Abstract
Torsion-angle sampling, as implemented in the
Protein Local Optimization Program
(
PLOP
), is used to generate multiple structurally variable single-conformer models which are in good agreement with X-ray data. An ensemble-refinement approach to differentiate between positional uncertainty and conformational heterogeneity is proposed.
Modeling structural variability is critical for understanding protein function and for modeling reliable targets for
in silico
docking experiments. Because of the time-intensive nature of manual X-ray crystallographic refinement, automated refinement methods that thoroughly explore conformational space are essential for the systematic construction of structurally variable models. Using five proteins spanning resolutions of 1.0–2.8 Å, it is demonstrated how torsion-angle sampling of backbone and side-chain libraries with filtering against both the chemical energy, using a modern effective potential, and the electron density, coupled with minimization of a reciprocal-space X-ray target function, can generate multiple structurally variable models which fit the X-ray data well. Torsion-angle sampling as implemented in the
Protein Local Optimization Program
(
PLOP
) has been used in this work. Models with the lowest
R
free
values are obtained when electrostatic and implicit solvation terms are included in the effective potential. HIV-1 protease, calmodulin and SUMO-conjugating enzyme illustrate how variability in the ensemble of structures captures structural variability that is observed across multiple crystal structures and is linked to functional flexibility at hinge regions and binding interfaces. An ensemble-refinement procedure is proposed to differentiate between variability that is a consequence of physical conformational heterogeneity and that which reflects uncertainty in the atomic coordinates.