https://www.sciencedaily.com/releases/2019/10/191021135037.htm

To engineer them, they use two very different approaches. In "directed evolution," they randomly vary the linear sequence of amino acid building blocks encoding a natural protein and screen for variants with the desired activity; or they use "rational design" to model proteins based on their actual 3D structures to identify amino acids that likely will impact protein function. However, directed evolution can only cover a small part of the enormous space of possible protein sequences, while rational design approaches are limited by the relative scarcity of painstakingly resolved 3D protein structures.

Now, a research team led by George Church, Ph.D. at Harvard's Wyss Institute for Biologically Inspired Engineering and Harvard Medical School (HMS) has created a third approach to engineering proteins that uses deep learning to distill the fundamental features of proteins directly from their amino acid sequence without the need for additional information. The approach robustly predicts the functions of both natural and de novo designed proteins, and moves a lot of laborious laboratory experiments to the computer, achieving up to two orders of magnitude cost reduction compared to existing approaches. The study is published in Nature Methods.