Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs
Fig 1
Comparison of average RMSD change using various alignment methods.
A total of 100 models were produced for each alignment method. The average RMSD of the models were normalized to the average RMSD of the models produced with the knowledge-based alignment (black). Values above 1 represent an alignment method that produced on average worse models while values below 1 represent an alignment method that produced on average better models. For (A) all receptors regardless of family, the knowledge-based modeling performs the best regardless of region analyzed. When split between (B) Class A and (C) Classes B, C, and F, the majority of the improvements are found in the Classes B, C, and F where template availability is limited.