Using this model, the researchers can enter any possible sequence of Gag proteins and determine how prevalent it will be. That prevalence correlates with the fitness of a virus carrying that particular protein sequence, a relationship that the researchers demonstrated by using the model to predict the fitness of a few dozen Gag protein sequences, and verified by engineering those sequences into HIV viruses and testing their ability to replicate in cells grown in the lab. They also tested their predictions against human clinical data.
The model also allows the researchers to visualize viral fitness using "fitness landscapes" - topographical maps that show how fit the virus is for different possible amino-acid sequences for the Gag proteins. In these landscapes, each hill represents sequences that are very fit; valleys represent sequences that are not.
Ideally, vaccine-induced immune responses would target viral proteins in such a way that mutant strains that escape the immune response correspond to the fitness valleys. Thus, the virus would either be destroyed by the immune response or forced to mutate to strains that cannot replicate well and are less able to infect more cells.
This would mimic the immune response mounted by people known as "elite controllers," who are exposed to the virus but able to control it without medication. Immune cells in those people target the same peptide sequences that the model predicted would produce the biggest loss of fitness when mutated.
This general approach could also be used to identify vaccine targets for other viruses, Chakraborty says.
"The reason we are excited about this is that we now have a method that combines two technologies that are getting cheaper all the time: sequencing and computation," he says. "We think that if this continues to be validated, it could become a general method of obtaining the fitness landscapes of viruses, allowing you to do rational design of the active components of vaccines."
"This work is a great example of how integrating expertise from different scientific disciplines - in this case physics, computational biology, virology and immunology - can accelerate progress toward an HIV vaccine," Walker says.
Source: Massachusetts Institute of Technology