Comparative Molecular Field Analysis Field Research Articles

The Universal 3D QSAR Model for Dopamine D2 Receptor Antagonists.

In order to search for novel antipsychotics acting through the D2 receptor, it is necessary to know the structure–activity relationships for dopamine D2 receptor antagonists. In this context, we constructed the universal three-dimensional quantitative structure–activity relationship (3D- QSAR) model for competitive dopamine D2 receptor antagonists. We took 176 compounds from chemically different groups characterized by the half maximal inhibitory concentration (IC50)from the CHEMBL database and docked them to the X-ray structure of the human D2 receptor in the inactive state. Selected docking poses were applied for Comparative Molecular Field Analysis (CoMFA) alignment. The obtained CoMFA model is characterized by a cross-validated coefficient Q2 of 0.76 with an optimal component of 5, R2 of 0.92, and an F value of 338.9. The steric and electrostatic field contributions are 67.4% and 32.6%, respectively. The statistics obtained prove that the CoMFA model is significant. Next, the IC50 of the 16 compounds from the test set was predicted with R2 of 0.95. Finally, a progressive scrambling test was carried out for additional validation. The CoMFA fields were mapped onto the dopamine D2 receptor binding site, which enabled a discussion of the structure–activity relationship based on ligand–receptor interactions. In particular, it was found that one of the desired steric interactions covers the area of a putative common allosteric pocket suggested for some other G protein-coupled receptors (GPCRs), which would suggest that some of the known dopamine receptor antagonists are bitopic in their essence. The CoMFA model can be applied to predict the potential activity of novel dopamine D2 receptor antagonists.

Toward the identification of a reliable 3D-QSAR model for the protein tyrosine phosphatase 1B inhibitors

Protein tyrosine phosphatase 1B (PTP1B) is an intracellular non-receptor phosphatase that is implicated in signal transduction of insulin and leptin pathways, thus PTP1B is considered as potential target for treating type II diabetes and obesity. The present article is an attempt to formulate the three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling of a series of compounds possessing PTP1B inhibitory activities using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The optimum template ligand-based models are statistically significant with great CoMFA (R2cv = 0.600, R2pred = 0.6760) and CoMSIA (R2cv = 0.624, R2pred = 0.8068) values. Molecular docking was employed to elucidate the inhibitory mechanisms of this series of compounds against PTP1B. In addition, the CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of PTP1B active site. The knowledge of structure-activity relationship and ligand-receptor interactions from 3D-QSAR model and molecular docking will be useful for better understanding the mechanism of ligand-receptor interaction and facilitating development of novel compounds as potent PTP1B inhibitors.

Comparative QSAR studies using HQSAR, CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors

The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC50: 0.002–2.75μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q2 value of 0.693 and r2 value of 0.981; a CoMFA q2 value of 0.534 and r2 value of 0.913; and a CoMSIA q2 value of 0.512 and r2 value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.

Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2)=0.988, q (2)=0.757, ONC=6; r (2)=0.966, q (2)=0.645, ONC=5; and r (2)=0.957, q (2)=0.736, ONC=6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

Studies of N(9)-arenthenyl purines as novel DFG-in and DFG-out dual Src/Abl inhibitors using 3D-QSAR, docking and molecular dynamics simulations.

Recently, the development of Src/Abl (c-Src/Bcr-Abl tyrosine kinases) dual inhibitors has attracted considerable attention from the research community for treatment of malignancies. In order to explore the different structural features impacting the Src and Abl inhibitory activities of N(9)-arenethenyl purines and to investigate the molecular mechanisms of ligand-receptor interactions, a molecular modeling study combining the three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics (MD) simulations was performed. The obtained CoMFA (comparative molecular field analysis) models exhibited satisfactory internal and external predictability. The plots of the CoMFA fields could be used to investigate the structural differences between DFG-in (targeting the active enzyme conformation) and DFG-out (targeting the inactive enzyme conformation) inhibitors. The key amino acid residues were identified by docking studies, and the detailed binding modes of the compounds with different activities were determined by MD simulations. The binding free energies gave a good correlation with the experimental determined activities. In an energetic analysis, the MM-PBSA (molecular mechanics Poisson-Boltzmann surface) energy decomposition revealed that the van der Waals interactions were the major driving force for the binding of the DFG-in and DFG-out compounds to Src and Abl, especially the hydrophobic interactions between ligands and residues Ala403/380, Asp404/381, and Phe405/382 in DFG-out Src and Abl complexes. They also help to stabilize the DFG-out conformations. These results can offer useful references for designing novel potential DFG-in and DFG-out dual Src/Abl inhibitors.

Investigations and design of pyridine-2-carboxylic acid thiazol-2-ylamide analogs as methionine aminopeptidase inhibitors using 3D-QSAR and molecular docking

Methionine amino peptidases (MetAPs) are metalloproteases that remove co-translational N-terminal methionine from nascent polypeptide chains. Due to their essential role in protein synthesis, MetAPs are considered as potential targets for antibacterial drugs. In the present work, three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out on a series of pyridine-2-carboxylic acid thiazol-2-ylamide-based MetAP inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models were developed using 30 training set molecules. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients (q 2) of 0.799 and 0.704 and conventional correlation coefficients (r 2) of 0.989 and 0.954, respectively. These inhibitors were docked into MetAP active site. The CoMFA and CoMSIA field contour maps correlate well with the structural characteristics of the binding pocket of MetAP active site. Using the knowledge of structure–activity relationship and receptor–ligand interactions from 3D-QSAR model and the docked complexes, four new pyridine-2-carboxylic acid thiazol-2-ylamide analogs were designed. These analogs exhibit significantly better predicted activity than the reported molecules. The present work has implications for the development of novel antibiotics as potent MetAP inhibitors.

A systematic quantitative approach to rational drug design and discovery of novel human carbonic anhydrase IX inhibitors

Drug design involves the design of small molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed for a series of carbonic anhydrase IX inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques with the help of SYBYL 7.1 software. The large set of 36 different aromatic/heterocyclic sulfamates carbonic anhydrase (CA, EC 4.2.1.1) inhibitors, such as hCA IX, was chosen for this study. The conventional ligand-based 3D-QSAR studies were performed based on the low energy conformations employing database alignment rule. The ligand-based model gave q2 values 0.802 and 0.829 and r2 values 1.000 and 0.994 for CoMFA and CoMSIA, respectively, and the predictive ability of the model was validated. The predicted r2 values are 0.999 and 0.502 for CoMFA and CoMSIA, respectively. SEA (steric, electrostatic, hydrogen bond acceptor) of CoMSIA has the significant contribution for the model development. The docking of inhibitors into hCA IX active site using Glide XP (Schrödinger) software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps are well in agreement with the structural characteristics of the binding pocket of hCA IX active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved hCA IX inhibitors as leads for various types of metastatic cancers including those of cervical, renal, breast and head and neck origin.

Study of differences in the VEGFR2 inhibitory activities between semaxanib and SU5205 using 3D-QSAR, docking, and molecular dynamics simulations

Semaxanib (SU5416) and 3-[4′-fluorobenzylidene]indolin-2-one (SU5205) are structurally similar drugs that are able to inhibit vascular endothelial growth factor receptor-2 (VEGFR2), but the former is 87 times more effective than the latter. Previously, SU5205 was used as a radiolabelled inhibitor (as surrogate for SU5416) and a radiotracer for positron emission tomography (PET) imaging, but the compound exhibited poor stability and only a moderate IC 50 toward VEGFR2. In the current work, the relationship between the structure and activity of these drugs as VEGFR2 inhibitors was studied using 3D-QSAR, docking and molecular dynamics (MD) simulations. First, comparative molecular field analysis (CoMFA) was performed using 48 2-indolinone derivatives and their VEGFR2 inhibitory activities. The best CoMFA model was carried out over a training set including 40 compounds, and it included steric and electrostatic fields. In addition, this model gave satisfactory cross-validation results and adequately predicted 8 compounds contained in the test set. The plots of the CoMFA fields could explain the structural differences between semaxanib and SU5205. Docking and molecular dynamics simulations showed that both molecules have the same orientation and dynamics inside the VEGFR2 active site. However, the hydrophobic pocket of VEGFR2 was more exposed to the solvent media when it was complexed with SU5205. An energetic analysis, including Embrace and MM-GBSA calculations, revealed that the potency of ligand binding is governed by van der Waals contacts.

3D-QSAR and molecular docking studies of 2-pyrimidinecarbonitrile derivatives as inhibitors against falcipain-3

The three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on a series of falcipain-3 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 42 molecules served to establish the QSAR models. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients r(cv)(2) (q(2)) of 0.549 and 0.608, and conventional correlation coefficients (r(2)) of 0.976 and 0.932, respectively. An independent test set of 12 molecules validated the external predictive power of both models with predicted correlation coefficients (r(pred)(2)) for CoMFA and CoMSIA as 0.697 and 0.509, respectively. The docking of inhibitors into falcipain-3 active site using GOLD software revealed the vital interactions and binding conformation of the inhibitors. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of falcipain-3 active site, which suggests that the information rendered by 3D-QSAR models and the docking interactions can provide guidelines for the development of improved falcipain-3 inhibitors.

Insights into the structure of urea-like compounds as inhibitors of the juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta: Analysis of the binding modes and structure–activity relationships of the inhibitors by docking and CoMFA calculations

Substituted urea compounds are well-known as potent inhibitors of juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta. Docking simulations of 47 derivatives inside JHEH were performed to gain insight into the structural characteristics of these complexes. The obtained orientations show a strong similitude with the observed in the known X-ray crystal structures of human soluble epoxide hydrolase (sEH) complexed with dialkylurea inhibitors. In addition, the predicted inhibitor concentration (IC50) of the above-mentioned compounds as JHEH inhibitors were obtained by a quantitative structure–activity relationship (QSAR) method by using comparative molecular field analysis (CoMFA) applied to aligned dataset. The best models included steric and electrostatic fields and had adequate predictive abilities. In addition, these models were used to predict the activity of an external test set of compounds that was not used for building the model. Furthermore, plots of the CoMFA fields allowed conclusions to be drawn for the choice of suitable inhibitors.

Comparison between steroid binding to membrane progesterone receptor α (mPRα) and to nuclear progesterone receptor: Correlation with physicochemical properties assessed by comparative molecular field analysis and identification of mPRα-specific agonists

Recent results showing that the binding characteristics of 33 steroids for human membrane progesterone receptor alpha (hu-mPRα) differ from those for the nuclear progesterone receptor (nPR) suggest that hu-mPRα-specific agonists can be identified for investigating its physiological functions. The binding affinities of an additional 21 steroids for hu-mPRα were determined to explore the structure–activity relationships in more detail and to identify potent, specific mPRα agonists. Four synthetic progesterone derivatives with methyl or methylene groups on positions 18 or 19, 18a-methylprogesterone (18-CH 3P4, Org OE 64-0), 13-ethenyl-18-norprogesterone (18-CH 2P4, Org 33663-0), 19a-methylprogesterone (19-CH 3P4, Org OD 13-0) and 10-ethenyl-19-norprogesterone (19-CH 2P4, Org OD 02-0), showed similar or higher affinities than progesterone for hu-mPRα and displayed mPRα agonist activities in G-protein and MAP kinase activation assays. All four steroids also bound to the nPR in cytosolic fractions of MCF-7 cells. However, two compounds, 19-CH 2P4 and 19-CH 3P4, showed no nPR agonist activity in a nPR reporter assay and therefore are selective mPRα agonists suitable for physiological investigations. The structure–binding relationships of the combined series of 54 steroids for hu-mPRα deviated strikingly from those of a published set of 60 3-keto or 3-desoxy steroids for nPR. Close correlations were observed between the receptor binding affinities of the steroids and their physicochemical properties calculated by comparative molecular field analysis (CoMFA) for both hu-mPRα and nPR. A comparison of the CoMFA field graphs for the two receptors revealed several differences in the structural features required for binding to hu-mPRα and nPR which could be exploited to develop additional mPR-specific ligands.

Computational Study of the Interactions between Guanine Derivatives and Cyclin-Dependent Kinase 2 (CDK2) by CoMFA and QM/MM

Comparative molecular field analysis (CoMFA) and QM/MM hybrid calculations were performed on 9H-purine derivatives as CDK2 inhibitors. CoMFA was carried out to describe the activities of 78 analogues. The models were applied to a training set including 64 compounds. The best CoMFA model included steric and electrostatic fields, had a good Q(2) value of 0.845, and adequately predicted the compounds contained in the test set. Furthermore, plots of the steric CoMFA field allowed conclusions to be drawn for the choice of suitable inhibitors. In addition, the dynamical behavior of compounds with 4-(aminosulfonyl)phenyl, 4-[(methylamino)sulfonyl]phenyl, 4-[(dimethylamino)sulfonyl]phenyl, and [3-methoxy-4-(aminosulfonyl)]phenyl groups at position 2 of the 9H-purine scaffold inside the CDK2 active site were analyzed by QM/MM calculations. The interactions of these compounds with residues Lys89, Asp86, and Ile10 were characterized.

Molecular docking and 3D-QSAR studies on triazolinone and pyridazinone, non-nucleoside inhibitor of HIV-1 reverse transcriptase

Nonnucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors of the HIV-1 reverse transcriptase. Recently a series of Triazolinone and Pyridazinone were reported as potent inhibitors of HIV-1 wild type reverse transcriptase. In the present study, docking and 3D quantitative structure activity relationship (3D QSAR) studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 31 molecules. Ligands were built and minimized using Tripos force field and applying Gasteiger-Hückel charges. These ligands were docked into protein active site using GLIDE 4.0. The docked poses were analyzed; the best docked poses were selected and aligned. CoMFA and CoMSIA fields were calculated using SYBYL6.9. The molecules were divided into training set and test set, a PLS analysis was performed and QSAR models were generated. The model showed good statistical reliability which is evident from the r2 nv, q2 loo and r2 pred values. The CoMFA model provides the most significant correlation of steric and electrostatic fields with biological activities. The CoMSIA model provides a correlation of steric, electrostatic, acceptor and hydrophobic fields with biological activities. The information rendered by 3D QSAR model initiated us to optimize the lead and design new potential inhibitors.

3D QSAR studies on ketoamides of human cathepsin K inhibitors based on two different alignment methods

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 64 ketoamides as human cathepsin K (CatK) inhibitors, using ROCS ligand-based alignment and receptor-based alignment. Results generated from the ligand-based model were found to be superior to those obtained by the receptor-based model. CoMFA and CoMSIA field distributions are in good agreement with the structural characteristics of the binding groove of CatK, suggesting moderate substitutes at the P1, P2, P3 and P1′ may favor the inhibitory activity of ketoamides. These results provide useful information in understanding the structural and chemical features of CatK in designing and finding novel potential CatK inhibitors as osteoporosis therapeutic agents.

Molecular Modeling and Design of Arylthioindole Derivatives as Tubulin Inhibitors

Three-dimensional quantitative structure activity relationship (3D-QSAR) and docking studies of a series of arylthioindole derivatives as tubulin inhibitors against human breast cancer cell line MCF-7 have been carried out. An optimal 3D-QSAR model from the comparative molecular field analysis (CoMFA) for training set with significant statistical quality (R2 = 0.898) and predictive ability (q2 = 0.654) was established. The same model was further applied to predict pIC50 values of the compounds in test set, and the resulting predictive correlation coefficient R2(pred) reaches 0.816, further showing that this CoMFA model has high predictive ability. Moreover, the appropriate binding orientations and conformations of these compounds interacting with tubulin are located by docking study, and it is very interesting to find the consistency between the CoMFA field distribution and the 3D topology structure of active site of tubulin. Based on CoMFA along with docking results, some important factors improving the activities of these compounds were discussed in detail and were summarized as follows: the substituents R3R5 (on the phenyl ring) with higher electronegativity, the substituent R6 with higher electropositivity and bigger bulk, the substituent R7 with smaller bulk, and so on. In addition, five new compounds with higher activities have been designed. Such results can offer useful theoretical references for experimental works.

3D-QSAR studies of boron-containing dipeptides as proteasome inhibitors with CoMFA and CoMSIA methods

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed for a series of dipeptide boronate proteasome inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 46 molecules served to establish the models. The optimum CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients ( q 2) of 0.676 and 0.630 and conventional coefficients ( r 2) of 0.989 and 0.956, respectively. The predictive capacities of both models were successfully validated by calculating a test set of 13 molecules that were not included in the training set. The predicted correlation coefficients ( r 2 pred) of CoMFA and CoMSIA are 0.963 and 0.919, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of β5 subunit of 20S proteasome, which suggests that the 3D-QSAR models constructed in this paper can be used to guide the development of novel dipeptide boronate inhibitors of 20S proteasome.

A Combined 2D‐ and 3D‐QSAR Study on Analogues of ARC‐111 with Antitumor Activity

Two-Dimensional (2D) and Three-Dimensional (3D) Quantitative Structure–Activity Relationships (QSARs) of a series of analogues of ARC-111 with antitumor activity expressed as pIC50, which is defined as the negative value of the logarithm of necessary molar concentration of these compounds to cause 50% growth inhibition against tumor cell lines P388, have been studied by using a combined method of the DFT, MM2, and statistics for 2D, as well as the Comparative Molecular Field Analysis (CoMFA) for 3D. The established 2D-QSAR model shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient of 0.869 and the square of the crossvalidation coefficient q2 of 0.834 as well as the square of predictive correlation coefficient of 0.867 for the test set. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2 (0.993) and q2 (0.674) when CoMFA fields are used for the whole studied compounds. The results from 2D- and 3D-QSAR analyses conformably show that the steric interaction plays a crucial role in determining the cytotoxicity of the compounds and that selecting a moderate-size substituent R as well as increasing the negative charge of C28 at the same time are advantageous to improving the antitumor activity. Such results might offer some useful theoretical references for understanding the action mechanism and directing the molecular design of this kind of compound with antitumor activity.

Comparative molecular field analysis of flavonoid inhibitors of the PIM-1 kinase

The PIM-1 protein, the product of the pim-1 oncogene, is a serine/threonine kinase. Dysregulation of the PIM-1 kinase has been implicated in the development of human malignancies including lymphomas, leukemias, and prostate cancer. Comparative molecular field analysis (CoMFA) is a 3-D QSAR technique that has been widely used, with notable success, to correlate biological activity with the steric and electrostatic properties of ligands. We have used a set of 15 flavonoid inhibitors of the PIM-1 kinase, aligned de novo by common substructure, to generate a CoMFA model for the purpose of elucidating the steric and electrostatic properties involved in flavonoid binding to the PIM-1 kinase. Partial least squares correlation between observed and predicted inhibitor potency (expressed as −log IC 50), using a non-cross-validated partial least squares analysis, generated a non-cross-validated q 2 = 0.805 for the training set ( n = 15) of flavonoids. The CoMFA generated steric map indicated that the PIM-1-binding site was sterically hindered, leading to more efficient binding of planar molecules over ( R) or ( S) compounds. The electrostatic map identified that positive charges near the flavonoid atom C8 and negative charges near C4′ increased flavonoid binding. The CoMFA model accurately predicted the potency of a test set of flavonoids ( n = 6), generating a correlation between observed and predicted potency of q 2 = 0.825. CoMFA models generated from additional alignment rules, which were guided by co-crystal structure ligand orientations, did not improve the correlative value of the model. Superimposing the PIM-1 kinase crystal structure onto the CoMFA contours validated the steric and electrostatic maps, elucidating the amino acid residues that potentially contribute to the CoMFA fields. Thus we have generated the first predictive model that may be used for the rational design of small-molecule inhibitors of the PIM-1 kinase.

3D-QSAR and docking studies of aldehyde inhibitors of human cathepsin K

In order to better understand the structural and chemical features of human cathepsin K (CatK), which is an important cysteine protease in the pathogenesis of osteoporosis, the 3D-QSAR (CoMFA) studies were conducted on recently explored aldehyde compounds with known CatK inhibitory activities. The genetic algorithm of GOLD2.2 has been employed to position 59 aldehyde compounds into the active sites of CatK to determine the probable binding conformation. Good correlations between the predicted binding free energies and the experimental inhibitory activities suggested that the identified binding conformations of these potential inhibitors are reliable. The docking results also provided a reliable conformational alignment scheme for 3D-QSAR model. Based on the docking conformations, highly predictive comparative molecular field analysis (CoMFA) was performed with q 2 value of 0.723. The predictive ability was validated by some compounds that were not included in the training set. Furthermore, the CoMFA model was mapped back to the binding sites of CatK, to get a better understanding of vital interactions between the aldehyde compounds and the protease. The CoMFA field distributions are in good agreement with the structural characteristics of the binding groove of the CatK, which suggested that the n-Bu in R4 position is the favor group substitute at P1 and moderate groups in R2 group are required on P2 substitute. In addition, 3D-QSAR results also demonstrated that aldehyde is an important pharmacophore because of electrostatic effect. These results, together with the good correlations between the inhibitory activities and the binding free energies predicted by GOLD2.2, demonstrated the power of combining docking/QSAR approach to explore the probable binding conformations of compounds at the active sites of the protein target, and further provided useful information in understanding the structural and chemical features of CatK in designing and finding new potential inhibitors.

3D-QSAR Comparative Molecular Field Analysis on Opioid Receptor Antagonists: Pooling Data from Different Studies

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molecular field analysis (CoMFA) on a series of opioid receptor antagonists. To obtain statistically significant and robust CoMFA models, a sizable data set of naltrindole and naltrexone analogues was assembled by pooling biological and structural data from independent studies. A process of "leave one data set out", similar to the traditional "leave one out" cross-validation procedure employed in partial least squares (PLS) analysis, was utilized to study the feasibility of pooling data in the present case. These studies indicate that our approach yields statistically significant and highly predictive CoMFA models from the pooled data set of delta, mu, and kappa opioid receptor antagonists. All models showed excellent internal predictability and self-consistency: q(2) = 0.69/r(2) = 0.91 (delta), q(2) = 0.67/r(2) = 0.92 (mu), and q(2) = 0.60/r(2) = 0.96 (kappa). The CoMFA models were further validated using two separate test sets: one test set was selected randomly from the pooled data set, while the other test set was retrieved from other published sources. The overall excellent agreement between CoMFA-predicted and experimental binding affinities for a structurally diverse array of ligands across all three opioid receptor subtypes gives testimony to the superb predictive power of these models. CoMFA field analysis demonstrated that the variations in binding affinity of opioid antagonists are dominated by steric rather than electrostatic interactions with the three opioid receptor binding sites. The CoMFA steric-electrostatic contour maps corresponding to the delta, mu, and kappa opioid receptor subtypes reflected the characteristic similarities and differences in the familiar "message-address" concept of opioid receptor ligands. Structural modifications to increase selectivity for the delta over mu and kappa opioid receptors have been predicted on the basis of the CoMFA contour maps. The structure-activity relationships (SARs) together with the CoMFA models should find utility for the rational design of subtype-selective opioid receptor antagonists.