Accurate Measurement of the Effects of All Amino-Acid Mutations on Influenza Hemagglutinin

By Michael B. Doud, Jesse D. Bloom. 2016

220 citations recorded by the source corpus.

Influenza genes evolve mostly via point mutations, and so knowing the effect of every amino-acid mutation provides information about evolutionary paths available to the virus. We previously used high-throughput mutagenesis and deep sequencing to estimate the effects of all mutations to an H1 influenza hemagglutinin on viral replication in cell culture (Thyagarajan and Bloom, 2014); however, these measurements suffered from sub-stantial noise. Here we describe advances that greatly improve the accuracy and reproducibility of our measurements. The largest improvements come from using a helper virus to reduce bottlenecks when generating viruses from plasmids. Our measurements confirm that antigenic sites on the globular head of hemagglutinin are highly tolerant of mutations. However, other regions – including stalk epitopes targeted by broadly neutralizing antibodies – have a limited capacity to evolve. The ability to accurately measure the effects of all influenza mutations should enhance efforts to understand and predict viral evolution.

Subfields: Astronomy and Astrophysics, Geography, Planning and Development, History and Philosophy of Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Kevin Kaichuang Yang
Zachary Wu
Frances H. Arnold
Amy K. F. Davis
Jesse D. Bloom
K. McCormick
Megan E. Gumina
J. Petrie

Citations in the corpus

Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, unknown): “Viral replication assays, performed by quantitative sequencing after a certain time point, were applied to all of the viral proteins (Wu et al , 2015; Doud & Bloom, 2016; Haddox et al , 2016; Lee et al , 2018).”
Sera from Individuals with Narrowly Focused Influenza Virus Antibodies Rapidly Select Viral Escape Mutations In Ovo (2018, methodology): “Future experiments can use more recent technological advances, such as deep mutational scanning (24, 25), to better define the specificity of serum antibodies prior to passaging experiments.”
Machine learning in protein engineering (2018, background): “…β -glucosidase (Romero et al., 2015), HIV envelope protein (Haddox et al., 2016), inﬂuenza nucleoprotein (Ashenberg et al., 2017), inﬂuenza hemagglutinin (Doud & Bloom, 2016), PhoQ (Podgornaia & Laub, 2015), GB1 (Wu et al., 2016), the DNA-binding domain of a steroid hormone receptor (Starr et…”