Exploiting the glycan receptor-binding site of PltB subunit in salmonella typhi toxin for novel inhibitors: An in-silico approach.

Salmonella typhi (S. typhi), a gram-negative bacterium responsible for gastroenteritis - typhoid - has continually evolved into drug-resistant strains with the most recent being the haplotype H58 strain. The haplotype H58 strain has spread across the globe causing outbreaks in countries such as Pakistan, Zimbabwe, and several underdeveloped regions located in parts of Asia, Central and Southern Africa. Treatment by conventional antibiotics is gradually failing as recorded in the affected countries, including Nigeria and Barcelona - Spain. Therefore, the research presented herein aims to identify novel compounds targeting the typhoid toxin of S. typhi which is responsible for several virulence factors associated with typhoid. In-silico methods that include virtual screening, molecular dynamics (MD) and computation of binding free energies were utilized. Our research identified furan derivatives as top-scoring lead compounds from a database of more than 1,5 million compounds curated from the ZINC20 database. Post docking analysis and trajectory analysis post-MD simulations showed that π - π interactions are vital to holding the ligand within the receptor pocket whereas hydrophobic and Van der Waals interactions are crucial for the overall bonding. Through docking, MD simulations and free energy computations, we hypothesize that ZINC000114543311, ZINC000794380763 and ZINC000158992484 (docking scores of -9.06, -8.20 and -8.12 in conjunction with ΔG values of -64.691, -63.670 and -59.024 kcal/mol, respectively) bear a great potential to pave the way to fighting antibiotic resistance for typhoid in both humans and animals. The compounds presented here can also be used as lead materials for designing other compounds targeting the Salmonella typhi toxin.

Energy and reactivity profile and proton affinity analysis of rimegepant with special reference to its potential activity against SARS-CoV-2 virus proteins using molecular dynamics.

Rimegepant is a new medicine developed for the management of chronic headache due to migraine. This manuscript is an attempt to study the various structural, physical, and chemical properties of the molecules. The molecule was optimized using B3LYP functional with 6-311G + (2d,p) basis set. Excited state properties of the compound were studied using CAM-B3LYP functional with same basis sets using IEFPCM model in methanol for the implicit solvent atmosphere. The various electronic descriptors helped to identify the reactivity behavior and stability. The compound is found to possess good nonlinear optical properties in the gas phase. The various intramolecular electronic delocalizations and non-covalent interactions were analyzed and explained. As the compound contain several heterocyclic nitrogen atoms, they have potential proton abstraction features, which was analyzed energetically. The most important result from this study is from the molecular docking analysis which indicates that rimegepant binds irreversibly with three established SARS-CoV-2 proteins with ID 6LU7, 6M03, and 6W63 with docking scores - 9.2988, - 8.3629, and - 9.5421 kcal/mol respectively. Further assessment of docked complexes with molecular dynamics simulations revealed that hydrophobic interactions, water bridges, and π-π interactions play a significant role in stabilizing the ligand within the binding region of respective proteins. MMGBSA-free energies further demonstrated that rimegepant is more stable when complexed with 6LU7 among the selected PDB models. As the pharmacology and pharmacokinetics of this molecule are already established, rimegepant can be considered as an ideal candidate with potential for use in the treatment of COVID patients after clinical studies.

Ligand-based pharmacophore modelling and virtual screening for the identification of amyloid-beta diagnostic molecules.

Currently, only three molecules, flutemetamol, florbetaben and florbetapir, have been approved for clinical use towards the definitive diagnosis of Alzheimer's disease (AD). Despite the clinically approved drugs' advantages, there still exists a need for new diagnostic molecules with improved properties (physicochemical and pharmacokinetic) in comparison to the current molecules in clinical use and research. In this work, we report a pharmacophore model and a quantitative structure activity relationship (QSAR) model, constructed from a series of 166 amyloid beta diagnostic compounds (targeting Alzheimer's disease) with the purpose of identifying functional groups influencing and predicting bioactivity. Subsequently, pharmacophore based virtual screening and QSAR predictions were used to identify new amyloid beta diagnostic molecules. In addition, docking and molecular dynamics simulations were conducted to explore the type and nature of interactions required for ligands to bind effectively in the binding regions of amyloid beta fibrils (PDB 2MXU). In our findings, the highest-ranked 4 feature pharmacophore model possessed one hydrogen bond acceptor, one hydrophobic feature and two ring features (AHRR). Systematically, the same dataset of molecules used for pharmacophore modelling was used to generate an atom-based 3D QSAR hypothesis to illustrate the activity relationship of amyloid-beta diagnostic molecules. The partial least squares (PLS) 3D QSAR model obtained showed good correlation as indicated by respective statistical parameters, R^2, Q^2 and Pearson values of 0.76, 0.72 and 0.86 respectively. Virtual screening against ZINC15 database and the ChemBridge CNS-Set yielded 7 molecules, 4 of which had satisfactory ADME properties. Docking and molecular dynamics simulations showed that hydrogen bonding, hydrophobic and π-π interactions are crucial towards the binding of ligands (as predicted by our pharmacophore and QSAR models) to amyloid beta fibrils. In conclusion, the findings of this work present a wealth of information that can be useful in future research towards identifying and design of new amyloid diagnostic molecules. The pharmacophore presented here can be used to filter independent databases to identify new structurally related molecules with improved activity whereas the QSAR model can be useful in predicting bioactivities of the predicted hits.

Computational investigation of the binding characteristics of ß-amyloid fibrils.

Timely and accurate diagnosis of Alzheimer's disease (AD) remains a major challenge in the medical arena. ß-amyloid (Aß) imaging techniques such as positron emission tomography and single photon emission computed tomography require the use of an imaging probe. To date, only flutemetamol, florbetaben and florbetapir have been approved for clinical use as imaging probes. Design of imaging probes requires a detailed understanding of disease mechanism(s) and receptor-ligand interaction. In this study, molecular docking, molecular dynamics and binding free energies were used to investigate the multiple binding sites exhibited by ß-amyloid fibrils. Protein atomic models 2BEG, 5KK3, 2M4J, 2LMN, 5OQV, 2NAO, 2MVX and 2MXU (protein databank codes) were used to investigate the nature and location of binding sites and binding profiles of selected molecules with known affinities. Although amyloid fibrils are known to have multiple binding sites, we demonstrated that model 2MXU possesses one site which is druggable and can bind with common scaffolds currently being used in the imaging of amyloid fibrils. Models 2NAO, 5KK3 and 2M4J revealed that even though multiple sites may be available in some fibrils, the entire protein may not have a druggable site. Molecular dynamics revealed atomic models 2MXU and 2MVX to be the least flexible among the list. The outcomes of this investigation can be translated to assist in designing novel molecules that can be used for brain imaging in Alzheimer's disease.