Percentage of secondary structure 0.3020 0.3125 Percentage of Helix 0.2744 0.2729 Percentage of Sheet 0.2431 0.2003 Percentage of Coil 0.0148 0.0256 Consistence Score of Secondary Structure 0.3164 0.3214 Percentage of matching Solvent Accessibility 0.3351 0.3562 Matching of Bury Amino Acid 0.2721 0.3087 Matching of expose Amino Acid 0.1726 0.1558 DDFIRE 0.3171 0.3142 DFIRE (too many Inf) 0 0 DOPE 0.2708 0.2465 OPUS_CA 0.3642 0.3518 Proq2 0.3292 0.3200 RAPDF 0.3087 0.3240 RW 0.2585 0.2838 Based on the correlation coefficient results showed above, we can see that in CASP10 all model dataset (dataset one) and QA stage one dataset (dataset two), secondary structure based features, solvent accessibility based features and Proq2 showed better performance. The common ground between these two datasets is that they all contain the knowledge of all the models from different servers. That means they include good, medium, and bad models in the datasets. Likewise, structure based features and Proq2 work well on jagged-quality models pool. QA stage two dataset consist of 150 best models in dataset one, which means most of the models in this dataset got a better quality. When tested on QA stage two dataset
Percentage of secondary structure 0.3020 0.3125 Percentage of Helix 0.2744 0.2729 Percentage of Sheet 0.2431 0.2003 Percentage of Coil 0.0148 0.0256 Consistence Score of Secondary Structure 0.3164 0.3214 Percentage of matching Solvent Accessibility 0.3351 0.3562 Matching of Bury Amino Acid 0.2721 0.3087 Matching of expose Amino Acid 0.1726 0.1558 DDFIRE 0.3171 0.3142 DFIRE (too many Inf) 0 0 DOPE 0.2708 0.2465 OPUS_CA 0.3642 0.3518 Proq2 0.3292 0.3200 RAPDF 0.3087 0.3240 RW 0.2585 0.2838 Based on the correlation coefficient results showed above, we can see that in CASP10 all model dataset (dataset one) and QA stage one dataset (dataset two), secondary structure based features, solvent accessibility based features and Proq2 showed better performance. The common ground between these two datasets is that they all contain the knowledge of all the models from different servers. That means they include good, medium, and bad models in the datasets. Likewise, structure based features and Proq2 work well on jagged-quality models pool. QA stage two dataset consist of 150 best models in dataset one, which means most of the models in this dataset got a better quality. When tested on QA stage two dataset