A computational method for predicting nucleocapsid protein in retroviruses

• Published in: Scientific reports Vol. 12; no. 1; pp. 524 - 10
• Main Authors: Guo, Manyun, Ma, Yucheng, Liu, Wanyuan, Yuan, Zuyi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.01.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/663/2009; 631/1647/48; Algorithms; Amino acids; Annotations; Antigens; Computational Biology - methods; Computer applications; Factor analysis; Gene Products, gag - chemistry; Gene Products, gag - genetics; Gene Products, gag - metabolism; Genes; Genetic engineering; Homology; Humanities and Social Sciences; multidisciplinary; Nucleocapsid Proteins - chemistry; Nucleocapsid Proteins - genetics; Nucleocapsid Proteins - metabolism; Nucleocapsids; Predictions; Proteins; Research methodology; Retroviridae - genetics; Science; Science (multidisciplinary); Support Vector Machine
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-03182-2

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 gag s 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.