Systematic Derivation and Testing of AMBER Force Field Parameters for Fatty Ethers from Quantum Mechanical Calculations

We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in XYZ and PDB formats, or optionally directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license.

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A molecular dynamics simulation study on the propensity of Asn-Gly-containing heptapeptides towards β-turn structures: Comparison with ab initio quantum mechanical calculations

PLoS ONE ◽

10.1371/journal.pone.0243429 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243429

Author(s):

Dimitrios A. Mitsikas ◽

Nicholas M. Glykos

Keyword(s):

Molecular Dynamics ◽

Force Field ◽

Molecular Dynamics Simulations ◽

Dynamics Simulation ◽

Quantum Mechanical ◽

Type I ◽

Quantum Mechanical Calculations ◽

Water Solvent ◽

Simulation Protocol ◽

Dynamics Simulations

Both molecular mechanical and quantum mechanical calculations play an important role in describing the behavior and structure of molecules. In this work, we compare for the same peptide systems the results obtained from folding molecular dynamics simulations with previously reported results from quantum mechanical calculations. More specifically, three molecular dynamics simulations of 5 μs each in explicit water solvent were carried out for three Asn-Gly-containing heptapeptides, in order to study their folding and dynamics. Previous data, based on quantum mechanical calculations within the DFT framework have shown that these peptides adopt β-turn structures in aqueous solution, with type I’ β-turn being the most preferred motif. The results from our analyses indicate that at least for the given systems, force field and simulation protocol, the two methods diverge in their predictions. The possibility of a force field-dependent deficiency is examined as a possible source of the observed discrepancy.

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Comparison of different approaches to derive classical bonded force-field parameters for a transition metal cofactor: a case study for non-heme iron site of ectoine synthase

Theoretical Chemistry Accounts ◽

10.1007/s00214-021-02796-z ◽

2021 ◽

Vol 140 (8) ◽

Author(s):

Justyna Andrys ◽

Johann Heider ◽

Tomasz Borowski

Keyword(s):

Transition Metal ◽

Force Field ◽

Metal Ion ◽

Geometry Optimization ◽

Heme Iron ◽

Energy Structure ◽

Test Case ◽

Amber Force Field ◽

Force Field Parameters ◽

And Function

AbstractComputational investigations into the structure and function of metalloenzymes with transition metal cofactors require proper preparation of the model, which requires obtaining reliable force field parameters for the cofactor. Here, we present a test case where several methods were used to derive amber force field parameters for a bonded model of the Fe(II) cofactor of ectoine synthase. Moreover, the spin of the ground state of the cofactor was probed by DFT and post-HF methods, which consistently indicated the quintet state is lowest in energy and well separated from triplet and singlet. The performance of the obtained force field parameter sets, derived for the quintet spin state, was scrutinized and compared taking into account metrics focused on geometric features of the models as well as their energetics. The main conclusion of this study is that Hessian-based methods yield parameters which represent the geometry around the metal ion, but poorly reproduce energy variance with geometrical changes. On the other hand, the energy-based method yields parameters accurately reproducing energy-structure relationships, but with bad performance in geometry optimization. Preliminary tests show that admixing geometrical criteria to energy-based methods may allow to derive parameters with acceptable performance for both energy and geometry.

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FragBuilder: An efficient Python library to setup quantum chemistry calculations on peptides models

10.7287/peerj.preprints.169v1 ◽

2013 ◽

Author(s):

Anders Steen Christensen ◽

Jan H Jensen ◽

Thomas Hamelryck

Keyword(s):

Quantum Chemistry ◽

Force Field ◽

Molecular Mechanics ◽

Side Chain ◽

Quantum Mechanical ◽

Quantum Mechanical Calculations ◽

Model Peptide ◽

Quantum Chemistry Calculations ◽

Set Up ◽

Model Structures

We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in XYZ and PDB formats, or optionally directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license.

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