Computational protein structure refinement: Almost there, yet still so far to go

Abstract

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

Figure 1:

Typical refinement protocols via MD-based sampling (left) and iterative structure optimization (right). Grey colors indicate optional elements. KB: Knowledge-based.

Figure 2:

Examples of successful protein structure refinement based on CASP12 targets (from CASP12 web site: http://www.predictioncenter.org/casp12/index.cgi). Experimental, initial, and refined structures are shown in red, green, and orange, respectively. RMSD values refer to Cα atom deviations. The GDT_HA score is explained in the text.