Literature DB >> 31056514

Incorporating the Coevolving Information of Substrates in Predicting HIV-1 Protease Cleavage Sites.

Lun Hu, Pengwei Hu, Xin Luo, Xiaohui Yuan, Zhu-Hong You.   

Abstract

Human immunodeficiency virus 1 (HIV-1) protease (PR) plays a crucial role in the maturation of the virus. The study of substrate specificity of HIV-1 PR as a new endeavor strives to increase our ability to understand how HIV-1 PR recognizes its various cleavage sites. To predict HIV-1 PR cleavage sites, most of the existing approaches have been developed solely based on the homogeneity of substrate sequence information with supervised classification techniques. Although efficient, these approaches are found to be restricted to the ability of explaining their results and probably provide few insights into the mechanisms by which HIV-1 PR cleaves the substrates in a site-specific manner. In this work, a coevolutionary pattern-based prediction model for HIV-1 PR cleavage sites, namely EvoCleave, is proposed by integrating the coevolving information obtained from substrate sequences with a linear SVM classifier. The experiment results showed that EvoCleave yielded a very promising performance in terms of ROC analysis and f-measure. We also prospectively assessed the biological significance of coevolutionary patterns by applying them to study three fundamental issues of HIV-1 PR cleavage site. The analysis results demonstrated that the coevolutionary patterns offered valuable insights into the understanding of substrate specificity of HIV-1 PR.

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Year:  2020        PMID: 31056514     DOI: 10.1109/TCBB.2019.2914208

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  2 in total

1.  Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning.

Authors:  Zhenfeng Li; Lun Hu; Zehai Tang; Cheng Zhao
Journal:  Front Genet       Date:  2021-03-26       Impact factor: 4.599

2.  A computational method for predicting nucleocapsid protein in retroviruses.

Authors:  Manyun Guo; Yucheng Ma; Wanyuan Liu; Zuyi Yuan
Journal:  Sci Rep       Date:  2022-01-11       Impact factor: 4.379

  2 in total

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