Abstract
Protein is one of the most significant components of all living creatures. All significant and essential biological structures and functions relies on proteins and their respective biological functions. However, proteins cannot perform their unique biological significance independently. They have to interact with each other to realize the complicated biological processes in all living creatures including human beings. In other words, proteins depend on interactions (protein-protein interactions) to realize their significant effects. Thus, the significance comparison and quantitative contribution of candidate PPI features must be determined urgently. According to previous studies, 258 physical and chemical characteristics of proteins have been reported and confirmed to definitively affect the interaction efficiency of the related proteins. Among such features, essential physiochemical features of proteins like stoichiometric balance, protein abundance, molecular weight and charge distribution have been validated to be quite significant and irreplaceable for protein-protein interactions (PPIs). Therefore, in this study, we, on one hand, presented a novel computational framework to identify the key factors affecting PPIs with Boruta feature selection (BFS), Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and on the other hand, built a quantitative decision-rule system to evaluate the potential PPIs under real conditions with random forest (RF) and RIPPER algorithms, thereby supplying several new insights into the detailed biological mechanisms of complicated PPIs. The main datasets and codes can be downloaded at https://github.com/xypan1232/Mass-PPI.
Highlights
Protein–protein interactions (PPI) are core biochemical events that directly execute biological functions in all living creatures (Qian et al, 2014; Wang et al, 2014)
According to the three-stage feature selection scheme, remaining 167 features were analyzed by the powerful Monte Carlo feature selection (MCFS) method
For each feature subset constructed from F, random forest (RF) classifiers with different number of decision trees (10, 20, 50, and 100) were built on the training dataset and evaluated through 10-fold cross validation
Summary
Protein–protein interactions (PPI) are core biochemical events that directly execute biological functions in all living creatures (Qian et al, 2014; Wang et al, 2014). Biochemical features of PPIs (e.g., protein concentration, protein binding ligands, presence of adaptors, and covalent modifications) have been recognized as candidate factors that may affect PPIs (Pan et al, 2010; Raj et al, 2013; Modell et al, 2016). Most of such extracted features are ambiguous qualitative characteristics. These features may be directly or indirectly related to PPIs, but whether PPIs with optimal biological features may be determined in certain cell types is difficult. Accurate and quantitative/semi-quantitative characteristics of PPIs must be identified through continuous studies and exploration
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