Did Google’s DeepMind just revolutionize medicine?
I remember when I was taught in college that amino acids are “the building blocks of life”. I was fascinated by the idea that our complex shape, and the shape of other living organisms, was like a little Lego set, built to make us who we were. Even then, in the early 1980s, researchers had already been trying for nearly a decade to figure out how these amino acids tell proteins what shape to take. Since then, with ever more powerful computers and complex algorithms, researchers have applied machine learning techniques to answer the same biological question.
Google‘s (NASDAQ: GOOG)(NASDAQ: GOOGL) DeepMind has just provided an answer, and it amazes researchers. For nearly 50 years, scientists have wondered how proteins know what shape to fold and do so over and over again. In a modeling competition, researchers at DeepMind just cracked the code, creating a model that translates chains of amino acids into three-dimensional protein structures. To understand how this might impact medicine (and investment), it’s important to understand what this new knowledge will allow scientists to do. Furthermore, what are the downstream effects? What areas of biological research may be the most affected? And which companies have the most to gain – or to lose – the most?
Tens of thousands of proteins exist in humans, and there are billions of them in other species, viruses and bacteria. The way these proteins fold directly determines what they do. In fact, in molecular biology there is a saying that “structure is function”. The folded form is key to the role proteins play, such as infection-fighting antibodies or insulin in regulating blood sugar. This is why, since 1994, the Critical Assessment of Protein Structure Prediction (CASP) has been organized. This is an event that challenges teams to advance the accuracy of predictions in the area of protein structure.
AlphaFold, the winning model from DeepMind, was trained on public data of 170,000 protein structures. The program required 128 high-end cloud computing cores running for several weeks to create the algorithm. Ultimately, two-thirds of the model’s accuracy scores represent design errors smaller than the width of a single atom. DeepMind was a head and shoulders above the rest of the attendees at the event, which consisted mostly of varsity teams, but included entries from Microsoft (NASDAQ: MSFT) and Chinese internet giant Tencent (OTC: TCEHY).
Why it matters
Most of the drugs prescribed today were discovered by chance or through time-consuming trial-and-error experiments. Understanding how amino acids direct proteins to twist and fold, taking their three-dimensional shape, will provide a better understanding of why each protein becomes what it does and how those signals are transmitted across cell membranes. This could allow scientists to better design drugs that will be used by cells in the way that they want, understand diseases causing the wrong folds, and allow drug makers to identify the cause of genetic variations that lead to disease.
In one example during the event, the AlphaFold model provided the structure of a bacterial protein in just 30 minutes. The Max Planck Institute in Germany had been working on this problem for over a decade. Then the team could start tackling the thousands of unresolved proteins in the human genome and the hundreds of millions of proteins in nature that have not been modeled. This raises the question of when we can all get drugs designed for our own specific biology.
What to watch out for
For now, drug discovery applications will have to wait. It’s unclear when or how DeepMind will share their model, and while impressive, it had its limits. For example, the model struggled to predict complexes or groups of proteins, where interactions between proteins can distort shapes. As more proteins are involved, the potential possibility of interactions to be modeled becomes nearly impossible. This mathematical constraint – known as a combinatorial explosion – is common in advanced modeling, but could eventually be overcome with more computing power. Addressing this issue will be important, as protein-protein interactions are one of the key mechanisms targeted to discover new drugs.
Despite the caveats, the discovery promises to fuel the fire of scientific research into how the human body works. A better understanding of the translation of amino acids into proteins validates the potential impact of gene editing and companies like CRISPR Therapeutic (NASDAQ: CRSP), Intellia Therapeutic (NASDAQ: NTLA), and Modifications Drug (NASDAQ: MODIFY). Additionally, solving this problem should ultimately lead to less trial and error in the lab and make genome sequencing even more important, benefiting Illumina (NASDAQ: ILMN), Thermo Sinner Scientist (NYSE: TMO), and Agilent (NYSE: A). After all, DNA contains information to make proteins.
The benefits of DeepMind’s discovery will remain largely behind the curtain of research, appearing to most of us just as other medical breakthroughs have – in the form of new or better drugs to treat disease. But don’t be fooled by the importance. A CASP judge, a computational biologist at Columbia University, called it one of the most significant breakthroughs of his life. Even the CASP co-founder added, “I never thought I would see this in my lifetime.” I think this is the first salvo in a new battle against human disease. Armed with a better understanding of the building blocks of life and once unthinkable computing power, we could soon return to our drug discovery process as we now look at treating infections before penicillin becomes available, or monitoring pregnancy before ultrasound scans – two advancements made in the 1950s. Seventy years from now people might marvel at the efforts of drug discovery and wonder how we ever developed drugs with such a haphazard process.
This article represents the opinion of the author, who may disagree with the “official” recommendation position of a premium Motley Fool consulting service. We are heterogeneous! Questioning an investment thesis – even one of our own – helps us all to think critically about investing and make decisions that help us become smarter, happier, and richer.