Design and automated generation of artificial estrogen receptor as potential endocrine disruptor chemical binders

We here report on our results concerning the systematic design and synthesis of a new type of artificial estrogen receptor candidates. Our interest herein originates from the known estrogenic activity of endocrine disrupting chemicals, environmental pollutants that have attracted increased attention due to their interference with the endocrine and reproductive system of wildlife and humans. An automated protocol for the generation of libraries of potential artificial receptors is described. The resulting structures could find application in novel SPE cartridges for EDC-preconcentration.     

Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation

The computational determination of binding modes for a ligand into a protein receptor is much more successful than the prediction of relative binding affinities (RBAs) for a set of ligands. Here we consider the binding of a set of 26 synthetic A-CD ligands into the estrogen receptor ERα. We show that the MOE default scoring function (London dG) used to rank the docked poses leads to a negligible correlation with experimental RBAs. However, switching to an energy-based scoring function, using a multiple linear regression to fit experimental RBAs, selecting top-ranked poses and then iteratively repeating this process leads to exponential convergence in 4–7 iterations and a very strong correlation. The method is robust, as shown by various validation tests. This approach may be of general use in improving the quality of predicted binding affinities.     

Automated affinity selection for rapid discovery of peptide binders

In-solution affinity selection (AS) of large synthetic peptide libraries affords identification of binders to protein targets through access to an expanded chemical space. Standard affinity selection methods, however, can be time-consuming, low-throughput, or provide hits that display low selectivity to the target. Here we report an automated bio-layer interferometry (BLI)-assisted affinity selection platform. When coupled with tandem mass spectrometry (MS), this method enables both rapid de novo discovery and affinity maturation of known peptide binders with high selectivity. The BLI-assisted AS-MS technology also features real-time monitoring of the peptide binding during the library selection process, a feature unattainable by current selection approaches. We show the utility of the BLI AS-MS platform toward rapid identification of novel nanomolar (dissociation constant, K_D < 50 nM) non-canonical binders to the leukemia-associated oncogenic protein menin. To our knowledge, this is the first application of BLI to the affinity selection of synthetic peptide libraries. We believe our approach can significantly accelerate the use of synthetic peptidomimetic libraries in drug discovery.

This work reports an automated affinity selection-mass spectrometry (AS-MS) approach amenable to both de novo peptide binder discovery and affinity maturation of known binders in a high-throughput and selective manner.     

Generation and selection of novel estrogen receptor ligands using the de novo structure-based design tool, SkelGen

A de novo design approach to generating novel estrogen receptor (ER) ligands is described. The SkelGen program was used to generate ligands in the active sites of seven crystal structures of ERalpha. Seventeen high-scoring, diverse structures were selected from the SkelGen output and synthesized without introducing any modifications to the structures. Five ligands, four of which are novel, showed < or = 25 microM affinity, with the best compound displaying an IC50 of 340 nM. SkelGen can, therefore, be a powerful tool for designing active molecules.     

Boosting model performance and interpretation by entangling preprocessing selection and variable selection

The aim of data preprocessing is to remove data artifacts—such as a baseline, scatter effects or noise—and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames.     

Quantitative structure-affinity relationship of 5-HT1A receptor ligands by the classification tree method

The influence of molecular structure of 346 ligands on their affinity for 5-HT1A receptors was investigated. It was shown that the effectiveness of the proposed novel approach for interpretation of decision tree models compared favourably with the PLS method. In the context of the proposed approach, molecular fragments and their values of the relative influence on the affinity for 5-HT1A receptors were defined.     

Quantitative structure-affinity relationship of 5-HT1A receptor ligands by the classification tree method

The influence of molecular structure of 346 ligands on their affinity for 5-HT_1A receptors was investigated. It was shown that the effectiveness of the proposed novel approach for interpretation of decision tree models compared favourably with the PLS method. In the context of the proposed approach, molecular fragments and their values of the relative influence on the affinity for 5-HT_1A receptors were defined.     

Determination of estrogen presence in water by SPR using estrogen receptor dimerization

Estrogenic compounds are a class of pharmaceutical products harmful to animals and a cause of environmental damage. The biological activity of these compounds is high since they have been designed to act at low concentrations. Thus, even at the low concentrations found in the environment, they may produce deleterious effects on aquatic organisms as well as on humans, who might be contaminated in a number of ways (via drinking water or contaminated food, for example). We used the property of these compounds to bind a specific protein (estrogen receptor, ER) to develop a quantification method of these chemical entities. Estrogenic compound detection was performed using ER dimerization properties monitored by surface plasmon resonance (SPR). The ligand-activated ER dimer was detected by its interaction with a specific DNA consensus sequence estrogen response element. The concentration and the nature of the estrogenic compounds modified the SPR signal and were characteristic of the ligand-dependent homodimerization of ER. For 17β-estradiol, dimerization of ER was experimentally determined at an ER to 17β-estradiol ratio near 1:1. Estrogenic compounds (17β-estradiol, estriol, estrone, ethynyl estradiol) activated the dimerization process at different concentration levels, while some others (tamoxiphen, resveratrol, genistein, bisphenol A) did not seem to have any effects on it. We demonstrated that this method allows the direct detection of 17β-estradiol at concentrations above 1.4 μg/L (5 nM).     

基于雌激素β受体结构雌激素类化合物的三维定量结构-活性关系与分子对接研究

雌激素类化合物由于其对人和野生动物健康的负面影响而受到广泛关注.雌激素受体存在α和β两种亚型,由于雌激素β受体（ERβ）与α受体（ERα）两者结合腔中的氨基酸序列存在明显差异,因此配体化合物在与雌激素β受体和α受体的结合活性和模式上也可能存在较大差别.本文以50个与雌激素β受体结合的化合物为研究对象,应用比较分子相似性指数分析（COMSIA）的三维定量结构-活性关系（3D-QSAR）分析方法研究化合物结构与活性之间的关系,比较了原子契合和基于受体结构两种分子叠合方式对模型质量的影响,建立了相关性显著、预测能力强的定量活性预测模型（R^2=0.961,qLOO^2=0.671,R^2Pred=0.722）,并结合分子对接方法揭示了影响化合物活性的分子结构特征和分子机理.     

Environmetric modeling and interpretation of river water monitoring data

This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water.As a result more objective ecological policy and decision making is possible.     

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data

In the present work, microwave heating initiated precipitation polymerization was developed to prepare podophyllotoxin (PPT) molecularly imprinted polymers (MIPs), resulting in much shorter polymerization time and better particle morphology. Prior to the polymerization, ultraviolet and FTIR spectroscopy were used to study the interactions between PPT and the functional monomers. The synthesized parameters were respectively optimized and the optimal conditions for the efficient adsorption property were template: PPT, 1 mmol; functional monomer: acrylamide, 6 mmol; bi-crosslinker: ethylene glycol dimethacrylate, 20 mmol and divinylbenzene, 20 mmol; porogen: acetonitrile, 40 mL; initiator: azobisisobutyronitrile, 0.01mol L?1; polymerization temperature: 60°C. FTIR spectroscopy, SEM and thermal analysis were used to characterize the MIPs. The results of the equilibrium rebinding experiments and the competitive adsorption experiments showed that these imprinted polymers exhibited good adsorption ability for the PPT. Scatchard analysis illustrated that two and one types of binding sites were generated in the MIPs and non-imprinted polymers (NIPs), respectively. Using the prepared MIPs as the solid phase extraction (SPE) sorbent, PPT was extracted selectively and efficiently from Dysosma versipellis, Sinopodophyllum hexandrum and Diphylleia sinensis. The regression equation was y=5.873×10?x+17075.659 with the correlation coefficient of 0.9994 in the concentration range of 0.005-0.4 mg mL?1. After washing and eluting the SPE column with methanol and MeOH/acetic acid solution (v/v, 9:1), the limits of detection were 0.12-0.18 μg mL?1 and their recoveries were in the range of 89.5-91.1% with all RSDs lower than 3.7.     

Numerical interpretation of molecular surface field in dielectric modeling of solvation

Continuum solvent models, particularly those based on the Poisson‐Boltzmann equation (PBE), are widely used in the studies of biomolecular structures and functions. Existing PBE developments have been mainly focused on how to obtain more accurate and/or more efficient numerical potentials and energies. However to adopt the PBE models for molecular dynamics simulations, a difficulty is how to interpret dielectric boundary forces accurately and efficiently for robust dynamics simulations. This study documents the implementation and analysis of a range of standard fitting schemes, including both one‐sided and two‐sided methods with both first‐order and second‐order Taylor expansions, to calculate molecular surface electric fields to facilitate the numerical calculation of dielectric boundary forces. These efforts prompted us to develop an efficient approximated one‐dimensional method, which is to fit the surface field one dimension at a time, for biomolecular applications without much compromise in accuracy. We also developed a surface‐to‐atom force partition scheme given a level set representation of analytical molecular surfaces to facilitate their applications to molecular simulations. Testing of these fitting methods in the dielectric boundary force calculations shows that the second‐order methods, including the one‐dimensional method, consistently perform among the best in the molecular test cases. Finally, the timing analysis shows the approximated one‐dimensional method is far more efficient than standard second‐order methods in the PBE force calculations. © 2017 Wiley Periodicals, Inc.     

Ligand-selective inhibition of the interaction of steroid receptor coactivators and estrogen receptor isoforms

Ligand-dependent nuclear hormone receptor (NR) signaling requires direct interaction between NR and the steroid receptor coactivators (SRC). Herein we utilize a library of SRC2 peptidomimetics to select for specific inhibitors of the interaction of SRC2 with the two estrogen receptor (ER) isoforms, ERalpha and ERbeta, in the presence of three different ligands: 17beta-estradiol, diethylstilbesterol, and genistein. The pattern of inhibitor selectivity for each ER isoform varied depending upon which ligand was present, thus demonstrating that the ligands exert unique allosteric effects upon the surface of the SRC binding pocket. Several of the lead compounds are highly (>100-fold) selective for blocking the binding of SRC2 to ERalpha, in preference to ERbeta, in the presence of one ligand and therefore may prove useful for decoupling ERbeta signaling from ERalpha signaling.     

Theoretical modeling and interpretation of X-ray absorption spectra of liquid water

We report extensive calculations to examine the capability of theory to explain the XAS spectra of liquid water. Several aspects that enter the theoretical model are addressed, such as the quantum mechanical methods, the statistics and the XAS model. As input into our quantum mechanical calculations we will use structural information on liquid water obtained from first principles and from classical molecular dynamics simulations. As XAS models, we will examine the full core hole and the half core hole approximations to transition state theory. The quantum mechanics is performed on the basis of density functional theory. We conclude from this study that recent experimental results are fully consistent with, and can be completely explained by, present day theory, in particular, the pre-edge peak is reproduced. We also find that the average bond coordination in liquid water is 3.1 and that the assertion in a recent paper that the hydrogen bond number is much less than that cannot be substantiated. Our calculations emphasize that further advances in our understanding of water can only be made by more sophisticated spectroscopy with significantly increased resolution.     

Variable selection in multivariate calibration based on clustering of variable concept

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.