The recent success of AlphaFold2 (AF2) and other deep
learning
(DL) tools in accurately predicting the folded three-dimensional (3D)
structure of proteins and enzymes has revolutionized the structural
biology and protein design fields. The 3D structure indeed reveals
key information on the arrangement of the catalytic machinery of enzymes
and which structural elements gate the active site pocket. However,
comprehending enzymatic activity requires a detailed knowledge of
the chemical steps involved along the catalytic cycle and the exploration
of the multiple thermally accessible conformations that enzymes adopt
when in solution. In this Perspective, some of the recent studies
showing the potential of AF2 in elucidating the conformational landscape
of enzymes are provided. Selected examples of the key developments
of AF2-based and DL methods for protein design are discussed, as well
as a few enzyme design cases. These studies show the potential of
AF2 and DL for allowing the routine computational design of efficient
enzymes.