Structural Alphabets for Protein Structure Classification: A Comparison Study
نویسندگان
چکیده
منابع مشابه
Using linear algebra for protein structural comparison and classification
In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) a...
متن کاملClassification of protein 3D folds by hidden Markov learning on sequences of structural alphabets
Fragment-based analysis of protein three-dimensional (3D) structures has received increased attention in recent years. Here, we used a set of pentamer local structure alphabets (LSAs) recently derived in our laboratory to represent protein structures, i.e. we transformed the 3D structures into one-dimensional (1D) sequences of LSAs. We then applied Hidden Markov Model training to these LSA sequ...
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MOTIVATION The 3D structure of a protein sequence can be assembled from the substructures corresponding to small segments of this sequence. For each small sequence segment, there are only a few more likely substructures. We call them the 'structural alphabet' for this segment. Classical approaches such as ROSETTA used sequence profile and secondary structure information, to predict structural f...
متن کاملCharacterization and Prediction of Protein Flexibility Based on Structural Alphabets
Motivation. To assist efforts in determining and exploring the functional properties of proteins, it is desirable to characterize and predict protein flexibilities. Results. In this study, the conformational entropy is used as an indicator of the protein flexibility. We first explore whether the conformational change can capture the protein flexibility. The well-defined decoy structures are con...
متن کاملGENERATING FUZZY RULES FOR PROTEIN CLASSIFICATION
This paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein superfamily. Since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. These features are the occurrence probabilities of six exchange groups in the seque...
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ژورنال
عنوان ژورنال: Journal of Molecular Biology
سال: 2009
ISSN: 0022-2836
DOI: 10.1016/j.jmb.2008.12.044