scholarly journals On the faithfulness of molecular mechanics representations of proteins towards quantum-mechanical energy surfaces

2020 ◽  
Vol 10 (6) ◽  
pp. 20190121
Author(s):  
Gerhard König ◽  
Sereina Riniker
Keyword(s):  
Amino Acids ◽  
Force Field ◽  
Molecular Mechanics ◽  
Force Fields ◽  
Mechanical Energy ◽  
Reorganization Energy ◽  
Quantum Mechanical ◽  
Energy Minimum ◽  
Structural Reorganization ◽  
Reorganization Energies

Force fields based on molecular mechanics (MM) are the main computational tool to study the relationship between protein structure and function at the molecular level. To validate the quality of such force fields, high-level quantum-mechanical (QM) data are employed to test their capability to reproduce the features of all major conformational substates of a series of blocked amino acids. The phase-space overlap between MM and QM is quantified in terms of the average structural reorganization energies over all energy minima. Here, the structural reorganization energy is the MM potential-energy difference between the structure of the respective QM energy minimum and the structure of the closest MM energy minimum. Thus, it serves as a measure for the relative probability of visiting the QM minimum during an MM simulation. We evaluate variants of the AMBER, CHARMM, GROMOS and OPLS biomolecular force fields. In addition, the two blocked amino acids alanine and serine are used to demonstrate the dependence of the measured agreement on the QM method, the phase, and the conformational preferences. Blocked serine serves as an example to discuss possible improvements of the force fields, such as including polarization with Drude particles, or using tailored force fields. The results show that none of the evaluated force fields satisfactorily reproduces all energy minima. By decomposing the average structural reorganization energies in terms of individual energy terms, we can further assess the individual weaknesses of the parametrization strategies of each force field. The dominant problem for most force fields appears to be the van der Waals parameters, followed to a lesser degree by dihedral and bonded terms. Our results show that performing a simple QM energy optimization from an MM-optimized structure can be a first test of the validity of a force field for a particular target molecule.

Testing and Optimizing the Drude Polarizable Force Field for Blocked Amino Acids Based on High-Level Quantum-Mechanical Energy Surfaces

2022 ◽  
pp. 1-9
Author(s):  
Jinfeng Chen ◽  
Gerhard König
Keyword(s):  
Amino Acids ◽  
Force Field ◽  
Energy Surface ◽  
Mechanical Energy ◽  
Quantum Mechanical ◽  
Polarizable Force Field ◽  
Conformational Substates ◽  
Reorganization Energies ◽  
High Level ◽  
Energy Surfaces

The correct reproduction of conformational substates of amino acids was tested for the CHARMM Drude polarizable force field. This was achieved by evaluating the reorganization energies for all low lying energy minima occurring in all 15 neutral blocked amino acids on a quantum-mechanical (QM) energy surface at the MP2/cc-pVDZ level. The results indicate that the bonded parameters of the N-acetyl (ACE) and N-Methylamide (CT3) blocking groups lead to significant discrepancies. A reparametrization of five bond angles significantly improved the agreement with the QM energy surface. The corrected Drude force field exhibits almost the same average reorganization energies relative to the MP2 energy surface as the AM1 and PM3 semi-empirical methods.

scholarly journals Computation of protein–ligand binding free energies using quantum mechanical bespoke force fields

MedChemComm ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 1116-1120 ◽  
Author(s):  
Daniel J. Cole ◽  
Israel Cabeza de Vaca ◽  
William L. Jorgensen
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Molecular Mechanics ◽  
Force Fields ◽  
Quantum Mechanical ◽  
T4 Lysozyme ◽  
Free Energies ◽  
Binding Free Energies

A quantum mechanical bespoke molecular mechanics force field is derived for the L99A mutant of T4 lysozyme and used to compute absolute binding free energies of six benzene analogs to the protein.

scholarly journals Adsorption of Amino Acids on Graphene: Assessment of Current Force Fields

2019 ◽  
Author(s):  
Siva Dasetty ◽  
John K. Barrows ◽  
Sapna Sarupria
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Force Field ◽  
Force Fields ◽  
Principal Component ◽  
Free Energies ◽  
Adsorbed State ◽  
Free Energy Of Adsorption ◽  
Density Based Clustering ◽  
Structural Preferences

<div> <div> <div> <p>We compare the free energies of adsorption (∆Aads) and the structural preferences of amino acids obtained using the force fields — Amberff99SB-ILDN/TIP3P, CHARMM36/modified-TIP3P, OPLS-AA/M/TIP3P, and Amber03w/TIP4P/2005. The amino acid–graphene interactions are favorable irrespective of the force field. While the magnitudes of ∆Aads differ between the force fields, the trends in the free energy of adsorption with amino acids are similar across the studied force fields. ∆Aads positively correlates with amino acid–graphene and negatively correlates with graphene–water interaction energies. Using a combination of principal component analysis and density-based clustering technique, we grouped the structures observed in the graphene adsorbed state. The resulting population of clusters, and the conformation in each cluster indicate that the structures of the amino acid in the graphene adsorbed state vary across force fields. The differences in the conformations of amino acids are more severe in the graphene adsorbed state compared to the bulk state for all the force fields. Our findings suggest that while the thermodynamics of adsorption of proteins and peptides would be described consistently across different force fields, the structural preferences of peptides and proteins on graphene will be force field dependent. </p> </div> </div> </div>

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

2013 ◽  
Author(s):  
Anders Steen Christensen ◽  
Thomas Hamelryck ◽  
Jan H Jensen
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.

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

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.

Introducing QUBE: Quantum Mechanical Bespoke Force Fields for Protein Simulations

2019 ◽  
Author(s):  
Alice Allen ◽  
Michael J. Robertson ◽  
Michael C. Payne ◽  
Daniel Cole
Keyword(s):  
Force Field ◽  
Force Fields ◽  
Specific Protein ◽  
Biological Molecules ◽  
Quantum Mechanical ◽  
Conformational Preferences ◽  
J Coupling ◽  
Force Field Parameters ◽  
Folded Proteins ◽  
Dynamics Simulations

<div><div><div><p>Molecular mechanics force field parameters for macromolecules, such as proteins, are traditionally fit to reproduce experimental properties of small molecules, and thus they neglect system-specific polarization. In this paper, we introduce a complete QUantum mechanical BEspoke (QUBE) protein force field, which derives non-bonded parameters directly from the electron density of the specific protein under study. The main backbone and sidechain protein torsional parameters are re-derived in this work by fitting to quantum mechanical dihedral scans for compatibibility with QUBE non-bonded parameters. Software is provided for the preparation of QUBE input files. The accuracy of the new force field, and the derived torsional parameters, are tested by comparing the conformational preferences of a range of peptides and proteins with experimental measurements. Accurate backbone and sidechain conformations are obtained in molecular dynamics simulations of dipeptides, with NMR J coupling errors comparable to the widely-used OPLS force field. In simulations of five folded proteins, the secondary structure is generally retained and the NMR J coupling errors are similar to standard transferable force fields, although some loss of the experimental structure is observed in certain regions of the proteins. Overall, with several avenues for further development, the use of system-specific non-bonded force field parameters is a promising approach for next-generation simulations of biological molecules.</p></div></div></div>

A critical assessment of force field accuracy against NMR data for cyclic peptides containing β-amino acids

2018 ◽  
Vol 20 (23) ◽  
pp. 15807-15816 ◽  
Author(s):  
C. Paissoni ◽  
F. Nardelli ◽  
S. Zanella ◽  
F. Curnis ◽  
L. Belvisi ◽  
...  
Keyword(s):  
Amino Acids ◽  
Force Field ◽  
Cyclic Peptides ◽  
Force Fields ◽  
Critical Assessment ◽  
Isoaspartic Acid ◽  
Nmr Parameters ◽  
Nmr Data ◽  
Conformational Properties

A critical assessment of the reproducibility of NMR parameters of β amino acids pinpoints the major weaknesses of eight widely used force fields in reproducing the equilibrium conformational properties of highly constrained cyclic peptides containing isoAspartic acid.

scholarly journals Polarizable Drude Model with s-type Gaussian or Slater Charge Density for General Molecular Mechanics Force Fields

2018 ◽  
Author(s):  
Mohammad Ghahremanpour ◽  
Paul J. van Maaren ◽  
Carl Caleman ◽  
Geoffrey Hutchison ◽  
David van der Spoel
Keyword(s):  
Force Field ◽  
Charge Density ◽  
Molecular Mechanics ◽  
Force Fields ◽  
Distribution Functions ◽  
Drude Model ◽  
Atomic Polarization ◽  
Amber Force Field

Submitted manuscript that describes derivation of atomic polarization and exponents for Gaussian or Slater distribution functions to describe polarizable atoms in force fields. Parameters are provided based on the General Amber Force Field, for H, C, N, O, F, P, S, Cl, Br, I.<br>

scholarly journals Adsorption of Amino Acids on Graphene: Assessment of Current Force Fields

2019 ◽  
Author(s):  
Siva Dasetty ◽  
John K. Barrows ◽  
Sapna Sarupria
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Force Field ◽  
Force Fields ◽  
Principal Component ◽  
Free Energies ◽  
Adsorbed State ◽  
Free Energy Of Adsorption ◽  
Density Based Clustering ◽  
Structural Preferences

<div> <div> <div> <p>We compare the free energies of adsorption (∆Aads) and the structural preferences of amino acids obtained using the force fields — Amberff99SB-ILDN/TIP3P, CHARMM36/modified-TIP3P, OPLS-AA/M/TIP3P, and Amber03w/TIP4P/2005. The amino acid–graphene interactions are favorable irrespective of the force field. While the magnitudes of ∆Aads differ between the force fields, the trends in the free energy of adsorption with amino acids are similar across the studied force fields. ∆Aads positively correlates with amino acid–graphene and negatively correlates with graphene–water interaction energies. Using a combination of principal component analysis and density-based clustering technique, we grouped the structures observed in the graphene adsorbed state. The resulting population of clusters, and the conformation in each cluster indicate that the structures of the amino acid in the graphene adsorbed state vary across force fields. The differences in the conformations of amino acids are more severe in the graphene adsorbed state compared to the bulk state for all the force fields. Our findings suggest that while the thermodynamics of adsorption of proteins and peptides would be described consistently across different force fields, the structural preferences of peptides and proteins on graphene will be force field dependent. </p> </div> </div> </div>

scholarly journals Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1

2018 ◽  
Author(s):  
David L. Mobley ◽  
Caitlin C. Bannan ◽  
Andrea Rizzi ◽  
Christopher I. Bayly ◽  
John D. Chodera ◽  
...  
Keyword(s):  
Force Field ◽  
Molecular Mechanics ◽  
Small Molecule ◽  
Force Fields ◽  
Chemical Space ◽  
Dielectric Constants ◽  
Intermediate Step ◽  
Amber Force Field ◽  
Molecular Systems ◽  
Chemical Perception

AbstractHere, we focus on testing and improving force fields for molecular modeling, which see widespread use in diverse areas of computational chemistry and biomolecular simulation. A key issue affecting the accuracy and transferrability of these force fields is the use of atom typing. Traditional approaches to defining molecular mechanics force fields must encode, within a discrete set of atom types, all information which will ever be needed about the chemical environment; parameters are then assigned by looking up combinations of these atom types in tables. This atom typing approach leads to a wide variety of problems such as inextensible atom-typing machinery, enormous difficulty in expanding parameters encoded by atom types, and unnecessarily proliferation of encoded parameters. Here, we describe a new approach to assigning parameters for molecular mechanics force fields based on the industry standard SMARTS chemical perception language (with extensions to identify specific atoms available in SMIRKS). In this approach, each force field term (bonds, angles, and torsions, and nonbonded interactions) features separate definitions assigned in a hierarchical manner without using atom types. We accomplish this using direct chemical perception, where parameters are assigned directly based on substructure queries operating on the molecule(s) being parameterized, thereby avoiding the intermediate step of assigning atom types — a step which can be considered indirect chemical perception. Direct chemical perception allows for substantial simplification of force fields, as well as additional generality in the substructure queries. This approach is applicable to a wide variety of (bio)molecular systems, and can greatly reduce the number of parameters needed to create a complete force field. Further flexibility can also be gained by allowing force field terms to be interpolated based on the assignment of fractional bond orders via the same procedure used to assign partial charges. As an example of the utility of this approach, we provide a minimalist small molecule force field derived from Merck’s parm@Frosst (an Amber parm99 descendant), in which a parameter definition file only ≈ 300 lines long can parameterize a large and diverse spectrum of pharmaceutically relevant small molecule chemical space. We benchmark this minimalist force field on the FreeSolv small molecule hydration free energy set and calculations of densities and dielectric constants from the ThermoML Archive, demonstrating that it achieves comparable accuracy to the Generalized Amber Force Field (GAFF) that consists of many thousands of parameters.

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