LCB1 for COVID-19

Abstract De novo protein binders have emerged as a powerful platform for molecular recognition, yet achieving high affinity while maintaining favorable biophysical properties remains a major challenge. Here, using the SARS-CoV-2 receptor-binding domain (RBD) as a model target, we generated two de novo protein binder design campaigns that deliberately imposed different trade-offs between binding and monomeric behavior, and benchmarked them against a set of experimentally validated binders. Through integrated experimental and computational analyses, we show that binder design is constrained by two distinct failure modes that require different optimization strategies. None of the 22 original designs showed measurable binding to the RBD, with one class remaining monomeric but failing to achieve sufficient target binding, while the other had more competent binding interfaces but exhibited excessive surface hydrophobicity that promoted self-association. By integrating AlphaFold-based predictions, molecular dynamics simulations, docking analyses, and surface aggregation propensity (SAP) calculations, we establish complementary computational signatures that distinguish these failure modes and reveal their underlying structural determinants. Guided by these insights, we prospectively redesigned a representative failed binder by reducing noninterface hydrophobicity while preserving folding and binding interface quality. Experimental characterization by SEC-MALS, surface plasmon resonance, and circular dichroism confirmed that the redesigned variants were stable monomers and acquired low μM binding affinities. Together, our results define the mechanistic trade-offs between binding and self-association in de novo protein binder design and provide a framework for diagnosing and rationally overcoming common design failure modes.

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), is spreading worldwide. Although the COVID-19 epidemic has passed its peak of transmission, the harm it has caused deserves our attention. Scientists are striving to develop medications that can effectively treat COVID-19 symptoms without causing any adverse reactions. SARS-CoV-2 inhibitory peptides derived from animal proteins have a wide range of functional activities in addition to safety. Identifying animal protein sources is crucial to obtaining SARS-CoV-2 inhibitory peptides from animal sources. This review aims to reveal the mechanisms of action of these peptides on SARS-CoV-2 and the possibility of animal proteins as a material source of SARS-CoV-2 inhibitory peptides. Also, it introduces the utilization of computer-aided design methods, phage display, and drug delivery strategies in the research on SARS-CoV-2 inhibitor peptides from animal proteins. In order to identify new antiviral peptides and boost their efficiency, we recommend investigating the interaction between SARS-CoV-2 inhibitory peptides from animal protein sources and non-structural proteins (Nsps) using a variety of technologies, including computer-aided drug approaches, phage display techniques, and drug delivery techniques. This article provides useful information for the development of novel anti-COVID-19 drugs.

Viruses with rapid replication and easy mutation can become resistant to antiviral drug treatment. With novel viral infections emerging, such as the recent COVID-19 pandemic, novel antiviral therapies are urgently needed. Antiviral proteins, such as interferon, have been used for treating chronic hepatitis C infections for decades. Natural-origin antimicrobial peptides, such as defensins, have also been identified as possessing antiviral activities, including direct antiviral effects and the ability to induce indirect immune responses to viruses. To promote the development of antiviral drugs, we constructed a data repository of antiviral peptides and proteins (DRAVP). The database provides general information, antiviral activity, structure information, physicochemical information, and literature information for peptides and proteins. Because most of the proteins and peptides lack experimentally determined structures, AlphaFold was used to predict each antiviral peptide’s structure. A free website for users (http://dravp.cpu-bioinfor.org/, accessed on 30 August 2022) was constructed to facilitate data retrieval and sequence analysis. Additionally, all the data can be accessed from the web interface. The DRAVP database aims to be a useful resource for developing antiviral drugs.

COVID-19, caused by SARS-CoV-2, remains a global health challenge because of viral evolution and immune escape. Although current therapies primarily target viral entry and replication, agents that directly interfere with the Spike receptor-binding domain (S-RBD) remain limited. Here, we combined virtual screening with biological validation to identify compounds capable of interfering with S-RBD function. A total of 3014 compounds from a customized drug library were screened by molecular docking. Candidate binding and functional activity were subsequently evaluated using cellular thermal shift assays, surface plasmon resonance, immunoprecipitation, immunofluorescence, and pseudovirus-entry assays against 2019-nCoV, Delta, and Omicron pseudoviruses. DOTAP chloride (KD = 49.94 μM), cefotiam hexetil hydrochloride (KD = 142.26 μM), melittin (KD = 34.98 μM), and teicoplanin (KD = 73.58 μM) showed detectable binding to the S-RBD and inhibited Spike-mediated pseudovirus entry. Molecular docking suggested that these interactions were mediated by potential binding modes involving hydrogen bonds and π-interactions. The compounds interfered with S-RBD/hACE2 colocalisation and inhibited pseudovirus entry with variant-dependent efficacy. DOTAP chloride (25 μM) inhibited entry of the 2019-nCoV and Omicron pseudoviruses, whereas the other three compounds showed activity across the tested variants. These findings identify four compounds with RBD-binding and entry-inhibition properties for further development as entry inhibitors.