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Molecules. 2018 Jun 29;23(7). pii: E1584. doi: 10.3390/molecules23071584.

Scoring Amino Acid Mutations to Predict Avian-to-Human Transmission of Avian Influenza Viruses.

Author information

1
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China. qiangxl@mail.scuec.edu.cn.
2
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China. kouzhengcn@foxmail.com.
3
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China. yuxiangqd@163.com.
4
Henan Key Lab of Information-Based Electrical Appliances, College of Electrical and Electronic Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China. wangyanfeng@zzuli.edu.cn.

Abstract

Avian influenza virus (AIV) can directly cross species barriers and infect humans with high fatality. Using machine learning methods, the present paper scores the amino acid mutations and predicts interspecies transmission. Initially, 183 signature positions in 11 viral proteins were screened by the scores of five amino acid factors and their random forest rankings. The most important amino acid factor (Factor 3) and the minimal range of signature positions (50 amino acid residues) were explored by a supporting vector machine (the highest-performing classifier among four tested classifiers). Based on these results, the avian-to-human transmission of AIVs was analyzed and a prediction model was constructed for virology applications. The distributions of human-origin AIVs suggested that three molecular patterns of interspecies transmission emerge in nature. The novel findings of this paper provide important clues for future epidemic surveillance.

KEYWORDS:

amino acid mutation; avian influenza virus; interspecies transmission; machine learning

PMID:
29966263
PMCID:
PMC6100476
DOI:
10.3390/molecules23071584
[Indexed for MEDLINE]
Free PMC Article

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