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R Y Ji

Publications and source records attributed to R Y Ji.

At least 19 recordsLinked to original sources
PubMed2001

A 3D structural model of memapsin 2 protease generated from theoretical study.

AIM: To build a 3D structural model of memapsin 2 (M2) protease for theoretical study and drug design. METHODS: Structural alignment was performed based on multiple and pairwise sequence alignment of three templates. After the initial model was generated, energy minimization was completed by applying molecular mechanics method. Molecular dynamics (MD) technique was used to do further structural optimization. RESULTS: The 3D structural model of memapsin 2 was constructed. The model is reasonable according to several validation criteria. The active-site motifs of M2 are structurally supported by a beta-sheet rich domain and linked together with this domain through alpha helices. Tyr132 contained in beta-hairpin is a general characteristic of aspartic protease. The Calpha atom superimposing result is a direct verification that M2 is structurally unique but still belongs to the aspartic protease superfamily. CONCLUSION: The 3D-structure model from our study is informative to guide future molecular biology study about M2 and drug design based on database searching.

Amino Acid Sequence↗
PubMed2000

Molecular modeling on solvent effect and interaction mechanism of fentanyl analogs to mu-opioid receptor.

AIM: To do theoretical study about solvation effect and interaction mechanism of fentanyl analogs (FA) to mu opioid receptor (microOR). METHODS: Flexible docking (FlexiDock) was performed by using the possible active conformations of FA and optimized 3D structure of mu opioid receptor. Binding energies were calculated. Comparative molecular force field analysis (CoMFA) and quantitative structure activity relationship (QSAR) studies were carried out based on results of flexible docking. Solvation effects were considered by studying interaction of FA with water molecules. Partial least square (PLS) analysis was used to calculate regression equation for analgesic activities using binding energies as descriptive factor. RESULTS: 1) Binding conformations of these analogs derived by flexible docking were reasonable. 2) It was most possible for the FA to exist in water solution in the form of binding conformations. 3) Energetic calculation and QSAR analysis showed a good correlation between the calculated binding energies of FA and their analgesic activities. 4) Based on the 3D-model, the possible interaction mechanism of FA with mu opioid receptor can be illustrated reasonably. CONCLUSION: The nature of the correlation between the binding affinities and analgesic activities of FA was explained by our modeling result.

Analgesics↗
PubMed2000

Building three-dimensional structures of HIV-1 coreceptor CCR5 and its interaction with antagonist TAK779 by comparative molecular modeling.

AIM: To study the mechanism of interaction of CCR5 receptor with its antagonist TAK779. METHODS: Comparative molecular modeling has been used to develop the 3D-structural models of CCR5 receptor and its complex with TAK779. Molecular mechanics has been applied to optimize the above molecular models. Quantum mechanics has been utilized to calculate the structural information of TAK779. DOCK4.0 program is employed to dock the TAK779 molecular into the binding site of CCR5 receptor. RESULTS: The 3D-structural model of CCR5 receptor is constructed using the 3D-model of frog rhodopsin as a template. The binding pocket is situated in the transmembrane helices 3, 5, 6, and 7, and it is composed of conserved residues of Tyr108, Gly111, Ser114, Glu283, Gly286, and Cys290, and conservatively varied residues including Thr105, Leu107, Phe112, Gly115, Lys197, and Met287. O1, N7, N17, and O19 of TAK779 are the active center of TAK779. The pyran cycle and the aminium group of TAK779 interact with residues in the binding pocket of CCR5 receptor, the other part of TAK779 interacts with residues from the extracellular loops of CCR5. The binding energy of TAK779 with CCR5 is -51.606 kcal/mol. CONCLUSION: The model constructed and the interaction mode reported in the present study are useful in further understanding the molecular mechanism of receptor-virus recognition and designing new inhibitors of HIV-1 infection.

Amides↗
PubMed2000

Comparative molecular modeling on 3D-structure of opioid receptor-like 1 receptor.

AIM: To build the three-dimensional structure of opioid receptor-like 1 (ORL1) receptor. METHODS: Structural elements of ORL1 receptor were predicted from sequence alignments of opioid and related receptors of G protein-coupled receptor (GPCR) based on (i) the consensus, biophysical interpretations of alignment-derived properties, and (ii) tertiary structural homology to frog rhodopsin; The extracellular loops of ORL1 were built by self-constructed database searching based on geometrical constraints; initial model was refined computationally with energy minimization by molecular mechanics method. RESULTS: The calculated structure of ORL1 receptor has clusters of hydrogen bonds existing in interhelices and extracellular loops; the ORL1 receptor has a possible ligand-binding "crevice" situated on the extraside of the transmembrane domains between helices 3, 5, 6, and 7, which is partially covered by the extracellular loop 2 (EL-2); The binding cavity may consist of a "highly conserved region" involving the residues of Asp130, Tyr131, and an outer "conservatively variable region" containing the residues near the interface of transmembrane (TM) helices-EL loops; The molecular model obtained is qualitatively consistent with ligand affinities, hybrid peptide studies, and other experimental data. CONCLUSION: The structural model of ORL1 receptor from this study is helpful for clarifying experimental observations of ligands interacting with opioid receptors, and for designing new biological investigations.

Amino Acid Sequence↗
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↗
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↗
PubMed1999

Antimycoplasmal activities of (S)-(-)-9-fluoro-2,3-dihydro-3-methyl-10 -[4-(2-pyridyl)-1-piperazinyl]-7-oxo-7H-pyrido[1,2,3-de][1,4]benzoxazine -6-carboxylic acid (YH-6) in comparison with other antibiotics in vitro.

AIM: To determine the susceptibilities of Mycoplasma and Ureaplasma to (S)-(-)-9-fluoro-2,3-dihydro-3-methyl-10 -[4-(2-pyridyl)-1-piperazinyl]-7-oxo-7H-pyrido[1,2,3-de][1,4]benzoxazine -6-carboxylic acid (YH-6) and to compare it with those referential quinolones, macrolides, and tetracyclines. METHODS: The minimum inhibitory concentration (MIC) were determined by microdilution method in vitro. RESULTS: The MIC of YH-6 for Ureaplasma urealyticum (Uu: 250 micrograms.L-1), Mycoplasma hominis (Mh: 500 micrograms.L-1), M orale (Mo: 125 micrograms.L-1) and M salivarium (Ms: 125 micrograms.L-1) were closely similar to those of macrolides (erythromycin and leucomycin) and were 2-8 folds greater than those of ofloxacin (Ofl). Uu and Mh easily induced resistance to erythromycin and tetracycline. They did not easily form resistance to quinolone (YH-6, Ofl), josamycin and tylosin. Tetracycline-resistance (Tcr) or erythromycin-resistance (EMr) strains of Uu (or Mh) had cross-resistance to erythromycin or tetracycline. However, they had no cross-resistance to quinolone, josamycin and tylosin. CONCLUSION: YH-6 was a highly active quinolone against Mycoplasma, but could hardly induce resistance to Uu. EMr- or Tcr- strains of Uu (or Mh) had no cross-resistance to YH-6.

Anti-Bacterial Agents↗
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

Interpreting the effect of methyl group at the three carbon bridge of (-)-huperzine A on its anticholinesterase activity by molecular dynamics method.

Based on the recently resolved crystal structure of complex (-)-huperzine A-AChE, we simulated the interaction between (-)-huperzine A analogues and AChE using molecular dynamics method. It was revealed that the methyl group at the three carbon bridge of (-)-huperzine A can form a weak hydrogen bond with the phenol hydroxyl oxygen of Tyr121 and the main-chain oxygen of Gly118 of AChE, respectively.

Alkaloids↗
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 voltage-gated potassium channel pore.

AIM: To build a structure model for the pore of voltage-gated Shaker potassium channel and examine its validity. METHODS: (1) Structural restraints were derived from experimental and theoretical studies; (2) An initial structural motif satisfying the derived restraints was first constructed, and further refined by restrained molecular mechanics; (3) The quality of the model was judged by the criterion that whether it could clarify molecular mechanisms of channel functions and explain the known experimental facts. RESULTS: (1) A computer pore structure was proposed, in which the residues within signature sequence (corresponding to Shaker 439-446) dipped into the membrane and formed the narrow part of the pore in a non-periodic conformation, while the other residues in the P region constituted the outer mouth of the pore; (2) The ion selectivity was achieved through cation-pi orbital interaction mechanism at position 445 and oxygen cage mechanism at position 447; (3) Different binding modes led to different affinity of CTX and AgTx2 to channel; and (4) The inside of pore was dominated by negative electrostatic potential. CONCLUSION: The model proposed was consistent with the derived restraints from the experimental results.

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↗