Figure 4.

The added value of re-ranking models. The difference in the cumulative observed model quality score of the top ranked models is shown after the 5 models for each target provided by each server are re-ranked using the ModFOLD or 3D-Jury methods. Each bar represents Σ(m_i-m_j), where m_i is the observed model quality of the top ranked model after the 5 server models are re-ranked and m_j is the observed model quality of the original top ranked model submitted by the server. N.B. Only the common subset of servers which had submitted 5 models for all targets are included in the plot. The error bars show the standard error of the mean observed quality. Overall there is a mean increase of 0.44 in the cumulative observed model quality of the top ranked models if the ModFOLD method is used to re-rank the models provided by individual servers, however, there is a mean decrease of 0.56 if models are re-ranked using the 3D-Jury method (see Table 9). On the x axis, the first asterisk indicates a fold recognition server where the quality of the top ranking model can be significantly improved. An additional asterisk indicates a significant improvement of the ModFOLD method over the 3D-Jury method.