Computational Immunology Leads Way to Developing Vaccines for Viruses
Mathematical methods use sequential data sets to understand how hepatitis and HIV viruses adapt and mutate – and to detect their weaknesses
Prof Matthew McKay, Hari Harilela Associate Professor of Electronic & Computer Engineering
Statistics and computational methods are being employed by a team at HKUST to further the understanding of how harmful viruses invade the human immune system. The research – the first of its kind in Hong Kong – is being led by Prof Matthew McKay, Hari Harilela Associate Professor of Electronic & Computer Engineering, in a multidisciplinary approach that includes collaboration with biologists at MIT, statisticians at Stanford University, and a genetics group at Cambridge University.

“We are making sense of biological sequential data sets, in particular for viruses such as Chronic Hepatitis C (HCV) and HIV, with an eventual aim of developing vaccines," says Prof McKay, who points out that currently no functioning vaccines exist for either of these diseases. 

During 2016, the HKUST team made a fundamental discovery by establishing that both these diseases show remarkable structure in genetic makeup not known before. This new knowledge is helping to comprehend how the viruses function, how they adapt, the patterns the viruses prefer – and those they do not – and to identify weaknesses that can eventually lead to the development of vaccines. Prof McKay describes this as a "very fundamental result." 

It may appear unconventional that this work is being carried out by the department of Electronic & Computer Engineering, rather than Chemistry, say, but computational immunology utilizes mathematical methods to understand data sets, just the same as those used by Prof McKay to understand Wi-fi and to develop tools for communication systems. "Effectively, our approach is based on statistical signal processing," he says. "Our analysis reveals parts of the virus that may be most susceptible to immune pressure, despite the high mutability of the virus." 

Taking HIV, Prof McKay explains that is a very mutable virus and it is hard for the immune system to latch on. "A key aspect in immune response is antibodies that can attach themselves to the virus – to the exterior of the HIV protein. These proteins are not well understood, which is why it is hard to design an antibody-based vaccine. We are using a statistical method to predict the sequences for these outside proteins, and have for the first time come up with a way to predict whether certain genetic sequences here are fit and which are not fit. This is another very challenging and fundamental problem: our analysis reveals parts of the virus that may be most susceptible to immune pressure, despite the high mutability of the virus."

The team is now working on consolidating the work done to date. "We still have a lot of work to do in getting the computational and predictive models right –  this is an essential first step that we must take before we can talk about real experiments," says Prof McKay. The team is targeting top journals and aims to submit the findings during 2017.

Computational immunology is a new field not just for HKUST, but in Hong Kong, too. Other local institutions are showing great interest in using the tools developed by Prof McKay's team to guide their own experiments. 

Prof Matthew McKay, Hari Harilela Associate Professor of Electronic & Computer Engineering

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DESIGNED BY PTC