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A Reduced Generalized Force Field for Biological Halogen Bonds.

Melissa Coates FordAnthony K RappéAlexander N Ho
Published in: Journal of chemical theory and computation (2021)
The halogen bond (or X-bond) is a noncovalent interaction that is increasingly recognized as an important design tool for engineering protein-ligand interactions and controlling the structures of proteins and nucleic acids. In the past decade, there have been significant efforts to characterize the structure-energy relationships of this interaction in macromolecules. Progress in the computational modeling of X-bonds in biological molecules, however, has lagged behind these experimental studies, with most molecular mechanics/dynamics-based simulation methods not properly treating the properties of the X-bond. We had previously derived a force field for biological X-bonds (ffBXB) based on a set of potential energy functions that describe the anisotropic electrostatic and shape properties of halogens participating in X-bonds. Although fairly accurate for reproducing the energies within biomolecular systems, including X-bonds engineered into a DNA junction, the ffBXB with its seven variable parameters was considered to be too unwieldy for general applications. In the current study, we have generalized the ffBXB by reducing the number of variables to just one for each halogen type and show that this remaining electrostatic variable can be estimated for any new halogenated molecule through a standard restricted electrostatic potential calculation of atomic charges. In addition, we have generalized the ffBXB for both nucleic acids and proteins. As a proof of principle, we have parameterized this reduced and more general ffBXB against the AMBER force field. The resulting parameter set was shown to accurately recapitulate the quantum mechanical landscape and experimental interaction energies of X-bonds incorporated into DNA junction and T4 lysozyme model systems. Thus, this reduced and generalized ffBXB is more readily adaptable for incorporation into classical molecular mechanics/dynamics algorithms, including those commonly used to design inhibitors against therapeutic targets in medicinal chemistry and materials in biomolecular engineering.
Keyphrases
  • single molecule
  • transition metal
  • molecular dynamics simulations
  • machine learning
  • high resolution
  • density functional theory
  • molecular dynamics
  • mass spectrometry
  • monte carlo