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Biology subjects

Maseko, S. B.

Publications and source records attributed to Maseko, S. B..

3 recordsLinked to original sources

Nanobodies against SARS-CoV-2 non-structural protein Nsp9 inhibit viral replication by targeting innate immunity

Nanobodies are emerging as critical tools for drug design. Several have been recently created to serve as inhibitors of SARS-Cov-2 entry in the host cell by targeting surface-exposed Spike protein. However, due to the high frequency of mutations that affect Spike, these nanobodies may not target it to their full potential and as a consequence, inhibition of viral entry may not be efficient. Here we have established a pipeline that instead targets highly conserved viral proteins that are made only after viral entry into the host cell when the SARS-Cov-2 RNA-based genome is translated. As proof of principle, we designed nanobodies against the SARS-CoV-2 non-structural protein Nsp9, required for viral genome replication. To find out if this strategy efficiently blocks viral replication, one of these anti-Nsp9 nanobodies, 2NSP23, previously characterized using immunoassays and NMR spectroscopy for epitope mapping, was encapsulated into lipid nanoparticles (LNP) as mRNA. We show that this nanobody, hereby referred to as LNP-mRNA- 2NSP23, is internalized and translated in HEK293 cells. We next infected HEK293-ACE2 cells with multiple SARS-CoV-2 variants and subjected them to LNP-mRNA-2NSP23 treatment. Analysis of total RNA isolated from infected cells treated or untreated with LNP-mRNA-2NSP23 using qPCR and RNA deep sequencing shows that the LNP-mRNA-2NSP23 nanobody protects HEK293-ACE2 cells and suppresses replication of several SARS-CoV-2 variants. These observations indicate that following translation, the nanobody 2NSP23 inhibits viral replication by targeting Nsp9 in living cells. We speculate that LNP-mRNA-2NSP23 may be translated into an innovative technology to generate novel antiviral drugs highly efficient across coronaviruses.

cell biology↗

A gut meta-interactome map reveals modulation of human immunity by microbiome effectors

The molecular mechanisms by which the gut microbiome influences human health remain largely unknown. Pseudomonadota is the third most abundant phylum in normal gut microbiomes. Several pathogens in this phylum can inject so-called virulence effector proteins into host cells. We report the identification of intact type 3 secretion systems (T3SS) in 5 - 20% of commensal Pseudomonadota in normal human gut microbiomes. To understand their functions, we experimentally generated a high-quality protein-protein meta-interactome map consisting of 1,263 interactions between 289 bacterial effectors and 430 human proteins. Effector targets are enriched for metabolic and immune functions and for genetic variation of microbiome-influenced traits including autoimmune diseases. We demonstrate that effectors modulate NF-{kappa}B signaling, cytokine secretion, and adhesion molecule expression. Finally, effectors are enriched in metagenomes of Crohns disease, but not ulcerative colitis patients pointing toward complex contributions to the etiology of inflammatory bowel diseases. Our results suggest that effector-host protein interactions are an important regulatory layer by which the microbiome impacts human health.

microbiology↗

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

systems biology↗