SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model

By Wei Zhang, Song Liu, Yaoqi Zhou. 2008

73 citations recorded by the source corpus.

How to recognize the structural fold of a protein is one of the challenges in protein structure prediction. We have developed a series of single (non-consensus) methods (SPARKS, SP2, SP3, SP4) that are based on weighted matching of two to four sequence and structure-based profiles. There is a robust improvement of the accuracy and sensitivity of fold recognition as the number of matching profiles increases. Here, we introduce a new profile-profile comparison term based on real-value dihedral torsion angles. Together with updated real-value solvent accessibility profile and a new variable gap-penalty model based on fractional power of insertion/deletion profiles, the new method (SP5) leads to a robust improvement over previous SP method. There is a 2% absolute increase (5% relative improvement) in alignment accuracy over SP4 based on two independent benchmarks. Moreover, SP5 makes 7% absolute increase (22% relative improvement) in success rate of recognizing correct structural folds, and 32% relative improvement in model accuracy of models within the same fold in Lindahl benchmark. In addition, modeling accuracy of top-1 ranked models is improved by 12% over SP4 for the difficult targets in CASP 7 test set. These results highlight the importance of harnessing predicted structural properties in challenging remote-homolog recognition. The SP5 server is available at http://sparks.informatics.iupui.edu.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Jinbo Xu
2. Sheng Wang
3. Zhiyong Wang
4. Jianlin Cheng
5. Jian Peng
6. Taeho C. Jo
7. Jianzhu Ma
8. Siqi Sun

In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianlin Cheng
4. Zhiyong Wang
5. Badri Adhikari
6. Jianzhu Ma
7. Jian Peng
8. Yang Zhang

Citations in the corpus

• Template-based protein structure modeling using the RaptorX web server (2012, background): “It is thus often found that evolutionarily distant or unrelated protein sequences share common structural elements, which is used by threading methods 8,9 such as MUSTER 10 , SPARKS 11,12 and RAPTOR 13–15 .”
• Improving protein fold recognition by random forest (2014, methodology): “…folds when there is little sequence similarity between a target and a template protein, such as PSI-BLAST [18], HMMER [19], SAM-T98 [20], SSHMM [21],THREADER [22], FUGUE [23], SPARKS [24], SP3 [25], HHpred [26], FOLDpro [5], SP4 [27], SP5 [28], RAPTOR [29], SPARKS-X [30], and BoostThreader [31].”