Receptor-based 3D QSAR analysis of estrogen receptor ligands--merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods

A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches

Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.

An Insight into the Structural Requirements and Pharmacophore Identification of Carbonic Anhydrase Inhibitors to Combat Oxidative Stress at High Altitudes: An In-Silico Approach

Carbonic anhydrases (CA) inhibitory action could be linked to the treatment of a number of ailments, including cancer, osteoporosis, glaucoma, and several neurological problems. For the development of effective CA inhibitors, a variety of heterocyclic rings have been investigated. Furthermore, at high altitudes, oxygen pressure drops, resulting in the formation of reactive oxygen and nitrogen species, and CA inhibitors having role in combating this oxidative stress. Acetazolamide contains thiadiazole ring, which has aroused researchers’ interest because of its CA inhibitory action. In the present study, we used a number of drug design tools, such as pharmacophore modeling, 3D QSAR, docking, and virtual screening on twenty-seven 1,3,4-thiadiazole derivatives that have been described as potential CA inhibitors in the literature. An atom-based 3D-QSAR analysis was carried out to determine the contribution of individual atoms to model generation, while a pharmacophore mapping investigation was carried out to find the common unique pharmacophoric properties required for biological activity. The coefficient of determination for both the training and test sets were statistically significant in the generated model. The best QSAR model was chosen based on the values of R² (0.8757) and Q² (0.7888). A molecular docking study was also conducted against the most potent analogue 4m, which has the highest SP docking score (−5.217) (PDB ID: 6g3v). The virtual screening revealed a number of promising compounds. The screened compound ZINC77699643 interacted with the amino acid residues, Pro201 and Thr199, in the virtual screening study (PDB ID: 6g3v). These interactions demonstrated the significance of the CA inhibitory activity of the compound. Furthermore, ADME study revealed useful information regarding compound’s drug-like properties. Therefore, the findings of the present investigation could aid in the development of more potent CA inhibitors, which could benefit the treatment of oxidative stress at high altitudes.

An in silico approach to study the interaction of BHA with selected steroid hormone receptors and investigating it's agonistic and antagonistic properties

Antioxidant food additives were routinely used for increasing the keeping quality of packaged food items. Butylated Hydroxyanisole (BHA) is one of the most widely used synthetic phenolic antioxidants of such kind. Although quantity of antioxidants in packaged eatables and admissible daily intake (ADI) per person per day are limited by laws, the urbanisation and changes in lifestyle has cross these limits. Although studies on BHA has been carried out, there exists a great deal of uncertainty about the exact molecular mechanism of interaction of BHA with various receptors in the body. Since earlier reports suggested BHA plausibly interferes with reproductive system development, we opted docking of critical receptors of endogenous hormones controlling growth and development of reproductive system with BHA. Nuclear receptors of estrogen (ER), androgen (AR) and progesterone (PR) were selected for this purpose. This manuscript describes the comparison of binding pattern of BHA towards AR, ER and PR along with their agonists and antagonist. Lamarckian Genetic Algorithm of AutoDock 4.0 was used for analysing the mode of binding of ligands with the receptors. It is evident form the docking studies that, BHA exhibited similar binding pattern` with antagonists of AR and agonists of ER. But the interaction of BHA with PR was not compatible with either agonists or antagonists. The docking patterns produced could reliably demonstrate the interactions of BHA with selected receptors and also predict its possible agonistic and antagonistic action.

Molecular docking, 3D-QSAR, molecular dynamics, synthesis and anticancer activity of tyrosine kinase 2 (TYK 2) inhibitors

A therapeutic rationale is proposed by selectively targeting tyrosine kinase 2 (TYK 2) to obtain potent TYK 2 inhibitors by molecular modeling studies. In the present study, we have taken tyrosine kinase (TYK 2) inhibitors and carried out molecular docking, 3 D quantitative structure-activity relationship (3D-QSAR) analysis and molecular dynamics (MD). Based on the 3D-QSAR results thirteen new compounds (R-1 to R-13) were designed and synthesized in good yields. The synthesized molecules were evaluated for their in vitro anticancer activity against LnCap and A549 cell lines. The molecules R-1, R-3, R-5, R-7, and R-10 exhibited considerable anti cancer activity.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

BACKGROUND

PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding.

METHODS

Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors).

RESULTS

The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values.

CONCLUSION

Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

The role of LasR active site amino acids in the interaction with the Acyl Homoserine Lactones (AHLs) analogues: A computational study

The present work combines molecular docking calculations, 3D-QSAR, molecular dynamics simulations and free binding energy calculations (MM/PBSA and MM/GBSA) in a set of 28 structural analogues of acyl homoserine lactones with Quorum Sensing antagonist activity. The aim of this work is to understand how ligand binds and is affected by the molecular microenvironment in the active site of the LasR receptor for pseudomonas aeruginosa. We also study the stability of the interaction to find key structural characteristics that explain the antagonist activities of this set of ligands. This information is relevant for the rational modification or design of molecules and their identification as powerful LasR modulators. The analysis of molecular docking simulations shows that the 28 analogues have a similar binding mode compared to the native ligand. The carbonyl groups belonging to the lactone ring and the amide group of the acyl chain are oriented towards the amino acids forming hydrogen bond like interactions. The difference in antagonist activity is due to location and orientation of the LasR side chains within the hydrophobic pocket in its binding site. Additionally, we carried out molecular dynamics simulations to understand the conformational changes in the ligand-receptor interaction and the stability of each complex. Results show a direct relationship among the interaction energies of the ligands and the activities as an antagonist of the LasR receptor.

Computational biochemical investigation of the binding energy interactions between an estrogen receptor and its agonists

We present the energy profiles of estrogen receptor–agonist ligand interactions in atomic detail using a quantum biochemical approach.

Identification of estrogen receptor α ligands with virtual screening techniques

Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC50 value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC50 values from 5.5 to 6.5.

aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction

MOTIVATION

Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design.

RESULTS

We propose a novel graph-based-binding pocket signature called aCSM, which proved to be efficient and effective in handling large-scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitor's techniques. Finally, we predict novel ligands for proteins from Trypanosoma cruzi, the parasite responsible for Chagas disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario.

AVAILABILITY AND IMPLEMENTATION

Datasets and the source code are available at http://www.dcc.ufmg.br/∼dpires/acsm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Receptor-based QSAR study for a series of 3,3-disubstituted-5-aryl oxindoles and 6-aryl benzimidazol-2-ones derivatives as progesterone receptor inhibitors

Receptor-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 54 progesterone receptor (PR) inhibitors. The established CoMFA model from the training set gives statistically significant results with the cross-validated q (2) of 0.534 and non-cross-validated [Formula: see text] of 0.947. The best CoMSIA model was derived by the combination of steric field and hydrophobic field with a q (2) of 0.615 and [Formula: see text] of 0.954. A test set of 18 compounds was used to validate the predictive ability of the two models. The predicted correlation coefficients [Formula: see text] are 0.681 and 0.677 for CoMFA and CoMSIA models, respectively. Based on the CoMFA maps, the key structural characters of progesterone receptor inhibitors are identified. Moreover, the binding modes of oxindoles and benzimidazol-2-ones are also given by the quantum mechanical/molecular mechanical (QM/MM) calculations. This may provide useful information for drug design.

Does the oestrogen receptor encourage oestrogenicity in environmental pollutants? The case of 4-nonylphenol

A computer-aided docking study was conducted to explore in detail the binding interactions between the structurally unlikely environmental oestrogen 4-nonylphenol (4NP) and three of its metabolites with the human oestrogen receptor alpha (hERα). Docking was done within the Schrodinger Suite 2008 using both a conventional rigid receptor with flexible ligand and the induced-fit docking protocol. Induced-fit docking allows side-chain and backbone movement in the receptor to accommodate the ligand. This study has revealed unconventional interactions between the ligands and the hERα binding pocket that could explain the observed oestrogen-like behaviour of 4NP and suggests some of the metabolites of 4NP may also be oestrogenic.

Structure-based quantitative structure–activity relationship modeling of estrogen receptor β-ligands

Background: A variety of chemotypes have been studied as estrogen receptor (ER) β-selective ligands for potential drugs against various indications, including neurodegenerative diseases. Their structure–activity relationship data and the x-ray structures of the ERβ ligand-binding domain bound with different ligands have become available. Thus, it is vitally important for future development of ERβ-selective ligands that robust quantitative structure–activity relationship (QSAR) models be built. Methods/results: We employed a newly developed structure-based QSAR method (structure-based pharmacophore keys QSAR) that utilizes both the structure–activity relationship data and the 3D structural information of ERβ, as well as a robust QSAR workflow to analyze 37 ligands. Four sets of QSAR models were obtained, among which approximately 30 models afforded high (>0.60) training-r2 and test set-R2 statistics. Conclusion: We have obtained an ensemble of predictive models of ERβ ligands that will be useful in the future discovery of novel ERβ-selective molecules.

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis - a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

Synthesis and SAR comparative studies of 2-allyl-4-methoxy-1-alkoxybenzenes as 15-lipoxygenase inhibitors

A group of 2-alkoxy-5-methoxyallylbenzene were designed, synthesised and evaluated as potential inhibitors of the soybean 15-lipoxygenase (SLO) on the basis of the eugenol and esteragol structures. Compound 4d showed the best half maximal inhibitory concentration (IC₅₀) for SLO inhibition (IC₅₀ = 5.9 ± 0.6 µM). All the compounds were docked in the SLO active site retrieved from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB entry: 1IK3) and showed that the allyl group of the synthetic compounds similar to the linoleic acid double bond, were oriented toward the Fe³+-OH moiety in the active site of the enzyme and this conformation was especially fixed by the hydrophobic interaction of the 2-alkoxy group with Leu⁵¹⁵, Trp⁵¹⁹, Val⁵⁶⁶ and Ile⁵⁷². It was concluded that the molecular volume and shape of the alkoxy moiety was a major factor in the inhibitory potency variation of the synthetic compounds.

3D-QSAR Studies on C24-Monoalkylated Vitamin D3 26,23-Lactones and their C2α-Modified Derivatives with Inhibitory Activity to Vitamin D Receptor

The ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR) for 82 inhibitors of 25-dehydro-1α-hydroxyvitamin D3 -26,23-lactone analogs has been studied by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. The established CoMFA model in training set gives a cross-validated q(2) value of 0.516 and a non-cross-validated rncv (2) value of 0.667, while the CoMSIA model results in q(2) =0.517 and rncv (2) =0.632. In general, the predictive ability of the CoMFA model is superior to that of the CoMSIA model, with rpred (2) =0.639 for the CoMFA and rpred (2) =0.619 for the CoMSIA model. Based on the CoMFA contour maps, some key structural characters of vitamin D3 analogs responsible for inhibitory activity are identified, and some new C2α-modified 24-alkylvitamin D3 lactone analogs with high predicted pIC50 values are designed. The ligand functional group mutations by FEP simulation and docking studies reveal the rationality of the molecular design.

Design, synthesis and pharmacological evaluation of 6-hydroxy-4-methylquinolin-2(1H)-one derivatives as inotropic agents

Selective PDE3 inhibitors improve cardiac contractility and may be used in congestive heart failure. However, their proarrhythmic potential is the most important side effect. In this research we designed, synthesized and evaluated the potential cardiotonic activity of thirteen PDE3 inhibitors (4-[(4-methyl-2-oxo-1,2-dihydro-6-quinolinyl)oxy]butanamide analogs) using the spontaneously beating atria model. The design strategy was based on the structure of cilostamide, a selective PDE3 inhibitor. In each experiment, atrium of reserpine-treated rat was isolated and the contractile and chronotropic effects of a synthetic compounds were assessed. All experiments were carried out in comparison with IBMX, amrinone and cilostamide as standard compounds. The results showed that, among the new compounds, the best pharmacological profile was obtained with the compound 6-[4-(4-methylpiperazine-1-yl)-4-oxobutoxy]-4-methylquinolin-2(1H)-one, 4j, which displayed selectivity for increasing the force of contraction (165 +/- 4% change over the control) rather than the frequency rate (115 +/- 7% change over the control) at 100 microM and potent inhibitory activity of PDE3 with IC(50) = 0.20 microM.

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

Insights into ligand-elicited activation of human constitutive androstane receptor based on novel agonists and three-dimensional quantitative structure-activity relationship

The human constitutive androstane receptor (CAR, NR1I3) is an important regulator of xenobiotic metabolism and other physiological processes. So far, only few CAR agonists are known and no explicit mechanism has been proposed for their action. Thus, we aimed to generate a 3D QSAR model that could explain the molecular determinants of CAR agonist action. To obtain a sufficient number of agonists that cover a wide range of activity, we applied a virtual screening approach using both structure- and ligand-based methods. We identified 27 novel human CAR agonists on which a 3D QSAR model was generated. The model, complemented by coregulator recruitment and mutagenesis results, suggests a potential activation mechanism for human CAR and may serve to predict potential activation of CAR for compounds emerging from drug development projects or for chemicals undergoing toxicological risk assessment.

Exploring interactions of endocrine-disrupting compounds with different conformations of the human estrogen receptor alpha ligand binding domain: a molecular docking study

Endocrine-disrupting compounds (EDCs) accumulating in nature are known to interact with nuclear receptors. Especially important is the human estrogen receptor alpha (hERalpha), and several EDCs are either known or suspected to influence the activity of the ligand-binding domain (LBD). We here present a comparative docking study of both well-known hERalpha ligands and small organic compounds, including selected polychlorinated biphenyls (PCBs), plasticizers, and pesticides, that are all potentially endocrine-disrupting,into different conformations of the hERalpha LBD. Three newly found quasi-stable structures of the hERalhpa LBD are examined along with three crystallographic conformations of the protein, either theapo structure or using a protein structure with a bound agonist or antagonist ligand. The possible interactions between the protein and the potentially EDCs are described. It is found that most suspected EDCs can bind in the steroid binding cavity, interacting with at least one of the two hydrophilic ends of the steroid binding site. DDE, DDT, and HPTE are predicted to bind most strongly to the hERalpha LBD. It is predicted that these compounds can interact with the three conformations of hERalpha LBD with comparable affinities.The metabolic hydroxylation of aromatic compounds is found to lead to an increase in the binding affinity of PCBs as well as DDT. Docking into the quasi-stable conformations of the hERalpha LBD leads to computed binding affinities similar to or better than those calculated for the three X-ray structures, revealing that the new structures may be of importance for assessing the function of the influence of EDCs on nuclear receptors.

Design and synthesis of 4-methoxyphenylacetic acid esters as 15-lipoxygenase inhibitors and SAR comparative studies of them

A group of 4-methoxyphenylacetic acid esters were designed, synthesized and evaluated as potential inhibitors of soybean 15-lipoxygenase (SLO) on the basis of eugenol and esteragol structures. Compounds 7d-e showed the best IC(50) in SLO inhibition (IC(50)=3.8 and 1.9 microM, respectively). All compounds were docked in SLO active site and showed that carbonyl group of compounds is oriented toward the Fe(III)-OH moiety in the active site of enzyme and fixed by hydrogen bonding with hydroxyl group. It is assumed that lipophilic interaction of ligand-enzyme would be in charge of inhibiting the enzyme activity. The selectivity of the synthetic esters in inhibiting of 15-HLOb was also compared with 15-HLOa by molecular modeling and multiple alignment techniques.

Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis

P-glycoprotein (Pgp) is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents. This problem prompted several research groups to search for effective P-gp inhibitors. Cyclosporine A (CsA), aureobasidin A (AbA) and related analogues were reported to possess potent inhibitory actions against Pgp. In this work we employed receptor surface analysis (RSA) to construct two satisfactory receptor surface models (RSMs) for cyclosporine- and aureobasidin-based Pgp inhibitors. These pseudoreceptors were combined to achieve satisfactory three-dimensional quantitative structure activity relationship (3D-QSAR) for 68 different cyclosporine and aureobasidin derivatives. Upon validation against an external set of 16 randomly selected Pgp inhibitors, the optimal 3D-QSAR was found to be self-consistent and predictive (r(LOO)(2)=0.673, r(PRESS)(2)=0.600). The resulting 3D-QSAR was employed to probe the structural factors that control the inhibitory activities of cyclosporine and aureobasidin analogues against Pgp.

SAR comparative studies on pyrimido[4,5-b][1,4] benzothiazine derivatives as 15-lipoxygenase inhibitors, using ab initio calculations

The enzyme inhibitory activity of a new group of 2-substituted pyrimido[4,5-b][1,4]benzothiazines on soybean 15-lipoxygenase (15-LO) was evaluated and compared with those of their 4-methyl analogs using ab initio calculations. The results of these studies showed that the lack of 4-methyl substituent in the pyrimido[4,5-b][1,4] benzothiazine molecules greatly reduces their 15-LO inhibitory activities.

Receptor guided 3D-QSAR: a useful approach for designing of IGF-1R inhibitors

Research by other investigators has established that insulin-like growth factor‐1 receptor (IGF-1R) is a key oncological target, and that derivatives of 1, 3-disubstituted-imidazo[1,5-α] pyrazine are potent IGF-1R inhibitors. In this paper, we report on our three-dimensional quantitative structure activity relationship (3D-QSAR) studies for this series of compounds. We validated the 3D-QSAR models by the comparison of two major alignment schemes, namely, ligand-based (LB) and receptor-guided (RG) alignment schemes. The latter scheme yielded better 3D-QSAR models for both comparative molecular field analysis (CoMFA) (q² = 0.35, r² = 0.95) and comparative molecular similarity indices analysis (CoMSIA) (q² = 0.51, r² = 0.86). We submit that this might arise from the more accurate inhibitor alignment that results from using the structural information of the active site. We conclude that the receptor-guided 3D-QSAR may be helpful to design more potent IGF-1R inhibitors, as well as to understand their binding affinity with the receptor.

Receptor-specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery

The adaptation of forcefield-based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype alpha (ERalpha) and four ligands to the human retinoic acid receptor of isotype gamma (RARgamma). Using the FlexScreen all-atom receptor-ligand docking approach, we compare docking simulations parameterized by quantum-mechanical calculation of a large protein fragment with purely forcefield-based models. The use of receptor flexibility in the FlexScreen permits the treatment of all ligands in the same receptor model. We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R=0.81 for ERalpha and R=0.95 for RARgamma using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model.

3D-quantitative structure-activity relationship studies on benzothiadiazepine hydroxamates as inhibitors of tumor necrosis factor-alpha converting enzyme

A set of 29 benzothiadiazepine hydroxamates having selective tumor necrosis factor-alpha converting enzyme inhibitory activity were used to compare the quality and predictive power of 3D-quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices models for the atom-based, centroid/atom-based, data-based, and docked conformer-based alignment. Removal of two outliers from the initial training set of molecules improved the predictivity of models. Among the 3D-quantitative structure-activity relationship models developed using the above four alignments, the database alignment provided the optimal predictive comparative molecular field analysis model for the training set with cross-validated r(2) (q(2)) = 0.510, non-cross-validated r(2) = 0.972, standard error of estimates (s) = 0.098, and F = 215.44 and the optimal comparative molecular similarity indices model with cross-validated r(2) (q(2)) = 0.556, non-cross-validated r(2) = 0.946, standard error of estimates (s) = 0.163, and F = 99.785. These models also showed the best test set prediction for six compounds with predictive r(2) values of 0.460 and 0.535, respectively. The contour maps obtained from 3D-quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular similarity indices models exhibited good external predictivity as compared with that of comparative molecular field analysis models. The data generated from the present study helped us to further design and report some novel and potent tumor necrosis factor-alpha converting enzyme inhibitors.

Design and synthesis of eugenol derivatives, as potent 15-lipoxygenase inhibitors

A group of 4-allyl-2-methoxyphenol (eugenol) esters were designed, synthesized, and evaluated as potential inhibitors of soybean 15-lipoxygenase (SLO). Compounds 4c, 4d 4f, 4p, and 4q showed the best IC(50) in SLO inhibition (IC(50)=1.7, 2.3, 2.1, 2.2, and 0.017microM, respectively). All compounds were docked into SLO active site and showed that allyl group of compounds is oriented toward the iron atom in the active site of SLO. It is assumed that lipophilic interaction of ligand-enzyme would be in charge of inhibiting the enzyme activity. The selectivity of eugenol derivatives in inhibiting 15-HLOb was also compared with 15-HLOa by molecular modeling and multiple alignment techniques.

Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering? An investigation of the advantages and pitfalls of post-filtering

We have investigated the influence of post-filtering virtual screening results, with pharmacophoric features generated from an X-ray structure, on enrichment rates. This was performed using three docking softwares, zdock+, Surflex and FRED, as virtual screening tools and pharmacophores generated in UNITY from co-crystallized complexes. Sets of known actives along with 9997 pharmaceutically relevant decoy compounds were docked against six chemically diverse protein targets namely CDK2, COX2, ERalpha, fXa, MMP3, and NA. To try to overcome the inherent limitations of the well-known docking problem, we generated multiple poses for each compound. The compounds were first ranked according to their scores alone and enrichment rates were calculated using only the top scoring pose of each compound. Subsequently, all poses for each compound were passed through the different pharmacophores generated from co-crystallized complexes and the enrichment factors were re-calculated based on the top-scoring passing pose of each compound. Post-filtering with a pharmacophore generated from only one X-ray complex was shown to increase enrichment rates in all investigated targets compared to docking alone. This indicates that this is a general method, which works for diverse targets and different docking softwares.

Multistructure 3D-QSAR studies on a series of conformationally constrained butyrophenones docked into a new homology model of the 5-HT2A receptor

The present study is part of a long-term research project aiming to gain insight into the mechanism of action of atypical antipsychotics. Here we describe a 3D-QSAR study carried out on a series of butyrophenones with affinity for the serotonin-2A receptor, aligned by docking into the binding site of a receptor model. The series studied has two peculiarities: (i) all the compounds have a chiral center and can be represented by two enantiomeric structures, and (ii) many of the structures can bind the receptor in two alternative orientations, posing the problem of how to select a single representative structure for every compound. We have used an original solution consisting of the simultaneous use of multiple structures, representing different configurations, binding conformations, and positions. The final model showed good statistical quality (n = 426, r2 = 0.84, q2LOO = 0.81) and its interpretation provided useful information, not obtainable from the simple inspection of the ligand-receptor complexes.

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 -logIC50), using a non-cross-validated partial least squares analysis, generated a non-cross-validated q2=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 q2=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.

A novel structure-based virtual screening model for the hERG channel blockers

The hERG potassium channel is a key effector of cardiac repolarization and the blockade of this channel could cause arrhythmia. Thus, hERG channel blockade plays an important role for the potential pro-arrhythmic liability. In this report, binding of blockers to the hERG potassium channel is investigated using a combination of homology modeling, molecular docking, and molecular simulations, where blockade activities are evaluated using the linear regression model of GoldScore fitness. This structure-based virtual screening model is able to estimate the pIC(50) value of a wide range of ligands for the hERG potassium channel. The docked poses for ligands are also consistent with published mutation. Therefore, this model for the prediction of hERG channel blockade has the potential to provide cost-effective virtual screening tools for the evaluation of the cardiac liability of new chemical entities.

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function

We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand interactions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd < or = 2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five conformations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screening tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.