Soft Matter Science - Infectious Disease Modeling


Infectious Disease Modeling


I. Influenza Virus

Influenza virus is one of the most emergent and fatal diseases of human and poultry. The wide spread of avian flu, including the 1918 H1N1, 1968 H3N2, and more recent H5N1, has caused a great public health concern due to the emergence of potential pandemic threats. Though new vaccines are in development against both H3N2 and H5N1, it is unclear if they will be effective against future strains due to the high mutation rates of the influenza virus. Thus, new techniques that allow for both the prediction of future mutations and the development of appropriate vaccines (consisting of antibodies that can target influenza virus) are in great need for better preparation of future pandemics.

1. Mutual Information-based Site Transition Network

Given that the influenza A virus is under rapid mutations, with substitution rates estimated to be 0.0057 substitutions per site per year for HA1 domain , it is critical to predict future mutations and make an efficient influenza vaccine before a potential variant causes an epidemicor even a pandemic.

In this project, more than 4000 A/H3N2 hemagglutinin (HA) sequences from 1968 to 2008 were analyzed to model the evolutionary path of the influenza virus, which allows us to predict its future potential drifts with specific mutations. The mutual information (MI) method was used to design a site transition network (STN) for each amino acid site in the A/H3N2 HA sequence. The STN network indicates that most of the dynamic interactions are positioned around the epitopes and the receptor binding domain regions, with strong preferences in both the mutation sites and amino acid types being mutated to. The network also shows that antigenic changes accumulate over time, with occasional large changes due to multiple co-occurring mutations at antigenic sites. Furthermore, the cluster analysis by subdividing the STN into several subnetworks reveals a more detailed view about the features of the antigenic change: the characteristic inner sites and the connecting inter-subnetwork sites are both responsible for the drifts. A novel five-step prediction algorithm based on the STN shows a reasonable accuracy in reproducing historical HA mutations. The STN approach also agrees well with the phylogenetic tree and antigenic maps based on HA inhibition assays. Our current prediction strategy might shed light in identifying the trends in the HA sequence evolution, and provide guidelines for future vaccine development.

2. Free Energy Perturbation Study

The resistance of influenza virus to antibody (Ab) neutralization is due to mutatitions in the viral surface glycoprotein hemagglutinin (HA), which is the principal antigen on the influenza virus surface. Both HA and neuraminidase (NA) have emerged as critical targets for both vaccine and drug development in recent years.

The goal of this project is to provide a better understanding of the molecular mechanism behind the single mutation induced antibody neutralization, and to further predict other possible mutations with even greater capability in escaping antibody neutralization. We used the free energy perturbation (FEP) method which has been widely applied for calculating the binding affinities for a variety of biophysical phenomena to study this single mutation effect on the antibody-antigen binding affinity of H3N2 hemagglutinin (HA) and a monoclonical antibody fragment (Fab). Our simulation results showed a 5.2 ( 0.9 kcal/mol decrease in the HA/Ab binding affinity due to the T131I single mutation, in excellent agreement with experiment, but with a somewhat different molecular mechanism. We have also predicted a few more mutations on the T131 site which show even larger decreases in the binding affinity (meaning they are even better candidates for antibody neutralization). These predictions from the current FEP calculations can provide a testing ground for the experimental validation of the antibody reactivity, which might ultimately help us to chart a path toward effective influenza virus neutralization.


Related Publications:

  • Z. Xia, J. Gu, J. Zhu and R. H. Zhou,
    Using a Mutual Information Based Site Transition Network to Map the Genetic Evolution of Influenza A/H3N2 Virus,
    Bioinformatics, 25, 2309-2317, 2009
  • R. H. Zhou, P. Das, A. K. Royyuru,
    Single Mutation Induced H3N2 Hemagglutinin Antibody Neutralization: A Free Energy Perturbation Study, Cover article,
    J. Phys. Chem. B, 112, 15813-15820, 2008

II. HIV

Immune control of viral infections is modulated by diverse T cell receptor (TCR) clonotypes engaging peptide-MHC class I complexes on infected cells, but the relationship between TCR structure and antiviral function is unclear.

In this project, we used a combined approach employing both experimental and theoretical techniques to address the relationship between HIV-1 peptide (KK10)-HLA-TCR complex structure and function. We took advantage of unique reagents generated from persons with untreated HIV-1 infection: intrapatient CTL clones with distinct TCR clonotypes, all induced in vivo against the same HLA B*2705-restricted epitope in Gag, but differing in measures of antiviral function. Using molecular dynamics (MD) simulations coupled with functional assays, we not only captured structural and energetic details of particular substitutions, but also showed that specific binding patterns among KK10-HLA (B*2705)-TCR interactions are associated with enhanced antiviral efficacy and cross-reactivity of the clonotypes. This study provides structural and mechanistic insights into T cell-mediated antiviral immunity in a chronic human viral infection.


Related Publications:

    • Z. Xia, H. B. Chen, S. G. Kang, T. Huynh, J. W. Fang, P. A. Lamothe, B. D. Walker, and R. H. Zhou,
      The complex and specific pMHC interactions with diverse HIV-1 TCR clonotypes reveal a structural basis for alterations in CTL function,
      (Nature) Sci. Rep.4, 4087, 2014
    • Angelique Hölzemer, Christina F. Thobakgale, Camilo A. Jimenez-Cruz, Wilfredo F. Garcia-Beltran, Jonathan M. Carlson, Nienke H. van Teijlingen, Jaclyn K. Mann, Manjeetha Jaggernath, Seung-gu Kang, Christian Körner, Amy W. Chung, Jamie L. Schafer, David T. Evans, Galit Alter, Bruce D. Walker, Philip J. Goulder, Mary Carrington, Pia Hartmann, Thomas Pertel, R. H. Zhou, Thumbi Ndung’u, Marcus Altfeld,
      Selection of an HLA-C*03:04-Restricted HIV-1 p24 Gag Sequence Variant Is Associated with Viral Escape from KIR2DL3+ Natural Killer Cells: Data from an Observational Cohort in South Africa,
      PLoS Medicine, 12, e1001900, 2015

 

 

 

 

 

 




Members


Alumni

  • Otitoaleke G. Akinola
  • David R. Bell
  • Matteo Castelli
  • Camilo Jimenez
  • Yuxing Peng
  • Michael Pitman
  • Raul Araya Secchi
  • Frank Suits
  • Jacinta Wubben
  • Zhen Xia
  • Payel Das