Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment
<p>Abstract</p> <p>Background</p> <p>The quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize direc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-10-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/13/259 |
| _version_ | 1828438072807653376 |
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| author | Daniels Noah M Nadimpalli Shilpa Cowen Lenore J |
| author_facet | Daniels Noah M Nadimpalli Shilpa Cowen Lenore J |
| author_sort | Daniels Noah M |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>The quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize directly protein sequence similarity, and in fact, determining the proper way to incorporate sequence similarity measures into the construction and assessment of protein multiple structure alignments has proved surprisingly difficult.</p> <p>Results</p> <p>We present Formatt, a multiple structure alignment based on the Matt purely geometric multiple structure alignment program, that also takes into account sequence similarity when constructing alignments. We show that Formatt outperforms Matt and other popular structure alignment programs on the popular HOMSTRAD benchmark. For the SABMark twilight zone benchmark set that captures more remote homology, Formatt and Matt outperform other programs; depending on choice of embedded sequence aligner, Formatt produces either better sequence and structural alignments with a smaller core size than Matt, or similarly sized alignments with better sequence similarity, for a small cost in average RMSD.</p> <p>Conclusions</p> <p>Considering sequence information as well as purely geometric information seems to improve quality of multiple structure alignments, though defining what constitutes the best alignment when sequence and structural measures would suggest different alignments remains a difficult open question.</p> |
| first_indexed | 2024-12-10T19:57:27Z |
| format | Article |
| id | doaj.art-30eacdaac4b44845bc159810720060ac |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T19:57:27Z |
| publishDate | 2012-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-30eacdaac4b44845bc159810720060ac2022-12-22T01:35:37ZengBMCBMC Bioinformatics1471-21052012-10-0113125910.1186/1471-2105-13-259Formatt: Correcting protein multiple structural alignments by incorporating sequence alignmentDaniels Noah MNadimpalli ShilpaCowen Lenore J<p>Abstract</p> <p>Background</p> <p>The quality of multiple protein structure alignments are usually computed and assessed based on geometric functions of the coordinates of the backbone atoms from the protein chains. These purely geometric methods do not utilize directly protein sequence similarity, and in fact, determining the proper way to incorporate sequence similarity measures into the construction and assessment of protein multiple structure alignments has proved surprisingly difficult.</p> <p>Results</p> <p>We present Formatt, a multiple structure alignment based on the Matt purely geometric multiple structure alignment program, that also takes into account sequence similarity when constructing alignments. We show that Formatt outperforms Matt and other popular structure alignment programs on the popular HOMSTRAD benchmark. For the SABMark twilight zone benchmark set that captures more remote homology, Formatt and Matt outperform other programs; depending on choice of embedded sequence aligner, Formatt produces either better sequence and structural alignments with a smaller core size than Matt, or similarly sized alignments with better sequence similarity, for a small cost in average RMSD.</p> <p>Conclusions</p> <p>Considering sequence information as well as purely geometric information seems to improve quality of multiple structure alignments, though defining what constitutes the best alignment when sequence and structural measures would suggest different alignments remains a difficult open question.</p>http://www.biomedcentral.com/1471-2105/13/259 |
| spellingShingle | Daniels Noah M Nadimpalli Shilpa Cowen Lenore J Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment BMC Bioinformatics |
| title | Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment |
| title_full | Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment |
| title_fullStr | Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment |
| title_full_unstemmed | Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment |
| title_short | Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment |
| title_sort | formatt correcting protein multiple structural alignments by incorporating sequence alignment |
| url | http://www.biomedcentral.com/1471-2105/13/259 |
| work_keys_str_mv | AT danielsnoahm formattcorrectingproteinmultiplestructuralalignmentsbyincorporatingsequencealignment AT nadimpallishilpa formattcorrectingproteinmultiplestructuralalignmentsbyincorporatingsequencealignment AT cowenlenorej formattcorrectingproteinmultiplestructuralalignmentsbyincorporatingsequencealignment |