Studying the complex interactions between antibodies and viral targets

Dr. Tal Einav, a postdoctoral fellow in Dr. Jesse Bloom’s lab, is among the first class of Damon Runyon Quantitative Biology Fellows. The prestigious award supports three years of cancer-related computational research jointly mentored by a computational biologist and cancer biologist. Einav’s co-mentor on the project is Dr. Jonathan W. Yewdell of the National Institute of Allergy and Infectious Disease. Einav is using computational methods to create maps that model how complex mixtures of specialized immune proteins known as antibodies interact with their viral targets.

These maps are a step toward Einav’s ultimate goal, still many years in the future, which is “to be able to take a sample of your blood and predict how much protection you have, from your antibodies, against all of the different viruses out there,” he said.

“Getting this fellowship was a game-changer,” Einav said. The new fellowship already has allowed him to prioritize a high-risk, high-reward “moonshot” project: using his maps to untangle the composition of a mixture of antibodies. Though he’s starting with antibodies against influenza, the principles could be applied to antibodies against any pathogen, including the novel coronavirus, SARS-CoV-2, and those that increase the risk of cancer.

Antibodies also form the basis of some genetically engineered anti-cancer immunotherapies.

"Tal comes from a physics background and so has many interesting ideas about how to apply quantitative models to study viruses and the immune system. I look forward to seeing the ideas that he develops as a Damon Runyon Fellow," Bloom said.

A complex mixture

Antibodies, which we make after exposure to pathogens like the flu virus, can protect us from infection by binding to and disabling these molecular invaders as well as by serving as a beacon for other components of our immune system. We have a dizzying variety of antibodies. Thanks to some genetic shuffling and tweaking, our antibodies could come in potentially billions of varieties, and we make an ever-shifting mix to protect against the equally dizzying array of pathogens we encounter.

As part of our immune memory — the strategy our body uses to mount a faster, more effective defense against a pathogen the second time we see it (and the basis for vaccines) — we continue making many antibodies long after we’re no longer infected with the pathogen they recognize. This means that our blood contains hundreds or thousands of different antibodies — and it’s unclear how they all work together, Einav said.

Do they behave independently (in which case, if you understand each one’s behavior by itself, you understand how they behave together), or is there functional interplay between antibodies to the same pathogen? The maps coalesce information about individual antibodies together in a larger, more refined picture, which hasn’t been done before, Einav said.

“These kinds of interactions are potentially very important, but frankly, a big question mark,” he said.

Predictive power

“One of the things that I am currently working on is how to make a map that puts all of the antibodies and all of the viruses on equal footing. This means that you could take any antibody out there against any virus, and predict how effective it will be,” Einav said.

In the map, everything is quantitative, he noted: “You know exactly how much you need of each antibody to neutralize [block infection]. It's a way to really quantify the question of neutralization, which I'm so excited about.”

Einav is working to characterize how antibodies function individually and together. The maps will coalesce information about individual antibodies into a larger, more refined picture, which hasn’t been done before, he said.

The number of molecular players makes creating a map of an interacting mix of hundreds of antibodies and their viral targets an experimentalist’s nightmare come to life — but luckily Einav is a theorist. Because he started his scientific career as a physicist, he views the problem through a computational, mathematical lens — which makes the problem surmountable, if still quite challenging, he said.

Our mix of antibodies — known as our antibody repertoire — changes over time, as we’re exposed to new germs, re-exposed to old germs, and vaccinated. But changes to our antibody repertoire are impossible to quantify right now. We know that protection against flu fades, but how these changes are reflected in our immune repertoire is unclear, Einav said.

Right now, Einav is applying his maps to mixtures of different antibodies to see if he can understand their composition: How many antibodies are in them, where do they bind and how much protection does each one provide against flu?

“If it works, and this is actually something you can solve with this map, you're solving a problem that is probably one of the prime open questions in serology [the study of antibodies],” Einav said.

His maps could also have therapeutic uses one day. The ability to spot the holes in an individual’s immune protection could be used to tailor an antibody-based treatment to their specific needs. They could also be used to help gain greater insight into how our antibody repertoires respond to vaccines.

Einav credits his success to Bloom’s mentorship and the Hutch research environment.

“Jesse is an amazing mentor,” he said. "There is a wealth of opportunity here because everybody is working on virology or cancer, or some immunology component. It's a privilege to be at the Hutch.”