Abstract
BackgroundAlthough multiple templates are frequently used in comparative modeling, the effect of inclusion of additional template(s) on model accuracy (when compared to that of corresponding single-template based models) is not clear. To address this, we systematically analyze two-template models, the simplest case of multiple-template modeling. For an existing target-template pair (single-template modeling), a two-template based model of the target sequence is constructed by including an additional template without changing the original alignment to measure the effect of the second template on model accuracy.ResultsEven though in a large number of cases a two-template model showed higher accuracy than the corresponding one-template model, over the entire dataset only a marginal improvement was observed on average, as there were many cases where no change or the reverse change was observed. The increase in accuracy due to the structural complementarity of the templates increases at higher alignment accuracies. The combination of templates showing the highest potential for improvement is that where both templates share similar and low (less than 30%) sequence identity with the target, as well as low sequence identity with each other. The structural similarity between the templates also helps in identifying template combinations having a higher chance of resulting in an improved model.ConclusionInclusion of additional template(s) does not necessarily improve model quality, but there are distinct combinations of the two templates, which can be selected a priori, that tend to show improvement in model quality over the single template model. The benefit derived from the structural complementarity is dependent on the accuracy of the modeling alignment. The study helps to explain the observation that a careful selection of templates together with an accurate target:template alignment are necessary to the benefit from using multiple templates in comparative modeling and provides guidelines to maximize the benefit from using multiple templates. This enables formulation of simple template selection rules to rank targets of a protein family in the context of structural genomics.
Highlights
Multiple templates are frequently used in comparative modeling, the effect of inclusion of additional template(s) on model accuracy is not clear
Combinations of templates with S1 ≅ S2, S1 < 30%, S3 < 30%, and Template root mean squared deviation (RMSD) 3.5–5.5 Å show a high probability of improved model accuracy over the single-template model, while most remaining combinations tend to deteriorate the model
Since most modeling cases fall in the sequence identity range below 30%, our results enable judicious choice of additional templates to improve model accuracy
Summary
Multiple templates are frequently used in comparative modeling, the effect of inclusion of additional template(s) on model accuracy (when compared to that of corresponding single-template based models) is not clear. Is comparative modeling the most accurate method of structure prediction [4], but it allows a priori estimation of the approximate quality of the models [5]. Due to their added value [6], models are suitable for comparative studies over complete protein families [7,8,9]. Improving the quality of comparative models, especially for models where Target:Template sequence identity is less than 30% still remains a challenge [10]
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