Significantly Improved Protein Folding Thermodynamics Using a Dispersion-Corrected Water Model and a New Residue-Specific Force Field

Transcription

1 Significantly Improved Protein Folding Thermodynamics Using a Dispersion-Corrected Water Model and a New Residue-Specific Force Field Hao-Nan Wu, Fan Jiang, *, and Yun-Dong Wu, Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School, Shenzhen , China College of Chemistry and Molecular Engineering, Peking University, Beijing , China * jiangfan@pku.edu.cn Supporting Information 1

2 1. Simulated Systems and Simulation Settings. A variety of peptides and proteins were chosen in this study, including dipeptides, the polyalanine-based peptides A14 and AQ15, the Trp-cage miniprotein (PDB: 1L2Y), Trpzip-2 (PDB: 1LE1), the GB1 hairpin (PDB: 1PGB, residues 41-56) and a few intrinsically disordered peptides: the GS10 peptide, arginine-rich motif of HIV-1 Rev, histatin 5 and the RS peptide. Their sequences are given below: A14 Ac-Ala14-NHMe AQ15 Ac-(AAQAA)3-NH2 Trp-cage NLYIQ WLKDG GPSSG RPPPS Trpzip-2 SWTWE NGKWT WK-NH2 GB1 hairpin GEWTY DDATK TFTVT E GS10 GSGSG SGSGS GSGSG SGSGS HIV-1 Rev GAMAT RQARR NRRRR WRERQ RAAAA R Histatin 5 DSHAK RHHGY KRKFH EKHHS HRGY RS GAMGP SYGRS RSRSR SRSRS RSRS We also simulated two well-folded globular proteins: ubiquitin (PDB: 1UBQ) and hen egg white lysozyme (HEWL, PDB: 6LYT). The settings of simulations are shown in Table S1. Each peptide was solvated in explicit water molecules and ions (Cl or Na+) were added for charge neutralization. Replica-exchange molecular dynamics (REMD) simulations were used to enhance the conformational sampling. The initial periodic box volume was equilibrated by using a 3 ns NPT MD simulation at 300 K and 1 atm. The initial structures for each replica were drawn at regular intervals from a 10 ns NVT MD trajectory at 500 K unless otherwise specified. It should be noted that all REMD simulations were carried out under NVT conditions, which means higher pressures would appear at relatively high temperatures. However, Garcia et al. 1 3 and Best et al. 4 have shown that the effects of pressure on the folding equilibrium of both α- and β- peptides are very small. Thus, the melting curves can be fitted to obtain the folding thermodynamic data. GROMACS version was used for all simulations. 5 Electrostatics were treated using the particle-mesh Ewald (PME) method with a real-space cutoff of 0.9 nm. Van der Waals interactions were cut off at 0.9 nm with the long-range dispersion correction for energy and pressure. All bonds involving hydrogen were constrained using LINCS. 6 For all folding/unfolding simulations, we used the hydrogen mass repartitioning (HMR) method described previously. 7 All hydrogen masses were increased to 3 amu, while the masses of the 2

3 bonded non-hydrogen atoms were reduced to keep the total system mass unaltered. Thus, for these relatively time-consuming simulations, an integration time step of 4 fs can be used. In other simulations, including simulations of dipeptides, IDPs and folded globular proteins, HMR was not used and time step was set to 3 fs. Table S1. Peptides and proteins simulated in this work, along with the settings of simulations peptide/protein N aa t trj (ns) a N replica T (K) dipeptide A AQ Trp-cage Trpzip GB1 β-hairpin GS HIV-1 Rev b 298 Histatin b 300 RS b 298 a trajectory length per replica (for REMD), simulation time was depended on the system and water model. b three independent trajectories were simulated. 2. Trajectory Analyses. For simulations of dipeptides, the trajectory from the lowest-temperature replica was used for statistical analysis, with the first 20 ns discarded. For folding simulations of α-helical peptides, β-hairpins, and the Trp-cage miniprotein, the first 40 ns, 200 ns, and 400ns (per replica) were discarded respectively, unless otherwise specified. To obtain the fraction of helix formation from the simulations, three or more successive residues with backbone ϕ, ψ in the range 105 ϕ 25 and 77 ψ 3 are defined to form the α-helix. The fraction of folded conformations in each replica was calculated as the fraction of structures within backbone root-mean-square deviation (RMSD) of 2.2, 2.0, and 2.0 Å for Trp-cage, Trpzip-2 and the GB1 hairpin respectively, similar to the 3

4 criteria in our previous work. 8 For residue-specific force fields, the representative structure of the most populated native-like cluster from ~300 K replica was used as the reference to calculate the RMSDs. For other force fields, the experimental structures were used, as the most populated clusters generally differ from the native structures. The single linkage algorithm implemented in Gromacs was used in clustering analysis, with backbone RMSD cutoff of 0.8, 0.9 and 0.7 Å for Trp-cage, Trpzip-2 and the GB1 hairpin, respectively. On the basis of the two-state model of protein folding, the melting curve of the equilibrium folded fraction ff(t) was fitted to obtain the folding free energy: G F (T) = RTln [ f F(T) 1 f F (T) ] (1) where ΔGF(T)= ΔHF TΔSF is the difference between the free energy of the folded state and that of the unfolded state. ΔHF and ΔSF were the folding enthalpy and entropy, respectively. At the melting temperature Tm (folding midpoint), Tm = ΔHF/ΔSF. We assume ΔCp 0 in the fitting. The 3 JHNHα coupling constant is related to the dihedral angle ϕ distribution by the empirical Karplus equation: 9 J(φ) = A cos 2 (φ + Δ) + B cos(φ + Δ) + C (2) where A, B, C, and are Karplus parameters. For all calculated 3 JHNHα coupling constants in this work, the Karplus parameter set from Vo geli et al. (2007) was employed. 10 The Gromacs utility g_rotacf was used to calculate the N H order parameters (S 2 ) by rotational autocorrelation function of N-H vector: S 2 = lim t 3 2 [r NH(t) r NH (0)] (3) Experimental S 2 order parameter data for ubiquitin and HEWL were obtained from the BioMagResBank (BMRB) Residue-Specific Force Fields. We recently developed two residue-specific force fields, RSFF1 12 and RSFF2, 8 as improvements of OPLS-AA/L 13,14 and AMBER-ff99SB 15 force fields, by optimizing the backbone (ϕ, ψ) and side-chain χ parameters based on our statistical analysis of the PDB coil library. 16,17 In these new force fields, all bond stretching and angle bending potentials were adopted from the original force field without modifications. Besides, all the atomic Lennard- Jones σ and ε parameters and atomic charges were also not modified. On the other hand, all rotatable dihedral-angles for each amino acid residue were optimized independently from the 4

5 parent force fields. Their parameters were optimized such that the ϕ, ψ, χ distributions from REMD simulations of dipeptides (blocked amino acids) fit very well with the corresponding statistical ϕ, ψ, χ distributions. In addition, special L-J parameters were used for some local non-bonded (1-5, 1-6) interactions to better describe the coupling between neighboring dihedral-angles. RSFF1 and RSFF2 are optimized with the TIP4P-Ew and TIP3P water model, respectively. More details can be found in our previous work. 8,12 The similarity coefficient (S) of two ϕ, ψ distributions n1(ϕ, ψ) and n2(ϕ, ψ) was calculated according to: S = cosα = n 1 n 2 n 1 n 2 = where each summation is over all ϕ, ψ grids. [n 1(φ,ψ) n 2 (φ,ψ)] n 1 (φ,ψ) 2 n 2 (φ,ψ) 2 (4) Interestingly, the obtained ϕ, ψ distributions for each of the three side-chain 1 rotamers keep nearly the same when the water model is changed from TIP3P to TIP4P-D and the three rotamers give quite different backbone conformational features (Figure S1). In most cases, the similarity coefficients (S) between the ϕ, ψ plots from the original and new water models are > 0.99 (Figure S2A, Table S2). It is worth noting that almost all cases of S < 0.99 were for less populated rotamer(s). 17 We also calculated the 3 JHNHα coupling constant, which is a popular NMR observable for force field validation, from these dipeptide simulations. For the 19 AA dipeptides (excluding Pro), their 3 JHNHα coupling constants have also been measured by NMR experiments. 18 As shown in Figure S2B, TIP4P-D gives near the same 3 JHNHα values compared with those of TIP3P, as the Pearson correlation coefficient (R) is Both water models give 3 JHNHα values in excellent agreement with experimental data (Figure S4). When the AMBER-99SBildn force field was used, the simulations using TIP4P-D also give 3 JHNHα quite similar to those from the TIP3P simulations, with the correlation coefficient of R = between the two water models, but in worse agreement with the experimental data (Figure S4). These results indicate that the new water model does not significantly change the local conformational preferences, which is consistent with previous work of Florova et al. 19 5

6 Reference (1) Paschek, D.; Gnanakaran, S.; Garcia, A. E. Simulations of the Pressure and Temperature Unfolding of an α-helical Peptide. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, (2) Paschek, D.; Hempel, S.; Garcia, A. E. Computing the Stability Diagram of the Trp-Cage Miniprotein. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, (3) Paschek, D.; García, A. E. Reversible Temperature and Pressure Denaturation of a Protein Fragment: A Replica Exchange Molecular Dynamics Simulation Study. Phys. Rev. Lett. 2004, 93, (4) Best, R. B.; Hummer, G. Optimized Molecular Dynamics Force Fields Applied to the Helix-Coil Transition of Polypeptides. J. Phys. Chem. B 2009, 113, (5) Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 2008, 4, (6) Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. G. E. M. LINCS: A Linear Constraint Solver for Molecular Simulations. J. Comput. Chem. 1997, 18, (7) Hopkins, C. W.; Le Grand, S.; Walker, R. C.; Roitberg, A. E. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. J. Chem. Theory Comput. 2015, 11, (8) Zhou, C. Y.; Jiang, F.; Wu, Y. D. Residue-Specific Force Field Based on Protein Coil Library. RSFF2: Modification of AMBER ff99sb. J. Phys. Chem. B 2015, 119, (9) Karplus, M. Contact Electron Spin Coupling of Nuclear Magnetic Moments. J. Chem. Phys. 1959, 30, (10) Vögeli, B.; Ying, J.; Grishaev, A.; Bax, A. Limits on Variations in Protein Backbone Dynamics from Precise Measurements of Scalar Couplings. J. Am. Chem. Soc. 2007, 129, (11) Ulrich, E. L.; Akutsu, H.; Doreleijers, J. F.; Harano, Y.; Ioannidis, Y. E.; Lin, J.; Livny, M.; Mading, S.; Maziuk, D.; Miller, Z.; et al. BioMagResBank. Nucleic Acids Res. 2007, 36, D402 D408. (12) Jiang, F.; Zhou, C.; Wu, Y. Residue-Specific Force Field Based on the Protein Coil Library. RSFF1: Modification of OPLS-AA/L. J. Phys. Chem. B 2014, 118,

7 (13) Kaminski, G. a; Friesner, R. a; Tirado-Rives, J.; Jorgensen, W. L. Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides. J. Phys. Chem. B 2001, 105, (14) Jorgensen, W. L.; Maxwell, D. S.; Tirado-Rives, J. Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids. J. Am. Chem. Soc. 1996, 118, (15) Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of Multiple Amber Force Fields and Development of Improved Protein Backbone Parameters. Proteins: Struct., Funct., Genet. 2006, 65, (16) Jiang, F.; Han, W.; Wu, Y.-D. The Intrinsic Conformational Features of Amino Acids from a Protein Coil Library and Their Applications in Force Field Development. Phys. Chem. Chem. Phys. 2013, 15, (17) Jiang, F.; Han, W.; Wu, Y. Influence of Side Chain Conformations on Local Conformational Features of Amino Acids and Implication for Force Field Development. J. Phys. Chem. B 2010, 114, (18) Avbelj, F.; Grdadolnik, S. G.; Grdadolnik, J.; Baldwin, R. L. Intrinsic Backbone Preferences Are Fully Present in Blocked Amino Acids. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, (19) Florova, P.; Sklenovsky, P.; Bana š, P.; Otyepka, M. Explicit Water Models Affect the Specific Solvation and Dynamics of Unfolded Peptides While the Conformational Behavior and Flexibility of Folded Peptides Remain Intact. J. Chem. Theory Comput. 2010, 6, (20) Schneidman-Duhovny, D.; Hammel, M.; Tainer, J. A.; Sali, A. Accurate SAXS Profile Computation and Its Assessment by Contrast Variation Experiments. Biophys. J. 2013, 105, (21) Henriques, J.; Cragnell, C.; Skepö, M. Molecular Dynamics Simulations of Intrinsically Disordered Proteins: Force Field Evaluation and Comparison with Experiment. J. Chem. Theory Comput. 2015, 11, (22) Rauscher, S.; Gapsys, V.; Gajda, M. J.; Zweckstetter, M.; de Groot, B. L.; Grubmüller, 7

8 H. Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: A Comparison to Experiment. J. Chem. Theory Comput. 2015, 11, (23) Casu, F.; Duggan, B. M.; Hennig, M. The Arginine-Rich RNA-Binding Motif of HIV-1 Rev Is Intrinsically Disordered and Folds upon RRE Binding. Biophys. J. 2013, 105,

9 Figure S1. χ1-dependent ϕ, ψ plots for all AAs from the dipeptide simulations using RSFF2/TIP4P-D. (χ1-dependent ϕ, ψ plots using RSFF2/TIP3P are showed in our previous work) 8 9

10 Table S2. Similarity coefficients (S) between ϕ, ψ distributions from the RSFF2/TIP3P and RSFF2/TIP4P-D simulations of the 20 amino acid dipeptides total g+ t g- A G P E Q K R M L F Y H W C V I S T D N avg

11 Figure S2. (A) Similarity coefficients (S) between ϕ, ψ distributions from the RSFF2/TIP3P and RSFF2/TIP4P-D simulations of the 20 amino acid dipeptides. (B) Calculated 3 JHNHα couplings of 19 dipeptides (without Pro) from the RSFF2 simulations using TIP3P and TIP4P- D water models. 11

12 Table S3. Folding thermodynamics data a (ΔGf) from experiments and simulations systems T(K) expt. RSFF1 RSFF2 T4PEw T4PD ΔΔG T3P T4PEw T4PD ΔΔG 1 b ΔΔG 2 c A Trp-cage Trpzip-2 GB1 hairpin ~ ~ a ΔG f in kj/mol. b The difference between the relative free energy of the TIP4P-D and TIP3P water model. c The difference between the relative free energy of the TIP4P-D and TIP4P-Ew water model. 12

13 Figure S3. Folding free energy (ΔGf) from experiments and simulations. 13

14 Figure S4. Calculated 3 JHNHα couplings of 19 dipeptides (without Pro) from simulations using TIP3P and TIP4P-D water models, plotted against the corresponding experimental data. 14

15 Figure S5. Time evolutions of the helicity of A14 using RSFF2/TIP4P-D with different depth of the correction potential. The gray line represents experimental value at 300 K. The depth of the potential ε = 1.3 is chosen in the RSFF2+ force field. 15

16 Figure S6. Radius of gyration (Rg)of unfolded peptides GS10 and HIV-1 Rev. The trajectories at 298 K were used for the analysis. The Gromacs utility g_rotacf was used to calculate Rg. 16

17 Table S4. Average radius of gyration (Rg) from RSFF2+/TIP4P-D simulations, a compared with the small angle X-ray scattering (SAXS) data Histatin 5 RS peptide SAXS ± 0.04 Å ± 0.07 Å RSFF2+/TIP4P-D 13.9 ± 0.7 Å 13.2 ± 0.5 Å a The Gromacs utility g_rotacf was used to calculate Rg. 17

18 Figure S7. The small angle X-ray scattering (SAXS) curves. The curves from the RSFF2+/TIP4P-D simulations are obtained using FoXS program. 20 The gray lines represent the experimental curves with error. 21,22 18

19 Figure S8.Calculated 3 JHNHα coupling constants of the HIV-1 Rev peptide plotted against the corresponding experimental data

20 Figure S9. The stability and backbone dynamics of two folded globular proteins with different methods for (A) ubiquitin and (B) HEWL. The stability of native structures is measured by the backbone RMSD to the crystal structure. The backbone dynamics is measured by N-H order parameter (S 2 ), compare with NMR experimental data. Lower S 2 indicates more flexible (disorder) motions. 20