do so well. it was found that nobody could predict protein structure. they would get it completely wrong. this respected experimentalist stood on the stages, sort of rolled about laughing. "look at how silly you guys are!" we would joke like, oh, well, we still have a job for another 10 years. the CASP challenge nevertheless, had an immediate impact. kind of organized the community around this one metric and enabled us all to see what worked and didn't work.. for biochemist David Baker, the CASP-1 outcome was a source of sequence is entered into the system.. the algorithm then searches several genetic databases for similar protein sequences found in other organisms.. these related sequences are aligned in an array to create a representation called a multiple sequence alignment, or MSA.. the MSA contains information about the evolution of the protein across different organisms.. Next,, AlphaFold generates a matrix to encode the spatial relationships between every pair of amino acids in the target sequence called a pairwise representation.. you can generate a two--dimensional image of which bits of the folded protein are near to each other.. this matrix can be thought of as a two--dimensional map of the protein's 3d shape. the MSA and pairwise representations are then entered into the evoformer module, which is a powerful neural network called a transformer.. the evoformer uses a technique known as self--attention to efficiently extract meaningful information while dynamically updating the data. so we're setting up a conversation between the evolution of the protein and what we believe about the geometry of the protein. the refined pairwise information is then passed into another transformer called the structure module, which calculates the geometry at play to produce an initial guess of the protein's folded structure. this prediction is then refined by cycling it through the whole algorithm before producing a final output. Alphafold also reveals a score of how confident it is in its predictions of different parts of the protein structure. casP 14 This segment showcases how AI is revolutionizing protein design, describing the process of creating novel proteins with specific functionalities using AI-powered tools like RFdiffusion and AlphaFold 2, and highlighting the potential applications of this technology in medicine, energy, and other fields. This segment details the groundbreaking advancements in AI-powered protein structure prediction, highlighting the release of RoseTTAFold All-Atom and AlphaFold 3, their capabilities in predicting protein structures and interactions, and the impact of these tools on scientific research. The discussion also touches upon the evolution of the CASP challenge in light of these advancements and culminates in the announcement of the 2024 Nobel Prize in Chemistry awarded for this transformative work, emphasizing the ongoing importance of research in this field. the most important tools for structural biologists. in the first step of X--ray crystallography, a desired protein is purified and concentrated as a crystal. forming this highly ordered array is often the most difficult part of the entire process. the crystal is then placed into the path of a high-intensity x--ray beam and rotated. the x--rays deflect off the electrons surrounding the crystal's atoms, scattering before striking a detector. the resulting diffraction pattern is then transformed by a computer into a 3d map of the protein's electron density. from here, it's like solving a jigsaw puzzle. amino acids from the known protein sequence are fitted into the 3d map to produce a structural model of the protein. initially, researchers solved this jigsaw puzzle by hand using balls and sticks to construct the models. later, computational tools were developed to accelerate the task. the effort to doing this is enormous. you can think of it as something like a hundred thousand dollars in expense. a couple of years of a Phd student's time. really an enormous investment to get even a single structure. in the 1970s, a consortium of researchers started the Protein data Bank,or PDB, to catalog determined structures. the location of each atom inside a protein structure was recorded as a set of three--dimensional coordinates. today the PDB contains the structural data for more than 200,000 proteins. newer technologies like nuclear magnetic resonance and cryo-electron microscopy, or cryo-EM, allow researchers to probe