Impact
Developing a better malaria vaccine with the help of AI that could save hundreds of thousands of lives every year
When biochemist Matthew Higgins established his research group in 2006, he had malaria in his sights. This mosquito-borne disease is second only to tuberculosis in terms of devastating global impact. Malaria killed an estimated 627,000 people in 2020, most of them children under five, and nearly half the world's population is within its reach, although Africa is by far the hardest hit. Symptoms of infection may start with a simple fever and headache, making the infection easily overlooked or misdiagnosed – and therefore untreated.
Malaria prevention is therefore the priority. That's why Higgins, professor of molecular parasitology at the University of Oxford, is working tirelessly with his team to understand how the malaria parasite interacts with proteins in the human host. Their goal is to use this knowledge to design improved therapies, including a vaccine that will be much more effective than the one currently available.
When a human is bitten by an infected female mosquito, one of five types of malaria parasites can enter the bloodstream. These single-celled parasites are usually carried to the liver, where they mature and multiply, releasing more into the bloodstream. Symptoms such as fever, chills, fatigue and nausea may not appear until 10 days to four weeks after infection, but prompt diagnosis is crucial. Among the five species of parasites responsible for malaria in humans, two are particularly dangerous. For example, a Plasmodium falciparum infection can, if left untreated, suddenly progress to severe illness and death within a day.
The main challenge for Higgins is the changing nature of malaria parasites. Their ability to constantly change their appearance as well as that of their host's cells (red blood) allows them to evade the human immune system. “In terms of drug or vaccine discovery, it’s difficult to pin them down and decide what to target,” he says. The possibility of developing a fully effective vaccine – the only way to stop malaria – seemed remote.
The urgency of the race to develop an effective vaccine is underscored by the number of teams working to achieve this goal. Currently, RTS,S, widely known by its brand name Mosquirix, is the only approved inoculation. It was designed for children and in October 2021. Its arrival was a “huge breakthrough” and “very good news”, says Higgins. Since RTS,S only targets the first stage of an infection, during which the malaria parasite is transported to the liver, its effectiveness rate is only about 30%. “30% is a big deal. That means a lot of lives saved,” he says. “But we are still far from the 100% we want. »
When we combined our model with the structure predicted by Alphafold, we could suddenly see how the whole system worked.
Matthew Higgins, biochemist
More recently, another team at the University of Oxford – the Jenner Institute – reported promising results with another similar vaccine. Its approach, which consists of three doses followed by a booster a year later, has an effectiveness rate of 77%. However, like Mosquirix, this vaccine intercepts the first pre-hepatic stage of the malaria parasite's life cycle.
In contrast, Higgins – with his Oxford-based collaborators Simon Draper and Sumi Biswas – is developing immunogens for a multi-step vaccine that can work simultaneously in each phase of the infection cycle. Beyond the initial entry of the parasite into human liver cells, the laboratory's ultimate goal is to achieve a vaccine capable of not only targeting the invasion of blood cells that follows infection, but also the final step reproductive life cycle of the parasite, which involves the fusion of its male. and female gametes. This step is important to address because infected humans can otherwise transmit the parasite to uninfected mosquitoes if they are bitten again, continuing the cycle.
Progress has been difficult and slow. To illustrate why, consider the COVID-19 virus. This type of coronavirus has only one spike protein on its surface that a vaccine must target. Malaria parasites, on the other hand, have hundreds or even thousands of surface proteins, Higgins says. And he's a slippery shapeshifter.
Crucially, developing a vaccine containing an essential component to disrupt infection requires knowing the molecular structure of a gamete surface protein – Pfs48/45 – essential for parasite development in the mosquito midgut. This is where Higgins and his team went off the rails. For years, they tried to decipher the shape of the protein, with limited success. Even using two of the best experimental techniques available for discerning a protein's structure – X-ray crystallography and cryo-electron microscopy – the researchers were only able to obtain blurry, low-resolution images. As a result, their structural models of Pfs48/45 were necessarily imperfect and incomplete.
That was until AlphaFold came along.
“We had been wrestling with this problem for years, trying to get the details we needed,” Higgins says. “Then we added AlphaFold to the mix. And when we combined our model with the structure predicted by Alphafold, we could suddenly see how the whole system worked. Higgins remembers the exciting moment when his Ph.D. Kuang-Ting Ko – “who had tried all sorts of different things to improve the experimental images” – burst into the office with the news.
AlphaFold allowed us to take our project to the next level, from a basic science stage to the preclinical and clinical development stage.
-Matthew Higgins
“It was a big relief,” says Higgins, and a turning point for the project. The combination of painstaking experimental work and AI prediction quickly yielded a precise view of Pfs48/45. “The crucial information from AlphaFold allowed us to decide which protein fragments we want to introduce into a vaccine and how we want to organize these proteins,” says Higgins. “AlphaFold allowed us to take our project to the next level, from basic science to preclinical and clinical development. »
AlphaFold is of course not without its flaws. Higgins noted that while the AI system worked well at predicting how each module of a protein adopted its structure, there were cases where its 3D visualizations were a little off. To get the most accurate and reliable results, it's best to use AlphaFold with more traditional tools such as cryo-electron microscopy, he explains. “I'm sure AlphaFold's predictions will get better and better. But for now, combining experimental knowledge with AlphaFold models is the optimal approach, because it allows us to piece everything together. This is the approach we take on many of our projects.
Higgins' collaborator, Professor Sumi Biswas, will conduct a human clinical trial of Pfs48/45 in early 2023. Now that the structure of Pfs48/45 is understood, this will allow the Biswas and Higgins groups to work together to understand the immune response generated. in these vaccination trials and to design improved vaccines. In an effort to develop a vaccine that works at every stage of the malaria life cycle, Higgins is also making progress in understanding another target, a large protein complex key in the stage of malaria where parasites infect red blood cells , causing the appearance of malaria. of symptoms. Using a combination of AlphaFold and cryo-EM, the team is working hard to understand how this complex fits together.
Looking further ahead, Higgins envisions AlphaFold as an essential technology for creating new useful proteins from scratch, a process known as de novo protein design. “The future of AlphaFold may not be so much about predicting molecules that already exist in cells, but rather about predicting the structures of molecules that people design for specific applications, such as vaccines,” says- he. “If we are able to design proteins and then use AlphaFold to predict whether they will fold the way we need them to, that will be very powerful.”