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Nucleic Acids Research





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Oxford Academic Press


Copyright © 2005 The Author(s) The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated.


The accuracy of comparative models of proteins is addressed here. A set of 12 732 single-template models of sequences of known high-resolution structures was built by an automated procedure. Accuracy of several structure-derived properties, such as surface area, residue accessibility, presence of pockets, electrostatic potential and others, was determined as a function of template:target sequence identity by comparing models with their corresponding experimental structures. As expected, the average accuracy of structure-derived properties always increases with higher template:target sequence identity, but the exact shape of this relationship can differ from one property to another. A comparison of structure-derived properties measured from NMR and X-ray structures of the same protein shows that for most properties, the NMR/X-ray difference is of the same order as the error in models based on ~ 40% template:target sequence identity. The exact sequence identity at which properties reach that accuracy varies between 25 and 50%, depending on the property being analyzed. A general characteristic of simple comparative models is that their surface has increased area as a consequence of being more rugged than that of experimental structures. This suggests that including solvent effects during model building or refinement could significantly improve the accuracy of surface properties in comparative models.