Supporting information:

Transcription

1 Supporting information: A simple method to measure protein side-chain mobility using NMR chemical shifts. Mark V. Berjanskii, David S. Wishart* Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8 *To whom correspondence should be addressed: Dr. David Wishart, Department of Computing Science, 341 Athabasca Hall, University of Alberta, Edmonton, AB, Canada T6G 2E8, Phone: (780) , david.wishart@ualberta.ca S1

2 Figure S1. Failure of the existing backbone RCI protocol (Berjanskii, M. V.; Wishart, D. S. J. Am. Chem. Soc. 2005, 127, ) to properly predict amplitudes of total sidechain motions in PyJ (PDB ID: 1FAF) when the protocol is applied to side-chain atoms. Black arrows indicate residues with rigid side-chains, for which the old RCI protocol incorrectly predicts high side-chain mobility. The backbone RCI protocol utilized uniform weighting coefficients for all chemical shifts. For comparison, the side-chain RCI protocol developed in this work is shown with a red line. RMSDs corresponding to the total side-chain motions in MD simulations were calculated as described later in the Supporting Information. S2

3 Molecular Dynamics Protocol MD simulations were conducted with Gromacs , using the GROMOS96 43a1 force field 2. Starting structures were first minimized via a steepest descent algorithm. To avoid large distortions of the protein structure due to the vacuum environment, positional restraints with force constants of kj mol -1 nm -2 were initially applied to all protein atoms and then gradually released by reducing the number of restrained atoms in the following order: all main-chain heavy atoms (Cα, N, O, and C), all main-chain heavy atoms except Cα, only O and N atoms, all atoms in rigid secondary structure elements (αhelices and β-sheets, as identified by DSSP 3 ), all main-chain heavy atoms except Cα in α-helices and β-sheets, only O and N atoms in α-helices and β-sheets. Each time, the double-precision minimization was conducted for 200 steps. Bond lengths were restrained with the LINCS algorithm 4 using eight iterations and eight order expansions. Each model was placed in a periodic triclinic system with a distance between the protein and periodic box edges of 1.5 nm. The cutoff radius for calculation of the van der Waals interactions was 0.9 nm. Neighbor lists within a radius of 1.4 nm was updated every 20 fs. The radius for calculating short-range electrostatic interactions was 1.4 nm. Longrange electrostatic interactions were treated with a Particle-Mesh Ewald summation 5. After initial minimization, each model was solvated with SPC water. Solvent was minimized before the addition of ions using 1000 steps of double-precision steepest descent minimization. Force constants of kj mol -1 nm -2 were applied to restraint protein atoms during the minimization to avoid distortion of the protein structure by nonequilibrated solvent. The net charge of the system was adjusted to zero by adding counter ions (Na+ or Cl-). Three cycles of steepest descent minimization (1000 steps per cycle, double precision) were performed with a decreasing number of restrained atoms in the following order: all protein atoms, all main-chain heavy atoms, and all main-chain heavy atoms except Cα. Four equilibration MD steps at 300K were performed. During the first step, positional restraints of kj mol -1 nm -2 were applied to all protein atoms. A short run of double precision NPT dynamics with a 1 fs timestep was conducted for 1 ps to adjust the size of periodic box. The second and third equilibration steps were done at constant volume and temperature to produce a NVT ensemble. During the second step, water and ions were equilibrated for 1000 ps while protein atoms were frozen. The third step consisted of eight 100 ps cycles and was employed to equilibrate the system while gradually decreasing restraints of the protein atoms. The restraint strength and the order of restrained protein regions were identical to the ones that were used during the first minimization step (see above). The final equilibration step of double precision dynamics with a 1 fs timestep was done at constant temperature and pressure for 100 ps to adjust the size of periodic cell. MD production runs had lengths of 2-4 ns and integration steps of 2 fs. In all dynamics steps, the temperature of the protein and solvent was maintained separately at 300K by coupling with Berendsen thermostats 6. The coupling time was 0.1 ps. In all NPT simulations, including the production run, the pressure was maintained at 1 atm with isotropic pressure coupling using the Berendsen algorithm 6, a time constant of 4 ps and a compressibility of bar. Only simulations that had overall RMSD from the starting structure below 2Å for secondary structure elements were used to optimize the side-chain Random Coil Index. A typical profile of secondary structure RMSDs that S3

4 were observed during our MD simulations is shown in the Figure 2 of the Supporting Information below. Mean per-residue root-mean-square deviations (RMSD) of atomic coordinates during MD simulations, which are also often called RMSF (root-mean-square fluctuations), were determined by Gromacs Prior to the RMSD calculations, MD ensembles were aligned by rigid secondary structure elements (α-helices and β-sheets). The per-residue backbone RMSDs and side-chain RMSDs for total side-chain motions were calculated using backbone C, N, C atoms and all heavy side-chain atoms, respectively. Figure S2. Fluctuations of the overall backbone nitrogen RMSD of PyJ s secondary structure elements during MD simulations. References: (1) Pronk, S.; Pall, S.; Schulz, R.; Larsson, P.; Bjelkmar, P.; Apostolov, R.; Shirts, M. R.; Smith, J. C.; Kasson, P. M.; van der Spoel, D.; Hess, B.; Lindahl, E. Bioinformatics 2013, 29, (2) Scott, W. R. P.; Hunenberger, P. H.; Tironi, I. G.; Mark, A. E.; Billeter, S. R.; Fennen, J.; Torda, A. E.; Huber, T.; Kruger, P.; van Gunsteren, W. F. J. Phys. Chem. A 1999, 103, (3) Kabsch, W.; Sander, C. Biopolymers 1983, 22, (4) Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. J. Comput. Chem. 1997, 18, (5) Darden, T.; York, D.; Pedersen, L. J. Chem. Phys. 1993, 98, (6) Berendsen, H. J. C.; Postma, J. P. M.; Vangunsteren, W. F.; Dinola, A.; Haak, J. R. J. Chem. Phys. 1984, 81, S4

5 Figure S3. Correlation of RCI SC with side-chain RMSD of MD ensembles of (A) Disulfide Isomerase, (B) Interleukin-4, (C) Basic fibroblast growth factor, (D) HopPmal (E) PDZ Domain of Human Atrophin-1 Interacting Protein. Breaks in the Y axis are marked with double-lined slashes. The backbone RCI component of RCI SC (green lines) is included to demonstrate that it alone cannot properly predict side-chain MD RMSD. S5

6 Figure S4. Correlation of RCI SC with side-chain RMSD of NMR ensembles of (A) LIM domain in Four and a half LIM domains protein 2, (B) Signal transducing adaptor molecule 2, (C) Sperm flagellar protein 1, (D) MM1357 protein (E) PH domain of Dynamin-2. Breaks in the Y axis are marked with double-lined slashes. The backbone RCI component of RCI SC (green lines) is included to demonstrate that it alone cannot properly predict side-chain NMR RMSD. S6

7 Figure S5. Correlation of RCI with normalized mean side-chain B-factor of (A) Sterol carrier protein-2, (B) Inhibitor protein Im9, (C) DUSP domain of HUSP15, (D) SH3 of Tyrosine Kinase Lck (E) Human RANTES. The backbone RCI component of RCI SC (green lines) is included to demonstrate that it alone cannot properly predict side-chain B- factor. S7

8 Figure S6. Localization of PyJ side-chains with low (<0.11, violet color) and high (>0.15, green color) RCI SC values. Rigid side-chains (low RCI SC ) are located primarily in the PyJ core (A), whereas flexible side-chains (high RCI SC ) are mostly solvent exposed (B). The figure was generated with MolMol (Koradi, R.; Billeter, M.; Wüthrich, K. J. Mol. Graphics 1996, 14, ) S8

9 Figure S7. Correlation of RCI with fractional accessible surface areas of (A) Ubiquitin, (B) yhef, (C) BRCT domain of human BRCA1, (D) MM1357 protein (E) N-terminal domain of NP_ The backbone RCI component of RCI SC (green lines) is included to demonstrate that it alone cannot properly predict side-chain ASAf. S9

10 Table S1. Correlation of RCI SC with per-residue side-chain RMSD of MD and NMR ensembles, and fractional ASA for the training set. Protein BMRB ID PDB ID MD RMSD NMR RMSD ASAf Spearman (S) or Pearson (P) correlation coefficients: S P S P S P BFGF BLD BRCT of BRCA OQA Disulfide isomerase BJX Fibronectin domain X5A Interleukin BCN NP L1T PH domain of Dynamin YS PHD domain X4I of ING3 PTB domain of SNT YT PyJ FAF Replication Factor A K RWD domain of ring finger DAY protein 25 SH3 of human UE intersectin 2 yhgg XN Zinc finger protein EM Average S10

11 Table S2. Correlation of RCI SC with per-residue side-chain RMSD of MD and NMR ensembles, and fractional ASA for the testing set. BMRB Protein PDB ID MD RMSD NMR RMSD ASAf ID Spearman (S) or Pearson (P) S P S P S P correlation coefficients: Arabidopsis thaliana I9Y At1g70830 Sperm flagellar protein 1 Engrailed homeodomain Arabidopsis thaliana F20O EE P6J WJJ HopPmaL LF LIM domain X4L MM YEZ OB-fold domain of replication protein A PDZ domain of Atrophin-1 Interacting Protein PWWP domain of Hepatoma growth factor Rhodanese-like domain of Alr K5V UEW N KL SpaI LVL Ubiquitin D3Z Signal transducing adaptor molecule X5B yxef JOZ Average S11

12 Table S3. Correlation of RCI SC with per-residue side-chain RMSD of MD ensembles that were obtained with different MD force-fields for a subset of 12 proteins. Protein PDB ID BMRB ID Gromos43a1 Gromos53a6 Charmm27 CMAP AMBER99 SB-ILDN Spearman (S) or Pearson (P) correlation coefficients: S P S P S P S P Ubiquitin 1D3Z Disulfide isomerase 2BJX PyJ 1FAF Interleukin 4 1BCN BFGF 1BLD yhgg 1XN Fibronectin type III 1X5A domain Engrailed homeodomain 2P6J LIM domain 1X4L Atrophin-1 Interacting 1UEW Protein Hepatomaderived growth 1N factor SpaI 2LVL Average S12

13 Table S4. Correlation of RCI SC with per-residue side-chain RMSD of MD ensembles that were obtained with short and long MD simulations for a subset of 6 proteins. Protein PDB ID BMRB ID Short MD Long MD Spearman (S) and Pearson (P) MD length MD length correlation coefficients S P S P (ns) (ns) and MD length: Interleukin-4 1BCN SpaI 2LVL PyJ 1FAF HopPmaL 2LF NP L1T yxef 2JOZ Average S13

14 Table S5. Correlation of RCI SC with normalized mean side-chain B-factor of X-ray protein models. Protein PDB ID Chain BMRB ID Spearman correlation coefficient Pearson correlation coefficient Ubiquitin 1UBQ A Inhibitor Protein Im9 1FR2 A PrgI 3ZQE B DUSP domain of HUSP15 3T9L A Polcalcin 1K9U A Interleukin-1beta 1I1B A Human RANTES 1U4P B Cold-shock Protein A 1MJC A SH3 of Tyrosine Kinase Lck 1LCK A Sterol carrier protein-2 1C44 A p53 1AIE A HSPCO34 1TVG A Staphylococcal Nuclease 1EQV A Desulforedoxin 1DHG B Insulin 7INS D Beta-2-microglobulin 1LDS A T4 Lysozyme peptide 3NY8 A Human Interleukin-4 1RCB A Average S14

15 Table S6. Coefficients in the side-chain RCI expression. Side-chain RCI coefficients: A: 0.5 B: 1.5 K: Residue Atom K A HB 3.43 A CB 3.77 C CB 2.64 C HB C HB E CG 2.12 E HG E HG E HB E HB E CB 0.00 D HB D HB D CB 0.00 F CB 3.57 F CE* 3.71 F CD* 3.28 F HB* 3.71 F HZ 3.56 F CZ 3.43 F HD* 3.28 F HE 3.71 I CG* 3.69 I HG* 3.90 I CB 4.00 I CD I HB* 3.90 I HD 3.48 H CD H HD H HE H CE H ND H NE H HB H HB H CB 0.00 K HD K HD K HE K HE S15

16 K HG K CG 2.13 K HG K CE 2.32 K CD 2.51 K HB K HB K CB 0.00 M CG 3.18 M HG* 3.07 M CB 2.66 M CE 2.44 M HB* 2.88 M HE 2.05 L HB* 3.73 L HD* 3.73 L CD* 4.00 L CG 3.91 L HG 3.19 L CB 0.00 N HD N HD N CB 2.56 N ND N HB N HB Q CG 1.85 Q HG Q HG Q HE Q HE Q HB Q HB Q CB 0.00 P HB* 3.82 P HD* 3.55 P CD 3.02 P CG 3.19 P HG* 3.64 P CB 0.00 S CB 3.95 S HB S HB R HD R HD R HG R CG 1.57 R HG S16

17 R CD 2.51 R HB R NE 3.07 R HB R HE 1.67 R CB 0.00 T HB 3.36 T CB 3.89 T CG T HG W CB 3.57 W CE* 3.53 W CD 3.30 W HH 4.00 W HB* 4.00 W CH W NE 2.49 W HZ* 4.00 W CZ* 3.57 W HD 3.35 W HE* 3.78 V CB 4.00 V HG V HG V CG V HB 2.93 V CG Y HE Y CD Y HD Y HD Y CB 3.53 Y CE Y CE Y CD Y HB Y HB Y HE Wild-card * indicates coefficients that are applied to the average of stereospecific secondary chemical shifts. S17

18 Table S7. Accuracy of prediction of per-residue MD RMSD, NMR RMSD, and fractional ASA from RCI SC for each residue type in the RCI training set. Residue type Mean MD RMSD (Å) Median Error standard deviation Mean NMR RMSD (Å) Median Error standard deviation Mean Fractional ASA Median Error standard deviation A C E D F I H K M L N Q P S R T W V Y S18

19 Table S8. Accuracy of prediction of per-residue MD RMSD, NMR RMSD, and fractional ASA from RCI SC for each residue type in the RCI SC testing set. Residue type Mean MD RMSD (Å) Median Error standard deviation Mean NMR RMSD (Å) Median Error standard deviation Mean Fractional ASA Median Error standard deviation A C E D F I H K M L N Q P S R T W V Y S19

20 Table S9. Accuracy of prediction of normalized mean side-chain B-factor from RCI SC for each residue type. Residue type Mean Median Error standard deviation A C E D F I H K M L N Q P S R T W V Y S20