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Similarity search for local protein structures at atomic resolution by exploiting a database management system

View Article: PubMed Central - PubMed

ABSTRACT

A method to search for local structural similarities in proteins at atomic resolution is presented. It is demonstrated that a huge amount of structural data can be handled within a reasonable CPU time by using a conventional relational database management system with appropriate indexing of geometric data. This method, which we call geometric indexing, can enumerate ligand binding sites that are structurally similar to sub-structures of a query protein among more than 160,000 possible candidates within a few hours of CPU time on an ordinary desktop computer. After detecting a set of high scoring ligand binding sites by the geometric indexing search, structural alignments at atomic resolution are constructed by iteratively applying the Hungarian algorithm, and the statistical significance of the final score is estimated from an empirical model based on a gamma distribution. Applications of this method to several protein structures clearly shows that significant similarities can be detected between local structures of non-homologous as well as homologous proteins.

No MeSH data available.


Related in: MedlinePlus

Comparison of GI score and IR score. Each point represents a template included in the top 50,000 hits for the query (PDB ID: 101m). The regression line is also shown. The correlation coefficient between the scores is 0.87.
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f3-3_75: Comparison of GI score and IR score. Each point represents a template included in the top 50,000 hits for the query (PDB ID: 101m). The regression line is also shown. The correlation coefficient between the scores is 0.87.

Mentions: Figure 3 shows the two scores of each of the 50,000 templates. In general, the two scores correlate with each other very well, with a correlation coefficient of 0.87 in this case. But the rank of some templates may change dramatically upon refinement. The refinement greatly improved the scores of some templates of relatively low GI scores.


Similarity search for local protein structures at atomic resolution by exploiting a database management system
Comparison of GI score and IR score. Each point represents a template included in the top 50,000 hits for the query (PDB ID: 101m). The regression line is also shown. The correlation coefficient between the scores is 0.87.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC5036654&req=5

f3-3_75: Comparison of GI score and IR score. Each point represents a template included in the top 50,000 hits for the query (PDB ID: 101m). The regression line is also shown. The correlation coefficient between the scores is 0.87.
Mentions: Figure 3 shows the two scores of each of the 50,000 templates. In general, the two scores correlate with each other very well, with a correlation coefficient of 0.87 in this case. But the rank of some templates may change dramatically upon refinement. The refinement greatly improved the scores of some templates of relatively low GI scores.

View Article: PubMed Central - PubMed

ABSTRACT

A method to search for local structural similarities in proteins at atomic resolution is presented. It is demonstrated that a huge amount of structural data can be handled within a reasonable CPU time by using a conventional relational database management system with appropriate indexing of geometric data. This method, which we call geometric indexing, can enumerate ligand binding sites that are structurally similar to sub-structures of a query protein among more than 160,000 possible candidates within a few hours of CPU time on an ordinary desktop computer. After detecting a set of high scoring ligand binding sites by the geometric indexing search, structural alignments at atomic resolution are constructed by iteratively applying the Hungarian algorithm, and the statistical significance of the final score is estimated from an empirical model based on a gamma distribution. Applications of this method to several protein structures clearly shows that significant similarities can be detected between local structures of non-homologous as well as homologous proteins.

No MeSH data available.


Related in: MedlinePlus