Modeling the semantics of 3d protein structures

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Scopus citations

Abstract

The post Human Genome Project era calls for reliable, integrated, flexible, and convenient data management techniques to facilitate research activities. Querying biological data that is large in volume and complex in structure such as 3D proteins requires expressive models to explicitly support and capture the semantics of the complex data. Protein 3D structure search and comparison not only enable us to predict unknown structures, but can also reveal distant evolutionary relationships that are otherwise undetectable, and perhaps suggest unsuspected functional properties. In this work, we model 3D protein structures by adding spatial semantics and constructs to represent the contributing forces such as hydrogen bonds and high-level structures such as protein secondary structures. This paper makes a contribution to modeling the specialty of life science data and develops methods to meet the novel challenges posed by such data.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsPaolo Atzeni, Wesley Chu, Hongjun Lu, Shuigeng Zhou, Tok Wang Ling
PublisherSpringer-Verlag
Pages696-708
Number of pages13
ISBN (Print)3540237232, 9783540237235
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3288
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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