Protein-folding in the Age of AlphaFold 2

Proteins are complex cellular macromolecules which provide crucial and oftentimes unique functions throughout the lifecycle of a cell such as DNA replication, cellular signaling, transcription, phosphorylation, and more.

These macromolecules begin as long polypeptide chains whose physical interactions between the connected amino acids and the cellular environment form a 3D structure whose biological function is correlated with a protein's underlying amino acid sequence and composition. However, the de novo protein structure prediction from its constituent amino acid sequence has been a challenging problem to overcome due to incomplete knowledge of the interplay of physical and chemical interactions that give rise to assembly.

Nonetheless, recent and novel machine learning-powered developments made by DeepMind's AlphaFold 2 in the latest biennial Critical Assessment of protein Structure Prediction (CASP) have achieved significant progress in accurately predicting protein structure, as well as illuminating some of the underlying rules that mediate protein folding and assembly. This talk will introduce core protein physics concepts and discuss AlphaFold 2 in context of the protein folding problem as well as possible directions for the field.