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Development of a Force Field for the Simulation of Single-Chain Proteins and Protein–Protein Complexes
journal contribution
posted on 2020-03-09, 18:33 authored by Stefano Piana, Paul Robustelli, Dazhi Tan, Songela Chen, David E. ShawThe accuracy of atomistic physics-based
force fields for the simulation
of biological macromolecules
has typically been benchmarked experimentally using biophysical data
from simple, often single-chain systems. In the case of proteins,
the careful refinement of force field parameters associated with torsion-angle
potentials and the use of improved water models have enabled a great
deal of progress toward the highly accurate simulation of such monomeric
systems in both folded and, more recently, disordered states. In living
organisms, however, proteins constantly interact with other macromolecules,
such as proteins and nucleic acids, and these interactions are often
essential for proper biological function. Here, we show that state-of-the-art
force fields tuned to provide an accurate description of both ordered
and disordered proteins can be limited in their ability to accurately
describe protein–protein complexes. This observation prompted
us to perform an extensive reparameterization of one variant of the
Amber protein force field. Our objective involved refitting not only
the parameters associated with torsion-angle potentials but also the
parameters used to model nonbonded interactions, the specification
of which is expected to be central to the accurate description of
multicomponent systems. The resulting force field, which we call DES-Amber, allows for more accurate simulations of protein–protein
complexes, while still providing a state-of-the-art description of
both ordered and disordered single-chain proteins. Despite the improvements,
calculated protein–protein association free energies still
appear to deviate substantially from experiment, a result suggesting
that more fundamental changes to the force field, such as the explicit
treatment of polarization effects, may simultaneously further improve
the modeling of single-chain proteins and protein–protein complexes.