To date, no effective vaccines or therapies are available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pandemic agent of the coronavirus disease 2019 (COVID-19). Due to their safety, efficacy and specificity, peptide inhibitors hold great promise for the treatment of newly emerging viral pathogens. Based on the known structures of viral proteins and their cellular targets, antiviral peptides can be rationally designed and optimized. The resulting peptides may be highly specific for their respective targets and particular viral pathogens or exert broad antiviral activity. Here, we summarize the current status of peptides inhibiting SARS-CoV-2 entry and outline the strategies used to design peptides targeting the ACE2 receptor or the viral Spike protein and its activating proteases furin, transmembrane serine protease 2 (TMPRSS2), or cathepsin L. In addition, we present approaches used against related viruses such as SARS-CoV-1 that might be implemented for inhibition of SARS-CoV-2 infection.

SARS-CoV-2 is a respiratory pathogen and primarily infects the airway epithelium. As our knowledge about innate immune factors of the respiratory tract against SARS-CoV-2 is limited, we generated and screened a peptide/protein library derived from bronchoalveolar lavage for inhibitors of SARS-CoV-2 spike-driven entry. Analysis of antiviral fractions revealed the presence of α₁-antitrypsin (α₁AT), a highly abundant circulating serine protease inhibitor. Here, we report that α₁AT inhibits SARS-CoV-2 entry at physiological concentrations and suppresses viral replication in cell lines and primary cells including human airway epithelial cultures. We further demonstrate that α₁AT binds and inactivates the serine protease TMPRSS2, which enzymatically primes the SARS-CoV-2 spike protein for membrane fusion. Thus, the acute phase protein α₁AT is an inhibitor of TMPRSS2 and SARS-CoV-2 entry, and may play an important role in the innate immune defense against the novel coronavirus. Our findings suggest that repurposing of α₁AT-containing drugs has prospects for the therapy of COVID-19.

Virtual screening of protein-protein and protein-peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein-protein complexes have been proposed, methods specifically developed to predict protein-peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein-protein and protein-peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein-protein and protein-peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.