The mature polypeptides of human ACE2 WT and variants were cloned into eukaryotic expression plasmid pFclg with à C-terminally fused Fc region of human IgG1 using the Gibson Assembly method. DH1OB competent cells were electroporated with assembly products and cultured on Luria-Bertan agarose plates containing 25 µg.ml Zeocin. Monoclonal colonies were selected and sequenced to confirm the mutations. Then monoclonal colonies were cultured in Luria-Bertani containing 25 µg.ml Zeocin overnight to enhance the plasmid yield. Recombinant plasmids were extracted using an endotoxin removal plasmid extraction kit. Then, 50 ml of HEK 293F cells were transfected with 25 ng of recombinant plasmids using FectoPRO to express target proteins. The culture medium was collected after 5 day of incubation, Recombinant ACE2 proteins were extracted using protein A dextran and purified using SDS-PAGE. The obtained recombinant ACE2 proteins were in the natively dimeric form.

Host-pathogen interactions and pathogen evolution are underpinned by protein-protein interactions between viral and host proteins. An understanding of how viral variants affect protein-protein binding is important for predicting viral-host interactions, such as the emergence of new pathogenic SARS-CoV-2 variants. Here we propose an artificial intelligence-based framework called UniBind, in which proteins are represented as a graph at the residue and atom levels. UniBind integrates protein three-dimensional structure and binding affinity and is capable of multi-task learning for heterogeneous biological data integration. In systematic tests on benchmark datasets and further experimental validation, UniBind effectively and scalably predicted the effects of SARS-CoV-2 spike protein variants on their binding affinities to the human ACE2 receptor, as well as to SARS-CoV-2 neutralizing monoclonal antibodies. Furthermore, in a cross-species analysis, UniBind could be applied to predict host susceptibility to SARS-CoV-2 variants and to predict future viral variant evolutionary trends. This in silico approach has the potential to serve as an early warning system for problematic emerging SARS-CoV-2 variants, as well as to facilitate research on protein-protein interactions in general.