title: New force field on modeling intrinsically disordered proteins.
abstract: Intrinsically disordered proteins or intrinsically disordered protein regions comprise a large portion of eukaryotic proteomes (between 35% and 51%). These intrinsically disordered proteins were found to link with cancer and various other diseases. However, widely used additive force field parameter sets are insufficient in quantifying the structural properties of intrinsically disordered proteins. Therefore, we explored to a systematic correction of a base additive force field parameter set (chosen as Amber ff99SBildn) to correct the biases that was first demonstrated in simulations with the base parameter set. Specifically, the φ/ψ distributions of disorder-promoting residues were systematically corrected with the CMAP method. Our simulations show that the CMAP corrected Amber parameter set, termed ff99IDPs, improves the φ/ψ distributions of the disorder-promoting residues with respect to the benchmark data of intrinsically disordered protein structures, with root mean-squared percentage deviation less than 0.15% between the simulation and the benchmark. Our further validation shows that the chemical shifts from the ff99IDPs simulations are in quantitative agreement with those from reported NMR measurements for two tested IDPs, MeV NTAIL , and p53. The predicted residue dipolar couplings also show high correlation with experimental data. Interestingly, our simulations show that ff99IDPs can still be used to model the ordered state when the intrinsically disordered proteins are in complex, in contrast to ff99SBildn that can be applied well only to the ordered complex structures. These findings confirm that the newly proposed Amber ff99IDPs parameter set provides a reasonable tool in further studies of intrinsically disordered protein structures. In addition, our study also shows the importance of considering intrinsically disordered protein structures in general-purposed force field developments for both additive and non-additive models.
authors.affiliation: State Key Laboratory of Microbial metabolism, Department of Bioinformatics and Biostatistics, College of Life Sciences and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai, 200240, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Wei
authors.initials: W
authors.lastname: Ye
authors.forename: Cheng
authors.initials: C
authors.lastname: Jiang
authors.forename: Ray
authors.initials: R
authors.lastname: Luo
authors.forename: Hai-Feng
authors.initials: HF
authors.lastname: Chen
mesh_terms: D030562:Databases, Protein; D064267:Intrinsically Disordered Proteins / Q000737:chemistry* / Q000378:metabolism; D056004:Molecular Dynamics Simulation; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D013816:Thermodynamics; D016159:Tumor Suppressor Protein p53 / Q000737:chemistry / Q000378:metabolism
chemical_list: D064267:Intrinsically Disordered Proteins; D016159:Tumor Suppressor Protein p53
keywords: CMAP; IDPs force field; MD simulation; ff99IDPs; φ/ψ dihedrals