Mutational fitness landscape of human influenza H3N2 neuraminidase

Ruipeng Lei, Andrea Hernandez Garcia, Timothy J.C. Tan, Qi Wen Teo, Yiquan Wang, Xiwen Zhang, Shitong Luo, Satish K. Nair, Jian Peng, Nicholas C. Wu

Research output: Contribution to journalArticlepeer-review


Influenza neuraminidase (NA) has received increasing attention as an effective vaccine target. However, its mutational tolerance is not well characterized. Here, the fitness effects of >6,000 mutations in human H3N2 NA are probed using deep mutational scanning. Our result shows that while its antigenic regions have high mutational tolerance, there are solvent-exposed regions with low mutational tolerance. We also find that protein stability is a major determinant of NA mutational fitness. The deep mutational scanning result correlates well with mutational fitness inferred from natural sequences using a protein language model, substantiating the relevance of our findings to the natural evolution of circulating strains. Additional analysis further suggests that human H3N2 NA is far from running out of mutations despite already evolving for >50 years. Overall, this study advances our understanding of the evolutionary potential of NA and the underlying biophysical constraints, which in turn provide insights into NA-based vaccine design.

Original languageEnglish (US)
Article number111951
JournalCell Reports
Issue number1
StatePublished - Jan 31 2023


  • CP: Molecular biology
  • deep mutational scanning
  • evolution
  • influenza
  • neuraminidase
  • protein language model
  • protein stability
  • protein structure

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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