AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE2 and the spike glycoprotein (S protein) of SARS-CoV-2 hold the key to understanding the molecular mechanism to develop treatment and vaccines, yet the dynamic nature of these interactions in fluctuating surroundings is very challenging to probe by those structure determination techniques requiring the structures of samples to be fixed. Here we demonstrate, by a proof-of-concept simulation of infrared (IR) spectra of S protein and hACE2, that time-resolved spectroscopy may monitor the real-time structural information of the protein−protein complexes of interest, with the help of machine learning. Our machine learning protocol is able to identify fine changes in IR spectra associated with variation of the secondary structures of S protein of the coronavirus. Further, it is three to four orders of magnitude faster than conventional quantum chemistry calculations. We expect our machine learning protocol would accelerate the development of real-time spectroscopy study of protein dynamics.

The ongoing pandemic of COVID-19, a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed tremendous threat to human health and well-being by having affected tens of millions of people and killed more than 1 million affected since December 2019 (1). It has spurred enormous efforts in biological and biomedical research to search for a solution to this fatal disease, which rapidly advance our knowledge about it, including the identity of the pathogen (i.e., SARS-CoV-2), the genome sequence of the virus, and the structural basis for coronavirus recognition and infection (2–5). SARS-CoV-2 recognizes human angiotensin-converting enzyme 2 (hACE2) as the entry receptor to host cells using its surface spike glycoprotein (S protein) (1). The interactions of S protein with hACE2 have been subjected to intensive investigations by several groups (6–10), which laid the foundation for comprehensive understanding of the invasion of SARS-CoV-2 into the human body at the atomic scale (11), helps the search for intermediate hosts of the coronavirus (12), and will guide the design of therapeutics and vaccines (11, 13). Since the physiological environment in which S protein and hACE2 interact is always fluctuated due to the dynamic nature of water, a dynamic picture of the interactions between them is needed for precise mechanistic understanding that will inspire modulation and application (14). Unfortunately, such information relies on real-time tracking of protein conformations, which cannot be achieved by powerful structure characterization techniques with atomic precision like X-ray diffraction and cryoelectron microscopy, because they require fixed structures in samples. It motivates us to develop alternative approaches to resolve the issue.Recently, time-resolved infrared (IR) spectroscopy techniques have realized successful monitoring of changes of secondary structure with time (15), signaling the feasibility of real-time observation of protein dynamics in ambient conditions using spectroscopy. However, to facilitate the monitoring of specific peptide fragments in a secondary structure typically requires isotope labeling (e.g., C=O in the amide of protein backbone is replaced with ¹³C=O or C=¹⁸O) in the preparation of samples, which is, unfortunately, tedious and expensive for systematic investigation on conformation changes in protein dynamics. Therefore, it is desirable to develop isotope labeling-free spectroscopy to accelerate structure study of proteins for biological and biomedical sciences. To achieve this goal, one needs to employ quantum chemistry calculations to complete spectra signal assignment and structure determination. In fact, it relies on computer simulations of various possible conformers to nail the job, which is, unfortunately, very expensive for macromolecules like proteins. One of the biggest bottleneck problems in spectroscopic measurement of proteins is lack of rapid theoretical interpretation that can timely translate spectra signals into structural information. As a result, it is nearly impossible for an experimental spectroscopic study to monitor continuous structural changes associated with protein functions. Developing a cost-effective spectra simulation protocol is a pressing task to advance the real-time spectroscopy study of protein structures.Machine learning (ML), a collection of statistics-based methods which gain prediction power from the learning of big data, has emerged as a powerful toolkit to reduce the barrier to revealing the structure−property relationship (16). It has been increasingly popular in the study of molecules and materials, such as predicting chemical reaction routes (17) and accelerating discovery of materials (18). Especially, neural networks (NN), a subclass of ML algorithms, are well recognized for handling complex nonlinear problems. NN established a predictive model for desired properties by iterative optimization of a complex high-dimensional function in a virtually infinite space of parameters. This feature makes it a transferrable tool for predicting protein spectra (19).In this article, we developed and applied a cost-effective ML protocol, to predict the IR spectra along with the kinetic process of a COVID-2019 virus (SARS-CoV-2) protein binding to hACE2. The efficient simulation of IR signals of different states of the coronavirus associated with the changes in its secondary structure is very encouraging for studying dynamic interactions between S protein of SARS-CoV-2 and human ACE2 with the help of ML techniques. This will enable a real-time spectroscopic monitoring of protein structure evolution for this deadly virus, providing valuable information for understanding its molecular mechanism, as well as developing cures and vaccines. ML should provide a cost-effective tool for simulating optical properties of SARS-CoV-2.