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Development of CHARMM Additive Potential Energy Parameters for α-Methyl Amino Acids

  • Anthony J. Pane
    Anthony J. Pane
    Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Rockville, Maryland 20892, United States
  • Wenbo Yu
    Wenbo Yu
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States
    More by Wenbo Yu
  • Asaminew Aytenfisu
    Asaminew Aytenfisu
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States
  • Jude Tunyi
    Jude Tunyi
    Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Rockville, Maryland 20892, United States
    More by Jude Tunyi
  • Richard M. Venable
    Richard M. Venable
    Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Rockville, Maryland 20892, United States
  • Alexander D. MacKerell Jr.
    Alexander D. MacKerell, Jr.
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, 20 Penn Street, Baltimore, Maryland 21201, United States
  • , and 
  • Richard W. Pastor*
    Richard W. Pastor
    Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Rockville, Maryland 20892, United States
    *Email: [email protected]. Phone: 301-435-2035.
Cite this: J. Phys. Chem. B 2021, 125, 42, 11687–11696
Publication Date (Web):October 15, 2021
https://doi.org/10.1021/acs.jpcb.1c07202
Copyright © 2021 American Chemical Society

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    Abstract

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    Potential energy parameters for α-methyl amino acids were generated with ab initio calculations on α-methyl-N-acetylalanyl-N′-methylamide (the α-methyl “alanine dipeptide”) which served as an input to a grid-based correction to the backbone torsional potential (known as CMAP) consistent with the CHARMM36m additive protein force field. The new parameters were validated by comparison with experimentally determined helicities of the 22 residue C-terminal peptide (H10) from apolipoprotein A1 and five α-methylated variants in water and 0.3:0.7 trifluoroethanol (TFE)/water. Conventional molecular dynamics simulation totaling 30 μs for each peptide is in overall good agreement with the experiment, including the increased helicity in 30% TFE. An additional 500 ns of simulation using two-dimensional dihedral biasing (bpCMAP) replica exchange reduced left-handed conformations, increased right-handed helices, and thereby mostly decreased agreement with the experiment. Analysis of side chain–side chain salt bridges suggests that the overestimation of the helical content may be, in part, due to such interactions. The increased helicity of the peptides in 30% TFE arises from decreased hydrogen bonding of the backbone atoms to water and a concomitant increase in intramolecular backbone hydrogen bonds.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcb.1c07202.

    • Ratios of left-to-right conformations by substitution; replica-exchange rates for water; replica-exchange rates for 30% TFE; dipeptide conformations and energies; replica-exchange conformations and left-handed helix; conventional MD conformations and left-handed helix; salt-bridge occurrence in water; salt-bridge occurrence in 30% TFE; peptide helical content in water with and without salt bridges; backbone hydrogen bond occupancy; CMAP energy coefficients; and QM energy calculations for m-AAMA (PDF)

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