  Computational protein design facilitates discovery of novel proteins with
prescribed structure and functionality. Exciting designs were recently reported
using novel data-driven methodologies that can be roughly divided into two
categories: evolutionary-based and physics-inspired approaches. The former
infer characteristic sequence features shared by sets of evolutionary-related
proteins, such as conserved or coevolving positions, and recombine them to
generate candidates with similar structure and function. The latter estimate
key biochemical properties such as structure free energy, conformational
entropy or binding affinities using machine learning surrogates, and optimize
them to yield improved designs. Here, we review recent progress along both
tracks, discuss their strengths and weaknesses, and highlight opportunities for
synergistic approaches.
