Identification of Phage Viral Proteins With Hybrid Sequence Features

Abstract

The uniqueness of bacteriophages plays an important role in bioinformatics research. In real applications, the function of the bacteriophage virion proteins is the main area of interest. Therefore, it is very important to classify bacteriophage virion proteins and non-phage virion proteins accurately. Extracting comprehensive and effective sequence features from proteins plays a vital role in protein classification. In order to more fully represent protein information, this paper is more comprehensive and effective by combining the features extracted by the feature information representation algorithm based on sequence information (CCPA) and the feature representation algorithm based on sequence and structure information. After extracting features, the Max-Relevance-Max-Distance (MRMD) algorithm is used to select the optimal feature set with the strongest correlation between class labels and low redundancy between features. Given the randomness of the samples selected by the random forest classification algorithm and the randomness features for producing each node variable, a random forest method is employed to perform 10-fold cross-validation on the bacteriophage protein classification. The accuracy of this model is as high as 93.5% in the classification of phage proteins in this study. This study also found that, among the eight physicochemical properties considered, the charge property has the greatest impact on the classification of bacteriophage proteins These results indicate that the model discussed in this paper is an important tool in bacteriophage protein research.

Predicting Cell Wall Lytic Enzymes Using Combined Features

“…Redundant or irrelevant features will decrease the accuracy of prediction and increase computational time. In order to remove redundant or irrelevant features, a variety of feature selection techniques have been proposed: the analysis of variance (ANOVA) ( Tan et al, 2018 ; Li et al, 2019 ; Zhang et al, 2020a ), Max-Relevance-Max-Distance algorithms (MRMD) ( Zou et al, 2016 ; Wan et al, 2017 ; Ru et al, 2019 ; Kwon et al, 2020 ), and Minimal-Redundancy-Maximal-Relevance (MRMR) ( Jiao and Du, 2016 ; Xu et al, 2016 ; Wang et al, 2018b ; Kabir et al, 2020 ) are the representative feature selection algorithms. In this study, we selected features using the F-score algorithm; the F-score algorithm was proposed by Yi-Wei ( Chen and Lin, 2006 ).…”

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

“…It could be noticed that the property of side-chain [47] of amino acid had a high negative correlation (R = −0.516), indicating that PVPs favor small amino acids. As seen in Table 8, five top-ranked important amino acids present in PVPs were Ala, Thr, Val, Gly, and Ser, while according to the property of side-chain [50], such five amino acids (PS, side-chain) were ranked at (1,19), (2,15), (3,16), (4,20), and (5,18), respectively. Our analysis result was well consistent with the work of a previous study [45].…”

“…Recently, many researchers have exploited various types of machine learning (ML) algorithms using sequence features to directly predict PVPs including Seguritan et al's method [8], Feng et al's method [9], PVPred [10], Zhang et al's method [11], PVP-SVM [12], PhagePred [13], Tan et al's method [14], Ru et al's method [15], and Pred-BVP-Unb [16], as summarized in Table 1. In 2012, Seguritan et al [8] proposed the first predictor to identify viral structural proteins using an artificial neural network cooperating with a feature combination of amino acid composition (AAC) and protein isoelectric points.…”

“…Secondly, few existing methods were not assessed using an independent dataset, indicating that these methods might provide misleading results with overestimated accuracy. Finally, no web server was provided for many methods in this area including Seguritan et al's method [8], Feng et al's method [9], Zhang et al's method [11], PhagePred [13], Tan et al's method [14], Ru et al's method [15], and Pred-BVP-Unb [16]. Although, these methods have demonstrated good prediction performances, they are not generalized or transferable to researchers with informatics background who can develop in-house prediction models.…”

DeePVP: Identification and classification of phage virion protein using deep learning

“…However, the diversity of PVPs is much higher than that of the enzymes encoded in the phage genome, which makes the identification of PVPs much more difficult (11). To overcome the difficulty of PVP identification, several de novo algorithms for PVP identification have been proposed (8,(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23). Most of these tools are twoclass classifiers to distinguish whether the given phage protein is PVP.…”