Protein structure prediction and model quality assessment

Abstract

Protein structures have proven to be a crucial piece of information for biomedical research. Of the millions of currently sequenced proteins only a small fraction is experimentally solved for structure and the only feasible way to bridge the gap between sequence and structure data is computational modeling. Half a century has passed since it was shown that the amino acid sequence of a protein determines its shape, but a method to translate the sequence code reliably into the 3D structure still remains to be developed. This review summarizes modern protein structure prediction techniques with the emphasis on comparative modeling, and describes the recent advances in methods for theoretical model quality assessment.

FIGURE 1. Alignment accuracy for the best model of each target in all CASPs as percentage of maximum alignability

Targets are ordered by sequence identity between the target and the closest template. Values on the vertical axis represent the ratio between the number of residues correctly aligned to the target structure in the best model and the maximum number of residues that can be aligned between the best available template and the target structure. An alignment of 100% indicates that all residues with an equivalent in the template were correctly aligned. A value greater than 100% indicates an improvement in model quality beyond that obtained by copying a template structure. Values above 100% may be obtained by using additional templates, through refinement or with template free methods. Trend lines show a steady though sometimes modest improvement over successive CASPs.

FIGURE 2. Fraction of targets for which the best submitted models in the template-based category are superior to the models naively built on the best template in CASPs 5–7