Abstract
Protein–protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein–protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein–protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.
Highlights
Protein is the material foundation of all living things [1]
Considering the fact that few Protein–protein interaction (PPI) studies with computational methods investigated which gene ontology (GO) terms were highly related to the determination of PPIs, the purpose of this study was to identify key GO terms or KEGG pathways that can indicate the difference between two proteins with and without functional associations
Apart from above-mentioned cellular component and molecular function associated GO terms, we identified a group of functional enrichment results that can be clustered into the biological processes cluster
Summary
Protein is the material foundation of all living things [1]. Protein–protein interaction (PPI) plays an extremely significant role in the growth, reproduction, and metabolism of any life, even in a single cell [2, 3]. Given that proteins influence different biological processes, even in single cells, conducting a study on PPI to further determine protein functions and life activities is a relevant endeavor. As for computational methods, several algorithms have been developed to identify PPI, and the two main ones are the topology-free approaches and the graph-based approaches, which are based on distances between proteins and specialized clustering techniques, respectively [9, 10]. Some other computational methods predict PPIs from protein sequences using machine learning. Pan et al first used latent Dirichlet allocation model to extract latent topic features from the conjoint triad features, the learned topic features were fed into a random forest classifier to predict PPIs [13].
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