Mathematical models could predict evolution of HIV proteins

Research from the University of Iowa (IA, USA) has applied concepts from financial mathematical modeling to help predict the evolution of HIV proteins. This information could be utilized in the future to design better vaccine candidates against the virus.

There has been limited success in the field of HIV vaccines thus far; this is in part due to the HIV envelope glycoprotein (Env), which mutates frequently causing a large diversity of variants. Senior author, Hillel Haim (University of Iowa) explained: “HIV is a highly dynamic virus. It continuously changes, both in an infected individual and, as a consequence of that, in the greater population.

“When we make a vaccine, we are essentially trying to mimic the virus so that the immune system will learn how to recognize and attack the real virus. The problem we are trying to solve for HIV is how can you design a vaccine to hit a moving and continuously changing target?”

Haim and his team utilized computational approaches inspired by financial mathematical models to predict the evolution of the Env protein in Iowa over 30 years. The basis of their model was formed by analyzing Env proteins from blood samples dating back to 1980s. –

The team initially examined changes in structural properties of Env, and then identified clues to predict the patterns of change. When they compared the Env proteins from different viruses in the same blood sample, they discovered that some properties were relatively conserved; however, others were highly variable. The team termed this characteristic variance as “volatility” noting that the volatility of each property was similar in different patients.

Inspired by financial modeling, where randomness has a defined and frequently predictable structure, the team constructed a model to predict how Env would evolve.

Haim  commented: “We found that volatilities of Env properties measured from a few patient samples from the 1980s allowed us to accurately predict how these properties of the virus evolved in the Iowa population over the course of 30 years,”

“Fortunately, relative to the financial market models that inspired this work, our predictions of changes in HIV are remarkably accurate, due to the highly conserved nature of randomness in this virus.”

The researchers hope that an ability to accurately predict the evolution of HIV proteins by testing a small number of patients would allow the tailoring of vaccines and improve their success in the future.

Sources: DeLeon O, Hodis H, O’Malley Y et al. Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model. PLoS Biol. doi:10.1371/journal.pbio.2001549 (2017) (Epub);


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