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Incorporating Ab Initio energy into threading approaches for protein structure prediction.

Shao M, Wang S, Wang C, Yuan X, Li SC, Zheng W, Bu D - BMC Bioinformatics (2011)

Bottom Line: Subsequently, a local search algorithm is utilized to optimize the scoring function.Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark.In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.Incorporating Ab Initio energy functions into threading can greatly improve alignment accuracy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn

ABSTRACT

Background: Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches--threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure; while the energy functions in Ab Initio methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the Ab Initio energy function can be used to improve threading, especially for the weakly/distant homologous templates.

Results: In this paper, we investigate the possibility to improve alignment-generating through incorporating distant interaction information into the alignment scoring function in a nontrivial approach. Specifically, the distant interaction information is introduced through employing an Ab Initio energy function to evaluate the "partial" decoy built from an alignment. Subsequently, a local search algorithm is utilized to optimize the scoring function.Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark. In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.

Conclusions: Incorporating Ab Initio energy functions into threading can greatly improve alignment accuracy.

Show MeSH
Linear correlation between local score and match state size. Both local score and match state size are calculated from reference alignment of query-template pairs in SALIGN [18] benchmark dataset.
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Figure 2: Linear correlation between local score and match state size. Both local score and match state size are calculated from reference alignment of query-template pairs in SALIGN [18] benchmark dataset.

Mentions: We also investigate the relationship between the scores with the match state size. Analysis suggests the linearity between local(global) scores and match state size. Specifically, the linear correlation coefficient between local(global) scores and the match state size is –0.762 (–0.968) (See Fig.2 and 3 for details). Thus, it is reasonable to normalize both local and global score through dividing by the match state size.


Incorporating Ab Initio energy into threading approaches for protein structure prediction.

Shao M, Wang S, Wang C, Yuan X, Li SC, Zheng W, Bu D - BMC Bioinformatics (2011)

Linear correlation between local score and match state size. Both local score and match state size are calculated from reference alignment of query-template pairs in SALIGN [18] benchmark dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3044312&req=5

Figure 2: Linear correlation between local score and match state size. Both local score and match state size are calculated from reference alignment of query-template pairs in SALIGN [18] benchmark dataset.
Mentions: We also investigate the relationship between the scores with the match state size. Analysis suggests the linearity between local(global) scores and match state size. Specifically, the linear correlation coefficient between local(global) scores and the match state size is –0.762 (–0.968) (See Fig.2 and 3 for details). Thus, it is reasonable to normalize both local and global score through dividing by the match state size.

Bottom Line: Subsequently, a local search algorithm is utilized to optimize the scoring function.Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark.In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.Incorporating Ab Initio energy functions into threading can greatly improve alignment accuracy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. shaomingfu@ict.ac.cn

ABSTRACT

Background: Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches--threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure; while the energy functions in Ab Initio methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the Ab Initio energy function can be used to improve threading, especially for the weakly/distant homologous templates.

Results: In this paper, we investigate the possibility to improve alignment-generating through incorporating distant interaction information into the alignment scoring function in a nontrivial approach. Specifically, the distant interaction information is introduced through employing an Ab Initio energy function to evaluate the "partial" decoy built from an alignment. Subsequently, a local search algorithm is utilized to optimize the scoring function.Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark. In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.

Conclusions: Incorporating Ab Initio energy functions into threading can greatly improve alignment accuracy.

Show MeSH