The amazing structures of proteins

The Nobel Prize in Chemistry 2024 is all about proteins. David Baker, the University of Washington and Howard Hughes Medical Institute, has succeeded in building entirely new kinds of proteins, and Demis Hassabis and John Jumper, Google DeepMind, have developed an AI model to predict proteins’ complex structures.

Jan Terje Andersen, Professor and head of the Laboratory of Adaptive Immunity and Homeostasis at University of Oslo and Oslo University Hospital and a member of the CoE Precision Immunotherapy Alliance, PRIMA, has always been fascinated in how proteins, the final products that are encoded by our DNA, work.

“They are essential for life and perform biological tasks that continuously go on in our body. As such they are like tiny machines or workhorses, always on a mission,” he says. 

Proteins can be very small or very large and complex and they are built up of amino acids that are connected in short or long chains. These chains fold into three-dimensional structures that define their architecture and function, explains Terje Andersen. “We have 20 amino acids that can be combined in a plethora of ways, and while some proteins consist of tens of such building blocks, others can be much more complex and contain several thousand.” 

“How the proteins fold into very specific shapes has been a mystery, but the laureates have opened doors to this universe and given us tools that help us understand how proteins are built and turned into machines with distinct tasks,” he says. “These computational tools allow predictions on how altering a specific amino acid may affect the protein’s function. Such knowledge has further guided structure-based engineering of designer proteins, and even led to engineering of completely new proteins.” 

The latter was shown by David Baker and his lab about 20 years ago. Since then, this research has had an enormous scientific impact as protein engineers are using and developing computational tools to gain a better understanding of biology and diseases, and also of how such strategies can be used to tailor new biomedical technologies and medicines, describes Terje Andersen. 

The Baker lab is a fantastic example of how groundbreaking research can generate businesses, value creation, and solutions to tomorrow’s challenges.

“The Baker lab is a fantastic example of how groundbreaking research can generate businesses, value creation, and solutions to tomorrow’s challenges. He is the director of the Institute for Protein Design at University of Washington School of Medicine that is often referred to as a start-up factory,” he adds.

From Rosetta to AI

In 2003 Professor David Baker and his colleagues published the design and crystallographic validation of a 93-residue α/β-protein named Top7 – a breakthrough in computational de novo protein design (the generation of new proteins with sequences unrelated to those in nature based on physical principles of intramolecular and intermolecular interactions (Huang et al., Nature, 2016)).

This was big news for several reasons; it was a relatively large protein, they sought to design a folding pattern not found for any globular protein in the Protein Data Bank, and the final design had no significant sequence similarity to any naturally occurring protein in the sequence databases. As such, Top7 was unlike any other protein, both structurally and sequence-wise, and it was designed by automated computation with full optimization of both backbone and sidechains.  

Baker and his colleagues showed that a wide range of protein structures could be designed using the Rosetta software, including proteins that can be used as pharmaceuticals and vaccines.

The Royal Swedish Academy of Sciences’ describes that the key to Baker’s success was his team’s initial development of the Rosetta computer program in 1999. The program assembles short structural fragments from unrelated protein structures with similar local sequences in the Protein Data Bank, and simultaneously optimizes sequence and structure with respect to the target backbone conformation. Baker and his colleagues showed that a wide range of protein structures could be designed using the Rosetta software, including proteins that can be used as pharmaceuticals and vaccines. In addition, in 2008, Baker and his coworkers reported the first attempts at de novo enzyme design, and the design of novel enzymes that can catalyze reactions for which no naturally occurring enzymes exist.

The second discovery awarded with a Nobel Prize this year concerns the prediction of protein structures. Ever since the 1970s researchers have had difficulties predicting protein structures from amino acid sequences. The breakthrough came just four years ago, in 2020, when Demis Hassabis and John Jumper, DeepMind, presented an artificial intelligence (AI) model, AlphaFold2. AlphaFold2 (AF2) showed a level of accuracy that was competitive with experimental structures for a majority of target proteins.

“The AF2 architecture can be described as an ingenious piece of neural network with a multitude of new inventions, and it can be viewed as the first real scientific breakthrough of AI,” stated the Royal Swedish Academy of Sciences, emphasizing the impact of this development.

The AF2 source code was also made public, which decisively contributed to its impact as it could be extensively tested and validated. Since 2020, more than two million people from 190 countries have used it.

Crucial tools

Thanks to the laureates’ protein design and prediction tools, today we can easily visualize the structure of proteins and better understand how life functions, including why some diseases develop or how antibiotic resistance occurs. 

Jan Terje Andersen’s lab takes advantage of such tools to gain a better understanding of how proteins behave and how they interact to mediate immunological reactions. 

“We need to learn nature’s rules and how they can be manipulated as such knowledge can be used to design protein variants with altered binding and transport properties. These engineered variants are not only great tools to study biology, but they can also lead to the development of biomedical technologies and therapeutic products that can be used to specifically treat diseases by avoiding adverse side effects,” he explains.

For instance, his lab is part of a Centre of Excellence called the Precision Immunotherapy Alliance (PRIMA), where seven research groups are working together with the common aim to develop new precision immunotherapy strategies over the coming years. As part of this work, computational methods are crucial for engineering antibodies with a tailored mode of action and immune receptors that are engineered to specifically recognize and eradicate tumor cells. 

The overall aim is to crack the code of how T-cells recognize cancer cells by bridging structural biology and genetic screening with molecular modeling, computational biology, and AI.

“In fact, one of the PIs and co-directors of PRIMA, Professor Johanna Olweus, is part of “MATCHMAKERS”, a team funded by a large Cancer Grand Challenges grant from Cancer Research UK and NIH, in which the laureate David Baker is also active,” says Terje Andersen. “The overall aim is to crack the code of how T-cells recognize cancer cells by bridging structural biology and genetic screening with molecular modeling, computational biology, and AI. In addition, my lab has an on-going project with one of the spin-out companies from the Baker lab.”

Designing new biomedical technologies

The overall focus of Jan Terje Andersen’s research is to gain in-depth insight into the cellular processes and molecular interplay underlying the functions of the two most abundant groups of proteins in blood, albumin and antibodies, and their respective receptors. He and his colleagues use the knowledge gained from combining structural, computational, and biophysical approaches with cellular and in vivo studies to design new biomedical technologies, such as engineering recombinant proteins with improved functions.

For example, we recently published an article on a new technology that can be used to improve the pharmacokinetics of monoclonal antibodies and also to enhance the killing of cancer cells and bacteria via potent engagement of the complement system.

“These proteins can be used as scaffolds in the development of protein-based therapeutics and also subunit vaccines, both for invasive and non-invasive delivery strategies. For example, we recently published an article on a new technology that can be used to improve the pharmacokinetics of monoclonal antibodies and also to enhance the killing of cancer cells and bacteria via potent engagement of the complement system,” he says.

“Importantly, we are collaborating extensively with biotech and pharmaceutical companies to enable the translation of our key findings and technologies into new solutions. Two years ago, we also spun out a biotech company called Authera AS from our lab, which has a unique technology platform that is used in collaboration with companies. We also hope to launch a new company next year.”