Guided by the classic notion that a protein's amino acid sequence determines its three-dimensional structure, protein structure prediction has long been a central problem in structural biology. Over the past decade, deep learning methods, exemplified by AlphaFold2 (AF2), have revolutionized the field, an achievement recognized by the 2024 Nobel Prize in Chemistry. Yet this success does not mean the protein folding problem is fully solved. Key challenges, each an opportunity for future breakthroughs, include single-sequence prediction, modeling protein dynamics, predicting multimeric complexes, and incorporating experimental constraints.
