Literature DB >> 30567479

Computational identification of physicochemical signatures for host tropism of influenza A virus.

Rui Yin1, Xinrui Zhou1, Jie Zheng2, Chee Keong Kwoh1.   

Abstract

Avian influenza viruses from migratory birds have managed to cross host species barriers and infected various hosts like human and swine. Epidemics and pandemics might occur when influenza viruses are adapted to humans, causing deaths and enormous economic loss. Receptor-binding specificity of the virus is one of the key factors for the transmission of influenza viruses across species. The determination of host tropism and understanding of molecular properties would help identify the mechanism why zoonotic influenza viruses can cross species barrier and infect humans. In this study, we have constructed computational models for host tropism prediction on human-adapted subtypes of influenza HA proteins using random forest. The feature vectors of the prediction models were generated based on seven physicochemical properties of amino acids from influenza sequences of three major hosts. Feature aggregation and associative rules were further applied to select top 20 features and extract host-associated physicochemical signatures on the combined model of nonspecific subtypes. The prediction model achieved high performance ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>Accuracy</mml:mtext><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>9</mml:mn><mml:mn>4</mml:mn><mml:mn>8</mml:mn></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>Precision</mml:mtext><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>9</mml:mn><mml:mn>5</mml:mn><mml:mn>4</mml:mn></mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>MCC</mml:mtext><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>9</mml:mn><mml:mn>2</mml:mn><mml:mn>2</mml:mn></mml:math> ). Support and confidence rates were calculated for the host class-association rules. The results indicated that secondary structure and normalized Van der Waals volume were identified as more important physicochemical signatures in determining the host tropism.

Entities:  

Keywords:  Influenza; association rules; hemagglutinin; host tropism; physicochemical signature

Mesh:

Year:  2018        PMID: 30567479     DOI: 10.1142/S0219720018400231

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  4 in total

1.  Influenza virus genotype to phenotype predictions through machine learning: a systematic review.

Authors:  Laura K Borkenhagen; Martin W Allen; Jonathan A Runstadler
Journal:  Emerg Microbes Infect       Date:  2021-12       Impact factor: 7.163

2.  Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences.

Authors:  Rui Yin; Zihan Luo; Chee Keong Kwoh
Journal:  Curr Genomics       Date:  2021-12-31       Impact factor: 2.689

3.  Identifying host-specific amino acid signatures for influenza A viruses using an adjusted entropy measure.

Authors:  Yixiang Zhang; Kent M Eskridge; Shunpu Zhang; Guoqing Lu
Journal:  BMC Bioinformatics       Date:  2022-08-12       Impact factor: 3.307

4.  HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction.

Authors:  Rui Yin; Xinrui Zhou; Shamima Rashid; Chee Keong Kwoh
Journal:  BMC Med Genomics       Date:  2020-01-23       Impact factor: 3.063

  4 in total

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