Advances in GPCR Modeling Evaluated by the GPCR Dock 2013 Assessment: Meeting New Challenges

Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems

Применение синтетического аналога простагландина Е1 для подготовки шейки матки и индукции родов

A clinical prospective examination of 90 women with complete pregnancy and indications for labor induction because of unsatisfactory maturity of uterus cervix has been made. The aim was to create a comparative analysis of efficiency of intravaginal introduction of prostaglandin synthetic analogue E1 misoprostol («Sytotec») and intracervical introduction of prostaglandin E2 dinoprostone («Prepidil» gel) for uterus cervix preparation and labor induction at complete pregnancy. Misoprostol in a dose of 25 mkg has been introduced to pregnant women of the 1 group (n=44), every 4 hours not more than 3 times. In case of discharge of waters or labor activity the second introduction has not been done. Dinoprostone has been introduced intracervically in a single dose to pregnant women of the 2 group (n=46). The use of misoprostol has been accompanied by spontaneous beginning of labor activity by 2 times more often than the use of dinoprostone. The quantity of vaginal births within 12 and 24 hours of observation has been surely greater and the duration of time between the beginning of introduction and labor has been surely smaller in the group of women received misoprostol as compared to the one received dinoprostone. It has not been revealed any differences between examined groups by the frequency of uterus hyperstimulation symptom development, labor duration, frequency of abdominal and vaginal labor, as well as perinatal outcomes.С целью сравнительного анализа эффективности интравагинального введения синтетического аналога простагландина Е1 мизопростола («Сайтотек») и интрацервикального введения простагландина Е2 динопростона (гель «Препидил») для подготовки шейки матки и индукции родов при доношенной беременности проведено клиническое проспективное исследование 90 женщин с доношенной беременностью, имеющих показания для родовозбуждения при неудовлетворительной зрелости шейки матки. Беременным 1-й группы (n = 44) интравагинально вводили мизопростол в дозе 25 мкг через каждые 4 ч не более 3 раз. Повторное введение препарата не проводили в случае отхождения вод или развития родовой деятельности. Беременным 2-й группы (n = 46) однократно интрацервикально вводили динопростон. Применение мизопростола в 2 раза чаще сопровождалось самостоятельным началом родовой деятельности, чем при использовании динопростона. Количество влагалищных родов в течение 12 и 24 ч наблюдения было достоверно больше, а продолжительность времени от начала введения препарата до родоразрешения достоверно меньше в группе женщин, получавших мизопростол, по сравнению с группой пациенток, получавших динопростон. Не было выявлено различий между исследуемыми группами в частоте развития симптомов гиперстимуляции матки, продолжительности родов, частоте абдоминального и влагалищного родоразрешений, а также перинатальных исходах

Improved methodical approach for quantitative BRET analysis of G protein coupled receptor dimerization

G Protein Coupled Receptors (GPCR) can form dimers or higher ordered oligomers, the process of which can remarkably influence the physiological and pharmacological function of these receptors. Quantitative Bioluminescence Resonance Energy Transfer (qBRET) measurements are the gold standards to prove the direct physical interaction between the protomers of presumed GPCR dimers. For the correct interpretation of these experiments, the expression of the energy donor Renilla luciferase labeled receptor has to be maintained constant, which is hard to achieve in expression systems. To analyze the effects of non-constant donor expression on qBRET curves, we performed Monte Carlo simulations. Our results show that the decrease of donor expression can lead to saturation qBRET curves even if the interaction between donor and acceptor labeled receptors is non-specific leading to false interpretation of the dimerization state. We suggest here a new approach to the analysis of qBRET data, when the BRET ratio is plotted as a function of the acceptor labeled receptor expression at various donor receptor expression levels. With this method, we were able to distinguish between dimerization and non-specific interaction when the results of classical qBRET experiments were ambiguous. The simulation results were confirmed experimentally using rapamycin inducible heterodimerization system. We used this new method to investigate the dimerization of various GPCRs, and our data have confirmed the homodimerization of V2 vasopressin and CaSR calcium sensing receptors, whereas our data argue against the heterodimerization of these receptors with other studied GPCRs, including type I and II angiotensin, β2 adrenergic and CB1 cannabinoid receptors

Identification of Anti-Malarial Compounds as Novel Antagonists to Chemokine Receptor CXCR4 in Pancreatic Cancer Cells

Despite recent advances in targeted therapies, patients with pancreatic adenocarcinoma continue to have poor survival highlighting the urgency to identify novel therapeutic targets. Our previous investigations have implicated chemokine receptor CXCR4 and its selective ligand CXCL12 in the pathogenesis and progression of pancreatic intraepithelial neoplasia and invasive pancreatic cancer; hence, CXCR4 is a promising target for suppression of pancreatic cancer growth. Here, we combined in silico structural modeling of CXCR4 to screen for candidate anti-CXCR4 compounds with in vitro cell line assays and identified NSC56612 from the National Cancer Institute's (NCI) Open Chemical Repository Collection as an inhibitor of activated CXCR4. Next, we identified that NSC56612 is structurally similar to the established anti-malarial drugs chloroquine and hydroxychloroquine. We evaluated these compounds in pancreatic cancer cells in vitro and observed specific antagonism of CXCR4-mediated signaling and cell proliferation. Recent in vivo therapeutic applications of chloroquine in pancreatic cancer mouse models have demonstrated decreased tumor growth and improved survival. Our results thus provide a molecular target and basis for further evaluation of chloroquine and hydroxychloroquine in pancreatic cancer. Historically safe in humans, chloroquine and hydroxychloroquine appear to be promising agents to safely and effectively target CXCR4 in patients with pancreatic cancer

Prediction of Peptide Reactivity with Human IVIg through a Knowledge-Based Approach

The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far

Spatial chemical distance based on atomic property fields

Similarity of compound chemical structures often leads to close pharmacological profiles, including binding to the same protein targets. The opposite, however, is not always true, as distinct chemical scaffolds can exhibit similar pharmacology as well. Therefore, relying on chemical similarity to known binders in search for novel chemicals targeting the same protein artificially narrows down the results and makes lead hopping impossible. In this study we attempt to design a compound similarity/distance measure that better captures structural aspects of their pharmacology and molecular interactions. The measure is based on our recently published method for compound spatial alignment with atomic property fields as a generalized 3D pharmacophoric potential. We optimized contributions of different atomic properties for better discrimination of compound pairs with the same pharmacology from those with different pharmacology using Partial Least Squares regression. Our proposed similarity measure was then tested for its ability to discriminate pharmacologically similar pairs from decoys on a large diverse dataset of 115 protein–ligand complexes. Compared to 2D Tanimoto and Shape Tanimoto approaches, our new approach led to improvement in the area under the receiver operating characteristic curve values in 66 and 58% of domains respectively. The improvement was particularly high for the previously problematic cases (weak performance of the 2D Tanimoto and Shape Tanimoto measures) with original AUC values below 0.8. In fact for these cases we obtained improvement in 86% of domains compare to 2D Tanimoto measure and 85% compare to Shape Tanimoto measure. The proposed spatial chemical distance measure can be used in virtual ligand screening

Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p

Predicting Inactive Conformations of Protein Kinases Using Active Structures: Conformational Selection of Type-II Inhibitors

Protein kinases have been found to possess two characteristic conformations in their activation-loops: the active DFG-in conformation and the inactive DFG-out conformation. Recently, it has been very interesting to develop type-II inhibitors which target the DFG-out conformation and are more specific than the type-I inhibitors binding to the active DFG-in conformation. However, solving crystal structures of kinases with the DFG-out conformation remains a challenge, and this seriously hampers the application of the structure-based approaches in development of novel type-II inhibitors. To overcome this limitation, here we present a computational approach for predicting the DFG-out inactive conformation using the DFG-in active structures, and develop related conformational selection protocols for the uses of the predicted DFG-out models in the binding pose prediction and virtual screening of type-II ligands. With the DFG-out models, we predicted the binding poses for known type-II inhibitors, and the results were found in good agreement with the X-ray crystal structures. We also tested the abilities of the DFG-out models to recognize their specific type-II inhibitors by screening a database of small molecules. The AUC (area under curve) results indicated that the predicted DFG-out models were selective toward their specific type-II inhibitors. Therefore, the computational approach and protocols presented in this study are very promising for the structure-based design and screening of novel type-II kinase inhibitors

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

Lestaurtinib Inhibits Histone Phosphorylation and Androgen-Dependent Gene Expression in Prostate Cancer Cells

Background: Epigenetics is defined as heritable changes in gene expression that are not based on changes in the DNA sequence. Posttranslational modification of histone proteins is a major mechanism of epigenetic regulation. The kinase PRK1 (protein kinase C related kinase 1, also known as PKN1) phosphorylates histone H3 at threonine 11 and is involved in the regulation of androgen receptor signalling. Thus, it has been identified as a novel drug target but little is known about PRK1 inhibitors and consequences of its inhibition. Methodology/Principal Finding: Using a focused library screening approach, we identified the clinical candidate lestaurtinib (also known as CEP-701) as a new inhibitor of PRK1. Based on a generated 3D model of the PRK1 kinase using the homolog PKC-theta (protein kinase c theta) protein as a template, the key interaction of lestaurtinib with PRK1 was analyzed by means of molecular docking studies. Furthermore, the effects on histone H3 threonine phosphorylation and androgen-dependent gene expression was evaluated in prostate cancer cells. Conclusions/Significance: Lestaurtinib inhibits PRK1 very potently in vitro and in vivo. Applied to cell culture it inhibits histone H3 threonine phosphorylation and androgen-dependent gene expression, a feature that has not been known yet. Thus our findings have implication both for understanding of the clinical activity of lestaurtinib as well as for future PRK