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Biomedical subjects

H L Jiang

Publications and source records attributed to H L Jiang.

At least 37 records · Page 2Linked to original sources
PubMed2000

Study on mechanism of interaction of nociceptin and opioids binding with opioid receptor-like 1 receptor.

AIM: To study the mechanism of interaction of nociceptin and opioids with ORL1 receptor. METHODS: Molecular dynamics study was carried out before nociceptin was manually docked into the binding site of ORL1 receptor; DOCK4.0 program was applied to dock four stereoisomers of lofentanyl and etorphine into the binding pocket of ORL1 receptor; Binding energies were calculated, the relationship between binding energy and binding affinity was studied. RESULTS: Nociceptin fits well into the binding pocket, the N-terminal FGGF tetrapeptide is located in the inner region of the binding cavity, the nociceptin (5-7) interacts with the conservatively variable residues near the other end of binding pocket, and maybe determines selectivity of ORL1 receptor over dynorphin A, the positively charged core of nociceptin (8-13) binds predominantly with negatively charged EL-2 loop, which is thought to be able to mediate receptor activation. The shortest fully active analogue of nociceptin (1-13) is also discussed. The main difference between these two opioids and nociceptin exists in the kinds and the number of conserved and variable residues in the binding pocket and thereafter in the strength of their interaction. Prediction for binding affinities of four stereoisomers of lofentanyl has been performed based on their binding energies, the similar pharmacophore of lofentanyl and other fentanyl analogs, and the good correlation between binding energies and their experimental binding affinities (-log Ki values). CONCLUSION: Ligand docking results from this study are helpful in clarifying experimental observations of ligands interaction with opioid receptors, thus furthering biological investigations.

Binding Sites↗
PubMed2000

Building 3D-structural model of kappa opioid receptor and studying its interaction mechanism with dynorphin A(1-8).

AIM: To construct the 3D-structural model of human kappa opioid receptor (HKOR) and study its interacting mechanism with dynorphin A(1-8) (Dyn8). METHODS: Comparative molecular modeling was applied to build the 7 transmembrane (TM) helical domain of HKOR using the bovine rhodopsin (OPSD) model as a template. Molecular dynamics was performed to minimize the HKOR model and to simulate the 3D-structure of Dyn8 based on the NMR results of dynorphin A(1-14). The extracellular loops (EL) were built by self-constructed database searching. DOCK4.0 program was performed to construct Dyn8 complex with HKOR. RESULTS: (1) The model of HKOR was obtained and validated by theoretical and experimental data. (2) The Dyn8-HKOR interacting mechanism is reasonably explained: Side chain of residue Asp138 interacts with protonated nitrogen atom at the N-terminal residues of Dyn8 through electrostatic and hydrogen bonding, which play an important role in ligand binding with receptor. (3) Negatively charged amino acids in the second extracellular loop (EL2) as Asp223 and Glu209 interact with the C-terminal positively charged residues in Dyn8, and Glu209 is a likely determinant of peptide ligand specificity. CONCLUSION: Some amino acid residues positioned in EL2, TM3, TM4, and TM5 form the binding site and therefore determine the selectivity of kappa peptide agonist.

Amino Acid Sequence↗
PubMed1999

Molecular modeling on kappa opioid receptor and its interaction with nonpeptide kappa opioid agonists.

AIM: To study the interaction between kappa-opioid receptor and its nonpeptide agonists. METHODS: The "conservation patterns" for G-protein coupled receptors (GPCR) were used to determine 7 transmembrane (TM) regions. Taking the crystallographic coordinates of bacteriorhodopsin (BR) as the template, the 3D structural model was constructed for 7 TM of kappa-opioid subtype with molecular mechanics (MM) method. Five highly active nonpeptide kappa-opioid agonists were docked into the 7 helices of kappa-opioid receptor to study the ligand-receptor interaction. RESULTS: Four important interactions between U-50488-like agonists and kappa-opioid receptors were drawn according to our modeling study: (1) the protonated pyrrolidine nitrogen of the ligands formed a hydrogen-bond with the carboxyl of Asp138; (2) the carbonyl oxygen of ligands forms a hydrogen bond to the hydroxyl of Ser187; (3) the aryl groups connected to acylamide of the agonists inserted into a hydrophobic cavity enclosed by residues Val239, Val236, Phe235, Val232, Leu186, and Trp183; (4) the pyrrolidine of the ligands in the complexes was surrounded by Ile290, Asp138, Ile194, Ile135, and Cys131. CONCLUSION: The proposed interaction mechanism is helpful for further mutant experiments and designing novel potent kappa-opioid agonists.

3,4-Dichloro-N-methyl-N-(2-(1-pyrrolidinyl)-cycloh↗
PubMed1998

A 3D-QSAR study on ginkgolides and their analogues with comparative molecular field analysis.

Comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) paradigm was used to study the correlation between the physicochemical properties and the in vitro bioactivities of ginkgolide analogues. The correlation derived from CoMFA analysis has a good predictive capability. Based on the result of CoMFA analysis, we designed some compounds. Pharmacological assay indicated that three of these new designed compounds are 2 and 4 times more potent than that of ginkgolides.

Diterpenes↗
PubMed1998

Analysis of electronic structures of physostigmine analogs.

AIM: To elucidate the action mechanism and structural prerequisites of 21 physostigmine analogs as acetylcholinesterase inhibitors at the molecular level, and help the rational design of these dihydroindoline inhibitors. METHODS: Initial structures of these compounds were built and minimized by SYBYL 6.2 molecular modeling software. Conformations of those molecules with the highest predictive abilities in the Comparative Molecular Field Analysis model were chosen to the semiempirical quantum chemical calculations. RESULTS: (1) The highest occupied molecular orbital (HOMO) consisted mainly of the orbitals in phenyl group and N1 atom; the lowest unoccupied molecular orbital (LUMO) of the molecules was contributed from phenyl group and C11 atom. While the HOMO energies did not show any recognizable relationship with activity, the LUMO energies showed a decreased tendency with increasing activity. The active compounds showed lower LUMO energies. (2) The carbon atom (C11) had the most positive net atom charge. The most active compound had the most positive charge on this carbon, but had the lower charges on the carbonyl oxygen (O12) which was the most negative charge atom. (3) The bond order of carbon-oxygen bond (C11-O10) was invariant across the series of the compounds. (4) Compounds with too high or too low total dipole moment had lower activities, while the most active one had a lower molecular polarizability. CONCLUSION: A molecular model was suggested to explain the possible mode of action by which these compounds inhibit acetylcholinesterase.

Cholinesterase Inhibitors↗
PubMed1998

Establishment of kappa opioid receptor agonists pharmacophore with molecular modeling method.

AIM: To build up nonpeptide kappa-opioid receptor agonists pharmacophore. METHODS: Five structurally diverse, highly active nonpeptide kappa-opioid agonists were retrieved from MDL MDDR database. Molecular mechanics method were used to seek out 50 lower energy conformations for each compound. Taking nitrogen atom of pyrrolidine and acyl acetamide as overlay points, 5 agonists were superimposed to each other with their most favorite conformation. From the overlay map, the structure specificity of nonpeptide kappa-opioid receptor agonists were elucidated. RESULTS: According to this pharmacophore, the pyrrolidine ring, the carbonyl group of acyl acetamide, and the hydrophobic group attached to acyl acetamide were suggested to be the structure-specific moieties of kappa-opioid agonists. Moreover, by comparing kappa 1-opioid receptor sequence of mouse with other G-protein-coupled receptors, we determined those conserve residues existing on transmembrane regions which might interact with the suggested groups. The carboxyl of Asp138 might interact with N atom of pyrrolidine by forming a hydrogen bond. The hydroxyl of Ser187 and the carbonyl group of kappa-opioid agonists might form another hydrogen bond, which was critical for its kappa selective affinity. The hydrophobic group attached to acyl acetamide might have hydrophobic interaction with aromatic residues of kappa-opioid receptors. CONCLUSION: These kappa agonists pharmacophore were helpful to select specific positions in the lead compounds to be occupied by hydrophobic moieties to limit their ability to across the blood-brain barrier.

Amino Acid Sequence↗
PubMed1997

Binding conformers searching method for ligands according to the structures of their receptors and its application to thrombin inhibitors.

AIM: To develop a method of finding binding conformers for ligands according to the three-dimensional structures of their receptors. METHODS: Combining the systematic search method of ligand with the molecular docking approach of ligand fitting into its receptor, we developed a binding conformer searching method for ligands. RESULTS: The binding conformers of phosphonopeptidyl thrombin inhibitors were recognized. The binding (interaction) energies between these inhibitors and thrombin were calculated with molecular mechanical method. CONCLUSION: Both of the total binding energies and steric binding energies have good correlations with the inhibitory activities of these thrombin inhibitors, demonstrating that our approach is reasonable. It can also be used to explain the inhibition mechanism of thrombin interacting with these inhibitors.

Binding Sites↗
PubMed1997

Molecular modeling of mu opioid receptor and receptor-ligand interaction.

AIM: To construct the 3D structural model of mu opioid receptor (mu OR) and study the interaction between mu OR and fentanyl derivatives. METHODS: The 3D structure of mu OR was modeled using the bacteriorhodopsin (bRh) as a template, in which the alignments of transmembrane (TM) of bRh and mu OR were achieved by scoring the alignment between the amino acid sequence of mu OR and the structure of bRh. The fentanyl derivatives were docked into the 7 helices of mu OR and the binding energies were calculated. RESULTS: (1) The receptor-ligand interaction models were obtained for fentanyl derivatives. (2) In these models, the fundamental binding sites were possibly Asp147 and His297. The negatively charged oxygen of Asp147 and the positively charged ammonium group of ligand formed the potent electrostatic and hydrogen-binding interactions. Whereas the interactions between the positively charged nitrogen of His297 and the carbonyl oxygen of ligand were weak. In addition, there were some pi-pi interactions between the receptor and the ligand. (3) The binding energies of the receptor-ligand complexes had a good correlation with the analgesic activities (-lg ED50) of the fentanyl derivatives. CONCLUSION: This model is helpful for understanding the receptor-ligand interaction and for designing novel mu OR selective ligands.

Amino Acid Sequence↗
PubMed1997

Molecular modeling of interaction between delta opioid receptor and 3-methylfentanylisothiocyanate.

AIM: To construct a 3D structural model of delta opioid receptor (delta OR) and study its interaction with 3-methylfentanylisothiocyanate (SuperFIT). METHODS: Using the bacteriohodopsin as a template, the 3D structure of delta OR was modeled; SuperFIT was docked into its inside. RESULTS: The interaction model between delta OR and (3R, 4S)-SuperFIT was achieved, in which the important binding sites possibly were Asp128, Ser106, Phe104, Tyr308, and Pro315. Asp128 formed the electrostatic and hydrogen-binding interactions with the protonated nitrogen on piperidine of the ligand. Ser106 formed the electrostatic interaction with the N atom of isothiocyano group of the ligand; whereas Phe104, Tyr308, and Pro315 formed the hydrophobic interactions with the S atom of isothiocyano group. In addition, there were some other interactions between delta OR and the ligand. CONCLUSION: The residues Phe104, Tyr308, Pro315, and Ser106 of delta OR are crucial to the delta selectivity of the ligand, which is beneficial for designing novel delta-selective ligand.

Amino Acid Sequence↗
PubMed1997

Interaction models of 3-methylfentanyl derivatives with mu opioid receptors.

AIM: To study the interaction model of 3-methylfentanyl derivatives with mu opioid receptor. METHODS: After a systematic conformational search, a three-dimensional quantitative structure-activity relationship study was carried out with comparative molecular field analysis (CoMFA). RESULTS: 1) The 6 CoMFA models had good predictive values and each model corresponded to the minimum-energy conformations of 13 compounds studied; 2) The important geometric parameters of mu pharmacophore d1 (A), d2 (A), d3 (A), d4 (A), d5 (A), and d6 (A) were 5.2, 5.4, 4.9, 10.6, 10.2, and 5.8 in Model A; 5.2, 6.5, 3.6, 10.6, 11.6, and 5.8 in Model B; 5.2, 4.6, 4.9, 11.6, 9.2, and 6.5 in Model C; 5.2, 5.4, 4.9, 10.5, 10.3, and 5.8 in Model D; 3.6, 5.4, 4.9, 5.7, 7.5, and 5.7 in Model E; 5.2, 4.7, 4.9, 11.2, 9.5, and 6.4 in Model F, respectively. CONCLUSIONS: The several bioactive conformations of fentanyl analogs possibly existed and did not need to be the absolute minimum-energy conformation, each of which was involved in the interaction with mu opioid receptor.

Analgesics↗
PubMed1997

Structure-activity relationship of channel binding affinity of quaternary ammonium ions.

AIM: To explore the structure-activity relationship of quaternary ammonium (QA) ions at the external binding site of K+ channel. METHODS: InsightII and MOPAC 6.0 molecular modeling package were used to calculate the free energy of hydration (delta Ghydration), the energy of the highest occupied orbital (EHOMO), and the energy of the lowest unoccupied orbital (ELUMO) for each QA ion, respectively. The partial least square method was used to analyze the relationship between the binding free energy and these descriptive parameters. RESULTS: Generally, the higher the ELUMO of a QA ion was, the weaker its solvation was and accordingly the stronger binding affinity. For a QA ion larger than tetraethylammonium (TEA), its large size was unfavorable to its channel binding affinity. CONCLUSION: The binding affinity of all QA ions correlated well with delta Ghydration and ELUMO.

Binding Sites↗
PubMed1996

Molecular modeling of interactions between tetrahydroprotoberberines and dopamine receptors.

AIM: To build up the structure models of dopamine receptors, then combined with the receptor models, to investigate the action mechanism of tetrahydroprotoberberines (THPB) on dopamine receptors at the molecular level. METHODS: Using the three-dimensional structure of bacteriorhodopsin as a template, we have constructed dopamine D1 and D2 receptor models on computer. l-Stepholidine was selected as the leading compound of THPB and docked into D1 and D2 receptor active sites. RESULTS: After manual adjustment and energy minimization, the ligand-receptor interaction models were achieved. Based on these models, the possible action mechanism of THPB on dopamine receptors was suggested that the protonated N atom of THPB form electrostatic interaction and hydrogen-bonding interaction with residue Asp in TM3 of the receptor, the two substituents in D ring of THPB form hydrogen-bonding interactions with two Ser residues in TM5 of the receptor, and the aryl groups form pi-pi interactions with some aryl residues of the receptor around ligand. CONCLUSION: Our ligand-receptor interaction models should be helpful for rational design of more potent drugs.

Amino Acid Sequence↗
PubMed1996

Molecular modeling of mu opioid receptor and its interaction with ohmefentanyl.

AIM: To build up the structure model of mu opioid receptor, then combined with the receptor model, to investigate the action mechanism of ohmefentanyl on the receptor. METHODS: Using the three-dimensional structure of bacteriorhodopsin as a template, we constructed mu opioid receptor model on computer. Ohmefentanyl was then docked into the supposed receptor binding sites. RESULTS: A good ligand-receptor interaction model was achieved. The possible binding sites were found to be Asp147 and His319. The protonated N atom of ohmefentanyl form potent electrostatic and hydrogen-bonding interactions with residue Asp147 of the receptor, the O atom of the carbonyl group form weak electrostatic and hydrogen-bonding interactions with residue His319, and the two phenyl groups form pi-pi interactions with some aryl residues of the receptor around ligand. CONCLUSION: The ligand-receptor interaction model should be helpful for rational design of novel analgesic.

Amino Acid Sequence↗
PubMed1994

3D-QSAR study on ether and ester analogs of artemisinin with comparative molecular field analysis.

Comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) paradigm, was used to study the correlations between the physicochemical properties and the in vitro activities of a series of ether and ester analogs of artemisinin. Four alignment models were used in the CoMFA investigation. The correlations derived from CoMFA analysis with the four alignments proved all to have good predictive values. The steric field predictive model of alignment B is accordant with the experimental results of Avery M A, et al: J Med Chem 1993; 36: 4264-75. The electrostatic field predictive results of alignments A, B, and C are consistent with our previous result of quantum chemical calculation. The highest rcross2 of alignment D, indicated that the side chain of -C6-O2-O1-C10-O3-C7-O4-C12-O5- and atom C16 are important groups of artemisinin analogs for antimalarial activity.

Antimalarials↗