Literature DB >> 24057351

In silico comparison of low- and high-risk human papillomavirus proteins.

Mahsa Alemi1, Hassan Mohabatkar, Mandana Behbahani.   

Abstract

Human papillomavirus (HPV) is an important pathogen which is classified into two, high- and low-risk groups. The proteins of high-risk and low-risk HPV types have different functions. Therefore, there is a need to develop a computational method for predicting these two groups. In the present study, the physiochemical properties of all early (E1, E2, E4, E5, E6, and E7) and late (L1 and L2) proteins in high- and low-risk HPV types have been studied. The concept of receiver operating characteristic analysis and support vector machines methods has been used for comparison of high- and low-risk HPV types. The results demonstrate that amino acid composition, physiochemical, and secondary structure of E2 protein are significantly different between these two groups. The results demonstrate that in silico properties can create useful information to predict high-risk and low-risk HPV types.

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Year:  2013        PMID: 24057351     DOI: 10.1007/s12010-013-0479-5

Source DB:  PubMed          Journal:  Appl Biochem Biotechnol        ISSN: 0273-2289            Impact factor:   2.926


  5 in total

1.  Prediction of high-risk types of human papillomaviruses using statistical model of protein "sequence space".

Authors:  Cong Wang; Yabing Hai; Xiaoqing Liu; Nanfang Liu; Yuhua Yao; Pingan He; Qi Dai
Journal:  Comput Math Methods Med       Date:  2015-04-20       Impact factor: 2.238

2.  Detection and genotyping of human papillomavirus (HPV) in HIV-infected women and its relationship with HPV/HIV co-infection.

Authors:  Rodolfo Miglioli Badial; Marina Carrara Dias; Bruna Stuqui; Patrícia Pereira Dos Santos Melli; Silvana Maria Quintana; Caroline Measso do Bonfim; José Antônio Cordeiro; Tatiana Rabachini; Marilia de Freitas Calmon; Paola Jocelan Scarin Provazzi; Paula Rahal
Journal:  Medicine (Baltimore)       Date:  2018-04       Impact factor: 1.889

3.  Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses.

Authors:  Junchao Zhang; Kechao Wang
Journal:  Comput Math Methods Med       Date:  2022-07-21       Impact factor: 2.809

4.  Human papillomavirus genotypes in women with invasive cervical cancer with and without human immunodeficiency virus infection in Botswana.

Authors:  Leabaneng Tawe; Emily MacDuffie; Mohan Narasimhamurthy; Qiao Wang; Simani Gaseitsiwe; Sikhulile Moyo; Ishmael Kasvosve; Sanghyuk S Shin; Nicola M Zetola; Giacomo M Paganotti; Surbhi Grover
Journal:  Int J Cancer       Date:  2019-08-02       Impact factor: 7.316

5.  Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes.

Authors:  Xinnan Xu; Rui Kong; Xiaoqing Liu; Pingan He; Qi Dai
Journal:  Comput Math Methods Med       Date:  2020-06-18       Impact factor: 2.238

  5 in total

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