Biology subjects

Whittaker, L.

Publications and source records attributed to Whittaker, L..

3 recordsLinked to original sources

bioRxiv2022

Stabilization of a Protein by a Single Halogen-Based Aromatic Amplifier

The utility of halogenation in protein design is investigated by a combination of quantitative atomistic simulations and experiment. The approach is applied to insulin, a small, therapeutically relevant domain amenable to simulation and semi-synthesis. In a singly halogenated aromatic ring, the simulations predicted regiospecific inductive effects to modulate multiple surrounding electrostatic (weakly polar) interactions, thereby amplifying changes in thermodynamic stability. In accordance with the simulations, stabilization of insulin is demonstrated by single halogen atoms at the ortho position of an invariant phenylalanine (2-F-PheB24, 2-Cl-PheB24 and 2-Br-PheB24; {Delta}{Delta}Gu = -0.5 to -1.0 kcal/mol) located at the edge of a protein crevice. Corresponding meta and para substitutions have negligible effects. The ortho-modified insulin analogs exhibit enhanced resistance to fibrillation above room temperature and retain biological activity in mammalian cells and in a rat model of diabetes mellitus. Consequently, halogen-based stabilization of insulin and other therapeutic proteins may provide a biophysical strategy to circumvent the requirement for a distribution "cold chain" in the developing world and enhance the shelf life of pharmaceutical formulations.

biochemistry↗

bioRxiv2022

A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses

Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharactrised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.

microbiology↗

bioRxiv2020

Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution

Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth.

evolutionary biology↗