The problem of protein folding is considered one of the fundamental challenges of molecular biology because a large number of degrees of freedom of bonds and atoms in a protein leads to a combinatorial explosion in the number of possible low-energy arrangements . In 2020, the DeepMind team announced a widely recognised breakthrough in the prediction of spatial protein 3D structures from their amino acid sequence with their deep learning-based system AlphaFold . The system participated in the 13th and 14th Critical Assessment of Protein Structure Prediction (CASP) competition [8], outperforming all competitors. Since then, it has been made openly available and used to fill the open AlphaFold Protein Structure Database which contains more than 200 million predicted protein 3D structures, covering nearly every known protein on earth . Within a short period of time, the structures of 98.5% of the human proteome have been predicted using AlphaFold, while the previous decades of experimental research yielded 17% . The system was trained on structural data openly deposited in the Protein Data Bank , which was founded and announced in 1971 . The success story of AlphaFold illustrates what is possible today when researchers are able to access the data that scientists have produced over the course of 50 years.