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Nanobodies are emerging as critical tools for drug design. Several have been recently created to serve as inhibitors of severe acute respiratory syndrome coronavirus s (SARS-CoV-2) entry in the host cell by targeting surface-exposed spike protein. Here we have established a pipeline that instead targets highly conserved viral proteins 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 (Nsp)9, which is required for viral genome replication. One of these anti-Nsp9 nanobodies, 2NSP23, previously characterized using immunoassays and nuclear magnetic resonance spectroscopy for epitope mapping, was expressed and found to block SARS-CoV-2 replication specifically. We next encapsulated 2NSP23 nanobody into lipid nanoparticles (LNPs) as mRNA. We show that this nanobody, hereby referred to as LNP-mRNA-2NSP23, is internalized and translated in cells and suppresses multiple SARS-CoV-2 variants, as seen by qPCR and RNA deep sequencing. These results are corroborated in three-dimensional reconstituted human epithelium kept at air-liquid interface to mimic the outer surface of lung tissue. These observations indicate that LNP-mRNA-2NSP23 is internalized and, after translation, it inhibits viral replication by targeting Nsp9 in living cells. We speculate that LNP-mRNA-2NSP23 may be translated into an innovative strategy to generate novel antiviral drugs highly efficient across coronaviruses., Competing Interests: P.P., G.E., H.T.H., and S.C.D. are part of a US patent filed by New York University in Abu Dhabi jointly with ISAR Biosciences., (© 2024 The Author(s).)

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 or 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., (© 2024. The Author(s).)

Human T-cell leukemia virus type-1 (HTLV-1) is the first pathogenic retrovirus discovered in human. Although HTLV-1-induced diseases are well-characterized and linked to the encoded Tax-1 oncoprotein, there is currently no strategy to target Tax-1 functions with small molecules. Here, we analyzed the binding of Tax-1 to the human homolog of the drosophila discs large tumor suppressor (hDLG1/SAP97), a multi-domain scaffolding protein involved in Tax-1-transformation ability. We have solved the structures of the PDZ binding motif (PBM) of Tax-1 in complex with the PDZ1 and PDZ2 domains of hDLG1 and assessed the binding of 10 million molecules by virtual screening. Among the 19 experimentally confirmed compounds, one systematically inhibited the Tax-1-hDLG1 interaction in different biophysical and cellular assays, as well as HTLV-1 cell-to-cell transmission in a T-cell model. Thus, our work demonstrates that interactions involving Tax-1 PDZ-domains are amenable to small-molecule inhibition, which provides a framework for the design of targeted therapies for HTLV-1-induced diseases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Understanding the mechanisms of coronavirus disease 2019 (COVID-19) disease severity to efficiently design therapies for emerging virus variants remains an urgent challenge of the ongoing pandemic. Infection and immune reactions are mediated by direct contacts between viral molecules and the host proteome, and the vast majority of these virus-host contacts (the 'contactome') have not been identified. Here, we present a systematic contactome map of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with the human host encompassing more than 200 binary virus-host and intraviral protein-protein interactions. We find that host proteins genetically associated with comorbidities of severe illness and long COVID are enriched in SARS-CoV-2 targeted network communities. Evaluating contactome-derived hypotheses, we demonstrate that viral NSP14 activates nuclear factor κB (NF-κB)-dependent transcription, even in the presence of cytokine signaling. Moreover, for several tested host proteins, genetic knock-down substantially reduces viral replication. Additionally, we show for USP25 that this effect is phenocopied by the small-molecule inhibitor AZ1. Our results connect viral proteins to human genetic architecture for COVID-19 severity and offer potential therapeutic targets., (© 2022. The Author(s).)

Competing Interests: Declaration of interests C.G. is the cofounder of two companies, Virtual Discovery, Inc. and Quantum Therapeutics, Inc., which are engaged in drug discovery activities, in part using VirtualFlow.

Virtual screening is a useful in silico approach to identify potential leads against various targets. It is known that carbapenems (doripenem and faropenem) do not show any reasonable inhibitory activities against L,D-transpeptidase 5 (Ldt Mt5 ) and also an adduct of meropenem exhibited slow acylation. Since these drugs are active against L,D-transpeptidase 2 (Ldt Mt2 ), understanding the differences between these two enzymes is essential. In this study, a ligand-based virtual screening of 12,766 compounds followed by molecular dynamics (MD) simulations was applied to identify potential leads against Ldt Mt5 . To further validate the obtained virtual screening ranking for Ldt Mt5 , we screened the same libraries of compounds against Ldt Mt2 which had more experimetal and calculated binding energies reported. The observed consistency between the binding affinities of Ldt Mt2 validates the obtained virtual screening binding scores for Ldt Mt5 . We subjected 37 compounds with docking scores ranging from - 7.2 to - 9.9 kcal mol -1 obtained from virtual screening for further MD analysis. A set of compounds (n = 12) from four antibiotic classes with ≤ - 30 kcal mol -1 molecular mechanics/generalized born surface area (MM-GBSA) binding free energies (ΔG bind ) was characterized. A final set of that, all β-lactams (n = 4), was considered. The outcome of this study provides insight into the design of potential novel leads for Ldt Mt5 . Graphical abstract.

HIV-1 is an infectious virus that causes acquired immunodeficiency syndrome (AIDS) and it is one of the major causes of deaths worldwide. The production of HIV-1 protease (PR) on a large scale has been a problem for scientists due to its cytotoxicity, low yield, insolubility, and low activity. HIV-1 C-SA protease has been cloned, expressed, and purified previously, however, with low recovery (0.25 mg/L). Herein we report an optimal expression and solubilisation procedure to recover active HIV-1 C-SA protease enzyme from inclusion bodies. The HIV protease was expressed in seven different vectors (pET11b, pET15b, pET28a pET32a, pET39b, pET41b and pGEX 6P-1). The highest expression was achieved when the vector pET32a (Trx tag) was employed. A total of 19.5 mg of fusion protein was refolded of which 5.5 mg of active protease was obtained after cleavage. The free protease had a high specific activity of 2.81 µmoles/min/mg. Interestingly the Trx-fusion protein also showed activity closer (1.24 µmoles/min/mg) to that of the free protease suggesting that the pET32a vector (Trx tag) expressed in BL21(DE3) pLysS provides a more efficient way to obtain HIV-1 protease.

The aspartate protease of the human immune deficiency type-1 virus (HIV-1) has become a crucial antiviral target in which many useful antiretroviral inhibitors have been developed. However, it seems the emergence of new HIV-1 PR mutations enhances drug resistance, hence, the available FDA approved drugs show less activity towards the protease. A mutation and insertion designated L38L↑N↑L PR was recently reported from subtype of C-SA HIV-1. An integrated two-layered ONIOM (QM:MM) method was employed in this study to examine the binding affinities of the nine HIV PR inhibitors against this mutant. The computed binding free energies as well as experimental data revealed a reduced inhibitory activity towards the L38L↑N↑L PR in comparison with subtype C-SA HIV-1 PR. This observation suggests that the insertion and mutations significantly affect the binding affinities or characteristics of the HIV PIs and/or parent PR. The same trend for the computational binding free energies was observed for eight of the nine inhibitors with respect to the experimental binding free energies. The outcome of this study shows that ONIOM method can be used as a reliable computational approach to rationalize lead compounds against specific targets. The nature of the intermolecular interactions in terms of the host-guest hydrogen bond interactions is discussed using the atoms in molecules (AIM) analysis. Natural bond orbital analysis was also used to determine the extent of charge transfer between the QM region of the L38L↑N↑L PR enzyme and FDA approved drugs. AIM analysis showed that the interaction between the QM region of the L38L↑N↑L PR and FDA approved drugs are electrostatic dominant, the bond stability computed from the NBO analysis supports the results from the AIM application. Future studies will focus on the improvement of the computational model by considering explicit water molecules in the active pocket. We believe that this approach has the potential to provide information that will aid in the design of much improved HIV-1 PR antiviral drugs.

This study intended to purify and characterise exo-inulinase of diesel-degrading Paenibacillus sp. D9. The whole genome sequencing of Paenibacillus sp. D9 revealed to possess the sacC gene that is encoded as exo-inulinase/levanase. This isolate was capable of producing a maximum of 50.9 IU/mL of exo-inulinase activity within 3 days at 30 °C, 200 rpm and pH of 7.0 on minimal salt medium agar supplemented with 1% (w/v) inulin. An exo-inulinase of 58.5 kDa was purified using ammonium sulphate precipitation, HiTrap QFF column and MMC column chromatographies with a specific activity of 4333 IU/mg, 7.1% recovery and a 4.3-fold increase in purity. The purified D9 exo-inulinase had temperature and pH optimum at 40 °C and pH 4.0, respectively, with the Michaelis constant of 5.5 mM and a maximal velocity of 476.2 IU/mg, respectively. Catalytic constant, k cat was calculated to be 42.6 s -1 with a catalytic efficiency (k cat /K m ) of 7.6 s -1  mM -1 . The presence of Ca 2+ enhanced the activity of D9 exo-inulinase while Hg 2+ completely inhibited the activity, other compounds such as Fe 3+ and Cu 2+ had an inhibitory effect. The results of amino acid alignment and the complete degradation of inulin into fructose by the purified enzyme confirmed that inulinase from Paenibacillus sp. D9 is an exo-form. The phylogenetic tree based on the protein sequences indicates that bacterial exo-inulinases possess a common ancestry.

The efficacy of HIV-1 protease (PR) inhibition therapies is often compromised by the emergence of mutations in the PR molecule that reduces the binding affinity of inhibitors while maintaining viable catalytic activity and affinity for natural substrates. In the present study, we used a recombinant HIV-1 C-SA PR and a recently reported variant for inhibition (Ki, IC50) and thermodynamic studies against nine clinically used inhibitors. This is the first time that binding free energies for C-SA PR and the mutant are reported. This variant PR harbours a mutation and insertion (I36T↑T) at position 36 of the C-SA HIV-1 PR, and did not show a significant difference in the catalytic effect of the HIV-1 PR. However, the nine clinically approved HIV PR drugs used in this study demonstrated weaker inhibition and lower binding affinities toward the variant when compared to the wild type HIV-1 PR. All the protease inhibitors (PIs), except Amprenavir and Ritonavir exhibited a significant decrease in binding affinity (p<0.0001). Darunavir and Nelfinavir exhibited the weakest binding affinity, 155- and 95-fold decreases respectively, toward the variant. Vitality values for the variant PR, against the seven selected PIs, confirm the impact of the mutation and insertion on the South African HIV-1 subtype C PR. This information has important clinical implications for thousands of patients in Sub-Saharan Africa.

Human immune virus subtype C is the most widely spread HIV subtype in Sub-Sahara Africa and South Africa. A profound structural insight on finding potential lead compounds is therefore necessary for drug discovery. The focus of this study is to rationalize the nine Food and Drugs Administration (FDA) HIV antiviral drugs complexed to subtype B and C-SA PR using ONIOM approach. To achieve this, an integrated two-layered ONIOM model was used to optimize the geometrics of the FDA approved HIV-1 PR inhibitors for subtype B. In our hybrid ONIOM model, the HIV-1 PR inhibitors as well as the ASP 25/25' catalytic active residues were treated at high level quantum mechanics (QM) theory using B3LYP/6-31G(d), and the remaining HIV PR residues were considered using the AMBER force field. The experimental binding energies of the PR inhibitors were compared to the ONIOM calculated results. The theoretical binding free energies (?G bind ) for subtype B follow a similar trend to the experimental results, with one exemption. The computational model was less suitable for C-SA PR. Analysis of the results provided valuable information about the shortcomings of this approach. Future studies will focus on the improvement of the computational model by considering explicit water molecules in the active pocket. We believe that this approach has the potential to provide much improved binding energies for complex enzyme drug interactions., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Human immunodeficiency virus (HIV) infections in sub-Saharan Africa represent about 56% of global infections. Many studies have targeted HIV-1 protease for the development of drugs against AIDS. Recombinant HIV-1 protease is used to screen new drugs from synthetic compounds or natural substances. Along with the wild type (C-SA) we also over-expressed and characterized two mutant forms from patients that had shown resistance to protease inhibitors. Using recombinant DNA technology, we constructed three recombinant plasmids in pGEX-6P-1 and expressed them containing a sequence encoding wild type HIV protease and two mutants (I36T↑T contains 100 amino acids and L38L↑N↑L contains 101 amino acids). These recombinant proteins were isolated from inclusion bodies by using QFF anion exchange and GST trap columns. In SDS-PAGE, we obtained these HIV proteases as single bands of approximately 11.5, 11.6 and 11.7 kDa for the wild type, I36T↑Tand L38L↑N↑L mutants, respectively. The enzyme was recovered efficiently (0.25 mg protein/L of Escherichia coli culture) and had high specific activity of 2.02, 2.20 and 1.33 μmol min(-1) mg(-1) at an optimal pH of 5 and temperature of 37 °C for the wild type, I36T↑T and L38L↑N↑L, respectively. The method employed here provides an easy and rapid purification of the HIV-1(C-SA) protease from the inclusion bodies, with high yield and high specific activities., (Copyright © 2016 Elsevier Inc. All rights reserved.)