Stop malaria with a truck

Develop a better malaria vaccine with the help of AI that can save hundreds of thousands of lives each year

When biochemist Matthew Higgins founded a research group in 2006, he was closely watching malaria. Mosquito-borne diseases are second only to tuberculosis in terms of its devastating global impact. Malaria killed an estimated 627,000 people in 2020, mostly children under the age of five, and almost half of the world’s population is within reach, but Africa is an overwhelming blow. Symptoms of an infection can begin with fever and headache, which can easily be overlooked or misdiagnosed, leaving untreated.

Therefore, as malaria prevention is a priority, Higgins, a professor of molecular parasitism at Oxford University, is working with the team and energy to understand how malaria parasites interact with human host proteins. We have cooperated with each other. Their aim is to use these insights to design improved treatments that include vaccines that are much more effective than those 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-cell parasites are usually carried to the liver where they mature and proliferate, and are released more into the bloodstream. Symptoms such as fever, chills, fatigue, and illness may not appear until 10 to 4 weeks after the infection occurs, but diagnosis speed is important. Of the five parasite species that cause malaria in humans, two are particularly dangerous. For example, infections caused by Plasmodium falciparum can suddenly escalate to severe illness or death within a day without treatment.

A key issue for Higgins is the nature of the shape of malaria parasites. The ability to constantly change the appearance and the appearance of host (red blood) cells allows us to avoid the human immune system. “It makes it difficult to pin it and decide what to target in terms of drugs, or vaccines, discovery,” he says. The possibility of a completely effective vaccine – the only way to stop malaria in that truck – looked far away.

The urgency of a race to develop an effective vaccine is highlighted by the number of teams working towards that goal. Currently, RTS, S, widely known under the brand name Mosquirix, is the only approved vaccination. Designed for children and designed in October 2021. Its arrival was “huge progress” and “very good news,” says Higgins. RTS,S targets only the first step of infection, and therefore malaria parasites are transported to the liver, making them only about 30% effective. “30% is a big deal. That means a lot of lives have been saved,” he says. “But that’s a longer journey than we want to be 100%.”

Recently, another team at Oxford University, Jenner Institute, reported promising results from another similar vaccine. The approach consists of three doses followed by a booster after a year, with an efficacy rate of 77%. However, like Mosquirix, this vaccine intercepts at the first pre-hepatic stage of the malaria parasite life cycle.

In contrast, Higgins, along with Oxford-based collaborators Simon Draper and Smi Biswas, are developing a multi-stage vaccine vaccine immunogen that works simultaneously at every stage of the infection cycle. Beyond the initial invasion of parasites into human liver cells, the lab’s ultimate goal targets the final reproductive stage of the parasite’s life cycle, including blood cell invasion following infection, as well as the fusion of the male. It is a vaccine that can be used. And female gametes. It is important to tackle this stage as an infected person, if bitten again, if the cycle continues, the infected person may transmit the parasite to a previously uninfected mosquito.

Progress is fierce and slow. To explain why, consider the Covid-19 virus. This type of coronavirus has one spike protein on the surface that the vaccine needs to target. Malaria parasites, on the other hand, have hundreds or even thousands of surface proteins. And it’s a slippery shapeshifter.

Importantly, developing a vaccine containing a critical infectious disintegration component requires knowledge of the molecular structure of one gamete surface protein (PFS48/45). This is where Higgins and his team got off track. For years they tried to decipher the form of proteins, but their success was limited. Using two best experimental techniques available to identify protein structures, researchers can only get fuzzy and low-resolution images with X-ray crystallography and cryo-electron microscopy . As a result, the structural model of PFS48/45 was inevitably incomplete and incomplete.

That was until Alphafold arrived.

“We’ve been fighting this issue for years and trying to get the details we needed,” says Higgins. “We then added AlphaFold to the mix. And when we combined the model with the AlphaFold’s prediction structure, we suddenly got to see how the whole system worked,” Higgins was a doctoral student. It reminds me of an exciting moment when one Kuang-Ting Ko (who was trying all sorts of different things to improve experimental images) exploded into the office on the news.

“It was a big relief,” says Higgins, a turning point for the project. A combination of laborious experimental studies and AI predictions prompted a sharp view of PFS48/45. “The key alphafold information allowed us to determine which bits of protein we wanted to put in the vaccine and how to organize those proteins,” says Higgins. “Alphafold has been able to take the project to the next level, from the basic science stage to the preclinical and clinical development stages.”

Of course, Alphafold is not without flaws. Higgins said that while AI systems worked well in predicting how each module within a protein would adopt its structure, 3D visualizations can be slightly off. For the most accurate and confident results, Alphafold is best used with more traditional tools such as ultra-low electron microscopy, he says. “I’m sure Alphafold’s predictions will continue to improve. But for now, combining experimental knowledge with the AlphaFold model is the best approach, as this will allow you to connect everything. This is what I would say. This is an approach that people are working on many projects.”

Higgins collaborator Professor Sumi Biswas will conduct a PFS48/45 human clinical trial in early 2023. Now that the structure of PFS48/45 is understood, this allows Biswa and the Higgins Group to work together to understand the immune responses in which the immune response was generated. These vaccination trials will design improved vaccines. In pursuit of developing a vaccine that works at every stage of the malaria lifecycle, Higgins is also moving forward in understanding alternative targets. This is the key to the large protein complexes at the stage of malaria, where parasites infect red blood cells and cause the onset of symptoms. Using the combination of Alphafold and Cryo-EM, the team is working hard to understand how this complex fits perfectly.

Higgins looks further on the way, envisioning AlphaFold as an important technique for creating new useful proteins from scratch, a process known as De Novo protein design. “The future of Alphafold may not be to predict the molecules that already exist in cells, but to predict the structure of the molecules that people are designing for specific applications, such as vaccines,” he said. I say it. “If you design a protein and then use AlphaFold to predict whether it will fold in the way you want it to, it will be very powerful.”