A modular molecular framework for quickly estimating the binding affinity of the spike protein of SARS-CoV-2 variants for ACE2, in presence of mutations at the spike receptor binding domain.
The rapid spread of new SARS-CoV-2 variants needs the development of rapid tools for predicting the affinity of the mutated proteins responsible for the infection, i.e., the SARS-CoV-2 spike protein, for the human ACE2 receptor, aiming to understand if a variant can be more efficient in invading host cells. Here we show how our computational pipeline, previously used for studying SARS-CoV-2 spike receptor binding domain (RBD)/ACE2 interactions and pre-/post-fusion conformational changes, can be used for predicting binding affinities of the human ACE2 receptor for the spike protein RBD of the characterized infectious variants of concern/interest B.1.1.7-UK (carrying the mutations N501Y, S494P, E484K at the RBD), P.1-Japan/Brazil (RBD mutations: K417N/T, E484K, N501Y), B.1.351-South Africa (RBD mutations: K417N, E484K, N501Y), B.1.427/B.1.429-California (RBD mutations: L452R), the B.1.141 variant (RBD mutations: N439K), and the recent B.1.617.1-India (RBD mutations: L452R; E484Q) and the B.1.620 (RBD mutations: S477N; E484K). Furthermore, we searched for ACE2 structurally related proteins that might be involved in interactions with the SARS-CoV-2 spike protein, in those tissues showing low ACE2 expression, revealing two new proteins, THOP1 and NLN, deserving to be investigated for their possible inclusion in the group of host-cell entry factors responsible for host-cell SARS-CoV-2 invasion and immunity response.