Handling context-sensitivity in protein structures using graph theory: bona fide prediction.

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TitleHandling context-sensitivity in protein structures using graph theory: bona fide prediction.
Publication TypeJournal Article
Year of Publication1997
AuthorsSamudrala, R, Moult, J
JournalProteins
VolumeSuppl 1
Pagination43-9
Date Published1997
ISSN0887-3585
KeywordsAmino Acid Sequence, Calcium-Binding Proteins, Cellulase, Cellulose 1,4-beta-Cellobiosidase, Computer Graphics, Ligases, Metalloproteins, Models, Molecular, Molecular Sequence Data, Nerve Tissue Proteins, Neurocalcin, Plant Proteins, Polyribonucleotide Nucleotidyltransferase, Protein Conformation, Proteins, Receptors, Calcium-Sensing, Sensitivity and Specificity, Sequence Alignment, Ubiquitin-Conjugating Enzymes
Abstract

We constructed five comparative models in a blind manner for the second meeting on the Critical Assessment of protein Structure Prediction methods (CASP2). The method used is based on a novel graph-theoretic clique-finding approach, and attempts to address the problem of interconnected structural changes in the comparative modeling of protein structures. We discuss briefly how the method is used for protein structure prediction, and detail how it performs in the blind tests. We find that compared to CASP1, significant improvements in building insertions and deletions and sidechain conformations have been achieved.

Alternate JournalProteins
PubMed ID9485494
Grant ListGM41034 / GM / NIGMS NIH HHS / United States