Semi‐parametric estimation of non‐separable models: a minimum distance from independence approach

Summary This paper studies non‐separable structural models that are of the form with U uniform on in which is a known real function parametrized by a structural parameter . We study the case in which contains a finite dimensional component and an infinite dimensional component h. We assume that the true value is identified by the restriction . Our proposal is to estimate by a minimum distance from independence (MDI) criterion. We show that: (a) our estimator for is consistent and we obtain rates of convergence and (b) the estimator for is consistent and asymptotically normally distributed.     

Insight into selectivity of peptidomimetic inhibitors with modified statine core for plasmepsin II of Plasmodium falciparum over human cathepsin D

Plasmepsin II (PlmII), an aspartic protease expressed in the food vacuole of Plasmodium falciparum (pf), cleaves the hemoglobin of the host during the erythrocytic stage of the parasite life cycle. Various peptidomimetic inhibitors of PlmII reported so far discriminate poorly between the drug target and aspartic proteases of the host organism, e.g., human cathepsin D (hCatD). hCatD is a protein digestion enzyme and signaling molecule involved in a variety of physiological processes; therefore, inhibition of hCatD by PlmII inhibitors may lead to pathophysiological conditions. In this study, binding of PlmII inhibitors has been modeled using the crystal structures of pfPlmII and hCatD complexes to gain insight into structural requirements underlying the target selectivity. A series of 26 inhibitors were modeled in the binding clefts of the pfPlmII and hCatD to establish QSAR models of the protease inhibition. In addition, 3D‐QSAR pharmacophore models were generated for each enzyme. It was concluded that the contributions of the P₂ and P_3′ residues to the inhibitor’s binding affinity are responsible for the target selectivity. Based on these findings, new inhibitor candidates were designed with predicted inhibition constants reaching 0.2 nm and selectivity index (S.I.) = > 1200.     

3D-QSAR studies of arylcarboxamides with inhibitory activity on InhA using pharmacophore-based alignment

Enoyl acyl carrier protein reductase (InhA) is a promising target for the development of antituberculosis drugs. The InhA-bound conformation of an indole-5-amide inhibitor (Genz 10850) (PDB code: IP44) was used to build a pharmacophore model by LigandScout. This model was then successfully used to identify the bioactive conformation and align 40 structurally diverse arylcarboxamide derivatives. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on arylcarboxamides-based InhA inhibitors based on pharmacophore alignment. The best prediction was obtained with CoMSIA model combining steric and electrostatic fields (, r² = 0.972). The model was validated by an external test set, which gave a good predictive value (). Graphical interpretation of the results revealed important structural features of the zarylcarboxamides related to the active site of InhA. The results may be exploited for further design and virtual screening for some novel InhA inhibitors.     

Orthogonal to backward mean transformation for dynamic panel data models

Summary The within‐groups estimator is inconsistent in dynamic panels with fixed T as the individual sample mean of the lagged dependent variable used in the within transformation is contemporaneously correlated with the idiosyncratic error term. This paper suggests transforming the lagged dependent variable into orthogonal deviations from its individual backward mean, which is contemporaneously uncorrelated with the idiosyncratic error term. As this transformation eliminates the individual effects as but not for T fixed, this alternative estimator is consistent for but inconsistent for and T fixed. The inconsistency for fixed T is shown to be negligibly small, though. Moreover, a Monte Carlo simulation shows that overall, it has superior small sample properties compared to other dynamic panel data estimators.     

Misspecification in moment inequality models: back to moment equalities?

Summary Consider the linear model where one is interested in learning about β given data on y and x and when y is interval measured; that is, we observe such that . Moment inequality procedures use the implication . As compared to least squares in the classical regression model, estimates obtained using an objective function based on these moment inequalities do not provide a clear approximation to the underlying unobserved conditional mean function. Most importantly, under misspecification, it is not unusual that no parameter β satisfies the previous inequalities for all values of x, and hence minima of an objective function based on these moment inequalities are typically tight. We construct set estimates for β in the linear model that have a clear interpretation when the model is misspecified. These sets are based on moment equality models. We illustrate these sets and compare them to estimates obtained using moment inequality‐based methods. In addition to the linear model with interval outcomes we also analyse the binary missing data model with a monotone instrument assumption (MIV), we find there that when this assumption is misspecified, bounds can still be non‐empty, and can differ from parameters obtained via maximum likelihood. We also examine a bivariate discrete game with multiple equilibria. In sum, misspecification in moment inequality models is of a different flavour than in moment equality models, and so care should be taken with (1) the_interpretation of the estimates and (2) the size of the ‘identified set’.     

Binding Orientations,QSAR, and Molecular Design of Thiophene Derivative Inhibitors

A theoretical study on binding orientations and quantitative structure–activity relationship of thiophene derivatives as inhibitors towards tubulin has been carried out by using the docking analysis and the comparative molecular field analysis. The appropriate binding orientations and conformations of these compounds interacting with tubulin were revealed by docking study; and a 3D‐quantitative structure–activity relationship model showing significant statistical quality and satisfying predictive ability was established, in which the correlation coefficient (R²) and cross‐validation coefficient (q²) are 0.949 and 0.743, respectively. The same model was further applied to predict the pIC₅₀ values for nine congeneric compounds as external test set, and the predictive correlation coefficient reaches 0.929, thus the predictive ability of this 3D‐quantitative structure–activity relationship model can be further confirmed. Some key structural factors of the compounds responsible for cytotoxicity were discussed in detail. Based on these structural factors, three new compounds with higher activity have been designed, and their cytotoxicities were also predicted by the established 3D‐quantitative structure–activity relationship model from comparative molecular field analysis as well as the docking analysis. We hope these theoretical results can be confirmed by experimental work.     

Conformational features responsible for the binding of cyclic analogues of enkephalin to opioid receptors

Models of μ- and δ-receptor-bound backbone conformations of enkephalin cyclic analogues containing Phe⁴ were determined by comparing geometrical similarity among the previously found low-energy, backbone structures of -enkephalinamide, -enkephalinamide, -enkephalin and -enkephalin. The present μ-receptor-bound conformation resembles a β-I bend in the peptide backbone centred on the Gly³-Phe⁴ region. Two slightly different models were found for the δ-receptor-bound conformation; both of them are more extended than the μ-receptor-bound conformation and include a γ-turn (or a γ-like turn) on the Gly³ residue. Energetically favourable rotamers of Tyr and Phc side chains were also determined for the μ- and δ-conformations. The present models of μ- and δ-conformations share geometrical similarity with the low-energy structures of Leu-enkephalin and the analogue.     

Computational Study and Modified Design of Selective Dopamine D3 Receptor Agonists

Dopamine D₃ receptor (D₃R) is considered as a potential target for the treatment of nervous system disorders, such as Parkinson's disease. Current research interests primarily focus on the discovery and design of potent D₃ agonists. In this work, we selected 40 D₃R agonists as the research system. Comparative molecular field analysis (CoMFA) of three‐dimensional quantitative structure–activity relationship (3D‐QSAR), structure–selectivity relationship (3D‐QSSR), and molecular docking was performed on D₃ receptor agonists to obtain the details at atomic level. The results indicated that both the CoMFA model (r² = 0.982, q² = 0.503, = 0.893, SEE = 0.057, F = 166.308) for structure–activity and (r² = 0.876, q² = 0.436, = 0.828, F = 52.645) for structure–selectivity have good predictive capabilities. Furthermore, docking studies on three compounds binding to D₃ receptor were performed to analyze the binding modes and interactions. The results elucidate that agonists formed hydrogen bond and hydrophobic interactions with key residues. Finally, we designed six molecules under the guidance of 3D‐QSAR/QSSR models. The activity and selectivity of designed molecules have been improved, and ADMET properties demonstrate they have low probability of hepatotoxicity (<0.5). These results from 3D‐QSAR/QSSR and docking studies have great significance for designing novel dopamine D₃ selective agonists in the future.     

Synthesis,Biological Evaluation,Mechanism of Action and Quantitative Structure–Activity Relationship Studies of Chalcones as Antibacterial Agents

Forty‐eight chalcone analogs were synthesized and their in vitro antibacterial activity against Staphylococcus aureus NCIM 5021, Bacillus subtilis NCIM 2718, Phaseolus vulgaris NCIM 2813, Escherichia coli NCIM 2931, Salmonella typhi 2501 and Enterobacter aerogenes NCIM 5139 were evaluated by microdilution broth assay. Quantitative structure–activity relationships were developed for all the cases (r ² = 0.68–0.79; = 0.58–0.78; q ² = 0.51–0.68; F = 13.02–61.51). Size, polarizability, electron‐donating/withdrawing and hydrophilic nature of the molecule determine the activity against these Gram‐positive and Gram‐negative bacteria. Staphylococcus aureus was the most and S. typhi was the least hydrophobic of these organisms. These chalcones act better against more hydrophobic organisms. The more active chalcones have log P between 1.5 and 3. Compound 24 , one of the most active compounds, was found to act by damaging the cell wall of S. aureus. Slimicidal activity of five of the most active compounds ( 24 , 31 , 32 , 34 and 37 ) was found to be in the range of 48–60% against S. aureus and 40–54% against E. coli. A correlation was observed among the hydrophobicity of the compounds, hydrophobicity of the bacterial cell surface and the antibacterial activity of the compound.     

Identification of novel HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking approach

The objective of this study is to identify novel HIV‐1 integrase (IN) inhibitors. Here, shape‐based screening and QSAR have been successfully implemented to identify the novel inhibitors for HIV‐1 IN, and in silico validation is performed by docking studies. The 2D QSAR model of benzodithiazine derivatives was built using genetic function approximation (GFA) method with good internal (cross‐validated r² = 0.852) and external prediction (). Best docking pose of highly active molecule of the benzodithiazine derivatives was used as a template for shape‐based screening of ZINC database. Toxicity prediction was also performed using Deductive Estimation of Risk from Existing Knowledge (DEREK) program to filter non‐toxic molecules. Inhibitory activities of screened non‐toxic molecules were predicted using derived QSAR models. Active, non‐toxic screened molecules were also docked into the active site of HIV‐1 IN using Auto Dock and dock program. Some molecules docked similarly as highly active molecule of the benzodithiazine derivatives. These molecules also followed the same docking interactions in both the programs. Finally, four benzodithiazine derivatives were identified as novel HIV‐1 integrase inhibitors based on QSAR predictions and docking interactions. ADME properties of these molecules were also computed using Discovery Studio.     

The practice of non‐parametric estimation by solving inverse problems: the example of transformation models

Summary This paper considers a semi‐parametric version of the transformation model φ (Y) =β′X+U under exogeneity or instrumental variables assumptions (E(U∣X) = 0 or . This model is used as an example to illustrate the practice of the estimation by solving linear functional equations. This paper is specially focused on the data‐driven selection of the regularization parameter and of the bandwidths. Simulations experiments illustrate the relevance of this approach.     

Cis/trans conformational equilibrium across the N‐methylphenylalanine2‐ N‐methylphenylalanine3 peptide bond of arginine vasopressin analogs

Abstract: Two cyclic analogs of vasopressin, ‐Pro‐Arg‐Gly‐NH₂ ( 1 ) and ‐Pro‐Arg‐Gly‐NH₂ ( 2 ) were synthesized by the solid phase method. Their structure was determined in aqueous solution by two‐dimensional NMR techniques and simulated annealing algorithm from an extended template in X‐PLOR. The total chemical shift correlation spectroscopy and rotating‐frame Overhauser enhancement spectroscopy of the peptides displayed four distinct sets of residual proton resonances. This suggests that both analogs adopt four families of conformations in H₂O/D₂O (9 : 1) (one major and three minor species). In further analysis only signals of major species (M) and of one minor species (m₁) were considered. The major species of both peptides include a trans peptide bond between the first and second residue, and a cis form between the second and third residue. In the minor species, all peptide bonds were found to exist in trans geometry.     

Semiparametric estimation of the Box–Cox transformation model

Summary In this paper, I propose a semiparametric estimation procedure for the Box–Cox transformation model. I show a global identification result under mild conditions that allow conditional heteroskedastic error terms. The proposed estimator minimizes a second order U‐process and does not require any user‐chosen values such as a smoothing parameter that sometimes induces unstable inference result. With a slight modification, it can also be applied to random censoring which depends on covariates in an arbitrary way. The estimator converges to an asymptotic normal distribution at the rate of and Monte Carlo experiments show adequate finite sample performance.     

Conformational modification of the PRP-hexapeptide by a direct covalent attachment of aromatic side chain groups

PRP-hexapeptide possessing the azo-bridge between Tyr¹ and Phe⁵ residues, called azo-PRP-hexapeptide: Tyr-Val-Pro-Leu-Phe-Pro, was synthesized and tested for immunoregulatory activity. High biological activity of the synthesized azo-PRP-hexapeptide suggests that the biologically active conformation of PRP-hexapeptide must be such that both aromatic rings (Tyr and Phe) are apparently close to each other.     

Identification and estimation of partially linear censored regression models with unknown heteroscedasticity

In this paper, we introduce a new identification and estimation strategy for partially linear regression models with a general form of unknown heteroscedasticity, that is, and , where ε is independent of and the functional forms of both and are left unspecified. We show that in such a model, β₀ and can be exactly identified while can be identified up to scale as long as permits sufficient nonlinearity in X. A two‐stage estimation procedure motivated by the identification strategy is described and its large sample properties are formally established. Moreover, our strategy is flexible enough to allow for both fixed and random censoring in the dependent variable. Simulation results show that the proposed estimator performs reasonably well in finite samples.