@inproceedings{Hinneburg2000Clust-5674,
  year={2000},
  doi={10.1109/BIBE.2000.889588},
  title={Clustering 3D-structures of Small Aminoacid-chains for Detecting Dependence from Their Sequential Context in Proteins},
  isbn={0-7695-0862-6},
  publisher={IEEE Comput. Soc},
  booktitle={Proceedings IEEE International Symposium on Bio-Informatics and Biomedical Engineering},
  pages={43--49},
  author={Hinneburg, Alexander and Keim, Daniel A. and Brandt, Wolfgang}
}

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    <dc:contributor>Brandt, Wolfgang</dc:contributor>
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    <dc:creator>Hinneburg, Alexander</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dcterms:abstract xml:lang="eng">In the past a good number of rotamer libraries have been published, which allow a deeper understanding of the conformational behavior of amino acid residues in proteins. Since the number of available high resolution X-ray protein structures has grown significantly over the last years, a more comprehensive analysis of the conformational behavior is possible today. In this paper, we present a method to compile a new class of rotamer libraries for detecting interesting relationships between residue conformations and their sequential context in proteins. The method is based on a new algorithm for clustering residue conformations. To demonstrate the effectiveness of our method we apply our algorithm to a library consisting of all 8000 tripeptide fragments formed by the 20 native amino acids. The analysis shows some very interesting new results, namely that some specific tripeptide fragments show some unexpected conformation of residues instead of the highly preferred conformation. In the neighborhood of two asparagine residues, for example, threonine avoids the conformation which is most likely to occur otherwise. The new insights obtained by the analysis are important in understanding the formation and prediction of secondary structure elements and will consequently be crucial for improving the state-of-the-art of protein folding.</dcterms:abstract>
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    <dc:creator>Keim, Daniel A.</dc:creator>
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    <dc:creator>Brandt, Wolfgang</dc:creator>
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    <dcterms:bibliographicCitation>First publ. in: 1st IEEE International Symposium on Bioinformatics &amp; Biomedical Engineering (BIBE), Arlington, Virginia, USA, 2000, pp. 43-49</dcterms:bibliographicCitation>
    <dcterms:title>Clustering 3D-structures of Small Aminoacid-chains for Detecting Dependence from Their Sequential Context in Proteins</dcterms:title>
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