Text). Our results indicate that in the approach simulation using the CamShift CV (Fig. 2, Inset C). These results we present here, this last variable is essential to fold GB3 and indicate that the folding events observed later in the simulation are reach convergence readily in the free-energy calculations. a result of the systematic bias induced by the CamShift CV toward the correct local topology in the native state. Folding of GB3 Using Chemical Shifts as CVs. The method that we introduce in this work makes it possible to visit efficiently a wide Thermodynamics
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