  The prediction of protein secondary and tertiary structures from the primary
amino acid sequence is both an incredibly important and incredibly difficult
problem. Accurate prediction of a protein's native structure can provide
critical insights about its function, ultimately leading to breakthoughs in
drug design and disease diagnosis. The field has a rich history, from the
earliest folding experiments in the 1960's to the use of state-of-the-art
algorithms today; this article reviews protein folding's history with an
emphasis on how modern methods are tackling the protein folding problem.
Assuming only a basic knowledge of biochemistry, we'll explore Christian
Anfinsen's classical experiments with bovine RNase, the paradox of protein
folding proposed by Cyrus Levinthal, the success of modern machine learning
methods, and the promise of quantum computation for protein folding.
