https://www.metaculus.com/notebooks/9191/methods-for-solving-protein-structures/

Proteins are complex molecules that comprise the bulk of functional parts of living things. They are encoded in DNA and RNA as genes. By sequencing DNA, or sometimes even the proteins themselves, we can learn the amino acid sequence that makes up a protein. But the way chemical strands fold into the correct 3D shape to form a functional protein is hard to predict. And it's also often difficult to observe the structure of a protein directly, as they are small enough that the important details involve the positions of individual atoms.

Nevertheless, there has been much effort to understand the structures of proteins in humans and other organisms. The structures can explain why a gene and the protein it encodes is essential, why a particular mutation causes cancer, or which drug molecules can fit in a protein pocket to alter the protein's activity. In short, we can learn how living things work (or don't work) and how we can intervene.

There has been an impressive diversity of approaches for predicting protein structures. For example, over the last decade I've been intrigued by Foldit, a computer game used to crowdsource human problem solving to find protein structures that best satisfy realistic chemical constraints. Of course, many techniques beyond human intuition are used for prediction too.