In silico predictive model to determine vector-mediated transport properties for the blood–brain barrier choline transporter

Abstract

The blood–brain barrier choline transporter (BBB-ChT) may have utility as a drug delivery vector to the central nervous system (CNS). We therefore initiated molecular docking studies with the AutoDock and AutoDock Vina (ADVina) algorithms to develop predictive models for compound screening and to identify structural features important for binding to this transporter. The binding energy predictions were highly correlated with r2=0.88, F=692.4, standard error of estimate =0.775, and P-value<0.0001 for selected BBB-ChT-active/inactive compounds (n=93). Both programs were able to cluster active (Gibbs free energy of binding <−6.0 kcal*mol−1) and inactive (Gibbs free energy of binding >−6.0 kcal*mol−1) molecules and dock them significantly better than at random with an area under the curve value of 0.86 and 0.84, respectively. In ranking smaller molecules with few torsional bonds, a size-related bias in scoring producing false-negative outcomes was detected. Finally, important blood–brain barrier parameters, such as the logBBpassive and logBBactive values, were assessed to predict compound transport to the CNS accurately. Knowledge gained from this study is useful to better understand the binding requirements in BBB-ChT, and until such time as its crystal structure becomes available, it may have significant utility in developing a highly predictive model for the rational design of drug-like compounds targeted to the brain.

Interaction studies of cholinium-based ionic liquids with calf thymus DNA: Spectrophotometric and computational methods

“…The resulting binding energy of the docked structure was found to be -3.86 kcal/mol, which is quite low. [57] Interestingly, redocking using Autodock vina program [58] produced similar results (binding affinity -3.9 kcal/mol with selectivity towards minor grooves, Fig. S3 supplementary information), indicating the accuracy of the tool as well affinity of the probes towards a biopolymer.…”

The steroid derivative 6-aminocholestanol inhibits the DEAD-box helicase eIF4A (LieIF4A) from the Trypanosomatid parasite Leishmania by perturbing the RNA and ATP binding sites

“…However, we independently found through 14 molecular modeling, molecular dynamics and docking simulations that 6-aminocholestanol 15 could target the L. infantum LieIF4A protein, which is a probable ortholog of the translation-16 initiation factor eIF4A [20, 23]. Moreover, it shows structural similarity to the previously 17 characterized hippuristanol that is an allosteric inhibitor of RNA binding in eukaryotic 18 eIF4A-like proteins that locks the RecA-like domains in a nonproductive closed conformation 19 [19]. 20…”

Thioguanine-based DENV-2 NS2B/NS3 protease inhibitors: Virtual screening, synthesis, biological evaluation and molecular modelling

“…The 24 top hit compounds were investigated for their interaction with the active site's residues and ranked in term of the free energy of binding (ΔG bind = -10 kcal/mol to -5 kcal/mol, see S1 Table. We identified that there are more than 50 ligands docked into the active site with that ΔG bind range, but only 24 of them shows interesting interactions with the essential amino acid residues. The selection of ΔG bind range is adopted from the study reported by Shityakov (2014) that Gibbs free energy of binding < 6.0 kcal/mol is clustered as active when this prediction is highly correlated with the experimental results with R 2 = 0.880; F = 692.4 standard error of estimate = 0.775 and p-value = 0.0001 [58]. This virtual screening study has a limitation in which no decoy database is being used to validate the docking protocol.…”