Targeting SARS-CoV-2 Spike Protein/ACE2 Protein-Protein Interactions: a Computational Study

LC-MS/MS profiling and analysis of Bacillus licheniformis extracellular proteins for antifungal potential against Candida albicans

Candida albicans, a significant human pathogenic fungus, employs hydrolytic proteases for host invasion. Conventional antifungal agents are reported with resistance issues from around the world. This study investigates the role of Bacillus licheniformis extracellular proteins (ECP) as effective antifungal peptides (AFPs). The aim was to identify and characterize the ECP of B. licheniformis through LC-MS/MS and bioinformatics analysis. LC-MS/MS analysis identified 326 proteins with 69 putative ECP, further analyzed in silico. Of these, 21 peptides exhibited antifungal properties revealed by classAMP tool and are predominantly anionic. Peptide-protein docking revealed interactions between AFPs like Peptide chain release factor 1 (Q65DV1_Seq1: SASEQLSDAK) and Putative carboxy peptidase (Q65IF0_Seq7: SDSSLEDQDFILESK) with C. albicans virulent SAP5 proteins (PDB ID 2QZX), forming hydrogen bonds and significant Pi-Pi interactions. The identification of B. licheniformis ECP is the novelty of the study that sheds light on their antifungal potential. The identified AFPs, particularly those interacting with bonafide pharmaceutical targets SAP5 of C. albicans represent promising avenues for the development of antifungal treatments with AFPs that could be the pursuit of a novel therapeutic strategy against C. albicans. SIGNIFICANCE OF STUDY: The purpose of this work was to carry out proteomic profiling of the secretome of B. licheniformis. Previously, the efficacy of Bacillus licheniformis extracellular proteins against Candida albicans was investigated and documented in a recently communicated manuscript, showcasing the antifungal activity of these proteins. In order to achieve high-throughput identification of ES (Excretory-secretory) proteins, the utilization of liquid chromatography tandem mass spectrometry (LC-MS) was utilized. There was a lack of comprehensive research on AFPs in B. licheniformis, nevertheless. The proteins secreted by B. licheniformis in liquid medium were initially discovered using liquid chromatography-tandem mass spectrometry (LC-MS) analysis and identification in order to immediately characterize the unidentified active metabolites in fermentation broth.

Leveraging shape screening and molecular dynamics simulations to optimize PARP1-Specific chemo/radio-potentiators for antitumor drug design

PARP1 plays a pivotal role in DNA repair within the base excision pathway, making it a promising therapeutic target for cancers involving BRCA mutations. Current study is focused on the discovery of PARP inhibitors with enhanced selectivity for PARP1. Concurrent inhibition of PARP1 with PARP2 and PARP3 affects cellular functions, potentially causing DNA damage accumulation and disrupting immune responses. In step 1, a virtual library of 593 million compounds has been screened using a shape-based screening approach to narrow down the promising scaffolds. In step 2, hierarchical docking approach embedded in Schrödinger suite was employed to select compounds with good dock score, drug-likeness and MMGBSA score. Analysis supplemented with decomposition energy, molecular dynamics (MD) simulations and hydrogen bond frequency analysis, pinpointed that active site residues; H862, G863, R878, M890, Y896 and F897 are crucial for specific binding of ZINC001258189808 and ZINC000092332196 with PARP1 as compared to PARP2 and PARP3. The binding of ZINC000656130962, ZINC000762230673, ZINC001332491123, and ZINC000579446675 also revealed interaction involving two additional active site residues of PARP1, namely N767 and E988. Weaker or no interaction was observed for these residues with PARP2 and PARP3. This approach advances our understanding of PARP-1 specific inhibitors and their mechanisms of action, facilitating the development of targeted therapeutics.

Recent advances in chemometric modelling of inhibitors against SARS-CoV-2

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused great harm to all countries worldwide. This disease can be prevented by vaccination and managed using various treatment methods, including injections, oral medications, or aerosol therapies. However, the selection of suitable compounds for the research and development of anti-SARS-CoV-2 drugs is a daunting task because of the vast databases of available compounds. The traditional process of drug research and development is time-consuming, labour-intensive, and costly. The application of chemometrics can significantly expedite drug R&D. This is particularly necessary and important for drug development against pandemic public emergency diseases, such as COVID-19. Through various chemometric techniques, such as quantitative structure-activity relationship (QSAR) modelling, molecular docking, and molecular dynamics (MD) simulations, compounds with inhibitory activity against SARS-CoV-2 can be quickly screened, allowing researchers to focus on the few prioritised candidates. In addition, the ADMET properties of the screened candidate compounds should be further explored to promote the successful discovery of anti-SARS-CoV-2 drugs. In this case, considerable time and economic costs can be saved while minimising the need for extensive animal experiments, in line with the 3R principles. This paper focuses on recent advances in chemometric modelling studies of COVID-19-related inhibitors, highlights current limitations, and outlines potential future directions for development.

Discovery of raffinose sulfate as a potential SARS CoV-2 inhibitor via blocking its binding with angiotensin converting enzyme 2

The present study aimed to characterize the possible binding sites on the SARS CoV-2 RBD-ACE2 complex and to highlight sulfated oligosaccharides as potential anti-SARS CoV-2 via inducing RBD-ACE2 complex destabilization and dissociation. By combining pharmacophore-based and structural-based virtual screening approaches we were able to discover raffinose sulfate (RS) as a potential antiviral sulfated oligosaccharide against two SARS CoV-2 variants (i.e., wild type and Omicron) (IC50 = 4.45 ± 0.28 μM and 4.65 ± 0.32 μM, respectively). Upon MD simulation, RS was able to establish stable binding at the RBD-ACE2 interface inducing a rapid dissociation. Accordingly, and by using bio-layer interferometry (BLI) assays, RS was able to significantly weaken the affinity between RBD (of both variants) and ACE2. Additionally, we found that RS has a poor cellular permeability indicating that its interaction with the RBD-ACE2 complex may be the main mechanism by which it mediates its antiviral activity against SARS CoV-2. Despite its proposed interaction with the RBD-ACE2 complex, RS did not show any inhibitory activity against ACE2 catalytic activity. In light of these findings, the RS scaffold can be further developed into a novel anti-SARS CoV-2 drug with improved activity and tolerability in comparison with other sulfated polysaccharides e.g., heparin and heparan.

Aloin A inhibits SARS CoV-2 replication by targeting its binding with ACE2 - Evidence from modeling-supported molecular dynamics simulation

The current study aimed to expand on the recently published results and assess the inhibitory efficacy of aloin A against SARS CoV-2. In vitro testing of aloin A against SARS CoV-2 proteases (i.e., MPro and PLPro) showed weak to moderate activity (IC50 = 68.56 ± 1.13 µM and 24.77 ± 1.57 µM, respectively). However, aloin A was able to inhibit the replication of SARS CoV-2 in Vero E6 cells efficiently with an IC50 of 0.095 ± 0.022 µM. Depending on the reported poor permeability of aloin A alongside its insignificant protease inhibitory activities presented in this study, we ran a number of extensive virtual screenings and physics-based simulations to determine the compound's potential mode of action. As a result, RBD-ACE2 was identified as a key target for aloin A. Results from 600 ns-long molecular dynamics (MD) simulation experiments pointed to aloin A's role as an RBD-ACE2 destabilizer. Therefore, the results of this work may pave the way for further development of this scaffold and the eventual production of innovative anti-SARS CoV-2 medicines with several mechanisms of action.Communicated by Ramaswamy H. Sarma.

Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface

After over two years of living with Covid-19 and hundreds of million cases worldwide there is still an unmet need to find proper treatments for the novel coronavirus, due also to the rapid mutation of its genome. In this context, a drug repositioning study has been performed, using in silico tools targeting Delta Spike protein/ACE2 interface. To this aim, it has been virtually screened a library composed by 4388 approved drugs through a deep learning-based QSAR model to identify protein-protein interactions modulators for molecular docking against Spike receptor binding domain (RBD). Binding energies of predicted complexes were calculated by Molecular Mechanics/Generalized Born Surface Area from docking and molecular dynamics simulations. Four out of the top twenty ranking compounds showed stable binding modes on Delta Spike RBD and were evaluated also for their effectiveness against Omicron. Among them an antihistaminic drug, fexofenadine, revealed very low binding energy, stable complex, and interesting interactions with Delta Spike RBD. Several antihistaminic drugs were found to exhibit direct antiviral activity against SARS-CoV-2 in vitro, and their mechanisms of action is still debated. This study not only highlights the potential of our computational methodology for a rapid screening of variant-specific drugs, but also represents a further tool for investigating properties and mechanisms of selected drugs.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Bioactive components of different nasal spray solutions may defeat SARS-Cov2: repurposing and in silico studies

The recent outbreak "Coronavirus Disease 2019 (COVID-19)" is caused by fast-spreading and highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). This virus enters into the human respiratory system by binding of the viral surface spike glycoprotein (S-protein) to an angiotensin-converting enzyme2 (ACE2) receptor that is found in the nasal passage and oral cavity of a human. Both spike protein and the ACE2 receptor have been identified as promising therapeutic targets to develop anti-SARS-CoV2 drugs. No therapeutic drugs have been developed as of today except for some vaccines. Therefore, potent therapeutic agents are urgently needed to combat the COVID-19 infections. This goal would be achieved only by applying drug repurposing and computational approaches. Thus, based on drug repurposing approach, we have investigated 16 bioactive components (1-16) from different nasal spray solutions to check their efficacies against human ACE2 and SARS-CoV2 spike proteins by performing molecular docking and molecular dynamic (MD) simulation studies. In this study, three bioactive components namely ciclesonide (8), levocabastine (13), and triamcinolone acetonide (16) have been found as promising inhibitory agents against SARS-CoV2 spike and human ACE2 receptor proteins with excellent binding affinities, comparing to reference drugs such as nafamostat, arbidol, losartan, and benazepril. Furthermore, MD simulations were performed (triplicate) for 100 ns to confirm the stability of 8, 13, and 16 with said protein targets and to compute MM-PBSA-based binding-free energy calculations. Thus, bioactive components 8, 13, and 16 open the door for researchers and scientist globally to investigate them against SARS-CoV2 through in vitro and in vivo analysis.

Artificial Intelligence Technologies for COVID-19 De Novo Drug Design

The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research.