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In 2021, Google published the methodology and source code for AlphaFold and within days, scientists adapted the code to allow virtually everyone to predict their own protein structures without prior knowledge.
Now, two years after its public release, AlphaFold has established itself as an essential tool in structural biology. Yet, with time, we've also gained a deeper insight into its limitations.
In this talk, I would like to delve into AlphaFold and similar machine learning techniques and explore their impact on science and structural biology. To truly appreciate their significance, we will first need to understand the role of protein structures and how they shape our daily lives. Additionally, we’ll have to examine how protein structures were traditionally solved prior to the advent of AlphaFold. We’ll then touch upon the concepts of protein evolution to better understand the biological basis behind this breakthrough, before we’ll look at the intricacies of the neural network itself and discuss the training data necessary to achieve its remarkable capabilities. Drawing from my experience as a practicing structural biologist, I will illustrate these points with real-life examples, showcasing instances where AlphaFold has succeeded and where it has encountered challenges. Lastly, we will peer into the future and speculate on the potential trajectory of this scientific journey and its potential to transform science and our approaches towards it.