Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field

Abstract

Background: Although experimental methods for determining protein structure are providing high resolution structures, they cannot keep the pace at which amino acid sequences are resolved on the scale of entire genomes. For a considerable fraction of proteins whose structures will not be determined experimentally, computational methods can provide valuable information. The value of structural models in biological research depends critically on their quality. Development of high-accuracy computational methods that reliably generate near-experimental quality structural models is an important, unsolved problem in the protein structure modeling.

Results: Large sets of structural decoys have been generated using reduced conformational space protein modeling tool CABS. Subsequently, the reduced models were subject to all-atom reconstruction. Then, the resulting detailed models were energy-minimized using state-of-the-art all-atom force field, assuming fixed positions of the alpha carbons. It has been shown that a very short minimization leads to the proper ranking of the quality of the models (distance from the native structure), when the all-atom energy is used as the ranking criterion. Additionally, we performed test on medium and low accuracy decoys built via classical methods of comparative modeling. The test placed our model evaluation procedure among the state-of-the-art protein model assessment methods.

Conclusion: These test computations show that a large scale high resolution protein structure prediction is possible, not only for small but also for large protein domains, and that it should be based on a hierarchical approach to the modeling protocol. We employed Molecular Mechanics with fixed alpha carbons to rank-order the all-atom models built on the scaffolds of the reduced models. Our tests show that a physic-based approach, usually considered computationally too demanding for large-scale applications, can be effectively used in such studies.

Figure 1

Accuracy-representative models from the CABS decoys set. Three 2GU3A example models with various distances from the native structure (the lowest energy model – 0.6 Å, intermediate 1.5 Å, and the worst one 3 Å from native). Models are plotted in gray, reference native structure in dark thin line.

Figure 2

Illustration of the secondary structure dependent character of differences between the accuracy-representative models. RMSD deviation from native for each residue of three 2GU3A example decoys (after the best superimposition of the entire structures – see Figure 1). On the sequence axis the secondary structure is symbolically depicted (helices in black and strands in grey).

Figure 3

Results of 1000 iteration minimization for the CABS decoys. For each protein, the energy was plotted as a function of Cα RMSD for all decoys (left panels) and without decoys with abnormal high energy values resulted from structural inaccuracies (right panels). On the left panels, energies of the native structures are denoted by asterisks. The native structures were subjected to the same rebuilding procedure from the Cα-traces as that applied to the decoys. Proteins are ordered in respect to their chain lengths (Table 1) – from the smallest on top (2GR8A) to the largest (2CJPA) on the bottom.

Figure 4

Results of 1000 iteration minimization for the Moulder decoys. For each subset of decoys, the energy was plotted as a function of Cα RMSD for the best scored decoys.