Ursolic acid interaction with transcription factors BRAF, V600E, and V600K: a computational approach towards new potential melanoma treatments.

CONTEXT: Melanoma is one of the cancers with the highest mortality rate for its ability to metastasize. Several targets have undergone investigation for the development of drugs against this pathology. One of the main targets is the kinase BRAF (RAF, rapidly accelerated fibrosarcoma). The most common mutation in melanoma is BRAFV600E and has been reported in 50-90% of patients with melanoma. Due to the relevance of the BRAFV600E mutation, inhibitors to this kinase have been developed, vemurafenib-OMe and dabrafenib. Ursolic acid (UA) is a pentacyclic triterpene with a privileged structure, the pentacycle scaffold, which allows to have a broad variety of biological activity; the most studied is its anticancer capacity. In this work, we reported the interaction profile of vemurafenib-OMe, dabrafenib, and UA, to define whether UA has binding capacity to BRAFWT, BRAFV600E, and BRAFV600K. Homology modeling of BRAFWT, V600E, and V600K; molecular docking; and molecular dynamics simulations were carried out and interactions and residues relevant to the binding of the inhibitors were obtained. We found that UA, like the inhibitors, presents hydrogen bond interactions, and hydrophobic interactions of van der Waals, and π-stacking with I463, Q530, C532, and F583. The ΔG of ursolic acid in complex with BRAFV600K (- 63.31 kcal/mol) is comparable to the ΔG of the selective inhibitor dabrafenib (- 63.32 kcal/mol) in complex to BRAFV600K and presents a ΔG like vemurafenib-OMe with BRAFWT and V600E. With this information, ursolic acid could be considered as a lead compound for design cycles and to optimize the binding profile and the selectivity towards mutations for the development of new selective inhibitors for BRAFV600E and V600K to new potential melanoma treatments. METHODS: The homology modeling calculations were executed on the public servers I-TASSER and ROBETTA, followed by molecular docking calculations using AutoGrid 4.2.6, AutoDockGPU 1.5.3, and AutoDockTools 1.5.6. Molecular dynamics and metadynamics simulations were performed in the Desmond module of the academic version of the Schrödinger-Maestro 2020-4 program, utilizing the OPLS-2005 force field. Ligand-protein interactions were evaluated using Schrödinger-Maestro program, LigPlot + , and PLIP (protein-ligand interaction profiler). Finally, all of the protein figures presented in this article were made in the PyMOL program.

In Silico Design of miniACE2 Decoys with In Vitro Enhanced Neutralization Activity against SARS-CoV-2, Encompassing Omicron Subvariants.

The COVID-19 pandemic has overwhelmed healthcare systems and triggered global economic downturns. While vaccines have reduced the lethality rate of SARS-CoV-2 to 0.9% as of October 2024, the continuous evolution of variants remains a significant public health challenge. Next-generation medical therapies offer hope in addressing this threat, especially for immunocompromised individuals who experience prolonged infections and severe illnesses, contributing to viral evolution. These cases increase the risk of new variants emerging. This study explores miniACE2 decoys as a novel strategy to counteract SARS-CoV-2 variants. Using in silico design and molecular dynamics, blocking proteins (BPs) were developed with stronger binding affinity for the receptor-binding domain of multiple variants than naturally soluble human ACE2. The BPs were expressed in E. coli and tested in vitro, showing promising neutralizing effects. Notably, miniACE2 BP9 exhibited an average IC50 of 4.9 µg/mL across several variants, including the Wuhan strain, Mu, Omicron BA.1, and BA.2 This low IC50 demonstrates the potent neutralizing ability of BP9, indicating its efficacy at low concentrations.Based on these findings, BP9 has emerged as a promising therapeutic candidate for combating SARS-CoV-2 and its evolving variants, thereby positioning it as a potential emergency biopharmaceutical.

Carbohydrate-Binding Mechanism of the Coagulant Lectin from Moringa oleifera Seeds (cMoL) Is Related to the Dimeric Protein Structure.

This study characterized the binding mechanisms of the lectin cMoL (from Moringa oleifera seeds) to carbohydrates using spectroscopy and molecular dynamics (MD). The interaction with carbohydrates was studied by evaluating lectin fluorescence emission after titration with glucose or galactose (2.0-11 mM). The Stern-Volmer constant (Ksv), binding constant (Ka), Gibbs free energy (∆G), and Hill coefficient were calculated. After the urea-induced denaturation of cMoL, evaluations were performed using fluorescence spectroscopy, circular dichroism (CD), and hemagglutinating activity (HA) evaluations. The MD simulations were performed using the Amber 20 package. The decrease in Ksv revealed that cMoL interacts with carbohydrates via a static mechanism. The cMoL bound carbohydrates spontaneously (ΔG < 0) and presented a Ka on the order of 102, with high selectivity for glucose. Protein-ligand complexes were stabilized by hydrogen bonds and hydrophobic interactions. The Hill parameter (h~2) indicated that the binding occurs through the cMoL dimer. The loss of HA at urea concentrations at which the fluorescence and CD spectra indicated protein monomerization confirmed these results. The MD simulations revealed that glucose bound to the large cavity formed between the monomers. In conclusion, the biotechnological application of cMoL lectin requires specific methods or media to improve its dimeric protein structure.

Pneumococcal surface protein A (PspA) prevents killing of Streptococcus pneumoniae by indolicidin.

Pneumococcal surface protein A (PspA) is an important virulence factor in Streptococcus pneumoniae that binds to lactoferrin and protects the bacterium from the bactericidal action of lactoferricins-cationic peptides released upon lactoferrin proteolysis. The present study investigated if PspA can prevent killing by another cationic peptide, indolicidin. PspA-negative pneumococci were more sensitive to indolicidin-induced killing than bacteria expressing PspA, suggesting that PspA prevents the bactericidal action of indolicidin. Similarly, chemical removal of choline-binding proteins increased sensitivity to indolicidin. The absence of capsule and PspA had an additive effect on pneumococcal killing by the AMP. Furthermore, anti-PspA antibodies enhanced the bactericidal effect of indolicidin on pneumococci, while addition of soluble PspA fragments competitively inhibited indolicidin action. Previous in silico analysis suggests a possible interaction between PspA and indolicidin. Thus, we hypothesize that PspA acts by sequestering indolicidin and preventing it from reaching the bacterial membrane. A specific interaction between PspA and indolicidin was demonstrated by mass spectrometry, confirming that PspA can actively bind to the AMP. These results reinforce the vaccine potential of PspA and suggest a possible mechanism of innate immune evasion employed by pneumococci, which involves binding to cationic peptides and hindering their ability to damage the bacterial membranes.

Quantum mechanical analysis of newly synthesized HIV-1 protease inhibitors: evaluation of wild-type and resistant strain binding interactions.

Inhibition of HIV-1 protease is a cornerstone of antiretroviral therapy. However, the notorious ability of HIV-1 to develop resistance to protease inhibitors (PIs), particularly darunavir (DRV), poses a major challenge. Using quantum chemistry and computer simulations, this study aims to investigate the interactions between two novel PIs, GRL-004 and GRL-063, as well as a wild-type (WT) HIV strain and a DRV-resistant mutant strain. To do this, we used molecular docking, molecular dynamics simulations, and quantum mechanical calculations to check how well GRL-004 and GRL-063 bound to both WT and DRV-resistant proteases. The results show that GRL-004 and GRL-063 bind very well to ASP29 in the WT strain. ASP29 is an important amino acid in the HIV protease dimer. Remarkably, amino acids such as ILE50 in the WT strains showed substantial binding energies to both drugs. Quantum energy calculations showed a slight reduction in the energy affinity of the interaction between the MUT strain and the ligand GRL-063, compared to the WT strain. GRL-004 showed similar interaction energy for both strains, suggesting that it has greater plasticity than GRL-063 despite its lower interaction affinity. Furthermore, GLY49B demonstrated strong binding energies regardless of mutations. Other relevant residues with strong binding energies include GLY49B, PHE82A, PRO81A, ASP29A, ASP25A and ALA28B. This study improves our understanding of receptor-ligand dynamics and the adaptability of new protease inhibitors (PIs), which has profound implications for the innovation of future antiretroviral drugs.

DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects.

In silico approach revealed the membrane receptor PHO36 as a new target for synthetic anticandidal peptides.

Aim: Synthetic antimicrobial peptides (SAMPs) present the potential to fight systemic fungal infections. Here, the PHO36 receptor from Candida albicans was analyzed by in silico tools as a possible target for three anticandidal SAMPs: RcAlb-PepIII, PepGAT and PepKAA.Materials & methods: Molecular docking, dynamics and quantum biochemistry were employed to understand the individual contribution of amino acid residues in the interaction region.Results: The results revealed that SAMPs strongly interact with the PHO36 by multiple high-energy interactions. This is the first study to employ quantum biochemistry to describe the interactions between SAMPs and the PHO36 receptor.Conclusion: This work contributes to understanding and identifying new molecular targets with medical importance that could be used to discover new drugs against systemic fungal infections.

Here, computers helped us find new proteins in Candida albicans that may guide the development of new medicines.

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus's infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; [Formula: see text] = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson's correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the [Formula: see text] loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

Exploring the potential of structure-based deep learning approaches for T cell receptor design.

Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optimization of existing ones designed for specific functions, such as binding a target protein. Despite the demonstrated potential of such approaches in designing general protein binders, their application in designing immunotherapeutics remains relatively underexplored. A relevant application is the design of T cell receptors (TCRs). Given the crucial role of T cells in mediating immune responses, redirecting these cells to tumor or infected target cells through the engineering of TCRs has shown promising results in treating diseases, especially cancer. However, the computational design of TCR interactions presents challenges for current physics-based methods, particularly due to the unique natural characteristics of these interfaces, such as low affinity and cross-reactivity. For this reason, in this study, we explored the potential of two structure-based deep learning protein design methods, ProteinMPNN and ESM-IF1, in designing fixed-backbone TCRs for binding target antigenic peptides presented by the MHC through different design scenarios. To evaluate TCR designs, we employed a comprehensive set of sequence- and structure-based metrics, highlighting the benefits of these methods in comparison to classical physics-based design methods and identifying deficiencies for improvement.

Countercurrent chromatography isolation of green propolis biomarkers: Potential blockers of SARS-COV-2 RBD and ACE2 interaction.

Propolis is a natural resinous mixture produced by honeybees with numerous biological activities. Considering the recently reported potential of propolis as an adjuvant in COVID-19 treatment, a methodology for the fractionation of the hexane extract of Brazilian green propolis (HEGP) was developed for the obtention of prenylated biomarkers by countercurrent chromatography. The inhibition of the interaction between the receptor binding domain (RBD) of spike and ACE2 receptor was evaluated by the Lumitᵀᴹ immunoassay. Fractionation of HEGP was performed by both normal (CCC1 and CCC2, with extended elution) and reversed (CCC3) phase elution-extrusion modes with the solvent system hexane-ethanol-water 4:3:1. The normal elution mode of CCC1 (471 mg HEGP in a 80 mL column volume, 1.6 mm id) was scaled-up (CCC5, 1211 mg HEGP in a 112 mL column volume, 2.1 mm id), leading to the isolation of 89.9 mg of artepillin C, 1; 52.7 mg of baccharin, 2; and 26.6 mg of chromene, with purities of 93 %, 83 % and 88 %, respectively, by HPLC-PDA. Among the isolated compounds, artepillin C, 1, and baccharin, 2, presented the best results in the Lumitᵀᴹ immunoassay, showing 67% and 51% inhibition, respectively, at the concentration of 10 µM. This technique proved to be of low operational cost and excellent reproducibility.

BAT2: an Open-Source Tool for Flexible, Automated, and Low Cost Absolute Binding Free Energy Calculations.

Absolute binding free energy (ABFE) calculations with all-atom molecular dynamics (MD) have the potential to greatly reduce costs in the first stages of drug discovery. Here, we introduce BAT2, the new version of the Binding Affinity Tool (BAT.py), designed to combine full automation of ABFE calculations with high-performance MD simulations, making it a potential tool for virtual screening. We describe and test several changes and new features that were incorporated into the code, such as relative restraints between the protein and the ligand instead of using fixed dummy atoms, support for the OpenMM simulation engine, a merged approach to the application/release of restraints, support for cobinders and proteins with multiple chains, and many others. We also reduced the simulation times for each ABFE calculation, assessing the effect on the expected robustness and accuracy of the calculations.

Unveiling crucial amino acids in the carbohydrate recognition domain of a viral protein through a structural bioinformatic approach.

Carbohydrate binding modules (CBMs) are protein domains that typically reside near catalytic domains, increasing substrate-protein proximity by constraining the conformational space of carbohydrates. Due to the flexibility and variability of glycans, the molecular details of how these protein regions recognize their target molecules are not always fully understood. Computational methods, including molecular docking and molecular dynamics simulations, have been employed to investigate lectin-carbohydrate interactions. In this study, we introduce a novel approach that integrates multiple computational techniques to identify the critical amino acids involved in the interaction between a CBM located at the tip of bacteriophage J-1's tail and its carbohydrate counterparts. Our results highlight three amino acids that play a significant role in binding, a finding we confirmed through in vitro experiments. By presenting this approach, we offer an intriguing alternative for pinpointing amino acids that contribute to protein-sugar interactions, leading to a more thorough comprehension of the molecular determinants of protein-carbohydrate interactions.

The Feline calicivirus Leader of the Capsid (LC) Protein Contains a Putative Transmembrane Domain, Binds to the Cytoplasmic Membrane, and Exogenously Permeates Cells.

Feline calicivirus (FCV), an important model for studying the biology of the Caliciviridae family, encodes the leader of the capsid (LC) protein, a viral factor known to induce apoptosis when expressed in a virus-free system. Our research has shown that the FCV LC protein forms disulfide bond-dependent homo-oligomers and exhibits intrinsic toxicity; however, it lacked a polybasic region and a transmembrane domain (TMD); thus, it was initially classified as a non-classical viroporin. The unique nature of the FCV LC protein, with no similarity to other proteins beyond the Vesivirus genus, has posed challenges for bioinformatic analysis reliant on sequence similarity. In this study, we continued characterizing the LC protein using the AlphaFold 2 and the recently released AlphaFold 3 artificial intelligence tools to predict the LC protein tertiary structure. We compared it to other molecular modeling algorithms, such as I-Tasser's QUARK, offering new insights into its putative TMD. Through exogenous interaction, we found that the recombinant LC protein associates with the CrFK plasmatic membrane and can permeate cell membranes in a disulfide bond-independent manner, suggesting that this interaction might occur through a TMD. Additionally, we examined its potential to activate the intrinsic apoptosis pathway in murine and human ovarian cancer cell lines, overexpressing survivin, an anti-apoptotic protein. All these results enhance our understanding of the LC protein's mechanism of action and suggest its role as a class-I viroporin.

Structural Characterization of Heat Shock Protein 90ß and Molecular Interactions with Geldanamycin and Ritonavir: A Computational Study.

Drug repositioning is an important therapeutic strategy for treating breast cancer. Hsp90ß chaperone is an attractive target for inhibiting cell progression. Its structure has a disordered and flexible linker region between the N-terminal and central domains. Geldanamycin was the first Hsp90ß inhibitor to interact specifically at the N-terminal site. Owing to the toxicity of geldanamycin, we investigated the repositioning of ritonavir as an Hsp90ß inhibitor, taking advantage of its proven efficacy against cancer. In this study, we used molecular modeling techniques to analyze the contribution of the Hsp90ß linker region to the flexibility and interaction between the ligands geldanamycin, ritonavir, and Hsp90ß. Our findings indicate that the linker region is responsible for the fluctuation and overall protein motion without disturbing the interaction between the inhibitors and the N-terminus. We also found that ritonavir established similar interactions with the substrate ATP triphosphate, filling the same pharmacophore zone.

Dissecting the structural and functional consequences of the evolutionary proline-glycine deletion in the wing 1 region of the forkhead domain of human FoxP1.

The human FoxP transcription factors dimerize via three-dimensional domain swapping, a unique feature among the human Fox family, as result of evolutionary sequence adaptations in the forkhead domain. This is the case for the conserved glycine and proline residues in the wing 1 region, which are absent in FoxP proteins but present in most of the Fox family. In this work, we engineered both glycine (G) and proline-glycine (PG) insertion mutants to evaluate the deletion events in FoxP proteins in their dimerization, stability, flexibility, and DNA-binding ability. We show that the PG insertion only increases protein stability, whereas the single glycine insertion decreases the association rate and protein stability and promotes affinity to the DNA ligand.

Class I TCP transcription factors TCP14 and TCP15 promote axillary branching in Arabidopsis by counteracting the action of Class II TCP BRANCHED1.

Shoot branching is determined by a balance between factors that promote axillary bud dormancy and factors that release buds from the quiescent state. The TCP family of transcription factors is classified into two classes, Class I and Class II, which usually play different roles. While the role of the Class II TCP BRANCHED1 (BRC1) in suppressing axillary bud development in Arabidopsis thaliana has been widely explored, the function of Class I TCPs in this process remains unknown. We analyzed the role of Class I TCP14 and TCP15 in axillary branch development in Arabidopsis through a series of genetic and molecular studies. In contrast to the increased branch number shown by brc1 mutants, tcp14 tcp15 plants exhibit a reduced number of branches compared with wild-type. Our findings provide evidence that TCP14 and TCP15 act by counteracting BRC1 function through two distinct mechanisms. First, they indirectly reduce BRC1 expression levels. Additionally, TCP15 directly interacts with BRC1 decoying it from chromatin and thereby preventing the transcriptional activation of a set of BRC1-dependent genes. We describe a molecular mechanism by which Class I TCPs physically antagonize the action of the Class II TCP BRC1, aligning with their opposite roles in axillary bud development.

Molecular insights into the regulatory landscape of PKC-related kinase-2 (PRK2/PKN2) using targeted small compounds.

The PKC-related kinases (PRKs, also termed PKNs) are important in cell migration, cancer, hepatitis C infection, and nutrient sensing. They belong to a group of protein kinases called AGC kinases that share common features like a C-terminal extension to the catalytic domain comprising a hydrophobic motif. PRKs are regulated by N-terminal domains, a pseudosubstrate sequence, Rho-binding domains, and a C2 domain involved in inhibition and dimerization, while Rho and lipids are activators. We investigated the allosteric regulation of PRK2 and its interaction with its upstream kinase PDK1 using a chemical biology approach. We confirmed the phosphoinositide-dependent protein kinase 1 (PDK1)-interacting fragment (PIF)-mediated docking interaction of PRK2 with PDK1 and showed that this interaction can be modulated allosterically. We showed that the polypeptide PIFtide and a small compound binding to the PIF-pocket of PRK2 were allosteric activators, by displacing the pseudosubstrate PKL region from the active site. In addition, a small compound binding to the PIF-pocket allosterically inhibited the catalytic activity of PRK2. Together, we confirmed the docking interaction and allostery between PRK2 and PDK1 and described an allosteric communication between the PIF-pocket and the active site of PRK2, both modulating the conformation of the ATP-binding site and the pseudosubstrate PKL-binding site. Our study highlights the allosteric modulation of the activity and the conformation of PRK2 in addition to the existence of at least two different complexes between PRK2 and its upstream kinase PDK1. Finally, the study highlights the potential for developing allosteric drugs to modulate PRK2 kinase conformations and catalytic activity.

Structural and Functional Characterization of New Lipid Transfer Proteins with Chitin-Binding Properties: Insights from Protein Structure Prediction, Molecular Docking, and Antifungal Activity.

Faced with the emergence of multiresistant microorganisms that affect human health, microbial agents have become a serious global threat, affecting human health and plant crops. Antimicrobial peptides have attracted significant attention in research for the development of new microbial control agents. This work's goal was the structural characterization and analysis of antifungal activity of chitin-binding peptides from Capsicum baccatum and Capsicum frutescens seeds on the growth of Candida and Fusarium species. Proteins were initially submitted to extraction in phosphate buffer pH 5.4 and subjected to chitin column chromatography. Posteriorly, two fractions were obtained for each species, Cb-F1 and Cf-F1 and Cb-F2 and Cf-F2, respectively. The Cb-F1 (C. baccatum) and Cf-F1 (C. frutescens) fractions did not bind to the chitin column. The electrophoresis results obtained after chromatography showed two major protein bands between 3.4 and 14.2 kDa for Cb-F2. For Cf-F2, three major bands were identified between 6.5 and 14.2 kDa. One band from each species was subjected to mass spectrometry, and both bands showed similarity to nonspecific lipid transfer protein. Candida albicans and Candida tropicalis had their growth inhibited by Cb-F2. Cf-F2 inhibited the development of C. albicans but did not inhibit the growth of C. tropicalis. Both fractions were unable to inhibit the growth of Fusarium species. The toxicity of the fractions was tested in vivo on Galleria mellonella larvae, and both showed a low toxicity rate at high concentrations. As a result, the fractions have enormous promise for the creation of novel antifungal compounds.

The power of computational proteomics platforms to decipher protein-protein interactions.

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

Development and characterization of a multimeric recombinant protein using the spike protein receptor binding domain as an antigen to induce SARS-CoV-2 neutralization.

BACKGROUND: SARS-CoV2 virus, responsible for the COVID-19 pandemic, has four structural proteins and 16 nonstructural proteins. S-protein is one of the structural proteins exposed on the virus surface and is the main target for producing neutralizing antibodies and vaccines. The S-protein forms a trimer that can bind the angiotensin-converting enzyme 2 (ACE2) through its receptor binding domain (RBD) for cell entry. AIMS: The goal of this study was to express in HEK293 cells a new RBD recombinant protein in a constitutive and stable manner in order to use it as an alternative immunogen and diagnostic tool for COVID-19. MATERIALS & METHODS: The protein was designed to contain an immunoglobulin signal sequence, an explanded C-terminal section of the RBD, a region responsible for the bacteriophage T4 trimerization inducer, and six histidines in the pCDNA-3.1 plasmid. Following transformation, the cells were selected with geneticin-G418 and purified from serum-fre culture supernatants using Ni2+-agarand size exclusion chromatography. The protein was structurally identified by cross-linking and circular dichroism experiments, and utilized to immunize mice in conjuction with AS03 or alum adjuvants. The mice sera were examined for antibody recognition, receptor-binding inhibition, and virus neutralization, while spleens were evaluated for γ-interferon production in the presence of RBD. RESULTS: The protein released in the culture supernatant of cells, and exhibited a molecular mass of 135 kDa with a secondary structure like the monomeric and trimeric RBD. After purification, it formed a multimeric structure comprising trimers and hexamers, which were able to bind the ACE2 receptor. It generated high antibody titers in mice when combined with AS03 adjuvant (up to 1:50,000). The sera were capable of inhibiting binding of biotin-labeled ACE2 to the virus S1 subunit and could neutralize the entry of the Wuhan virus strain into cells at dilutions up to 1:2000. It produced specific IFN-γ producing cells in immunized mouse splenocytes. DISCUSSION: Our data describe a new RBD containing protein, forming trimers and hexamers, which are able to induce a protective humoral and cellular response against SARS-CoV2. CONCLUSION: These results add a new arsenal to combat COVID-19, as an alternative immunogen or antigen for diagnosis.