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Editorial
. 2017 Jan;89(1):5-15.
doi: 10.1111/cbdd.12832. Epub 2016 Sep 19.

ff14IDPs force field improving the conformation sampling of intrinsically disordered proteins

Dong Song  1 , Wei Wang  1 , Wei Ye  1 , Dingjue Ji  1 , Ray Luo  2 , Hai-Feng Chen  1   3
Affiliations
Editorial

ff14IDPs force field improving the conformation sampling of intrinsically disordered proteins

Dong Song et al. Chem Biol Drug Des. 2017 Jan.

Abstract

Intrinsically disordered proteins are proteins which lack of specific tertiary structure and unable to fold spontaneously without the partner binding. These intrinsically disordered proteins are found to associate with various diseases, such as diabetes, cancer, and neurodegenerative diseases. However, current widely used force fields, such as ff99SB, ff14SB, OPLS/AA, and Charmm27, are insufficient in sampling the conformational characters of intrinsically disordered proteins. In this study, the CMAP method was used to correct the φ/ψ distributions of disorder-promoting amino acids. The simulation results show that the force filed parameters (ff14IDPs) can improve the φ/ψ distributions of the disorder-promoting amino acids, with RMSD less than 0.10% relative to the benchmark data of intrinsically disordered proteins. Further test suggests that the calculated secondary chemical shifts under ff14IDPs are in quantitative agreement with the data of NMR experiment for five tested systems. In addition, the simulation results show that ff14IDPs can still be used to model structural proteins, such as tested lysozyme and ubiquitin, with better performance in coil regions than the original general Amber force field ff14SB. These findings confirm that the newly developed Amber ff14IDPs is a robust model for improving the conformation sampling of intrinsically disordered proteins.

Keywords: CMAP correction; IDPs; ff14IDPs; force field.

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Conflict of interest statement

There is no conflict of interest.

Figures

Figure 1
Figure 1
Distribution of secondary structure and dihedral of IDRs in PDB. A: Distribution of secondary structure and coil in PDB. E for β-sheet, H for α-helix, G for 310 helix, I for π helix, T for turn, S for bend, C for coil. B: Distribution of dihedral for eight disorder-promoting amino acids.
Figure 2
Figure 2
The difference of dihedral distribution between benchmark of IDPs and CMAP correction for Glu, Lys and Pro.
Figure 3
Figure 3
RMSp of CMAP optimization for eight disorder-promoting amino acids.
Figure 4
Figure 4
Number of clusters occupying 70% or more conformations over increasing simulation time (checked every 5ns) with both tested force fields.
Figure 5
Figure 5
Simulation and thermodynamic data derived from ff14IDPs and ff14SB for HIVRev. (A)(B) Cα RMSD for five trajectories. (C) PMF free energy landscape on 2D space of radius gyration (RG) and RMSD, showing ff14IDPs could sample wider and more flexible conformation space. (D) RMSF. (E) Comparison of the average helicity under both force fields. (F) Comparison of the secondary chemical shift data. (G) Representative structures of top 10 clusters and their occupancies.
Figure 6
Figure 6
Simulation and thermodynamic data derived from ff14IDPs and ff14SB for apo-p53. (A)(B) Cα RMSD for five trajectories. (C) PMF free energy landscape on 2D space of radius gyration (RG) and RMSD, showing ff14IDPs could sample wider and more flexible conformation space. (D) RMSF. (E) Comparison of the average helicity under both force fields. (F) Comparison of the secondary chemical shift data. (G) Representative structures of top 10 clusters and their occupancies.
Figure 7
Figure 7
Simulation and thermodynamic data derived from ff14IDPs and ff14SB for α-synuclein. (A)(B) Cα RMSD for five trajectories. (C) PMF free energy landscape on 2D space of radius gyration (RG) and RMSD, showing ff14IDPs could sample wider and more flexible conformation space. (D) RMSF. (E) Comparison of the average helicity under both force fields. (F) Comparison of the secondary chemical shift data. (G) Representative structures of top 10 clusters and their occupancies.
Figure 8
Figure 8
Simulation and thermodynamic data derived from ff14IDPs and ff14SB for ubiquitin. (A)(B) Cα RMSD for five trajectories. (C) RMSF. (D) Comparison of the secondary chemical shift data. (E) Comparison of NOE data. (F) Comparison of order parameter (S). (G) Representative structures of top 4 clusters and their occupancies.
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