Large-scale identification of human protein function using topological features of interaction network

Abstract

The annotation of protein function is a vital step to elucidate the essence of life at a molecular level, and it is also meritorious in biomedical and pharmaceutical industry. Developments of sequencing technology result in constant expansion of the gap between the number of the known sequences and their functions. Therefore, it is indispensable to develop a computational method for the annotation of protein function. Herein, a novel method is proposed to identify protein function based on the weighted human protein-protein interaction network and graph theory. The network topology features with local and global information are presented to characterise proteins. The minimum redundancy maximum relevance algorithm is used to select 227 optimized feature subsets and support vector machine technique is utilized to build the prediction models. The performance of current method is assessed through 10-fold cross-validation test, and the range of accuracies is from 67.63% to 100%. Comparing with other annotation methods, the proposed way possesses a 50% improvement in the predictive accuracy. Generally, such network topology features provide insights into the relationship between protein functions and network architectures. The source code of Matlab is freely available on request from the authors.

Comparative analyses of three complete Primula mitogenomes with insights into mitogenome size variation in Ericales

“…Most of the predicted RNA editing sites could result in the conversion of the encoded amino acid from neutral to hydrophobic, ultimately increasing the hydrophobic character of the coding protein [ 53 ]. Hydrophobicity is conducive to protein folding and secondary structure formation [ 54 ]. The most frequent RNA editing events were found in ccmB and nad4L in P. smithiana .…”

Computational identification of protein-protein interactions in model plant proteomes

“…For example, PPIs are very useful for predicting the function of individual proteins 3 as well as pathways of protein groups 94 . Although large-scale PPIs of several model organisms have been revealed by experimental methods 95–97 and by computational methods 42,61,64,98 , the works for plant PPIs were sparse. This work is intended to fill the gap for plant PPIs by providing PPI predictions with the method that was calibrated on known PPIs in Arabidopsis .…”

Computational models for prediction of protein–protein interaction in rice and Magnaporthe grisea

“…Here, a fivefold cross-validation scheme was used for model development (Karan et al, 2019;Li et al, 2016). Training accuracies of 95% and 99% were obtained with AAC and CT features, respectively (Sahu et al, 2014).…”

“…Until now, few numbers have been reported of experimental PPIs between O. sativa and M. grisea that are inadequate to explore the pathogenic molecular mechanism ( Pellegrini et al., 1999 ; Jia et al. 2000 ; Krogh et al., 2001 ; Ng et al., 2003 ; Salwinski et al., 2004 ; Quevillon et al., 2005 ; Shoemaker and Panchenko, 2007 ; Wang et al., 2007 ; Najafabadi and Salavati, 2008 ; Parker et al., 2008 ; Ribot et al., 2008 ; Kumar and Nanduri, 2010 ; Mukhtar et al., 2011 ; Li et al., 2012 ; Maetschke et al., 2012 ; Mentlak et al., 2012 ; Park et al., 2012 ; Schleker et al., 2012 ; Simonsen et al., 2012 ; Meyer et al., 2013 ; Mosca et al., 2014 ; Rao et al., 2014 ; Sahu et al., 2014 ; Tully et al., 2014 ; Nourani et al., 2015 ; Li et al., 2016 ; Singh et al., 2016 ; Klopfenstein et al., 2018 ; Savojardo et al., 2018 ; Karan et al., 2019 ; Ma et al., 2019 ; Sahu et al., 2019 ; Loaiza et al., 2020 ; Lu et al., 2020 ; Singh et al., 2020 ; Wang et al., 2020 ; Rapposelli et al., 2021 ; Kumar et al., 2022 ; Mishra et al., 2022 ; Wu et al., 2015 ). Therefore, the computational approach is seen as an alternative method for the large-scale identification of PPIs.…”

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