ArticleComputational and structural biotechnology journal2014
3D representations of amino acids-applications to protein sequence comparison and classification.
Article in Computational and structural biotechnology journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 24 citations in OpenAlex.
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies.Computational and structural biotechnology journal · 2021Article
- Deep Learning in Protein Structural Modeling and Design.Patterns (New York, N.Y.) · 2020Review
- A novel numerical representation for proteins: Three-dimensional Chaos Game Representation and its Extended Natural Vector.Computational and structural biotechnology journal · 2020Article
- Numerical Encodings of Amino Acids in Multivariate Gaussian Modeling of Protein Multiple Sequence Alignments.Molecules (Basel, Switzerland) · 2018Article
- String kernels for protein sequence comparisons: improved fold recognition.BMC bioinformatics · 2017Article
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2 authors at 1 institution in 1 country.
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Abstract
The amino acid sequence of a protein is the key to understanding its structure and ultimately its function in the cell. This paper addresses the fundamental issue of encoding amino acids in ways that the representation of such a protein sequence facilitates the decoding of its information content. We show that a feature-based representation in a three-dimensional (3D) space derived from amino acid substitution matrices provides an adequate representation that can be used for direct comparison of protein sequences based on geometry. We measure the performance of such a representation in the context of the protein structural fold prediction problem. We compare the results of classifying different sets of proteins belonging to distinct structural folds against classifications of the same proteins obtained from sequence alone or directly from structural information. We find that sequence alone performs poorly as a structure classifier. We show in contrast that the use of the three dimensional representation of the sequences significantly improves the classification accuracy. We conclude with a discussion of the current limitations of such a representation and with a description of potential improvements.
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