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. 2019 Oct 9;21(39):21918-21931.
doi: 10.1039/c9cp03434j.

Extensive tests and evaluation of the CHARMM36IDPSFF force field for intrinsically disordered proteins and folded proteins

Hao Liu  1 , Dong Song, Yangpeng Zhang, Sheng Yang, Ray Luo, Hai-Feng Chen
Affiliations

Extensive tests and evaluation of the CHARMM36IDPSFF force field for intrinsically disordered proteins and folded proteins

Hao Liu et al. Phys Chem Chem Phys. .

Abstract

Intrinsically disordered proteins (IDPs) have received increasing attention in recent studies due to their structural heterogeneity and critical biological functions. To fully understand the structural properties and determine accurate ensembles of IDPs, molecular dynamics (MD) simulation was widely used to sample diverse conformations and reveal the structural dynamics. However, the classical state-of-the-art force fields perform well for folded proteins while being unsatisfactory for the simulations of disordered proteins reported in many previous studies. Thus, improved force fields were developed to precisely describe both folded proteins and disordered proteins. Preliminary tests show that our newly developed CHARMM36IDPSFF (C36IDPSFF) force field can well reproduce the experimental observables of several disordered proteins, but more tests on different types of proteins are needed to further evaluate the performance of C36IDPSFF. Here, we extensively simulate short peptides, disordered proteins, and fast-folding proteins as well as folded proteins, and compare the simulated results with the experimental observables. The simulation results show that C36IDPSFF could substantially reproduce the experimental observables for most of the tested proteins but some limitations are also found in the radius of gyration of large disordered proteins and the stability of fast-folding proteins. This force field will facilitate large scale studies of protein structural dynamics and functions using MD simulations.

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Conflict of interest statement

Conflicts of interest

The authors declare that there is no conflict of interest.

Figures

Fig. 1
Fig. 1
Time-dependent cumulative numbers of conformational clusters. (A) Ab40. (B) Ab42. The number of conformational clusters at a certain time is calculated with the simulated ensembles from 0 ns to this certain time.
Fig. 2
Fig. 2
Time-dependent RMS errors between the experimental data and cumulative-averaged NMR observables. (A) Chemical shifts of Cα and Cβ atoms for Aβ40. (B) Chemical shifts of Cα and Cβ atoms for Aβ40. (C) 3JHNHa couplings for Aβ40. (D) 3JHNHa couplings for Aβ42. The chemical shifts are reported in ppm, J-couplings are in Hz.
Fig. 3
Fig. 3
Comparison of the simulated and measured NMR observables for ALA5 and ALA7. The left panels stand for ALA5 and the right panels stand for ALA7. The top panels stand for Ca chemical shifts and the other panels stand for three backbone scalar couplings, 3JHNHα JHNCβ, and JHaC. The standard errors of the mean of residual chemical shifts and scalar couplings are very small and are not shown for the sake of clarity of the figures.
Fig. 4
Fig. 4
Comparison of simulated and measured NMR observables for Aβ40 and Aβ42. (A) Cα chemical shifts of Aβ40. (B) 3JHNHa scalar couplings of Aβ40. (C) Ca chemical shifts of Aβ42. (D) 3JHNHa scalar couplings of Aβ42. The standard errors of the mean of the residual chemical shifts and scalar couplings are very small and are not shown for the sake of clarity of the figures.
Fig. 5
Fig. 5
Structure stability and Ca chemical shifts of folded proteins in simulations with the C36IDPSFF force field. (A) BPTI. (B) GB3. (C) HEWL. The left panels stand for the RMSD, the middle panels stand for Rg and the right panels stand for Cα secondary chemical shifts. The residue name abbreviated “B” indicates the cysteine residue which forms a disulfide bond with another cysteine residue. The standard errors of the mean of the residual chemical shifts are very small and are not shown for the sake of clarity of the figures.
Fig. 6
Fig. 6
The fraction of helical structures of drkN SH3. The PED results were calculated from an ensemble with 1700 structures retrieved from the protein ensemble database (PED). The ᵟ2D results were calculated from the chemical shifts of drkN SH3 using the ᵟ2D software. The simulated results were calculated using the DSSP software.
Fig. 7
Fig. 7
The fraction of the secondary structures of Ab40 and Ab42 during the simulation. (A) Aβ40. (B) Aβ42. The simulation was divided into three parts. The first 200 ns of simulation trajectories were discarded because the initial structures of Aβ40 and Aβ42 are from PDB structures shown in the 0–200 ns simulations. The representative structures of the largest conformational cluster from 200–800 ns and 800–100 ns simulations are shown here. The fractions of the secondary structure were calculated using the DSSP software. In this calculation, DSSP codes “H”, “G” and “I” are considered as the “helix”; “B” and “E” are considered as the “sheet”; “T”, “S” and blanks are considered as the “coil”.
Fig. 8
Fig. 8
Temperature replica exchange simulations of the fast-folding peptides and proteins. (A) The helical structure of (AAQAA)3 and the superposition of the folded structures (orange) of villin, CLN025, and the GB1 hairpin from simulations and the corresponding PDB structures,, (cyan). The melting curves of (AAQAA)3 (B), villin (C), CLN025 (D), and the GB1 hairpin (E) are compared with the experimental data.,– The folded state is defined as RMSD which is less than 2.0, 2.5, and 3.0 Å for CLN025, the GB1 hairpin, and villin according to the length of each protein, respectively.
Fig. 9
Fig. 9
Probability distributions of the radii of gyration for disordered proteins simulated with mTIP3P and disp-water water models. (A) Aβ40. (B) ACTR. (C) drkN SH3. The averaged Rg values of simulations with mTIP3P and disp-water are marked by vertical solid lines in orange and blue, respectively. The experimental values are marked by vertical solid lines in gray and the errors are marked by two vertical dash lines in gray. The simulated and experimental Rg values are also labeled with the corresponding colors.
Fig. 10
Fig. 10
Simulation results of ACTR using the C36IDPSFF force field with mTIP3P and disp-water water models. (A) Conformational clusters. (B) Cα chemical shifts. (C) Time-dependent RMS errors of Cα chemical shifts. The standard errors of the mean of the residual chemical shifts and scalar couplings are very small and are not shown for the sake of clarity of the figures.
Fig. 11
Fig. 11
Calculated NMR observables in simulations of Aβ40 starting from the PDB structure and the extended structure. (A) Cα chemical shifts. (B) Time-dependent RMS error of Cα chemical shifts. (C) 3JHNHa scalar couplings. (D) Time-dependent RMS error of 3JHNHa scalar couplings. The standard errors of the mean of the residual chemical shifts and scalar couplings are very small and are not shown for the sake of clarity of the figures.
Fig. 12
Fig. 12
Difference between the experimental data and the simulated observables from C36IDPSFF in this study and a99SB-disp in previous work. (A) Disordered proteins, including Aβ40, ACTR, and drkN SH3. (B) Folded proteins, including 2JPU, 2JQN, and 2KL6. The differences of chemical shifts and J-couplings were estimated by RMS errors, and the differences of RDCs were estimated by Q factors. The differences of Rg were estimated by RgPenalty same as previous work. The 3JHNHa couplings were compared and referred to as 3J here. The missing values mean that the corresponding experimental data are not available.
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