Normal modes analysis and surface electrostatics of haemagglutinin proteins as fingerprints for high pathogenic type A i
- PDF / 1,851,130 Bytes
- 16 Pages / 595.276 x 793.701 pts Page_size
- 24 Downloads / 179 Views
RESEARCH
Open Access
Normal modes analysis and surface electrostatics of haemagglutinin proteins as fingerprints for high pathogenic type A influenza viruses Irene Righetto* and Francesco Filippini From 13th Bioinformatics and Computational Biology Conference - BBCC 2018 Naples, Italy. 19-21 November 2018
* Correspondence: irene.righetto@ bio.unipd.it Department of Biology, University of Padua, Synthetic Biology and Biotechnology Unit, via U. Bassi 58/ B, 35131 Padova, Italy
Abstract Background: Type A influenza viruses circulate and spread among wild birds and mostly consist of low pathogenic strains. However, fast genome variation timely results in the insurgence of high pathogenic strains, which when infecting poultry birds may cause a million deaths and strong commercial damage. More importantly, the host shift may concern these viruses and sustained human-to-human transmission may result in a dangerous pandemic outbreak. Therefore, fingerprints specific to either low or high pathogenic strains may represent a very important tool for global surveillance. Results: We combined Normal Modes Analysis and surface electrostatic analysis of a mixed strain dataset of influenza A virus haemagglutinins from high and low pathogenic strains in order to infer specific fingerprints. Normal Modes Analysis sorted the strains in two different, homogeneous clusters; sorting was independent of clades and specific instead to high vs low pathogenicity. A deeper analysis of fluctuations and flexibility regions unveiled a special role for the 110-helix region. Specific sorting was confirmed by surface electrostatics analysis, which further allowed to focus on regions and mechanisms possibly crucial to the low-to-high transition. Conclusions: Evidence from previous work demonstrated that changes in surface electrostatics are associated with the evolution and spreading of avian influenza A virus clades, and seemingly involved also in the avian to mammalian host shift. This work shows that a combination of electrostatics and Normal Modes Analysis can also identify fingerprints specific to high and low pathogenicity. The possibility to predict which specific mutations may result in a shift to high pathogenicity may help in surveillance and vaccine development. Keywords: Haemagglutinin, Avian influenza virus, H5N1, HPAI, LPAI, Homology modeling, Electrostatic distance, Normal modes analysis
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not
Data Loading...
