Doxycycline prevents blood-brain barrier dysfunction and microvascular hyperpermeability after traumatic brain injury.

The main objective of this study was to determine the cellular and molecular effects of doxycycline on the blood-brain barrier (BBB) and protection against secondary injuries following traumatic brain injury (TBI). Microvascular hyperpermeability and cerebral edema resulting from BBB dysfunction after TBI leads to elevation of intracranial pressure, secondary brain ischemia, herniation, and brain death. There are currently no effective therapies to modulate the underlying pathophysiology responsible for TBI-induced BBB dysfunction and hyperpermeability. The loss of BBB integrity by the proteolytic enzyme matrix metalloproteinase-9 (MMP-9) is critical to TBI-induced BBB hyperpermeability, and doxycycline possesses anti-MMP-9 effect. In this study, the effect of doxycycline on BBB hyperpermeability was studied utilizing molecular modeling (using Glide) in silico, cell culture-based models in vitro, and a mouse model of TBI in vivo. Brain microvascular endothelial cell assays of tight junction protein immunofluorescence and barrier permeability were performed. Adult C57BL/6 mice were subjected to sham versus TBI with or without doxycycline treatment and immediate intravital microscopic analysis for evaluating BBB integrity. Postmortem mouse brain tissue was collected to measure MMP-9 enzyme activity. It was found that doxycycline binding to the MMP-9 active sites have binding affinity of -7.07 kcal/mol. Doxycycline treated cell monolayers were protected from microvascular hyperpermeability and retained tight junction integrity (p < 0.05). Doxycycline treatment decreased BBB hyperpermeability following TBI in mice by 25% (p < 0.05). MMP-9 enzyme activity in brain tissue decreased with doxycycline treatment following TBI (p < 0.05). Doxycycline preserves BBB tight junction integrity following TBI via inhibiting MMP-9 activity. When established in human subjects, doxycycline, may provide readily accessible medical treatment after TBI to attenuate secondary injury.

Discovery of Anticancer Hybrid Molecules by Supervised Machine Learning Models and in Vitro Validation in Drug Resistant Chronic Myeloid Leukemia Cells.

The concept of hybrid drugs for targeting multiple aberrant pathways of cancer, by combining the key pharmacophores of clinically approved single-targeted drugs, has emerged as a promising approach for overcoming drug-resistance. Here, we report the design of unique hybrid molecules by combining the two pharmacophores of clinically approved BCR-ABL inhibitor (ponatinib) and HDAC inhibitor (vorinostat) and results of in vitro studies in drug-resistant CML cells. Robust 2D-QSAR and 3D-pharmacophore machine learning supervised models were developed for virtual screening of the hybrid molecules based on their predicted BCR-ABL and HDAC inhibitory activity. The developed 2D-QSAR model showed five information rich molecular descriptors while the 3D-pharmacophore model of BCR-ABL showed five different chemical features (hydrogen bond acceptor, donor, hydrophobic group, positive ion group, and aromatic rings) and the HDAC model showed four different chemical features (hydrogen bond acceptor, donor, positive ion group, and aromatic rings) for potent BCR-ABL and HDAC inhibition. Virtual screening of the 16 designed hybrid molecules identified FP7 and FP10 with better potential of inhibitory activity. FP7 was the most effective molecule with predicted IC50 using the BCR-ABL based 2D-QSAR model of 0.005 µM and that of the HDAC model of 0.153 µM, and that using the BCR-ABL based 3D-pharmacophore model was 0.02 µM and that with HDAC model was 0.014 µM. In vitro study (dose-response relationship) of FP7 in wild type and imatinib-resistant CML cell lines harboring Thr315Ile or Tyr253His mutations showed growth inhibitory IC50 values of 0.000 16, 0.0039, and 0.01 µM, respectively. This molecule also showed better biocompatibility when tested in whole blood and in PBMCs as compared to ponatinib or vorinostat.

A phytochemical-based medication search for the SARS-CoV-2 infection by molecular docking models towards spike glycoproteins and main proteases.

Identifying best bioactive phytochemicals from different medicinal plants using molecular docking techniques demonstrates a potential pre-clinical compound discovery against SARS-CoV-2 viral infection. The in silico screening of bioactive phytochemicals with the two druggable targets of SARS-CoV-2 by simple precision/extra precision molecular docking methods was used to compute binding affinity at its active sites. phyllaemblicin and cinnamtannin class of phytocompounds showed a better binding affinity range (-9.0 to -8.0 kcal mol-1) towards both these SARS-CoV-2 targets; the corresponding active site residues in the spike protein were predicted as: Y453, Q496, Q498, N501, Y449, Q493, G496, T500, Y505, L455, Q493, and K417; and Mpro: Q189, H164, H163, P168, H41, L167, Q192, M165, C145, Y54, M49, and Q189. Molecular dynamics simulation further established the structural and energetic stability of protein-phytocompound complexes and their interactions with their key residues supporting the molecular docking analysis. Protein-protein docking using ZDOCK and Prodigy server predicted the binding pose and affinity (-13.8 kcal mol-1) of the spike glycoprotein towards the human ACE2 enzyme and also showed significant structural variations in the ACE2 recognition site upon the binding of phyllaemblicin C compound at their binding interface. The phyllaemblicin and cinnamtannin class of phytochemicals can be potential inhibitors of both the spike and Mpro proteins of SARS-CoV-2; furthermore, its pharmacology and clinical optimization would lead towards novel COVID-19 small-molecule therapy.

Predicting the Molecular Mechanism of EGFR Domain II Dimer Binding Interface by Machine Learning to Identify Potent Small Molecule Inhibitor for Treatment of Cancer.

Epidermal growth factor receptor (EGFR) is a transmembrane druggable target controlling cellular differentiation, proliferation, migration, survival and invasion. EGFR activation mainly occurs by its homo/hetro dimerization molecular phenomenon leading to tumor development and invasion. Several tyrosine kinase based inhibitors were discovered as potent anti-cancer drugs. However, mutations in its kinase domain confer resistance to most of these drugs. To overcome this drug resistance, development of small molecule inhibitors disrupting the EGFR Domain II dimer binding by machine learning methods are promising. Based on this insight, a structure-based drug repurposing strategy was adopted to repurpose the existing FDA approved drugs in blocking the EGFR Domain II mediated dimerization. We identified five best repurposed drug molecules showing good binding affinity at its key arm-cavity dimer interface residues by different machine learning methods. The molecular mechanisms of action of these repurposed drugs were computationally validated by molecular electrostatics potential mapping, point mutations at the dimer arm-cavity binding interface, molecular docking and receptor interaction studies. The present machine learning strategy thus forms the basis of identifying potent and putative small molecule drugs for the treatment of different types of cancer.

Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition.

The bacterial ribosome is an established target for anti-bacterial therapy since decades. Several inhibitors have already been developed targeting both defined subunits (50S and 30S) of the ribosome. Aminoglycosides and tetracyclines are two classes of antibiotics that bind to the 30S ribosomal subunit. These inhibitors can target multiple active sites on ribosome that have a complex structure. To screen putative inhibitors against 30S subunit of the ribosome, the crystal structures in complex with various known inhibitors were analyzed using pharmacophore modeling approach. Multiple active sites were considered for building energy-based three-dimensional (3D) pharmacophore models. The generated models were validated using enrichment factor on decoy data-set. Virtual screening was performed using the developed 3D pharmacophore models and molecular interaction towards the 30S ribosomal unit was analyzed using the hits obtained for each pharmacophore model. The hits that were common to both streptomycin and paromomycin binding sites were identified. Further, to predict the activity of these hits a robust 2D-QSAR model with good predictive ability was developed using 16 streptomycin analogs. Hence, the developed models were able to identify novel inhibitors that are capable of binding to multiple active sites present on 30S ribosomal subunit.

Predictive models for designing potent tyrosine kinase inhibitors in chronic myeloid leukemia for understanding its molecular mechanism of resistance by molecular docking and dynamics simulations.

BCR-ABL fusion protein drives chronic myeloid leukemia (CML) which constitutively activates tyrosine kinase involved in the initiation and maintenance of CML phenotype. Ponatinib, an oral drug, was discovered as an efficient BCR-ABL inhibitor by addressing imatinib drug resistance arising due to the point mutations at its active sites. In this study, 44 BCR-ABL kinase inhibitors, which are derivatives of ponatinib, were used to develop a robust two-dimensional quantitative structure-activity relationship (2D-QSAR) and 3D-Pharmacophore models by dividing dataset into 32 training sets and 12 test set molecules. 2D-QSAR model was developed using Genetic Function Approximation (GFA) algorithm consisting of four types of information-rich molecular descriptors, electrotopological (ES_Count_aasN and ES_Sum_aaaC), electronic (Dipole_X), spatial (PMI_Y) and thermodynamic (LogD), primarily contributing to BCR-ABL kinase inhibitory activity. For the best 2D-QSAR model, the statistics were R2 = 0.8707, R2pred = 0.8142 and N = 32 for the training set molecules. Phase module of Schrödinger suit was employed for 3D-Pharmacophore model development showing five different pharmacophoric features - ADHHPRR with good R2 of 0.9629, F of 175.3, Q2 of 0.645 and root-mean-square error (RMSE) of 0.214 that are essential for an effective BCR-ABL kinase inhibition. These two models were further validated by cross-validation, test set predictions, enrichment factor calculations and predictions based on the external dataset. The molecular mechanism of resistance arising due to gate keeper mutation T315I of ABL kinase in complex with its inhibitors was also studied using molecular docking and molecular dynamics simulations. Our developed models predicted key chemical features for designing potent inhibitors against BCR-ABL kinase activity and its resistance mechanism to CML disease therapy. Communicated by Ramaswamy H. Sarma.

Structure-function studies of prothrombin Amrita, a dysfunctional prothrombin characterized by point mutation at Arg553 → Gln.

A dysfunctional prothrombin gene characterized by novel point mutation at Arg553 to Gln residue in Deep vein thrombosis (DVT) patient which we designated as "Prothrombin Amrita" was previously reported from our lab. The mutation occurred at nucleotide 20030 in exon 14 and was confirmed by restriction enzyme digestion. Arg553 has been reported as one of the key residues for the binding of cofactor Na+ ion in the thrombin protein. Structural analysis revealed the molecular mechanism behind the coagulant form of thrombin due to point Arg553Gln mutation near the cofactor Na+ ion region. Molecular electrostatic potential maps and molecular dynamics (MD) simulation of the wild type and mutated thrombin showed the key role played by the Na+ ion for its coagulant mechanism by analysing the charge distribution and nature of the hydrogen bonding at the mutated region of interest. We observed maintenance of the fast or procoagulant form of dysfunctional prothrombin due to changes in the charge distribution by this mutation and thereby also keeping strong hydrogen bonding network revealed by MD simulation between prothrombin and Na+ ion. This molecular mechanism might be the main cause for DVT in patients with this dysfunctional prothrombin gene.

Computational simulations and experimental validation of structure- physicochemical properties of pristine and functionalized graphene: Implications for adverse effects on p53 mediated DNA damage response.

Recent reports indicated DNA damaging potential of few-layer graphene in human cell systems. Here, we used computational technique to understand the interaction of both pristine (pG) or carboxyl functionalized graphene (fG) of different sizes (1, 6, and 10nm) with an important DNA repair protein p53. The molecular docking study revealed strong interaction between pG and DNA binding domains (DBD) of p53 with binding free energies (BE) varying from -12.0 (1nm) to -34 (6nm)kcal/mol, while fG showed relatively less interaction with BE varying from -6.7 (1nm) to -11.1 (6nm)kcal/mol. Most importantly, pG or fG bound p53-DBDs could not bind to DNA. Further, microarray analysis of human primary endothelial cells revealed graphene intervention on DNA damage and its structure-properties effect using comet assay studies. Thus, computational and experimental results revealed the structure-physicochemical property dependent adverse effects of graphene in DNA repair protein p53.

Epidermal growth factor receptor (EGFR) structure-based bioactive pharmacophore models for identifying next-generation inhibitors against clinically relevant EGFR mutations.

Present work elucidates identification of next generation inhibitors for clinically relevant mutations of epidermal growth factor receptor (EGFR) using structure-based bioactive pharmacophore modeling followed by virtual screening (VS) techniques. Three-dimensional (3D) pharmacophore models of EGFR and its different mutants were generated. This includes seven 3D pharmacophoric points with three different chemical features (descriptors), that is, one hydrogen bond donor, three hydrogen bond acceptors and three aromatic rings. Pharmacophore models were validated using decoy dataset, Receiver operating characteristic plot, and external dataset compounds. The robust, bioactive 3D e-pharmacophore models were then used for VS of four different small compound databases: FDA approved, investigational, anticancer, and bioactive compounds collections of Selleck Chemicals. CUDC101 a multitargeted kinase inhibitor showed highest binding free energy and 3D pharmacophore fit value than the well known EGFR inhibitors, Gefitinib and Erlotinib. Further, we obtained ML167 as the second best hit on VS from bioactive database showing high binding energy and pharmacophore fit value with respect to EGFR receptor and its mutants. Optimistically, presented drug discovery based on the computational study serves as a foundation in identifying and designing of more potent EGFR next-generation kinase inhibitors and warrants further experimental studies to fight against lung cancer.

In silico identification of T-type calcium channel blockers: A ligand-based pharmacophore mapping approach.

Limited progress has been made in the quest to identify both selective and non-toxic T-type calcium channel blocking compounds. The present research work was directed toward slaking the same by identifying the selective three dimensional (3D) pharmacophore map for T-type calcium channel blockers (CCBs). Using HipHop module in the CATALYST 4.10 software, both selective and non-selective HipHop pharmacophore maps for T-type CCBs were developed to identify its important common pharmacophoric features. HipHop pharmacophore map of the selective T-type CCBs contained six different chemical features, namely ring aromatic (R), positive ionizable (P), two hydrophobic aromatic (Y), hydrophobic aliphatic (Z), hydrogen bond acceptor (H) and hydrogen bond donor (D). However, non-selective T-type CCBs contain all the above mentioned features except ring aromatic (R). The present ligand-based pharmacophore mapping approach could thus be utilized in classifying selective vs. non-selective T-type CCBs. Further, the model can be used for virtual screening of several small molecule databases.