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      IDP-Specific Force Field ff14IDPSFF Improves the Conformer Sampling of Intrinsically Disordered Proteins

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          Abstract

          Intrinsically disordered proteins (IDPs) or intrinsically disordered regions have not fixed tertiary structure, but play key roles in signal regulation, molecule recognition, and drug target. However it is difficult to study the structure and function of IDPs by traditional experimental methods because of their diverse conformations. Limitations of current generic protein force fields and solvent models were reported in the previous simulations of IDPs. We have also explored to overcome these limitations by developing ff99IDPs and ff14IDPs force fields to correct the dihedral distribution for eight disordered promoting residues often observed in IDPs and found encouraging improvements. Here, we extend our correction of backbone dihedral terms to all 20 naturally occurring amino acids in the IDP-specific force field ( ff14IDPSFF) to further improve the quality in the modeling of IDPs. Extensive tests of seven IDPs and 14 unstructured short peptides show that the simulated Cα chemical shifts with the ff14IDPSFF force field are in quantitative agreement with those from NMR experiment and are more accurate than the base generic force field and also our previous ff14IDPs that only corrects the eight disorder-promoting amino acids. The influences of solvent models were also investigated and found to be less important. Finally our explicit solvent MD simulations further show that ff14IDPSFF can still be used to model structural and dynamical properties of two tested folded proteins, with a slightly better agreement in the loop regions for both structural and dynamical properties. These findings confirm that the newly developed IDP-specific force field ff14IDPSFF can improve the conformer sampling of intrinsically disordered proteins.

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          Author and article information

          Journal
          101230060
          32252
          J Chem Inf Model
          J Chem Inf Model
          Journal of chemical information and modeling
          1549-9596
          1549-960X
          6 December 2017
          04 May 2017
          22 May 2017
          15 December 2017
          : 57
          : 5
          : 1166-1178
          Affiliations
          [1 ]State Key Laboratory of Microbial metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, College of Life Sciences and Biotechnology, Shanghai Jiaotong University, Shanghai, 200240, China
          [2 ]Departments of Molecular Biology and Biochemistry, Chemical Engineering and Materials Science, and Biomedical Engineering, University of California, Irvine, CA 92697-3900, USA
          [3 ]Shanghai Center for Bioinformation Technology, Shanghai, 200235, China
          Author notes
          Corresponding authors: haifengchen@ 123456sjtu.edu.cn ; rluo@ 123456uci.edu , Tel: 86-21-34204348, Fax: 86-21-34204348
          Article
          PMC5731455 PMC5731455 5731455 nihpa925005
          10.1021/acs.jcim.7b00135
          5731455
          28448138
          5e2c152d-4864-4299-92b9-3c3cac5ab056
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