AI Enabled Deep Mutational Scanning of Interaction between SARS-CoV-2 Spike Protein S and Human ACE2 Receptor

We employ a recently developed platform, TLmutation, which could enable rapid investigation of the sequence-structure-function relationship between SARS-CoV-2 Spike Protein S and Human ACE2 Receptor. In particular, we employ a transfer learning approach to generate high-fidelity scans from noisy experimental data, and transfer the knowledge from single point mutation data to generate higher order mutational scans from the single amino-acid substitution data. Using deep mutagenesis, variants of ACE2 will be identified with increased binding to the receptor binding domain of S at a cell surface. In our preliminary results, we identify mutations across the interface and also at buried sites where they are predicted to enhance folding and presentation of the interaction epitope. The mutational landscape offers a blueprint for engineering high affinity ACE2 receptors to meet this unprecedented challenge. We plan to employ the information from the preliminary mutational landscape to generate the high order mutations in ACE2 receptor that could enhance binding to S protein and help in the design of future vaccines for treatment of SARS-CoV-2. We also aim to investigate this problem using distributed computing approaches to understand the underlying physics of the protein S and ACE2. In particular, we aim to perform molecular dynamics simulations to identify thermodynamic interactions that could enhance the ACE2 binding. Our preliminary results show that ACE2 variants identified from deep mutational scan not only stabilize the structural fluctuations but also strongly couple the motions of the two proteins. These simulations would be performed using Microsoft Azure and NCSA Blue Waters.