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De Novo Protein Structure Prediction

  • Ling-Hong Hung
  • Shing-Chung Ngan
  • Ram Samudrala
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)

Abstract

An unparalleled amount of sequence data is being made available from large-scale genome sequencing efforts. The data provide a shortcut to the determination of the function of a gene of interest, as long as there is an existing sequenced gene with similar sequence and of known function. This has spurred structural genomic initiatives with the goal of determining as many protein folds as possible (Brenner and Levitt, 2000; Burley, 2000; Brenner, 2001; Heinemann et al., 2001). The purpose of this is twofold: First, the structure of a gene product can often lead to direct inference of its function. Second, since the function of a protein is dependent on its structure, direct comparison of the structures of gene products can be more sensitive than the comparison of sequences of genes for detecting homology. Presently, structural determination by crystallography and NMR techniques is still slow and expensive in terms of manpower and resources, despite attempts to automate the processes. Computer structure prediction algorithms, while not providing the accuracy of the traditional techniques, are extremely quick and inexpensive and can provide useful low-resolution data for structure comparisons (Bonneau and Baker, 2001). Given the immense number of structures which the structural genomic projects are attempting to solve, there would be a considerable gain even if the computer structure prediction approach were applicable to a subset of proteins.

Keywords

Energy Function Structure Prediction Protein Structure Prediction Iterative Density Hydrophobic Moment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ling-Hong Hung
    • 1
  • Shing-Chung Ngan
    • 1
  • Ram Samudrala
    • 1
  1. 1.Department of MicrobiologyUniversity of WashingtonSeattle

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