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Protein structure search and local structure characterization.

Ku SY, Hu YJ - BMC Bioinformatics (2008)

Bottom Line: We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted.Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets.Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, National Chiao Tung University, 1001 University Rd. Hsinchu, Taiwan. gis92622@cis.nctu.edu.tw

ABSTRACT

Background: Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.

Results: We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at http://140.113.166.178/safast/.

Conclusion: The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

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Superimposition examples based on alignments identified by SA-FAST. (a) 1fxiA & 1ubq_ (b) 2azaA & 1paz_ (c) 1cewI & 1molA (d) 1cid_ & 2rhe.
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Figure 5: Superimposition examples based on alignments identified by SA-FAST. (a) 1fxiA & 1ubq_ (b) 2azaA & 1paz_ (c) 1cewI & 1molA (d) 1cid_ & 2rhe.

Mentions: To further evaluate the predictive validity of our alphabet, we examined pairwise alignment of difficult cases based on the number of residues aligned and the superposition root mean square deviation (RMSD). To avoid alignment process bias and to maintain consistency in our analysis of various structural alphabets, we applied the same FASTA-based alignment algorithm [27] in the alignment tests. We tested the alphabets and substitution matrices used in PBE-align, 3D-BLAST, and SA-FAST on ten difficult cases of previously studied pairwise alignments and compared the results with those produced using VAST, DALI, CE, and FATCAT [8,36]. Based on the alignments obtained using different alphabets and matrices, we used VMD [37] to calculate the superposition RMSD for PBE-align, 3D-BLAST, and SA-FAST. Table 9 shows that our alphabet had the lowest average RMSD per aligned residue among the three structural alphabets in the ten difficult alignment tests. Figure 5 shows four superimposition examples based on our structural alphabet.


Protein structure search and local structure characterization.

Ku SY, Hu YJ - BMC Bioinformatics (2008)

Superimposition examples based on alignments identified by SA-FAST. (a) 1fxiA & 1ubq_ (b) 2azaA & 1paz_ (c) 1cewI & 1molA (d) 1cid_ & 2rhe.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2529324&req=5

Figure 5: Superimposition examples based on alignments identified by SA-FAST. (a) 1fxiA & 1ubq_ (b) 2azaA & 1paz_ (c) 1cewI & 1molA (d) 1cid_ & 2rhe.
Mentions: To further evaluate the predictive validity of our alphabet, we examined pairwise alignment of difficult cases based on the number of residues aligned and the superposition root mean square deviation (RMSD). To avoid alignment process bias and to maintain consistency in our analysis of various structural alphabets, we applied the same FASTA-based alignment algorithm [27] in the alignment tests. We tested the alphabets and substitution matrices used in PBE-align, 3D-BLAST, and SA-FAST on ten difficult cases of previously studied pairwise alignments and compared the results with those produced using VAST, DALI, CE, and FATCAT [8,36]. Based on the alignments obtained using different alphabets and matrices, we used VMD [37] to calculate the superposition RMSD for PBE-align, 3D-BLAST, and SA-FAST. Table 9 shows that our alphabet had the lowest average RMSD per aligned residue among the three structural alphabets in the ten difficult alignment tests. Figure 5 shows four superimposition examples based on our structural alphabet.

Bottom Line: We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted.Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets.Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, National Chiao Tung University, 1001 University Rd. Hsinchu, Taiwan. gis92622@cis.nctu.edu.tw

ABSTRACT

Background: Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.

Results: We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at http://140.113.166.178/safast/.

Conclusion: The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

Show MeSH
Related in: MedlinePlus