Abstract
The performance of automated protein model building usually decreases with resolution, mainly owing to the lower information content of the experimental data. This calls for a more elaborate use of the available structural information about macromolecules. Here, a new method is presented that uses structural homologues to improve the quality of protein models automatically constructed using ARP/wARP. The method uses local structural similarity between deposited models and the model being built, and results in longer main-chain fragments that in turn can be more reliably docked to the protein sequence. The application of the homology-based model extension method to the example of a CFA synthase at 2.7 Å resolution resulted in a more complete model with almost all of the residues correctly built and docked to the sequence. The method was also evaluated on 1493 molecular-replacement solutions at a resolution of 4.0 Å and better that were submitted to the ARP/wARP web service for model building. A significant improvement in the completeness and sequence coverage of the built models has been observed.
Highlights
Model building is a key step in macromolecular crystallographic structure determination
The method was evaluated on 1493 molecular-replacement solutions at a resolution of 4.0 Aand better that were submitted to the ARP/wARP web service for model building
We have attempted to estimate the number of modelbuilding tasks that have recently been submitted to the ARP/ wARP web service and already had a homologous protein model available in the PDB before the model-building task was launched
Summary
Model building is a key step in macromolecular crystallographic structure determination. With the availability of X-ray diffraction data to a resolution of better than 3.0 Aand an initial map of reasonable quality, model building can often be accomplished using automated approaches. The performance of crystallographic model-building methods is reduced at lower resolution owing to the lower information content of the data (Karmali et al, 2009). For these cases the protein backbone models become fragmented, may contain insertions, deletions or incorrect connections, and may become difficult to assign to the target sequence (Chojnowski et al, 2019). Partial models can be expanded using databases of short continuous
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