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Alpha-synuclein (α-syn), as a highly soluble presynaptic protein expressed in the brain, plays an important role in recycling synaptic vesicles and regulating the synthesis, storage, and release of neurotransmitters. Accumulation of α-syn in Lewy bodies and Lewy neurites is the pathological hallmark of Parkinson’s disease (PD), so inhibition of α-syn aggregation may provide a novel approach for treating PD. In this study, the 3D structure of α-syn was downloaded from Protein Data Bank (PDB ID: 2N0A). A ligand-based pharmacophore model was conducted on a set of 43 diverse α-syn ligands, and the results suggested that two hydrogen-bond acceptors, one hydrophobic group, and two aromatic rings were significant to the inhibition of α-syn aggregation. A ligand-based 3D-QSAR model was also established with good statistical significance (R2 = 0.920) and excellent predictive ability (Q2 = 0.752). Novel indolinone derivatives were designed and synthesized based on the pharmacophore model. Subsequently, the 3D-QSAR model was used to predict the inhibitory activities towards α-syn aggregation, and the actual inhibitory activities were evaluated by thioflavin-T assay in vitro with the best inhibitory activity reaching 45.08%. The fitting results indicated that the built pharmacophore and 3D-QSAR models provided better reliability and accuracy for compound modification and prediction of the activity thereof. A ligand-based pharmacophore modeling and 3D-QSAR study have been performed on a set of 43 diverse ligands for α-synuclein for the first time. Based on the best pharmacophore modeling, novel indolinone derivatives were designed and synthesized, and the inhibitory activities for α-synuclein aggregation were evaluated by thioflavin-T assay in vitro, which preliminary indicated that five pharmacophore sites (two hydrogen bond acceptors (A), a hydrophobic group (H), and two aromatic rings (R)) in compounds contribute to the inhibitory activities. In the study, the built pharmacophore modeling and 3D-QSAR provided better reliability and accuracy for compound modification and prediction of the activity thereof.

Dipeptidyl peptidase IV (DPP4) is an important target for the treatment of type II diabetes mellitus. Inhibition of DPP4 will improve glycemic control in such patients by preventing the rapid breakdown and thereby prolonging the physiological actions of incretin hormones. Known DPP4 inhibitors (including marketed drugs and those drug candidates) appear to share similar structural features: the cyanopyrrolidine moieties, the xanthenes/pyrimidine parts and amino-like linkages. In this study, a multi-step virtual screening strategy including both rigid and flexible docking was employed to search for novel structures with DPP4 inhibition. From SPECS database, consisting of over 190,000 commercially available compounds, 99 virtual hits were picked up and 15 of them were eventually identified to have DPP4 inhibitory activities at 5 ~ 50 μM. Diverse structures of our compounds were out of usual structural categories. Hence a pharmacophore model was built to further explore their common binding features on the enzyme. The results provided a new pathway for the discovery of DPP4 inhibitors and would be helpful for further optimization of DPP4 inhibitors.

Cannabinoid receptor-1 (CB(1)) is widely expressed in the central nervous system and plays a vital role in regulating food intake and energy expenditure. CB(1) antagonists such as Rimonabant have been used in clinic to inhibit food intake, and therefore reduce body weight in obese animals and humans. To investigate the binding modes of CB(1) antagonists to the receptor, both receptor- and ligand-based methods were implemented in this study. At first, a pharmacophore model was generated based on 31 diverse CB(1) antagonists collected from literature. A test set validation and a simulated virtual screening evaluation were then performed to verify the reliability and discriminating ability of the pharmacophore. Meanwhile, the homology model of CB(1) receptor was constructed based on the crystal structure of human β (2) adrenergic receptor (β (2)-AR). Several classical antagonists were then docked into the optimized homology model with induced fit docking method. A hydrogen bond between the antagonists and Lys192 on the third transmembrane helix of the receptor was formed in the docking study, which has proven to be critical for receptor-ligand interaction by biological experiments. The structure obtained from induced fit docking was then confirmed to be a reliable model for molecular docking from the result of the simulated virtual screening. The consistency between the pharmacophore and the homology structure further proved the previous observation. The built receptor structure and antagonists' pharmacophore should be useful for the understanding of inhibitory mechanism and development of novel CB(1) antagonists.

5-hydroxytryptamine-2c (5-HT2c) receptor antagonists have clinical utility in the management of nervous system. In this work, ligand-based and receptor-based methods were used to investigate the binding mode of h5-HT2c receptor antagonists. First, the pharmacophore modeling of the h5-HT2c receptor antagonists was carried out by CATALYST. Then, the h5-HT2c antagonists were docked to the h5-HT2c receptor model. Subsequently, the comprehensive analysis of the pharmacophore and docking results revealed the structure-activity relationship of 5-HT2c receptor antagonists and the key residues involved in the interactions. For example, three hydrophobic points in the ligands corresponded to the region surrounded by Val135, Val208, Phe214, Ala222, Phe327, Phe328 and Val354 of the h5-HT2c receptor. The carbonyl group of compound 1 formed a hydrogen bond with Asn331. The nitrogen atom in the piperidine of compound 1 corresponding to the positive ionizable position of the best pharmacophore formed the electrostatic interactions with the carbonyl of Asp134, Asn331 and Val354, and with the hydroxyl group of Ser334. In addition, a predictive CoMFA model was developed based on the 24 compounds that were used as the training set in the pharmacophore modeling. Our results were not only useful to explore the detailed mechanism of the interactions between the h5-HT2c receptor and antagonists, but also provided suggestions in the discovery of novel 5-HT2c receptor antagonists.

Three-dimensional pharmacophore models of human adenosine receptor A2A antagonists were developed based on 23 diverse compounds selected from a large number of A2A antagonists. The best pharmacophore model, Hypo1, contained five features: one hydrogen bond donor , three hydrophobic points and one ring aromatic. Its correlation coefficient, root mean square deviation, and cost difference values were 0.955, 0.921 and 84.4, respectively, suggested that the Hypo1 model was reasonable and reliable. This model was validated by three methods: a test set of 106 diverse compounds, a simulated virtual screening, and superimposition with the crystal structure of A2A receptor. The results showed that Hypo1 was not only in agreement with the A2A crystal structure and literature reports, but also well identified active A2A antagonists from the virtual database. This methodology provides helpful information and a robust tool for the discovery of potent A2A antagonists.

Acetylcholinesterase (AChE) and its mutation recently emerged as a significant research area, due to its resistance against organophosphate and carbamate insecticides. Residue G265, which is always a conservative residue, mutated to A265 is the most frequent mutant of AChE in Drosophila populations. However, only this mutation caused a 'butterfly effect' that gives high insecticidal resistance. Herein, the models of sensitive strain (Dm-S) and the resistance strain (Dm-R) were constructed, to give a total of 2000 ps molecular dynamics simulation and to reveal the insecticidal resistance mechanism, with implied, the active gorge of Dm-R was much less flexible than that of Dm-S. The "back door" channel was widened to accelerate the detoxication against insecticides by the conformation changing of W83 and I161. All the distances (S238-H480, S238-G150, S238-G151, Y71-M153) in Dm-R became smaller than those in Dm-S, which may deeply influence the binding between the insecticides and DmAChE.

Two chemical function-based pharmacophore models of selective kappa-opioid receptor agonists were generated by using two different programs: Catalyst/HypoGen and Phase. The best output hypothesis (Hypo1) of HypoGen consisted of five features: one hydrogen-bond acceptor (HA), three hydrophobic points (HY), and one positive ionizable function (PI). The highest scoring model (Hypo2) produced by Phase comprised four features: one acceptor (A), one positive ionizable function (P), and two aromatic ring features (R). These two models (Hypo1 and Hypo2) were then validated by test set prediction and enrichment factors. They were shown to be able to identify highly potent kappa-agonists within a certain range, and satisfactory enrichments were achieved. The features of these two pharmacophore models were similar and consistent with experiment data. The models produced here were also generally in accord with other reported models. Therefore, our pharmacophore models were considered as valuable tools for 3D virtual screening, and could be useful for designing novel kappa-agonists.

Orvinols are potent analgesics that target opioid receptors. However, their analgesic mechanism remains unclear and no significant preference for subtype opioid receptor has been achieved. In order to find new orvinols that target the κ-receptor, comparative 3D–QSAR studies were performed on 26 orvinol analogs using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best predictions for the κ-receptor were obtained with the CoMFA standard model (q 2=0.686, r 2=0.947) and CoMSIA model combined steric, electrostatic, hydrophobic, and hydrogen bond donor/acceptor fields (q 2=0.678, r 2=0.914). The models built were further validated by a test set made up of seven compounds, leading to predictive r 2 values of 0.672 for CoMFA and 0.593 for CoMSIA. The study could be helpful for designing and prepare new category κ-agonists from orvinols.

Integration of viral-DNA into host chromosome mediated by the viral protein HIV-1 integrase (IN) is an essential step in the HIV-1 life cycle. In this process, human protein Lens epithelium-derived growth factor (LEDGF/p75) is discovered to function as a cellular co-factor for integration. LEDGF/p75-HIV-1 IN interaction represents an attractive target for anti-HIV therapy. In this study, approved drugs were investigated for the finding of potential inhibitors on this target. Via molecular docking against the LEDGF/p75-binding pocket of HIV-1 IN, 26 old drugs were selected from the DrugBank and purchased for bioassays. Among them, eight, namely Atorvastatin, Bumetanide, Candesartan, Carbidopa, Diclofenac, Diflunisal, Eprosartan, and Sulindac, were identified as potential inhibitors of LEDGF/p75- HIV-1 IN interaction, whose IC(50) values ranged from 6.5 μM to 36.8 μM. In addition, Atorvastatin was previously reported to block HIV-1 replication and may have an important implication for the treatment of AIDS. Our results suggested a mechanism of action for the anti-HIV effects of Atorvastatin. This work provides a new example of inhibitors targeting protein-protein interaction and confirmed that old drugs were valuable sources for antiviral drug discovery.

To probe the selective mechanism of agonists binding to three opioid receptor subtypes, ligand-based and receptor-based methods were implemented together and subtype characteristics of opioid agonists were clearly described. Three pharmacophore models of opioid agonists were generated by the Catalyst/HypoGen program. The best pharmacophore models for μ, δ and κ agonists contained four, five and five features, respectively. Meanwhile, the three-dimensional structures of three receptor subtypes were modeled on the basis of the crystal structure of β2-adrenergic receptor, and molecular docking was conducted further. According to these pharmacophore models and docking results, the similarities and differences among agonists of three subtypes were identified. μ or δ agonists, for example, could form one hydrogen bond separately with Tyr129 and Tyr150 at TMIII, whereas κ ones formed a π-π interaction in that place. These findings may be crucial for the development of novel selective analgesic drugs.

Three-dimensional pharmacophore models of human adenosine receptor A(₂A) antagonists were developed based on 23 diverse compounds selected from a large number of A(₂A) antagonists. The best pharmacophore model, Hypo1, contained five features: one hydrogen bond donor , three hydrophobic points and one ring aromatic. Its correlation coefficient, root mean square deviation, and cost difference values were 0.955, 0.921 and 84.4, respectively, suggested that the Hypo1 model was reasonable and reliable. This model was validated by three methods: a test set of 106 diverse compounds, a simulated virtual screening, and superimposition with the crystal structure of A(₂A) receptor. The results showed that Hypo1 was not only in agreement with the A(₂A) crystal structure and literature reports, but also well identified active A(₂A) antagonists from the virtual database. This methodology provides helpful information and a robust tool for the discovery of potent A(₂A) antagonists.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses using CoMFA and CoMSIA methods were conducted on a series of fluoropyrrolidine amides as dipeptidyl peptidase IV (DP-IV) inhibitors. The selected ligands were docked into the binding site of the 3D model of DP-IV using the GOLD software, and the possible interaction models between DP-IV and the inhibitors were obtained. Based on the binding conformations of these fluoropyrrolidine amides and their alignment inside the binding pocket of DP-IV, predictive 3D-QSAR models were established by CoMFA and CoMSIA analyses, which had conventional r 2 and cross-validated coefficient values ( $$ r^{2}_{{{\text{cv}}}} $$ ) up to 0.982 and 0.555 for CoMFA and 0.953 and 0.613 for CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were in the testing set. Structure-based investigations and the final 3D-QSAR results provide the guide for designing new potent inhibitors.

Structure-based 3D-QSAR studies were performed on 20 thiazoles against their binding affinities to the 5-HT3 receptor with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The thiazoles were initially docked into the binding pocket of a human 5-HT3A receptor homology model, constructed on the basis of the crystal structure of the snail acetylcholine binding protein (AChBP), using the GOLD program. The docked conformations were then extracted and used to build the 3D-QSAR models, with cross-validated % MathType!Translator!2!1!AMS LaTeX.tdl!TeX AMS-LaTeX! % MathType!MTEF!2!1!+- % feaaeaart1ev0aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbbjxAHX % garmWu51MyVXgatuuDJXwAK1uy0HwmaeHbfv3ySLgzG0uy0Hgip5wz % aebbnrfifHhDYfgasaacH8qrps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeWaeaaakeaaca % WGYbWaa0baaSqaaiaadogacaWG2baabaGaaGOmaaaaaaa!3B53! $$ r^{2}_{{cv}} $$ values 0.785 and 0.744 for CoMFA and CoMSIA, respectively. An additional five molecules were used to validate the models further, giving satisfactory predictive % MathType!Translator!2!1!AMS LaTeX.tdl!TeX AMS-LaTeX! % MathType!MTEF!2!1!+- % feaaeaart1ev0aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbbjxAHX % garmWu51MyVXgatuuDJXwAK1uy0HwmaeHbfv3ySLgzG0uy0Hgip5wz % aebbnrfifHhDYfgasaacH8qrps0lbbf9q8WrFfeuY-Hhbbf9v8qqaq % Fr0xc9pk0xbba9q8WqFfea0-yr0RYxir-Jbba9q8aq0-yq-He9q8qq % Q8frFve9Fve9Ff0dmeaabaqaciGacaGaaeqabaWaaeWaeaaakeaaca % WGYbWaaWbaaSqabeaacaaIYaaaaaaa!3970! $$ r^{2} $$ values of 0.582 and 0.804 for CoMFA and CoMSIA, respectively. The results would be helpful for the discovery of new potent and selective 5-HT3 receptor antagonists.

Molecular docking has been performed to investigate the binding mode of (-)-meptazinol (MEP) with acetylcholinesterase (AChE) and to screen bis-meptazinol (bis-MEP) derivatives for preferable synthetic candidates virtually. A reliable and practical docking method for investigation of AChE ligands was established by the comparison of two widely used docking programs, FlexX and GOLD. In our hands, we had more luck using GOLD than FlexX in reproducing the experimental poses of known ligands (RMSD1.5 A). GOLD fitness values of known ligands were also in good agreement with their activities. In the present GOLD docking protocol, (-)-MEP seemed to bind with the enzyme catalytic site in an open-gate conformation through strong hydrophobic interactions and a hydrogen bond. Virtual screening of a potential candidate compound library suggested that the most promising 15 bis-MEP derivatives on the list were mainly derived from (-)-MEP with conformations of (S,S) and (SR,RS) and with a 2- to 7-carbon linkage. Although there are still no biological results to confirm the predictive power of this method, the current study could provide an alternate tool for structural optimization of (-)-MEP as new AChE inhibitors. [Figure: see text].