RaptorX: exploiting structure information for protein alignment by statistical inference

Abstract

This work presents RaptorX, a statistical method for template-based protein modeling that improves alignment accuracy by exploiting structural information in a single or multiple templates. RaptorX consists of three major components: single-template threading, alignment quality prediction, and multiple-template threading. This work summarizes the methods used by RaptorX and presents its CASP9 result analysis, aiming to identify major bottlenecks with RaptorX and template-based modeling and hopefully directions for further study. Our results show that template structural information helps a lot with both single-template and multiple-template protein threading especially when closely-related templates are unavailable, and there is still large room for improvement in both alignment and template selection. The RaptorX web server is available at http://raptorx.uchicago.edu.

Figure 1

This figure compares RaptorX alignments with TMalign alignments for 38 CASP9 targets. A point above the diagonal line indicates that the RaptorX alignment is worse than the corresponding TMalign alignment.

Figure 2

This figure compares the predicted and real quality of the models built from the first-ranked templates. A point above the diagnoal line indicates that for a specific model its predicted quality overestimates the real value.

Figure 3

This figure illustrates the quality of the models built from the first-ranked templates and those from the best out of the top 5 or top 10 templates. A point on or close to the diagnoal line indicates that the first-ranked model is (or very close to) the best out of the top 5 or 10 models.

Figure 4

This figure illustrates the advantage of multiple-template models over single-template models: (a) multiple-template models vs. first-ranked template models; (b) multiple-template models vs. the best single-template models. A point under the diagonal line indicates that the multiple-template model is better.

Figure 5

This figure illustrates the quality of the models built from RaptorX multiple-template alignments and 3DCOMB alignments. A point above the red line indicates that the 3DCOMB model is at least 5 GDT units better than the RaptorX model.

Figure 6

This figure illustrates the quality of the models built by RaptorX and HHpred for the CASP9 targets. Each point represents one target. A point above the diagnoal line indicates that RaptorX generated a better 3D model than HHpred.

Figure 7

This figure illustrates the quality of the models built by RaptorX and HHpred for 1000 proteins. Both methods use only single templates to generate alignments and build models (with MODELLER). The template set consists of more than 6000 non-redundant protein chains generated by the PISCES server while the target set includes 1000 proteins randomly-chosen from these 6000 proteins. Each point represents one target. A point above the diagnoal line indicates that RaptorX generated a better 3D model than HHpred.

Figure 8

This figure illustrates the TMscore difference distribution of the models generated by RaptorX and HHpred for 1000 proteins. For both methods we generated alignments and builded models (with MODELLER) using only single templates. The template set consists of more than 6000 non-redundant protein chains generated by the PISCES server while the target set includes 1000 proteins randomly-chosen from these 6000 proteins. The blue columns indicate that the numbers of targets for which RaptorX is better while the red columns indicate that HHpred is better.