Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

Nicotine has no significant cytoprotective activity against SARS-CoV-2 infection.

When coronavirus disease 2019 (COVID-19) became a pandemic, one of most important questions was whether people who smoke are at more risk of COVID-19 infection. A number of clinical data have been reported in the literature so far, but controversy exists in the collection and interpretation of the data. Particularly, there is a controversial hypothesis that nicotine might be able to prevent SARS-CoV-2 infection. In the present study, motivated by the reported controversial clinical data and the controversial hypothesis, we carried out cytotoxicity assays in Vero E6 cells to examine the potential cytoprotective activity of nicotine against SARS-CoV-2 infection and demonstrated for the first time that nicotine had no significant cytoprotective activity against SARS-CoV-2 infection in these cells.

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning.

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II.

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates.

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

DREAM-in-CDM Approach and Identification of a New Generation of Anti-inflammatory Drugs Targeting mPGES-1.

Microsomal prostaglandin E2 synthase-1 (mPGES-1) is known as an ideal target for next generation of anti-inflammatory drugs without the side effects of currently available anti-inflammatory drugs. However, there has been no clinically promising mPGES-1 inhibitor identified through traditional drug discovery and development route. Here we report a new approach, called DREAM-in-CDM (Drug Repurposing Effort Applying Integrated Modeling-in vitro/vivo-Clinical Data Mining), to identify an FDA-approved drug suitable for use as an effective analgesic targeting mPGES-1. The DREAM-in-CDM approach consists of three steps: computational screening of FDA-approved drugs; in vitro and/or in vivo assays; and clinical data mining. By using the DREAM-in-CDM approach, lapatinib has been identified as a promising mPGES-1 inhibitor which may have significant anti-inflammatory effects to relieve various forms of pain and possibly treat various inflammation conditions involved in other inflammation-related diseases such as the lung inflammation caused by the newly identified COVID-19. We anticipate that the DREAM-in-CDM approach will be used to repurpose FDA-approved drugs for various new therapeutic indications associated with new targets.