De novo design of protein peptides to block association of the SARS-CoV-2 spike protein with human ACE2
Year: 2020
Type: article
Abstract: ABSTRACT The outbreak of COVID-19 has now become a global pandemic and it continues to spread rapidly worldwide, severely threatening lives and economic stability. Making the problem worse, there is no specific antiviral drug that can be used to treat COVID-19 to date. SARS-CoV-2 initiates its entry into human cells by binding to angiotensin-converting enzyme 2 (hACE2) via the receptor binding domain (RBD) of its spike protein. Therefore, molecules that can block SARS-CoV-2 from binding to hACE2 may potentially prevent the virus from entering human cells and serve as an effective antiviral drug. Based on this idea, we designed a series of peptides that can strongly bind to SARS-CoV-2 RBD in computational experiments. Specifically, we first constructed a 31-mer peptidic scaffold by linking two fragments grafted from hACE2 (a.a. 22-44 and 351-357) with a linker glycine, and then redesigned the peptide sequence to enhance its binding affinity to SARS-CoV-2 RBD. Compared with several computational studies that failed to identify that SARS-CoV-2 shows higher binding affinity for hACE2 than SARS-CoV, our protein design scoring function, EvoEF2, makes a correct identification, which is consistent with the recently reported experimental data, implying its high accuracy. The top designed peptide binders exhibited much stronger binding potency to hACE2 than the wild-type (−53.35 vs. −46.46 EvoEF2 energy unit for design and wild-type, respectively). The extensive and detailed computational analyses support the high reasonability of the designed binders, which not only recapitulated the critical native binding interactions but also introduced new favorable interactions to enhance binding. Due to the urgent situation created by COVID-19, we share these computational data to the community, which should be helpful to develop potential antiviral peptide drugs to combat this pandemic.
Source: Aging
Authors: Xiaoqiang Huang, Robin Pearce, Yang Zhang
Institution: University of Michigan
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Awards: IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus, III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction, G1492819423, G1556211573, G179862484, G2117560495, G2232767831, G3226045097, Advanced approaches to protein structure prediction, XSEDE 2.0: Integrating, Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement, G4584606584, G4630214672, G553838990, G5964975137, G6056929667, I-TASSER based atomic level protein structure prediction, Collaborative Research: ABI Development: Integrated platforms for protein structure and function predictions, G7063014021, G758395984, G7637742026, G7897283973, G8702286652, Structure-based functional annotation of microbial genomes
OpenAlex ID: W3034395518