by An advanced algorithm developed by Google DeepMind has tackled one of the greatest unsolved mysteries in biology. AlphaFold aims to predict the 3D structures of proteins from the “instruction code” in their building blocks. The latest upgrade has been released recently. The latest upgrade has been released recently.
Proteins are essential parts of living organisms and take part in almost every process in cells. But their shapes are often complex, and difficult to imagine. Therefore, if they are able to predict their 3D structures, this affects the processes within living things, including humans.
This provides new opportunities to create drugs to treat diseases. This in turn opens up new possibilities in what is known as molecular medicine. This is when scientists do their best to identify the causes of the disease at the molecular scale and also develop treatments to correct them at the molecular level.
The first version of DeepMind’s AI tool was revealed in 2018. AlphaFold3 is the latest iteration, released this year. A worldwide competition to evaluate new ways of predicting protein structures, the Critical Assessment of Structure Prediction (Casp) has been held twice a year since 1994. Since then, researchers eagerly await each a new incarnation of the algorithm.
However, as a master’s student, I was once reprimanded for using AlphaFold2 in some of my coursework. This was because it was considered to be only a predictive tool. In other words, how could anyone know if what was predicted matched the real protein without experimental verification?
This is a legitimate point. The field of experimental molecular biology has undergone its own revolution over the past decade with major advances in a microscope technique called cryo-electron microscopy (cryo-EM), which uses frozen samples and fine electron beams to reveal the structures of biomolecules. captured in high resolution. .
The advantage of AI tools like AlphaFold is that it can elucidate protein structures much faster (in a few minutes) at almost no cost. The results are readily available and more accessible online worldwide. They can also predict the structure of proteins that are difficult to verify experimentally, such as membrane proteins.
However, AlphaFold2 was not designed to address what is known as the quaternary structure of proteins, where multiple protein subunits form a larger protein. This involves a dynamic visualization of how different units of the protein molecule are folded. And some researchers reported that they sometimes seemed to have trouble predicting the structural elements of proteins called coils.
When my professor contacted me in May to share the news that AlphaFold3 had been released, my first question was its ability to predict quaternary structures. If he succeeded? Could we now take the big leap towards predicting complete structure? Initial reports indicate that the answers to these questions are positive.
Experimental methods are slower. And when they are able to capture the 3D structure of the molecules, it is more like looking at a statue — a picture of the protein — rather than seeing how it moves and interacts to perform actions in the body. In other words, we want a film, rather than a photograph.
Experimental methods have also traditionally struggled with membrane proteins – key molecules attached to or associated with cell membranes. These are often the key to understanding and treating many of the worst diseases.
This is where AlphaFold3 could truly change the landscape. If it succeeds in predicting quaternary structures at a level equal to or greater than experimental methods such as crystallography, cryo-EM and others, and can visualize membrane proteins better than the competition, then we will indeed have a giant leap forward . race towards true molecular medicine.
AlphaFold3 can only be accessed from the DeepMind server, but it is easy to use. Researchers can get their results in minutes simply from sequencing. The other promise of AlphaFold3 is additional interference. DeepMind is not alone in its ambitions to master the protein folding problem. As Casp’s next competition approaches others are trying to win the race. For example, Liam McGuffin and his team at the University of Reading are making progress in quality assessment and predicting the stoichiometry of protein complexes. Stoichiometry refers to the proportions in which elements or chemical compounds react with each other.
Not all scientists in this field are pursuing the goal in the same way. Others are trying to solve similar challenges in terms of the quality of the 3D models or specific barriers such as those presented by membrane proteins. The competition was great because of progress in this area.
However, experimental methods are not going away anytime soon, nor should they. Advances in cryo-EM are commendable, and X-ray crystallography gives us the best resolution of biomolecules yet. The next breakthrough could be the European XFEL laser in Germany. These technologies will only continue to improve.
My biggest question as we survey this new field is whether our human instinct will be to give up until we have full proof of AlphaFold folding. If this new technology is able to give results comparable to, or greater than, experimental verification, will we be willing to accept it? If we can, its speed and accuracy could have a major impact on areas such as drug development.
For the first time, with AlphaFold3, we have cleared perhaps the most significant hurdle in the protein prediction revolution. What will we make of this new life? And what medicine can we do with it?
This article from The Conversation is republished under a Creative Commons license. Read the original article.
Sam McKee does not work for any company or organization that would benefit from this article, does not consult with, share ownership of or be funded by any company or organization that would benefit from this article, and has disclosed no affiliations relevant beyond his academic appointment.
