Identifying novel protein phenotype annotations by hybridizing protein–protein interactions and protein sequence similarities

Abstract

Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein-protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.

Determination of Genes Related to Uveitis by Utilization of the Random Walk with Restart Algorithm on a Protein–Protein Interaction Network

“…Many computational methods have been developed using this information, such as the prediction engines for the identification of protein functions [118,119,120] and methods for identification of novel disease genes [36,37,38]. Several methods were built based on the hypothesis that two proteins in a PPI are more likely to share similar functions.…”

An integrated method for the identification of novel genes related to oral cancer

“…This algorithm has therefore been used to mine novel disease genes in recent years [30–32]. However, the RWR algorithm consistently provides false discoveries [40], supporting the need for additional screening rules. In this study, we used the RWR algorithm as the basic algorithm and added some rules to build the present RWR-based learning method for the identification of novel OC-related genes.…”

“…However, several false discoveries are inevitable, as mentioned in previous studies [33, 40]. To screen out these false discoveries, a series of screening rules were added to this section and integrated into the RWR-based and SP-based methods.…”

“…To indicate the effectiveness of the integrated method, disease genes of OC were employed. It has been reported in some studies that using only the RWR algorithm and SP algorithm consistently produces several false discoveries [33, 40], which may be caused by the structure of the network or lack of consideration of the essential properties of genes. Thus, further rules, produced by the permutation test, associations between candidates and the validation of genes according to their properties, were integrated into the RWR-based and SP-based methods.…”

Identifying novel fruit-related genes in Arabidopsis thaliana based on the random walk with restart algorithm

“…Some computational methods have been developed to identify novel PPIs [34–37], which can yield more abundant PPIs to investigate related problems. The accumulated information of PPIs can be used to construct a large PPI network, which is helpful to investigate different properties of proteins, such as protein functions [38–40], relationship with different diseases [21, 22, 41–45]. …”