Abstract
Cassava is a starchy root crop whose role in food security becomes more significant nowadays. Together with the industrial uses for versatile purposes, demand for cassava starch is continuously growing. However, in-depth study to uncover the mystery of cellular regulation, especially the interaction between proteins, is lacking. To reduce the knowledge gap in protein-protein interaction (PPI), genome-scale PPI network of cassava was constructed using interolog-based method (MePPI-In, available at http://bml.sbi.kmutt.ac.th/ppi). The network was constructed from the information of seven template plants. The MePPI-In included 90,173 interactions from 7,209 proteins. At least, 39 percent of the total predictions were found with supports from gene/protein expression data, while further co-expression analysis yielded 16 highly promising PPIs. In addition, domain-domain interaction information was employed to increase reliability of the network and guide the search for more groups of promising PPIs. Moreover, the topology and functional content of MePPI-In was similar to the networks of Arabidopsis and rice. The potential contribution of MePPI-In for various applications, such as protein-complex formation and prediction of protein function, was discussed and exemplified. The insights provided by our MePPI-In would hopefully enable us to pursue precise trait improvement in cassava.
Highlights
Proteins are macromolecules that play crucial roles in a range of biological processes in cells
Upon the homology-based principle of this method, seven plant species were selected as templates, based on one of the three criteria
The cassava orthologous proteins that showed identity percentage ≥60, coverage percentage ≥80 and e-value ≤ 10−10 were identified. If these orthologous proteins matched the proteins of template plants that had previously been identified to have protein-protein interaction, such interactions were regarded as orthologous PPIs in cassava
Summary
Proteins are macromolecules that play crucial roles in a range of biological processes in cells. Cooperation between proteins, called protein-protein interaction (PPI), allows cells to dynamically modulate when proteins and their counterparts are turned on to play roles in particular cellular processes Since these interactions are highly dependent on prevailing conditions of exposure, the PPI is considered a type of biological language utilized to synchronize cellular regulation, especially at post-translational level. The performance of the machine learning based-methods depends enormously on the numbers and quality of the employed data, especially the model-training information which are in general related to experimentally measured data. The previous research works are the good evidences of the appropriateness of such method for PPI inference in plants
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