Proteins rule everything around us. And inside us too. Squeeze the water out of the human body and what’s left—skin, bones, organ tissue—is mostly protein. Not only do proteins make up much of the physical substance of living things, they also govern and catalyze the chemical reactions that underpin life itself.
The human body contains tens of thousands of different proteins, each with a unique chemical structure and job to do. Some you’ve probably heard of: hemoglobin transports oxygen from the lungs to muscle and other tissues; insulin regulates blood sugar; antibodies, a diverse class of proteins, destroy invading pathogens. Collagen? Protein. Enzymes? Those are proteins too. Put simply: no protein, no life.
Of the four Cal-connected 2024 Nobel laureates, two were awarded for breakthroughs in protein-related work. Molecular biologist Gary Ruvkun ’73 was awarded the Nobel in physiology or medicine, along with colleague Victor Ambros, for the discovery of microRNA, a key to regulating the expression of genes within cells by controlling the amount of protein production. MicroRNA helped solve a central mystery in DNA science: how various types of cells differentiate despite containing the same genetic blueprints. And biochemist David Baker, Ph.D. ’89, received the Nobel in chemistry for work that supercharged the field of protein design.
Why design proteins, you ask? There are millions of naturally occurring proteins, after all. How many more could we possibly need? But proteins are powerful molecules, and for decades, biologists have been tempted by the idea that novel, lab-created proteins could be used as medicines, vaccines, enzymes, and even biological alternatives to industrial chemicals.
It would not prove easy. Proteins begin as long strings of amino acids that fold, origami-like, into complex, three-dimensional structures. The shape of a given protein molecule is key to its function. Recall how the virulence of COVID-19 was attributed to the shape of its spike protein (those spikes allow COVID to bind easily to our ACE2 receptors). Or, on the defensive side of the viral ballgame, take immunoglobulin G. This protein hangs out inside your nose until an unwelcome organism enters, such as a scrap of bacteria or virus. Like other antibodies, immunoglobulin G has a Y-shaped structure, resembling a crude fork. Using its “tines,” the antibody attaches to the invading antigen. Then its “handle,” the base of the Y structure, attaches to an immune cell, such as a macrophage or natural killer cell, which then engulfs the intruder. Voilà. The immune system at work.
The challenge for the would-be protein designer is that while the chemical makeup of a protein is relatively easy to work out or dream up, discerning its ultimate shape is extraordinarily hard. In some cases, it has taken researchers years to learn the shape of a single protein.
But that was in the “before times.” In the late 1990s, David Baker and his team at the University of Washington developed Rosetta, a software program with the ability to accurately predict the folded shape of an existing or theoretical protein by its amino acid sequence.
“Since then, his research group has produced one imaginative protein creation after another,” according to the Nobel Foundation, “including proteins that can be used as pharmaceuticals, vaccines, nanomaterials, and tiny sensors.”
The allure of protein science once called to former Berkeley professor and 2024 Nobelist John Hopfield as well. In the 1970s, he was interested in how enzymes catalyze chemical reactions with high accuracy in processes like DNA replication and protein synthesis. He created a model called kinetic proofreading that is fundamental to this day. As such, you might expect Hopfield to have won the prize in chemistry or perhaps physiology or medicine. Instead, he shares the award in physics, for his cornerstone work in artificial intelligence.
As you’re no doubt aware, the AI revolution is now upon us. We catch lifts from self-driving cars, and even the least creative among us can produce astonishing works of art using free AI programs. Our daily lives are guided by a slew of algorithms that determine the news we consume, the entertainment we watch, and even the friends and lovers we connect with. Powering it all are artificial neural networks (ANNs) inspired by the structure and function of the human brain.
In 1982, Hopfield laid the groundwork for today’s AI by theorizing a new type of ANN that he called the Hopfield network. ANNs of the time allowed information to flow only in one direction via a chain of nodes—input to output. The Hopfield network utilized recurrent connections, meaning all nodes were connected and information could flow back and forth in loops and throughout the system. This allowed the ANN to take a low-quality input, compare it to memorized data, and select the closest match. Hopfield laid the groundwork that enables today’s AI to outperform us in nearly every activity we hold dear.
Rounding out the haul for Cal-connected scientists, the Nobel Committee awarded former Berkeley Professor James Robinson, along with his colleagues Daron Acemoglu and Simon Johnson, with the prize in economics for their work probing the question, what makes one nation rich and another poor? The answer, according to the laureates, can be found in a nation’s institutions and whether they nurture or exploit the population. Robinson is now a fellow at the Institute of African Studies at the University of Nigeria at Nsukka, specializing in Sub-Saharan Africa and Latin America.
Robinson and Acemoglu’s theory, laid out in their influential 2012 book Why Nations Fail, is that national institutions can be either inclusive or extractive. Inclusive institutions foster fair distribution of power throughout society and allow broad engagement in the economy. Such institutions provide citizens with the necessary infrastructure, legal protections, and political freedoms to thrive. Predictably, nations with inclusive institutions tend to flourish economically, the authors write.
Extractive institutions, on the other hand, concentrate power among the few. Acemoglu and Robinson say these institutions often resist “creative destruction,” in which outdated ideas, technologies, and industries are replaced by new, innovative ones. They create oppressive policies that foster unpredictable economic conditions and often ignite social unrest. They restrict broad access to the economy, thereby limiting the talent pool, and instead they favor monopolies, hindering competition. By choking innovation and limiting prosperity to the few, Acemoglu and Robinson argue, countries with extractive institutions tend to stay poor.
Something to think about.
