Investigating the effects of four medicinal plants against dengue virus through QSAR modeling and molecular dynamics studies

The Dengue virus (DENV) has been increasingly recognized as a prevalent viral pathogen responsible for global transmission of infection. It has been established that DENV's NS5 methyltransferase (MTase) controls viral replication. As a result, NS5 MTase is considered a potentially useful drug target for DENV. In this study, the two phases of virtual screening were conducted using the ML-based QSAR model and molecular docking to identify potential compounds against NS5 of DENV. Four medicinal plants [Aloe vera, Cannabis sativa (Hemp), Ocimum sanctum (Holy Basil; Tulsi), and Zingiber officinale (Ginger)] that showed anti-viral properties were selected for sourcing the phytochemicals and screening them against NS5. Additionally, re-docking at higher exhaustiveness and interaction analysis were performed which resulted in the identification of the top four hits (135398658, 5281675, 119394, and 969516) which showed comparable results with the control Sinefungin (SFG). Post molecular dynamics simulation, 135398658 showed the lowest RMSD (0.4-0.5 nm) and the maximum number of hydrogen bonds (eight hydrogen bonds) after the control while 5281675 and 969516 showed comparable hydrogen bonds to the control. These compounds showed direct interactions with the catalytic site residues GLU111 and ASP131, in addition to this these compounds showed stable complex formation as depicted by principal component analysis and free energy landscape. 135398658 showed lower total binding free energy (ΔGTotal = -36.56 kcal/mol) than the control, while 5281675 had comparable values to the control (ΔGTotal = -34.1 kcal/mol). Overall, the purpose of this study was to identify phytochemicals that inhibit NS5 function, that could be further tested experimentally to treat dengue virus (DENV).Communicated by Ramaswamy H. Sarma.

Inhibition of SARS-CoV-2 NSP-15 by Uridine-5'-Monophosphate Analogues Using QSAR Modelling, Molecular Dynamics Simulations, and Free Energy Landscape

SARS-CoV-2 is accountable for severe social and economic disruption around the world causing COVID-19. Non-structural protein-15 (NSP15) possesses a domain that is vital to the viral life cycle and is known as uridylate-specific endoribonuclease (EndoU). This domain binds to the uridine 5'-monophosphate (U5P) so that the protein may carry out its native activity. It is considered a vital drug target to inhibit the growth of the virus. Thus, in this current study, ML-based QSAR and virtual screening of U5P analogues targeting Nsp15 were performed to identify potential molecules against SARS-CoV-2. Screening of 816 unique U5P analogues using ML-based QSAR identified 397 compounds ranked on their predicted bioactivity (pIC50). Further, molecular docking and hydrogen bond interaction analysis resulted in the selection of the top three compounds (53309102, 57398422, and 76314921). Molecular dynamics simulation of the most promising compounds showed that two molecules 53309102 and 57398422 acted as potential binders of Nsp15. The compound was able to inhibit nsp15 activity as it was successfully bound to the active site of the nsp15 protein. This was achieved by the formation of relevant contacts with enzymatically critical amino acid residues (His235, His250, and Lys290). Principal component analysis and free energy landscape studies showed stable complex formation while MM/GBSA calculation showed lower binding energies for 53309102 (ΔGTOTAL = -29.4 kcal/mol) and 57398422 (ΔGTOTAL = -39.4 kcal/mol) compared to the control U5P (ΔGTOTAL = -18.8 kcal/mol). This study aimed to identify analogues of U5P inhibiting the NSP15 function that potentially could be used for treating COVID-19.

Investigating the bispecific lead compounds against methicillin-resistant Staphylococcus aureus SarA and CrtM using machine learning and molecular dynamics approach

Methicillin-resistant Staphylococcus aureus (MRSA) is a notorious pathogen that has emerged as a serious global health concern over the past few decades. Staphylococcal accessory regulator A (SarA) and 4,4'-diapophytoene synthase (CrtM) play a crucial role in biofilm formation and staphyloxanthin biosynthesis. Thus, the present study used a machine learning-based QSAR model to screen 1261 plant-derived natural organic compounds in order to identify a medication candidate with both biofilm and virulence inhibitory potential. Additionally, the in-silico molecular docking analysis has demonstrated significant binding efficacy of the identified hit compound, that is 85137543, with SarA and CrtM when compared to the control compound, hesperidin. Post-MD simulation analysis of the complexes depicted strong binding of 85137543 to both SarA and CrtM. Moreover, 85137543 showed hydrogen bonding with the key residues of both proteins during docking (ALA138 of SarA and ALA134 of CrtM) and post-MD simulation (LYS273 of CrtM and ASN212 of SarA). The RMSD of 85137543 was stable and consistent when bound to both CrtM and SarA with RMSDs of 1.3 and 1 nm, respectively. In addition, principal component analysis and the free energy landscape showed stable complex formation with both proteins. Low binding free energy (ΔGTotal) was observed by 85137543 for SarA (-47.92 kcal/mol) and CrtM (-36.43 kcal/mol), which showed strong binding. Overall, this study identified 85137543 as a potential inhibitor of both SarA and CrtM in MRSA.Communicated by Ramaswamy H. Sarma.

Virtual Screening Based on QSAR and Molecular Docking of Possible Inhibitors Targeting Chagas CYP51

Chagas is a neglected tropical disease caused by the parasite Trypanosoma cruzi with no effective treatment in all its forms. There is a need to find more effective therapeutic alternatives with reduced toxicity. In this contribution, multiple linear regression models were used to identify the molecular descriptors that best describe the inhibitory activity of 52 fenarimol analogues against Trypanosoma cruzi. The topological, physicochemical, thermodynamic, electronic, and charge descriptors were evaluated to cover a wide range of properties that frequently encode biological activity. A model with high predictive value was obtained based on geometrical descriptors and descriptors encoding hydrophobicity and London dispersion forces as necessary for the inhibition of Trypanosoma cruzi-CYP51. Docking methodology was implemented to evaluate molecular interactions in silico. The virtual screening results in this study can be used for rational design of new analogues with improved activity against Chagas disease.