A novel predictor of ACE2-binding ability among betacoronaviruses

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in ~4.8 million deaths worldwide as of this writing. Almost all conceivable aspects of SARS-CoV-2 have been explored since the virus began spreading in the human population. Despite numerous proposals, it is still unclear how and when the virus gained the ability to efficiently bind to and infect human cells. In an effort to understand the evolution of receptor binding domain (RBD) of the spike protein of SARS-CoV-2, and specifically, how the ability of RBD to bind to angiotensin-converting enzyme 2 receptor (ACE2) of humans evolved in coronaviruses, we have applied an alignment-free technique to infer functional relatedness among betacoronaviruses. This technique, concurrently being optimized for identifying novel prions, was adapted to gain new insights into coronavirus evolution, specifically in the context of the ongoing COVID-19 pandemic. Novel methods for predicting the capacity for coronaviruses, in general, to infect human cells are urgently needed.

proposed method utilizes physicochemical properties of amino acids to develop fully dynamic waveform representations of proteins that encode both the amino acid content and the context of amino acids. These waveforms are then subjected to dynamic time warping (DTW) and distance evaluation to develop a distance metric that is relatively less sensitive to variation in sequence length and primary amino acid composition.

Using our proposed method, we show that in contrast to alignment-based maximum likelihood (ML) and neighbor-joining (NJ) phylogenetic analyses, all bat betacoronavirus spike protein RBDs known to bind to the ACE2 receptor are found within a single physicochemical cluster. Further, other RBDs within that cluster are from pangolin coronaviruses, two of which have already been shown to bind to ACE2 while the others are suspected, yet unverified ACE2 binding domains. This finding is important because both severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 use the host ACE2 receptor for cell entry. Surveillance for coronaviruses belonging to this cluster could potentially guide efforts to stifle or curtail potential and/or early zoonotic outbreaks with their associated deaths and financial devastation.

Robust methods for predicting human ACE2 receptor binding by the spike protein of coronaviruses are needed for the early detection of zoonotic coronaviruses and biosurveillance to prevent future outbreaks. Here we present a new waveform-based approach that utilizes the physicochemical properties of amino acids to determine the propensity of betacoronaviruses to infect humans. Comparison with the established phylogenetic methods demonstrates the usefulness of this new approach in the biosurveillance of coronaviruses.