Investigation of inhibition of human glucose 6-phosphate dehydrogenase by some 99mTc chelators by in silico and in vitro methods.

The inhibitory effects of methoxyisobutylisonitrile (MIBI), diethylene triamine pentaacetic acid (DTPA), dimercaptosuccinic acid (DMSA) and metilendifosfonat (MDP) on human erythrocyte glucose 6-phosphate dehydrogenase (hG6PD) activity were investigated. For this purpose, hG6PD was initially purified 557-fold at a yield of 51.43% using 2',5'-adenosine diphosphate (ADP) sepharose 4B affinity gel chromatography. The in vitro effects of these chelators on hG6PD enzyme were studied. IC50 values of MIBI, DTPA, DMSA and MDP were 0.056, 0.172, 0.274 and 0.175 mM, of hG6PD, respectively. It was detected in in vitro studies that the hG6PD enzyme is inhibited due to these radiopharmaceutical chelators. In addition to in vitro studies, in order to better understand the molecular mechanism of studied compounds, combined in silico approaches, including molecular docking and molecular dynamics (MD), simulations were successfully performed. MD simulations shed light on inhibition mechanisms of the individual inhibitors into the ligand-binding pocket of hG6PD. Essential amino acids for binding are also investigated using per-residue interaction analysis studies.

Kinetic and docking studies of cytosolic/tumor-associated carbonic anhydrase isozymes I, II and IX with some hydroxylic compounds.

A series of hydroxylic compounds (1-10, NK-154 and NK-168) have been assayed for the inhibition of three physiologically relevant carbonic anhydrase isozymes, the cytosolic isozymes I, II and tumor-associated isozyme IX. The investigated compounds showed inhibition constants in the range of 0.068-4003, 0.012-9.9 and 0.025-115 µm at the hCA I, hCA II and hCA IX enzymes, respectively. In order to investigate the binding mechanisms of these inhibitors, in silico studies were also applied. Molecular docking scores of the studied compounds are calculated using scoring algorithms, namely Glide/induced fit docking. The inhibitory potencies of the novel compounds were analyzed at the human isoforms hCA I, hCA II and hCA IX as targets and the KI values were calculated.

Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling.

Observed mass shifts associated with deuterium incorporation in hydrogen-deuterium exchange mass spectrometry (HDX-MS) frequently deviate from the initial signals due to back and forward exchange. In typical HDX-MS experiments, the impact of these disparities on data interpretation is generally low because relative and not absolute mass changes are investigated. However, for more advanced data processing including optimization, experimental error correction is imperative for accurate results. Here the potential for automatic HDX-MS data correction using models generated by deep neural networks is demonstrated. A multilayer perceptron (MLP) is used to learn a mapping between uncorrected HDX-MS data and data with mass shifts corrected for back and forward exchange. The model is rigorously tested at various levels including peptide level mass changes, residue level protection factors following optimization, and ability to correctly identify native protein folds using HDX-MS guided protein modeling. AI is shown to demonstrate considerable potential for amending HDX-MS data and improving fidelity across all levels. With access to big data, online tools may eventually be able to predict corrected mass shifts in HDX-MS profiles. This should improve throughput in workflows that require the reporting of real mass changes as well as allow retrospective correction of historic profiles to facilitate new discoveries with these data.

Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models.

An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS.

Integration of multi-scale molecular modeling approaches with experiments for the in silico guided design and discovery of novel hERG-Neutral antihypertensive oxazalone and imidazolone derivatives and analysis of their potential restrictive effects on cell proliferation.

AT1 antagonists is the most recent drug class of molecules against hypertension and they mediate their actions through blocking detrimental effects of angiotensin II (A-II) when acts on type I (AT1) A-II receptor. The effects of AT1 antagonists are not limited to cardiovascular diseases. AT1 receptor blockers may be used as potential anti-cancer agents - due to the inhibition of cell proliferation stimulated by A-II. Therefore, AT1 receptors and the A-II biosynthesis mechanisms are targets for the development of new synthetic drugs and therapeutic treatment of various cardiovascular and other diseases. In this work, multi-scale molecular modeling approaches were performed and it is found that oxazolone and imidazolone derivatives reveal similar/better interaction energy profiles compared to the FDA approved sartan molecules at the binding site of the AT1 receptor. In silico-guided designed hit molecules were then synthesized and tested for their binding affinities to human AT1 receptor in radioligand binding studies, using [125I-Sar1-Ile8] AngII. Among the compounds tested, 19d and 9j molecules bound to receptor in a dose response manner and with relatively high affinities. Next, cytotoxicity and wound healing assays were performed for these hit molecules. Since hit molecule 19d led to deceleration of cell motility in all three cell lines (NIH3T3, A549, and H358) tested in this study, this molecule is investigated in further tests. In two cell lines (HUVEC and MCF-7) tested, 19d induced G2/M cell cycle arrest in a concentration dependent manner. Adherent cells detached from the plates and underwent cell death possibly due to apoptosis at 19d concentrations that induced cell cycle arrest.

Identification of novel serotonin reuptake inhibitors targeting central and allosteric binding sites: A virtual screening and molecular dynamics simulations study.

The serotonin (5-hydroxytryptamine, 5HT) transporter (SERT) is a member of neurotransmitter sodium symporter (NSS) family, which maintains neurotransmitter by reuptaking 5HT into synapses. Decrease in serotonin concentrations in synaptic clefts have been reported to cause psychological and neurological disorders. Therefore, inhibition of SERT is a potent strategy for the treatment of related diseases such as depression. In this study, approximately 260,000 small molecules from an available chemical database have been virtually screened both at central and allosteric binding sites of SERT to identify potent novel candidate SERT inhibitors. A set of docking algorithms were used to predict binding modes and energies of compounds. Screening analyses led three top-ranked hit compounds (160234, Otava ID: 7118020138; 159166, Otava ID: 7117171303; and 69419, Otava ID: 118671819) for central binding site (S1) and one compound (93507, Otava ID: 6248262) for allosteric binding site (S2). These promising compounds are then subjected to long multiple molecular dynamics (MD) simulations to elucidate their structural and dynamical profiles at the binding cavities of SERT. Higher predicted binding affinities of identified compounds were also confirmed with binding free energy calculations (MM/GBSA) in comparison with the reference central and allosteric binding site inhibitors, paroxetine (8PR) and escitalopram (68P), respectively. To the best of our knowledge, the present work is the first structure-based high throughput virtual screening study reported using recently revealed crystal structure of SERT for screening inhibitors from chemical databases on S1 and S2 binding sites. Small molecule library screening study yielded candidate compounds both at central and allosteric binding site of SERT, and further experimentation may pave the way for developing novel strong inhibitors.