The COVID-19 pandemic has resulted in 198 million reported infections and more than 4 million deaths as of July 2021 ( covid19.who.int ). Research to identify effective therapies for COVID-19 includes: (1) designing a vaccine as future protection; (2) de novo drug discovery; and (3) identifying existing drugs to repurpose them as effective and immediate treatments. To assist in drug repurposing and design, we determine two apo structures of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease at ambient temperature by serial femtosecond X-ray crystallography. We employ detailed molecular simulations of selected known main protease inhibitors with the structures and compare binding modes and energies. The combined structural and molecular modeling studies not only reveal the dynamics of small molecules targeting the main protease but also provide invaluable opportunities for drug repurposing and structure-based drug design strategies against SARS-CoV-2.

Monoamine oxidase (MAO) enzymes MAO-A and MAO-B play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors are very important for the treatment of several neurodegenerative diseases such as Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, 256 750 molecules from Otava Green Chemical Collection were virtually screened for their binding activities as MAO-B inhibitors. Two hit molecules were identified after applying different filters such as high docking scores and selectivity to MAO-B, desired pharmacokinetic profile predictions with binary quantitative structure-activity relationship (QSAR) models. Therapeutic activity prediction as well as pharmacokinetic and toxicity profiles were investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular dynamics (MD) simulations were also performed to make more detailed assessments beyond docking studies. All these calculations were made not only to determine if studied molecules possess the potential to be a MAO-B inhibitor but also to find out whether they carry MAO-B selectivity versus MAO-A. The evaluation of docking results and pharmacokinetic profile predictions together with the MD simulations enabled us to identify one hit molecule (ligand 1, Otava ID: 3463218) which displayed higher selectivity toward MAO-B than a positive control selegiline which is a commercially used drug for PD therapeutic purposes.

Pseudomonas aeruginosa can regulate its virulence gene expressions by using a signal system called quorum sensing. It is known that inhibition of quorum sensing can block biofilm formation and leave the bacteria defenseless. Therefore, it is necessary to determine natural sources to obtain potential quorum sensing inhibitors. This study aims to investigate an alternative treatment approach by utilizing the carotenoid zeaxanthin to reduce the expressions of P. aeruginosa virulence factors through quorum sensing inhibition. The inhibition potential of zeaxanthin was determined by in silico screening from a library of 638 lichen metabolites. Fluorescent monitor strains were utilized for quorum sensing inhibitor screens, and quantitative reverse-transcriptase PCR assay was performed for evaluating gene expression. Results indicate that zeaxanthin is a better inhibitor than the lichen secondary metabolite evernic acid, which was previously shown to be capable of inhibiting P. aeruginosa quorum sensing systems.

In this virtual drug repurposing study, we used 7922 FDA approved drugs and compounds in clinical investigation from NPC database. Both apo and holo forms of SARS-CoV-2 Main Protease as well as Spike Protein/ACE2 were used for virtual screening. Initially, docking was performed for these compounds at target binding sites. The compounds were then sorted according to their docking scores which represent binding energies. The first 100 compounds from each docking simulations were initially subjected to short (10 ns) MD simulations (in total 300 ligand-bound complexes), and average binding energies during MD simulations were calculated using the MM/GBSA method. Then, the selected promising hit compounds based on average MM/GBSA scores were used in long (100-ns and 500-ns) MD simulations. In total around 15 µs MD simulations were performed in this study. Both docking and MD simulations binding free energy calculations showed that holo form of the target protein is more appropriate choice for virtual drug screening studies. These numerical calculations have shown that the following 8 compounds can be considered as SARS-CoV-2 Main Protease inhibitors: Pimelautide, Rotigaptide, Telinavir, Ritonavir, Pinokalant, Terlakiren, Cefotiam and Cefpiramide. In addition, following 5 compounds were identified as potential SARS-CoV-2 ACE-2/Spike protein domain inhibitors: Denopamine, Bometolol, Naminterol, Rotigaptide and Benzquercin. These compounds can be clinically tested and if the simulation results validated, they may be considered to be used as treatment for COVID-19.

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The dopamine D2 receptor (D2R) plays an important part in the human central nervous system and it is considered to be a focal target of antipsychotic agents. It is structurally modeled in active and inactive states, in which homodimerization reaction of the D2R monomers is also applied. The ASP2314 (also known as ACR16) ligand, a D2R stabilizer, is used in tests to evaluate how dimerization and conformational changes may alter the ligand binding space and to provide information on alterations in inhibitory mechanisms upon activation. The administration of the D2R agonist ligand ACR16 [H](+)-4-propyl-3,4,4a,5,6,10b-hexahydro-2H-naphtho[1,2-b][1,4]oxazin-9-ol ((+)PHNO) revealed K values of 32 nM for the D2R and 52 μM for the D2R. The calculated binding affinities of ACR16 with post processing molecular dynamics (MD) simulations analyses using MM/PBSA for the monomeric and homodimeric forms of the D2R were -9.46 and -8.39 kcal/mol, respectively. The data suggests that the dimerization of the D2R leads negative cooperativity for ACR16 binding. The dimerization reaction of the D2R is energetically favorable by -22.95 kcal/mol. The dimerization reaction structurally and thermodynamically stabilizes the D2R conformation, which may be due to the intermolecular forces formed between the TM4 of each monomer, and the result strongly demonstrates dimerization essential for activation of the D2R.

Dopamine receptor D2 (D2R) plays an important role in the human central nervous system and is a focal target of antipsychotic agents. The D2R and D2R dimeric models previously developed by our group are used to investigate the prediction of binding affinity of the LY404,039 ligand and its binding mechanism within the catalytic domain. The computational data obtained using molecular dynamics simulations fit well with the experimental results. The calculated binding affinities of LY404,039 using MM/PBSA for the D2R and D2R targets were -12.04 and -9.11 kcal/mol, respectively. The experimental results suggest that LY404,039 binds to D2R and D2R with binding affinities (K) of 8.2 and 1640 nM, respectively. The high binding affinity of LY404,039 in terms of binding to [H]domperidone was inhibited by the presence of a guanine nucleotide, indicating an agonist action of the drug at D2R. The interaction analysis demonstrated that while Asp114 was among the most critical amino acids for D2R binding, residues Ser193 and Ser197 were significantly more important within the binding cavity of D2R. Molecular modeling analyses are extended to ensemble docking as well as structure-based pharmacophore model (E-pharmacophore) development using the bioactive conformation of LY404,039 at the binding pocket as a template and screening of small-molecule databases with derived pharmacophore models.