Abstract
Nucleocapsid protein (NC) in the group-specific antigen (gag) of retrovirus is essential in the interactions of most retroviral gag proteins with RNAs. Computational method to predict NCs would benefit subsequent structure analysis and functional study on them. However, no computational method to predict the exact locations of NCs in retroviruses has been proposed yet. The wide range of length variation of NCs also increases the difficulties. In this paper, a computational method to identify NCs in retroviruses is proposed. All available retrovirus sequences with NC annotations were collected from NCBI. Models based on random forest (RF) and weighted support vector machine (WSVM) were built to predict initiation and termination sites of NCs. Factor analysis scales of generalized amino acid information along with position weight matrix were utilized to generate the feature space. Homology based gene prediction methods were also compared and integrated to bring out better predicting performance. Candidate initiation and termination sites predicted were then combined and screened according to their intervals, decision values and alignment scores. All available gag sequences without NC annotations were scanned with the model to detect putative NCs. Geometric means of sensitivity and specificity generated from prediction of initiation and termination sites under fivefold cross-validation are 0.9900 and 0.9548 respectively. 90.91% of all the collected retrovirus sequences with NC annotations could be predicted totally correct by the model combining WSVM, RF and simple alignment. The composite model performs better than the simplex ones. 235 putative NCs in unannotated gags were detected by the model. Our prediction method performs well on NC recognition and could also be expanded to solve other gene prediction problems, especially those whose training samples have large length variations.
Highlights
In this paper, computational models to identify nucleocapsid protein (NC) from retroviruses were proposed
We found that the combination of the first two of them could generate the best predicting performance[20]
First we introduce a simple alignment (SA)
Summary
Computational models to identify NCs from retroviruses were proposed. All available annotated NC sequences in retroviruses were collected for the training and testing process. The initiation and termination sites of NCs were separately predicted and combined together afterwards to acquire high prediction accuracy when dealing with sequences that are poorly conserved in their lengths. Their performance was tested by fivefold cross validation test. A composite ab initio model to predict intact NCs from genetic sequences was proposed. It performs better than the simplex ones. All of the 6651 available gag sequences without NC annotations were scanned with the composite model and 282 putative NCs in them were found
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