ArticleJournal of molecular modeling2014
Quantitative characterization of protein tertiary motifs.
Article in Journal of molecular modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Diversity and motif conservation in protein 3D structural landscape: exploration by a new multivariate simulation method.Journal of molecular modeling · 2018Article
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2 authors.
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Abstract
A quantitative feature-vector representation/model of tertiary structural motifs of proteins is presented. Multiclass logistic regression and a probabilistic neural network were employed to apply this representation to large data sets in order to classify them into major families of distinct motif types (including those of functional importance) with high statistical confidence. Scatter plots of random samples of these motifs were obtained through two-dimensional transformation of the feature vector by metric MDS (multidimensional scaling). The plots showed distinct clusters and shapes for different families and demonstrated the relevance and importance of the proposed quantitative feature-vector representation for characterizing protein tertiary structural motifs. The relative importance of the features was analyzed. The scope of the present work to investigate Nature's prioritization and optimization of functional motif structures is highlighted.
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Registered trials
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