  Proteins are intricate molecular machines whose complexity arises from the
heterogeneity of the amino acid building blocks and their dynamic network of
many-body interactions. These nanomachines gain function when put in the
context of a whole organism through interaction with other inhabitants of the
biological realm. And this functionality shapes their evolutionary histories
through intertwined paths of selection and adaptation. Recent advances in
machine learning have solved the decades-old problem of how protein sequence
determines their structure. However, the ultimate question regarding the basic
logic of protein machines remains open: How does the collective physics of
proteins lead to their functionality? and how does a sequence encode the full
range of dynamics and chemical interactions that facilitate function? Here, we
explore these questions within a physical approach that treats proteins as
mechano-chemical machines, which are adapted to function via concerted
evolution of structure, motion, and chemical interactions.
