Abstract
Considering that traditional biological experiments are expensive and time consuming, it is important to develop effective computational models to infer potential essential proteins. In this manuscript, a novel collaborative filtering model-based method called CFMM was proposed, in which, an updated protein–domain interaction (PDI) network was constructed first by applying collaborative filtering algorithm on the original PDI network, and then, through integrating topological features of PDI networks with biological features of proteins, a calculative method was designed to infer potential essential proteins based on an improved PageRank algorithm. The novelties of CFMM lie in construction of an updated PDI network, application of the commodity-customer-based collaborative filtering algorithm, and introduction of the calculation method based on an improved PageRank algorithm, which ensured that CFMM can be applied to predict essential proteins without relying entirely on known protein–domain associations. Simulation results showed that CFMM can achieve reliable prediction accuracies of 92.16, 83.14, 71.37, 63.87, 55.84, and 52.43% in the top 1, 5, 10, 15, 20, and 25% predicted candidate key proteins based on the DIP database, which are remarkably higher than 14 competitive state-of-the-art predictive models as a whole, and in addition, CFMM can achieve satisfactory predictive performances based on different databases with various evaluation measurements, which further indicated that CFMM may be a useful tool for the identification of essential proteins in the future.
Highlights
Researches show that essential proteins are important for survival of organisms and play critical roles in the development of life processes
Based on the updated protein–domain interaction (PDI) network, some key topological features and biological features of proteins were extracted, which would be further integrated together to infer potential essential proteins based on an improved PageRank algorithm
According to WBP, our prediction model CFMM can apply improved PageRank to identify potential proteins
Summary
Researches show that essential proteins are important for survival of organisms and play critical roles in the development of life processes. Zhao et al (2019) put forward a prediction model called RWHN to infer key proteins by integrating PPI networks with protein domains and some other biological information. Y. Fan et al (2016) proposed a novel prediction model by adopting Pearson correlation coefficients and subcellular localization to update the PPI network Qin et al (2017) put forward a method for recognizing essential proteins based on the topological information of PPI networks and orthologous information of proteins. Zhang W. et al (2018) presented a computational model called TEGS to recognize key proteins by combining biological information of proteins and topological features of PPI networks. Z. Chen et al (2020) presented a novel strategy named NPRI by combining various biological data of proteins and the topological features of PPI networks to infer key proteins. The detailed information of datasets downloaded from the DIP, Krogan, and Gavin databases are shown in the following Table 1
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