A visualization of 3D proteome universe: Mapping of a proteome ensemble into 3D space based on the protein-structure composition

• Published in: Molecular phylogenetics and evolution 2011-11, Vol.61 (2), p.484-494
• Main Authors: Aita, Takuyo, Nishigaki, Koichi
• Format: Article
• Language: English
• Subjects: Biodiversity; Biosystematics; Comparative genomics; correlation; Dimensionality reduction; genome; Metagenomics; Metaproteomics; Models, Molecular; new methods; nucleotide sequences; Principal Component Analysis; Protein Conformation; protein structure; proteins; proteome; Proteome - analysis; Proteomics - methods; researchers
• ISSN: 1055-7903; 1095-9513
• DOI: 10.1016/j.ympev.2011.06.020

[Display omitted] ► We developed a novel mapping of an ensemble of various species into 3D space. ► Each species is represented as a 1053D vector giving composition of protein folds. ► We mapped the 1053D vectors for 456 species into a 3D space. ► The properties of the 1053D vectors were quantitatively conserved in 3D space. To visualize a bird’s-eye view of an ensemble of proteomes for various species, we recently developed a novel method of mapping a proteome ensemble into Three-Dimensional (3D) vector space. In this study, the “proteome” is defined as the entire set of all proteins encoded in a genome sequence, and these proteins were dealt with at the level of the SCOP Fold. First, we represented the proteome of a species s by a 1053-dimensional vector x( s), where its length ∣ x( s)∣ represents the overall amount of all the SCOP Folds in the proteome, and its unit vector x( s)/∣ x( s)∣ represents the relative composition of the SCOP Folds in the proteome and the size of the dimension, 1053, is the number of all possible Folds in the proteome ensemble given. Second, we mapped the vector x( s) to the 3D vector y( s), based on the two simple principles: (1) ∣ y( s)∣ = ∣ x( s)∣, and (2) the angle between y( s) and y( t) maximally correlates with the angle between x( s) and x( t). We applied to the mapping of a proteome ensemble for 456 species, which were retrieved from the Genomes TO Protein structures and functions (GTOP) database. As a result, we succeeded in the mapping in that the properties of the 1053-dimensional vectors were quantitatively conserved in the 3D vectors. Particularly, the angles between vectors before and after the mapping highly correlated with each other (correlation coefficients were 0.95–0.96). This new mapping method will allow researchers to intuitively interpret the visual information presented in the maps in a highly effective manner.