Systematization of the Protein Sequence Diversity in Enzymes Related to Secondary Metabolic Pathways in Plants, in the Context of Big Data Biology Inspired by the KNApSAcK Motorcycle Database

Ikeda, Shun; Abe, Takashi; Nakamura, Yukiko; Kibinge, Nelson; Morita, Aki; Nakatani, Ayaka; Ono, Naoaki; Ikemura, Toshimichi; Nakamura, Kensuke; Altaf‐Ul‐Amin, Md.; Kanaya, Shigehiko

doi:10.1093/pcp/pct041

Cited by 19 publications

(11 citation statements)

References 143 publications

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“…The combined variance explained by the first two principal components is 30.02 (Figure 6 left) percent, whereas the variance explained by the first two components in binary encoded set is 14.84 percent (Figure 6 right). Triterpenoid synthases can be clearly distinguished from the other categories, which was also consistent with our previous findings [30]. …”

Section: Resultssupporting

confidence: 93%

Integration of Residue Attributes for Sequence Diversity Characterization of Terpenoid Enzymes

Kibinge

Ikeda

Ono

et al. 2014

BioMed Research International

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Progress in the “omics” fields such as genomics, transcriptomics, proteomics, and metabolomics has engendered a need for innovative analytical techniques to derive meaningful information from the ever increasing molecular data. KNApSAcK motorcycle DB is a popular database for enzymes related to secondary metabolic pathways in plants. One of the challenges in analyses of protein sequence data in such repositories is the standard notation of sequences as strings of alphabetical characters. This has created lack of a natural underlying metric that eases amenability to computation. In view of this requirement, we applied novel integration of selected biochemical and physical attributes of amino acids derived from the amino acid index and quantified in numerical scale, to examine diversity of peptide sequences of terpenoid synthases accumulated in KNApSAcK motorcycle DB. We initially generated a reduced amino acid index table. This is a set of biochemical and physical properties obtained by random forest feature selection of important indices from the amino acid index. Principal component analysis was then applied for characterization of enzymes involved in synthesis of terpenoids. The variance explained was increased by incorporation of residue attributes for analyses.

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Section: Resultssupporting

confidence: 93%

Integration of Residue Attributes for Sequence Diversity Characterization of Terpenoid Enzymes

Kibinge

Ikeda

Ono

et al. 2014

BioMed Research International

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“…Species‐metabolite information is accumulated in the KNApSAcK Core DB that has been utilized extensively in omics science 4,7. Metabolic reactions in enzymes are also accumulated in Motorcycle DB 4. Activity‐species and activity‐metabolite relations are accumulated in Biological Activity DB and Metabolite Activity DB, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…To attain this purpose, we have developed KNApSAcK Family Databases (DBs), which are utilized in a number of researches in metabolomics. A review of the KNApSAcK DB utilization in scientific work is presented by Ikeda et al 4. Data has been collected in the KNApSAcK database in order to facilitate the comprehensive understanding of the medicinal usage of plants based on traditional as well as modern knowledge of healthy cuisine ingredients and metabolomics 4–8…”

Section: Introductionmentioning

confidence: 99%

Clustering of 3D‐Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities

Ohtana

Abdullah

Altaf-Ul-Amin

et al. 2014

Molecular Informatics

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Developing database systems connecting diverse species based on omics is the most important theme in big data biology. To attain this purpose, we have developed KNApSAcK Family Databases, which are utilized in a number of researches in metabolomics. In the present study, we have developed a network-based approach to analyze relationships between 3D structure and biological activity of metabolites consisting of four steps as follows: construction of a network of metabolites based on structural similarity (Step 1), classification of metabolites into structure groups (Step 2), assessment of statistically significant relations between structure groups and biological activities (Step 3), and 2-dimensional clustering of the constructed data matrix based on statistically significant relations between structure groups and biological activities (Step 4). Applying this method to a data set consisting of 2072 secondary metabolites and 140 biological activities reported in KNApSAcK Metabolite Activity DB, we obtained 983 statistically significant structure group-biological activity pairs. As a whole, we systematically analyzed the relationship between 3D-chemical structures of metabolites and biological activities.

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“…This work further used SOM to examine codon usage heterogeneity in the E. coli O157 genome, which contains “O157-unique segments” (O-islands), and showed that SOM is a powerful tool for characterization of horizontally transferred genes. Another example of the application of BL-SOM is the investigation of the enzyme sequence diversity related to secondary metabolism [64]. Initially, a map was constructed by using a big data matrix that consisted of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria.…”

Section: Multivariate Analysis In Systems Biologymentioning

confidence: 99%

Systems Biology in the Context of Big Data and Networks

Altaf-Ul-Amin

Afendi

Kiboi

et al. 2014

BioMed Research International

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Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.

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Systematization of the Protein Sequence Diversity in Enzymes Related to Secondary Metabolic Pathways in Plants, in the Context of Big Data Biology Inspired by the KNApSAcK Motorcycle Database

Cited by 19 publications

References 143 publications

Integration of Residue Attributes for Sequence Diversity Characterization of Terpenoid Enzymes

Integration of Residue Attributes for Sequence Diversity Characterization of Terpenoid Enzymes

Clustering of 3D‐Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities

Systems Biology in the Context of Big Data and Networks

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