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Tripeptide analysis of protein structures
BACKGROUND: An efficient building block for protein structure prediction can be tripeptides. 8000 different tripeptides from a dataset of 1220 high resolution (≤ 2.0°A) structures from the Protein Data Bank (PDB) have been looked at, to determine which are structurally rigid and non-rigid. This data...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2002
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC140318/ https://www.ncbi.nlm.nih.gov/pubmed/12495440 http://dx.doi.org/10.1186/1472-6807-2-9 |
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author | Anishetty, Sharmila Pennathur, Gautam Anishetty, Ramesh |
author_facet | Anishetty, Sharmila Pennathur, Gautam Anishetty, Ramesh |
author_sort | Anishetty, Sharmila |
collection | PubMed |
description | BACKGROUND: An efficient building block for protein structure prediction can be tripeptides. 8000 different tripeptides from a dataset of 1220 high resolution (≤ 2.0°A) structures from the Protein Data Bank (PDB) have been looked at, to determine which are structurally rigid and non-rigid. This data has been statistically analyzed, discussed and summarized. The entire data can be utilized for the building of protein structures. RESULTS: Tripeptides have been classified into three categories: rigid, non-rigid and intermediate, based on the relative structural rigidity between C(α )and C(β )atoms in a tripeptide. We found that 18% of the tripeptides in the dataset can be classified as rigid, 4% as non-rigid and 78% as intermediate. Many rigid tripeptides are made of hydrophobic residues, however, there are tripeptides with polar side chains forming rigid structures. The bulk of the tripeptides fall in the intermediate class while very small numbers actually fall in the non-rigid class. Structurally all rigid tripeptides essentially form two structural classes while the intermediate and non-rigid tripeptides fall into one structural class. This notion of rigidity and non-rigidity is designed to capture side chain interactions but not secondary structures. CONCLUSIONS: Rigid tripeptides have no correlation with the secondary structures in proteins and hence this work is complementary to such studies. Tripeptide data may be used to predict plausible structures for oligopeptides and for denovo protein design. |
format | Text |
id | pubmed-140318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2002 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-1403182003-02-06 Tripeptide analysis of protein structures Anishetty, Sharmila Pennathur, Gautam Anishetty, Ramesh BMC Struct Biol Research Article BACKGROUND: An efficient building block for protein structure prediction can be tripeptides. 8000 different tripeptides from a dataset of 1220 high resolution (≤ 2.0°A) structures from the Protein Data Bank (PDB) have been looked at, to determine which are structurally rigid and non-rigid. This data has been statistically analyzed, discussed and summarized. The entire data can be utilized for the building of protein structures. RESULTS: Tripeptides have been classified into three categories: rigid, non-rigid and intermediate, based on the relative structural rigidity between C(α )and C(β )atoms in a tripeptide. We found that 18% of the tripeptides in the dataset can be classified as rigid, 4% as non-rigid and 78% as intermediate. Many rigid tripeptides are made of hydrophobic residues, however, there are tripeptides with polar side chains forming rigid structures. The bulk of the tripeptides fall in the intermediate class while very small numbers actually fall in the non-rigid class. Structurally all rigid tripeptides essentially form two structural classes while the intermediate and non-rigid tripeptides fall into one structural class. This notion of rigidity and non-rigidity is designed to capture side chain interactions but not secondary structures. CONCLUSIONS: Rigid tripeptides have no correlation with the secondary structures in proteins and hence this work is complementary to such studies. Tripeptide data may be used to predict plausible structures for oligopeptides and for denovo protein design. BioMed Central 2002-12-21 /pmc/articles/PMC140318/ /pubmed/12495440 http://dx.doi.org/10.1186/1472-6807-2-9 Text en Copyright © 2002 Anishetty et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. |
spellingShingle | Research Article Anishetty, Sharmila Pennathur, Gautam Anishetty, Ramesh Tripeptide analysis of protein structures |
title | Tripeptide analysis of protein structures |
title_full | Tripeptide analysis of protein structures |
title_fullStr | Tripeptide analysis of protein structures |
title_full_unstemmed | Tripeptide analysis of protein structures |
title_short | Tripeptide analysis of protein structures |
title_sort | tripeptide analysis of protein structures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC140318/ https://www.ncbi.nlm.nih.gov/pubmed/12495440 http://dx.doi.org/10.1186/1472-6807-2-9 |
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