GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures
<p>Abstract</p> <p>Background</p> <p>We introduce <it>GASH</it>, a new, publicly accessible program for structural alignment and superposition. Alignments are scored by the Number of Equivalent Residues (NER), a quantitative measure of structural similarity...
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| Format: | Article |
| Language: | English |
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BMC
2005-09-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/6/221 |
| _version_ | 1818386282036854784 |
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| author | Toh Hiroyuki Standley Daron M Nakamura Haruki |
| author_facet | Toh Hiroyuki Standley Daron M Nakamura Haruki |
| author_sort | Toh Hiroyuki |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>We introduce <it>GASH</it>, a new, publicly accessible program for structural alignment and superposition. Alignments are scored by the Number of Equivalent Residues (NER), a quantitative measure of structural similarity that can be applied to any structural alignment method. Multiple alignments are optimized by conjugate gradient maximization of the NER score within the genetic algorithm framework. Initial alignments are generated by the program Local ASH, and can be supplemented by alignments from any other program.</p> <p>Results</p> <p>We compare GASH to DaliLite, CE, and to our earlier program Global ASH on a difficult test set consisting of 3,102 structure pairs, as well as a smaller set derived from the Fischer-Eisenberg set. The extent of alignment crossover, as well as the completeness of the initial set of alignments are examined. The quality of the superpositions is evaluated both by NER and by the number of aligned residues under three different RMSD cutoffs (2,4, and 6Å). In addition to the numerical assessment, the alignments for several biologically related structural pairs are discussed in detail.</p> <p>Conclusion</p> <p>Regardless of which criteria is used to judge the superposition accuracy, GASH achieves the best overall performance, followed by DaliLite, Global ASH, and CE. In terms of CPU usage, DaliLite CE and GASH perform similarly for query proteins under 500 residues, but for larger proteins DaliLite is faster than GASH or CE. Both an http interface and a simple object application protocol (SOAP) interface to the GASH program are available at <url>http://www.pdbj.org/GASH/</url>.</p> |
| first_indexed | 2024-12-14T03:51:34Z |
| format | Article |
| id | doaj.art-c8cfa211898b454f91178ca97c543192 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T03:51:34Z |
| publishDate | 2005-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c8cfa211898b454f91178ca97c5431922022-12-21T23:18:12ZengBMCBMC Bioinformatics1471-21052005-09-016122110.1186/1471-2105-6-221GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structuresToh HiroyukiStandley Daron MNakamura Haruki<p>Abstract</p> <p>Background</p> <p>We introduce <it>GASH</it>, a new, publicly accessible program for structural alignment and superposition. Alignments are scored by the Number of Equivalent Residues (NER), a quantitative measure of structural similarity that can be applied to any structural alignment method. Multiple alignments are optimized by conjugate gradient maximization of the NER score within the genetic algorithm framework. Initial alignments are generated by the program Local ASH, and can be supplemented by alignments from any other program.</p> <p>Results</p> <p>We compare GASH to DaliLite, CE, and to our earlier program Global ASH on a difficult test set consisting of 3,102 structure pairs, as well as a smaller set derived from the Fischer-Eisenberg set. The extent of alignment crossover, as well as the completeness of the initial set of alignments are examined. The quality of the superpositions is evaluated both by NER and by the number of aligned residues under three different RMSD cutoffs (2,4, and 6Å). In addition to the numerical assessment, the alignments for several biologically related structural pairs are discussed in detail.</p> <p>Conclusion</p> <p>Regardless of which criteria is used to judge the superposition accuracy, GASH achieves the best overall performance, followed by DaliLite, Global ASH, and CE. In terms of CPU usage, DaliLite CE and GASH perform similarly for query proteins under 500 residues, but for larger proteins DaliLite is faster than GASH or CE. Both an http interface and a simple object application protocol (SOAP) interface to the GASH program are available at <url>http://www.pdbj.org/GASH/</url>.</p>http://www.biomedcentral.com/1471-2105/6/221 |
| spellingShingle | Toh Hiroyuki Standley Daron M Nakamura Haruki GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures BMC Bioinformatics |
| title | GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures |
| title_full | GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures |
| title_fullStr | GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures |
| title_full_unstemmed | GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures |
| title_short | GASH: An improved algorithm for maximizing the number of equivalent residues between two protein structures |
| title_sort | gash an improved algorithm for maximizing the number of equivalent residues between two protein structures |
| url | http://www.biomedcentral.com/1471-2105/6/221 |
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