  The SARS-CoV-2 pandemic has created a global race for a cure. One approach
focuses on designing a novel variant of the human angiotensin-converting enzyme
2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it
from human cells. Here we formulate a novel protein design framework as a
reinforcement learning problem. We generate new designs efficiently through the
combination of a fast, biologically-grounded reward function and sequential
action-space formulation. The use of Policy Gradients reduces the compute
budget needed to reach consistent, high-quality designs by at least an order of
magnitude compared to standard methods. Complexes designed by this method have
been validated by molecular dynamics simulations, confirming their increased
stability. This suggests that combining leading protein design methods with
modern deep reinforcement learning is a viable path for discovering a Covid-19
cure and may accelerate design of peptide-based therapeutics for other
diseases.
