3Drefine: consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization

Abstract

One of the major limitations of computational protein structure prediction is the deviation of predicted models from their experimentally derived true, native structures. The limitations often hinder the possibility of applying computational protein structure prediction methods in biochemical assignment and drug design that are very sensitive to structural details. Refinement of these low-resolution predicted models to high-resolution structures close to the native state, however, has proven to be extremely challenging. Thus, protein structure refinement remains a largely unsolved problem. Critical assessment of techniques for protein structure prediction (CASP) specifically indicated that most predictors participating in the refinement category still did not consistently improve model quality. Here, we propose a two-step refinement protocol, called 3Drefine, to consistently bring the initial model closer to the native structure. The first step is based on optimization of hydrogen bonding (HB) network and the second step applies atomic-level energy minimization on the optimized model using a composite physics and knowledge-based force fields. The approach has been evaluated on the CASP benchmark data and it exhibits consistent improvement over the initial structure in both global and local structural quality measures. 3Drefine method is also computationally inexpensive, consuming only few minutes of CPU time to refine a protein of typical length (300 residues). 3Drefine web server is freely available at http://sysbio.rnet.missouri.edu/3Drefine/.

Fig. 1

Changes in global and local structural qualities using 3Drefine on CASP8 Refinement Targets (A) Scatter plot of changes in GDT-HA and TM-score. A positive change indicates the quality of the model of a target has been improved by refinement. (B) Scatter plot of changes in RMSD and MolProbity-score. A negative change indicates the quality of the model of a target has been improved by refinement.

Fig. 2

Structural superposition of initial model (blue) and refined model using 3Drefine (red) on the native structure (green) for two CASP8 Targets. The values under each model indicate GDT-HA, TM-score, RMSD and MolProbity score respectively before (blue) and after (red) refinement. (A) Structural superposition for Target TR464. (B) Structural superposition for Target TR432. Figures were prepared in PyMOL (The PyMOL Molecular Graphics System, Version 1.4.1, Schrödinger, LLC.).

Fig. 3

Changes in local and global qualities of CASP9 Refinement Targets (A) Scatter plot for GDT-HA score changes for 3Drefine and FG-MD (B) Scatter plot for TM-score changes for 3Drefine and FG-MD (C) Scatter plot for RMSD-score changes for 3Drefine and FG-MD (D) Scatter plot for MolProbity-score changes for 3Drefine and FG-MD (E) Structural superposition of initial model (blue) and refined model using 3Drefine (red) on the native structure (green) for CASP9 target TR606. (F) Structural superposition of initial model (blue) and refined model using 3Drefine (red) on the native structure (green) for CASP9 target TR624. The values under each model indicate GDT-HA, TM-score, RMSD and MolProbity score respectively before (blue) and after (red) refinement.

Fig. 4

Scatter plot of RMSD changes for 107 CASP9 Targets (Initial models generated using MULTICOM-REFINE).

Fig. 5

Refinement results for 107 CASP9 Targets using 3Drefine (Initial structures generated using MULTICOM-REFINE) (A) GDT-HA score changes. A positive change indicates the quality of the model of a target has been improved by refinement (B) TM score changes. A positive change indicates the quality of the model of a target has been improved by refinement.