In the absence of experimental structures, comparative modeling continues to be the chosen method for retrieving structural information of target proteins. However, models lack the accuracy of experimental structures. Alignment error and structural divergence (between target and template) influence model accuracy the most. Here, we examine the potential additional impact of backbone geometry, as our previous studies have suggested that the structural class (all-α, αβ, all-β) of proteins may influence the accuracy of their models. In the twilight zone (sequence identity ≤ 30%) and at a similar level of target-template divergence, model accuracy of proteins does indeed follow the trend all-α > αβ > all-β. This is mainly a result of the alignment accuracy following the same trend (all-α > αβ > all-β) with backbone geometry playing only a minor role. Differences in the diversity of sequences belonging to different structural classes leads to the observed accuracy differences thus enabling a priori accuracy estimates of alignments/models in a class-dependent manner. This study provides a systematic description and quantification of structural class-dependent effect in comparative modeling. The study also suggests datasets for large-scale sequence/structure analyses should have equal representation of different structural classes to avoid class-dependent bias.
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