3DS3 and 3DS5 3D-SHOTGUN meta-predictors in CAFASP3

Abstract

The performance of the 3DS3 and 3DS5 3D-SHOTGUN meta-predictors in CAFASP3 is reported. The 3D-SHOTGUN meta-predictors are fully automatic fold recognition servers that attempt to incorporate into the prediction process a number of successful strategies that human predictors often apply. Namely, the input to 3D-SHOTGUN are the top five models predicted by a number of independent fold recognition servers and its output are hybrid models, assembled by using the recurrent structural information from the input models. The resulting hybrid models are, on average, more accurate and more complete than the input models. When evaluated on a large set of prediction targets, the 3D-SHOTGUN servers show increased sensitivities and significantly better specificities. For CAFASP3, the 3DS3 and 3DS3 and 3DS5 used a preliminary implementation of the 3D-SHOTGUN method, which lacked a refinement step. Although this did not have a significant effect on the easier targets, for the hardest prediction targets, where the input models had significant structural conflicts, the 3D-SHOTGUN models contained a number of non-native-like features such as fragmentation and overlaps. The CAFASP3 evaluation identified the 3D-SHOTGUN meta-predictors within the top three most sensitive and most specific servers. A fully automated refinement step to the 3D-SHOTGUN method is currently being implemented, and preliminary results indicate that in addition to “cleaning up” such undesirable features, it is able to further increase the accuracy of the resulting models. Proteins 2003;53:517–523. © 2003 Wiley-Liss, Inc.