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  1. Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics (StReBio '09)
  2. Identification of structurally important amino acids in proteins by graph-theoretic measures
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Using random forests to uncover bivariate interactions in high dimensional small data sets
Identification of structurally important amino acids in proteins by graph-theoretic measures
Lift-based search for significant dependencies in dense data sets
Finding optimal parameters for edit distance based sequence classification is NP-hard
Multi-class protein fold recognition using large margin logic based divide and conquer learning
Protein sequence alignment and structural disorder: a substitution matrix for an extended alphabet
Handling missing values and censored data in PCA of pharmacological matrices
Comparing graph-based representations of protein for mining purposes
Can we improve on the identification of transcription factor binding sites?

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Identification of structurally important amino acids in proteins by graph-theoretic measures

Content Provider ACM Digital Library
Author Lu, Yu-En Cheng, Tammy M. K. Lió, Pietro
Abstract Identifying key residues important for maintaining a protein structure is a non-trivial problem in Computational Biology. In this paper, we present results based on a graph model representing protein structures. This model considers the structure as residue-residue interactions in order to capture protein stability. We propose the application of approximate minimum vertex cover algorithms (MVC) as a novel approach for identifying the structurally important residues, which we shall refer to as key residues. We establish that MVC based algorithms captures the essence of protein structural stability by correlation analysis with ΔΔG, the change of protein free energies due to amino acid variations. We also benchmark our approach with popular approaches for analyzing large complex networks --- betweenness, and Eigenvector centrality. Our findings are such that they do not correlate well with ΔΔG. We give explanations from the free energy point of view, which shall benefit future development measures for protein structure stability.
Starting Page 7
Ending Page 11
Page Count 5
File Format PDF
ISBN 9781605586670
DOI 10.1145/1562090.1562092
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-06-28
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Vertex cover Protein stability Graph theory Protein structure Computational biology
Content Type Text
Resource Type Article
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