Exploring structural variability in X-ray crystallographic models using protein local optimization by torsion-angle sampling

Abstract

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 A, 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.

Figure 1

Structural variability among PLOP ensembles. (a) Median backbone (filled squares) and heavy-atom (empty squares) r.m.s.d. between PLOP models and the PDB structure as a function of resolution. Dashed lines indicate the corresponding minimum and maximum r.m.s.d. values in the respective ensembles of single-conformer PLOP models. (b) The number of distinct side chains that are in different conformations relative to the PDB structure was evaluated for every combination of n PLOP models. The reported percentage side-chain variability is the maximum number of variable side-chain conformations for a given number of PLOP models (n) divided by the number of nonglycine residues in the corresponding protein. (c) Side-chain variability in the respective PLOP ensembles relative to the PDB structure categorized by charged surface residues (filled squares), neutral surface residues (empty squares) and buried residues (circles).

Figure 2

Higher quality models generated using the full potential in PLOP. The distribution of R _free values is shown for the filtered ensembles of PLOP models that were generated using the full potential (‘sgbnp’, solid black) and without the electrostatics and solvation terms (‘noelec’, shaded) in PLOP. 40 structures are in the filtered ‘sgbnp’ ensemble and 18 structures are in the filtered ‘noelec’ ensemble. The R _free of the PDB structure is 0.225.

Figure 3

Contribution of variable side-chain conformations attributed to structural heterogeneity. Scatter plot of σ(S) and σ(B) values for each residue identified as ‘variable’ using the volumetric definition and a 1 Å cutoff. Residues for which there is poor side-chain density have been omitted for clarity. The dashed line indicates the threshold for attributing the modeled variability to structural heterogeneity (right of the line) or positional uncertainty (left of the line).

Figure 4

Structural variability in HIV-1 protease. (a) Cartoon representation of HIV-1 protease (1g35; Schaal et al., 2001 ▶). The variable loops described by Zoete et al. (2002 ▶) are colored in red and the residue numbers are indicated; the ligand for 1g35 is colored in green and the catalytic aspartate residues are represented by blue spheres. (b) Backbone r.m.s.d. values as a function of residue number for five PLOP structures. The first 99 residues correspond to chain A and the last 99 residues correspond to chain B.