Molecular dynamics simulation as an important complement of experiment is widely used to study protein structures and functions. However, previous studies indicate that current force fields cannot, simultaneously, provide accurate descriptions of folded proteins and intrinsically disordered proteins (IDPs). Therefore, a CMAP optimized force field based on the Amber ff03 force field (termed ff03CMAP herein) was developed for balanced sampling of folded proteins and IDPs. Extensive validations of short peptides, folded proteins, disordered proteins, and fast-folding proteins show that simulated chemical shifts, J-coupling constants, order parameters, and residual dipolar couplings with the ff03CMAP force field are in very good agreement with NMR measurements and are more accurate than other ff03-series force fields. The influence of solvent models was also investigated. It was found that the combination of ff03CMAP/TIP4P-Ew is suitable for folded proteins and that of ff03CMAP/TIP4PD is better for disordered proteins. These findings confirm that the newly developed force field ff03CMAP can improve the balance of conformer sampling between folded proteins and IDPs.

Intrinsically disordered proteins and regions (IDPs and IDRs) have attracted increasing interest with their abundance in the human proteome and critical roles in various human diseases. However, the characterization of structural dynamics of IDPs presents a challenge to general experimental methods due to their highly heterogeneous ensembles. Molecular dynamics (MD) simulation has been an alternative method with recent advances in computation power. Nevertheless, it is imperative that eligible predictions are determined by a highly precise force field, but traditional force fields sometimes give a collapsed disorder structure and overestimate the stability of IDPs. Here, we present a novel residue-specific force field, OPLSIDPSFF, to correct backbone dihedral terms for all 20 natural amino acids based on OPLS-AA/L. Extensive tests of 11 IDPs and two short peptides show that the simulated chemical shifts and J-coupling with the OPLSIDPSFF force field are in quantitative agreement with those from NMR experimental observables and are more accurate than the base generic force field. The influences of solvent models were also investigated, and it was found that TIP4P-D water had positive effects on limited observables. Furthermore, OPLSIDPSFF can still be used to model structural and dynamic properties of two tested folded proteins and fast-folding proteins. These findings confirm that the newly developed residue-specific force field OPLSIDPSFF can improve the conformer sampling of intrinsically disordered and folded proteins.

C36IDPSFF force field performs well in both disordered proteins and folded proteins, and achieves similar accuracy to a99SB-disp in relatively short-time simulations.

Intrinsically disordered proteins (IDPs) are proteins without a fixed three-dimensional (3D) structure under physiological conditions and are associated with Parkinson’s disease, Alzheimer’s disease, cancer, cardiovascular disease, amyloidosis, diabetes, and other diseases. Experimental methods can hardly capture the ensemble of diverse conformations for IDPs. Molecular dynamics (MD) simulations can sample continuous conformations that might provide a valuable complement to experimental data. However, the accuracy of MD simulations depends on the quality of force field. In particular, the evolutionary conservation and coevolution of IDPs introduce that current force fields could not precisely reproduce the conformation of IDPs. In order to improve the performance of force field, deep learning and reweighting methods were used to automatically generate personal force field parameters for intrinsically disordered and ordered proteins. At first, the deep learning method predicted more accuracy φ/ψ dihedral of residue than the previous method. Then, reweighting optimized the personal force field parameters for each residue. Finally, typical representative systems such as IDPs, structure protein, and fast-folding protein were used to evaluate this force field. The results indicate that two personal force field parameters (named PPFF1 and PPFF1_af2) could better reproduce the experimental observables than ff03CMAP force field. In summary, this strategy will provide feasibility for the development of precise personal force fields.