Structure-activity relationships for α-calcitonin gene-related peptide

Calcitonin gene-related peptide (CGRP) is a member of the calcitonin (CT) family of peptides. It is a widely distributed neuropeptide implicated in conditions such as neurogenic inflammation. With other members of the CT family, it shares an N-terminal disulphide-bonded ring which is essential for biological activity, an area of potential α-helix, and a C-terminal amide. CGRP binds to the calcitonin receptor-like receptor (CLR) in complex with receptor activity-modifying protein 1 (RAMP1), a member of the family B (or secretin-like) GPCRs. It can also activate other CLR or calcitonin-receptor/RAMP complexes. This 37 amino acid peptide comprises the N-terminal ring that is required for receptor activation (residues 1-7); an α-helix (residues 8-18), a region incorporating a β-bend (residues 19-26) and the C-terminal portion (residues 27-37), that is characterized by bends between residues 28-30 and 33-34. A few residues have been identified that seem to make major contributions to receptor binding and activation, with a larger number contributing either to minor interactions (which collectively may be significant), or to maintaining the conformation of the bound peptide. It is not clear if CGRP follows the pattern of other family B GPCRs in binding largely as an α-helix. Linked Articles This article is part of a themed section on Neuropeptides. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2013.170.issue-7 © 2012 The Authors. British Journal of Pharmacology © 2012 The British Pharmacological Society.

Structure-activity relationships of the N-terminus of calcitonin gene-related peptide:key roles of alanine-5 and threonine-6 in receptor activation

Background and purpose - The N-terminus of calcitonin gene-related peptide (CGRP) is important for receptor activation, especially the disulphide-bonded ring (residues 1-7). However, the roles of individual amino acids within this region have not been examined and so the molecular determinants of agonism are unknown. This study has examined the role of residues 1, 3-6 and 8-9, excluding Cys-2 and Cys-7. Experimental approach - CGRP derivatives were substituted with either cysteine or alanine; further residues were introduced at position 6. Their affinity was measured by radioligand binding and their efficacy by measuring cAMP production in SK-N-MC cells and ß-arrestin 2 translocation in CHO-K1 cells at the CGRP receptor. Key results - Substitution of Ala-5 by cysteine reduced affinity 270-fold and reduced efficacy for production of cAMP in SK-N-MCs. Potency at ß-arrestin translocation was reduced by 9-fold. Substitution of Thr-6 by cysteine destroyed all measurable efficacy of both cAMP and ß-arrestin responses; substitution with either alanine or serine impaired potency. Substitutions at positions 1, 4, 8 and 9 resulted in approximately 10-fold reductions in potency at both responses. Similar observations were made at a second CGRP-activated receptor, the AMY1(a) receptor. Conclusions and implications - Ala-5 and Thr-6 are key determinants of agonist activity for CGRP. Ala-5 is also very important for receptor binding. Residues outside of the 1-7 ring also contribute to agonist activity.

Structure-activity relationship of calcitonin gene-related peptide and its receptor

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Structure-activity relations in Cs-doped heteropolyacid catalysts for biodiesel production

A series of insoluble heteropolytungstate (H3PW12O40 HPW) salts, CsxH3−xPW12O40 (x=0.9–3x=0.9–3), were synthesized and characterized using a range of bulk and surface sensitive probes including N2 porosimetry, powder XRD, FTIR, XPS, 31P MAS NMR, and NH3 calorimetry. Materials with Cs content in the range x=2.0–2.7x=2.0–2.7 were composed of dispersed crystallites with surface areas ∼100 m2 g−1 and high Brönsted acid strengths [ΔH0ads(NH3)=−150 kJmol−1], similar to the parent heteropolyacid. The number of accessible surface acid sites probed by α -pinene isomerization correlated well with those determined by NH3 adsorption calorimetry and surface area measurements. CsxH3−xPW12O40 were active toward the esterification of palmitic acid and transesterification of tributyrin, important steps in fatty acid and ester processing for biodiesel synthesis. Optimum performance occurs for Cs loadings of x=2.0–2.3x=2.0–2.3, correlating with the accessible surface acid site density. These catalysts were recoverable with no leaching of soluble HPW.

Using Vibrational Infrared Biomolecular Spectroscopy to Detect Heat-Induced Changes of Molecular Structure in Relation to Nutrient Availability of Prairie Whole Oat Grains on a Molecular Basis

Background To our knowledge, there is little study on the interaction between nutrient availability and molecular structure changes induced by different processing methods in dairy cattle. The objective of this study was to investigate the effect of heat processing methods on interaction between nutrient availability and molecular structure in terms of functional groups that are related to protein and starch inherent structure of oat grains with two continued years and three replication of each year. Method The oat grains were kept as raw (control) or heated in an air-draft oven (dry roasting: DO) at 120 °C for 60 min and under microwave irradiation (MIO) for 6 min. The molecular structure features were revealed by vibrational infrared molecular spectroscopy. Results The results showed that rumen degradability of dry matter, protein and starch was significantly lower (P <0.05) for MIO compared to control and DO treatments. A higher protein α-helix to β-sheet and a lower amide I to starch area ratio were observed for MIO compared to DO and/or raw treatment. A negative correlation (−0.99, P < 0.01) was observed between α-helix or amide I to starch area ratio and dry matter. A positive correlation (0.99, P < 0.01) was found between protein β-sheet and crude protein. Conclusion The results reveal that oat grains are more sensitive to microwave irradiation than dry heating in terms of protein and starch molecular profile and nutrient availability in ruminants.

Preliminary structure-activity relationship studies on some novel s-substituted aliphatic analogues of 5-{1-[(4- chlorophenyl) sulfonyl]-3-piperidinyl}-1, 3, 4-oxadiazol-2-yl sulfide

Purpose: To study the structure-activity relationships of synthetic multifunctional sulfides through evaluation of lipoxygenase and anti-bacterial activities. Methods: S-substituted derivatives of the parent compound 5-(1-(4-chlorophenylsulfonyl) piperidin-3- yl)-1, 3, 4-oxadiazole-2-thiol were synthesized through reaction with different saturated and unsaturated alkyl halides in DMF medium, with NaH catalyst. Spectral characterization of each derivative was carried out with respect to IR, 1H - NMR, 13C - NMR and EI - MS. The lipoxygenase inhibitory and antibacterial activities of the derivatives were determined using standard procedures. Results: Compound 5e exhibited higher lipoxygenase inhibitory potential than the standard (Baicalein®), with % inhibition of 94.71 ± 0.45 and IC50 of 20.72 ± 0.34 μmoles/L. Compound 5b showed significant antibacterial potential against all the bacterial strains with % inhibition ranging from 62.04 ± 2.78, 69.49 ± 0.41, 63.38 ± 1.97 and 59.70 ± 3.70 to 78.32 ± 0.41, while MIC ranged from 8.18 ± 2.00, 10.60 ± 1.83, 10.84 ± 3.00, 9.81 ± 1.86 and 11.73 ± 5.00 μmoles/L for S. typhi, E. coli, P. aeruginosa, B. subtilis and S. aureus, respectively. Compounds 5d, 5e and 5g showed good antibacterial activity against S. typhi and B. subtilis bacterial strains. Conclusion: The results suggest that compound 5e bearing n-pentyl group is a potent lipoxygenase inhibitor, while compound 5b with n-propyl substitution is a strong antibacterial agent. In addition, compounds 5d, 5e and 5g bearing n-butyl, n-pentyl and n-octyl groups, respectively, are good antibacterial agents against S. typhi and B. subtilis.

New cholinesterase inhibitors for Alzheimer's disease: Structure Activity Studies (SARs) and molecular docking of isoquinolone and azepanone derivatives

A library of isoquinolinone and azepanone derivatives were screened for both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) activity. The strategy adopted included (a) in vitro biological assays, against eel AChE (EeAChE) and equine serum BuChE (EqBuChE) in order to determine the compounds IC50 and their dose-response activity, consolidated by (b) molecular docking studies to evaluate the docking poses and interatomic interactions in the case of the hit compounds, validated by STD-NMR studies. Compound (1f) was identified as one of these hits with an IC50 of 89.5 mu M for EeAChE and 153.8 mu M for EqBuChE, (2a) was identified as a second hit with an IC50 of 108.4 mu M (EeAChE) and 277.8 mu M (EqBuChE). In order to gain insights into the binding mode and principle active site interactions of these molecules, (R)-(1f) along with 3 other analogues (also as the R-enantiomer) were docked into both RhAChE and hBuChE models. Galantamine was used as the benchmark. The docking study was validated by performing an STD-NMR study of (1f) with EeAChE using galantamine as the benchmark.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

Cheaper Faster Drug Development Validated By The Repositioning Of Drugs Against Neglected Tropical Diseases.

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

Desenhos de estruturas químicas correlacionam-se com propriedades biológicas: MIA-QSAR

Descriptors in multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) are pixels of bidimensional images of chemical structures (drawings), which were used to model the trichomonicidal activities of a series of benzimidazole derivatives. The MIA-QSAR model showed good predictive ability, with r², q² and r val. ext.² of 0.853, 0.519 and 0.778, respectively, which are comparable to the best values obtained by CoMFA e CoMSIA for the same series. A MIA-based analysis was also performed by using images of alphabetic letters with the corresponding numeric ordering as dependent variables, but no correlation was found, supporting that MIA-QSAR is not arbitrary.

Descriptor-and fragment-based QSAR models for a series of Schistosoma mansoni purine nucleoside inhibitors

The enzyme purine nucleoside phosphorylase from Schistosoma mansoni (SmPNP) is an attractive molecular target for the treatment of major parasitic infectious diseases, with special emphasis on its role in the discovery of new drugs against schistosomiasis, a tropical disease that affects millions of people worldwide. In the present work, we have determined the inhibitory potency and developed descriptor- and fragment-based quantitative structure-activity relationships (QSAR) for a series of 9-deazaguanine analogs as inhibitors of SmPNP. Significant statistical parameters (descriptor-based model: r² = 0.79, q² = 0.62, r²pred = 0.52; and fragment-based model: r² = 0.95, q² = 0.81, r²pred = 0.80) were obtained, indicating the potential of the models for untested compounds. The fragment-based model was then used to predict the inhibitory potency of a test set of compounds, and the predicted values are in good agreement with the experimental results

Estudos de relações estrutura-atividade quantitativas (QSAR) de bis-benzamidinas com atividade antifúngica

This paper describes 2D-QSAR and 3D-QSAR studies against Candida albicans and Cryptococcus neofarmans for a set of 20 bisbenzamidines. In the studies of 2D-QSAR with C. albicans it was obtained a correlation between log MIC-1 and lipolo component-Z (r² = 0.68; Q² = 0.51). In the case of C. neofarmans a correlation between log MIC-1 and lipolo component-Z and of Balaban index (r² = 0.85; Q² = 0.6) was obtained. 3D-QSAR studies using CoMFA showed that the steric fields contributed more to the predicted activities for Candida albicans (94.9%) and Cryptococcus neofarmans (97.9%).

Obtenció de nous anàlegs amb activitat brassinoesteroide mitjançant modelització molecular i síntesi

Els brassinoesteroides són productes naturals que actuen com a potents reguladors del creixement vegetal. Presenten aplicacions prometedores en l’agricultura degut a que, aplicats exògenament, augmenten la qualitat i la quantitat de les collites. Ara bé, el seu ús s’ha vist restringit degut a la seva costosa obtenció. Aquest fet ha motivat la recerca de nous compostos actius més assequibles. En aquest projecte es planteja el disseny i obtenció de nous anàlegs seguint diferents estratègies que impliquen tant l’ús de mètodes de modelització molecular com de síntesi orgànica. La primera d’aquestes estratègies consisteix en buscar compostos actius en bases de dades de compostos comercials a través de processos de Virtual Screening desenvolupats amb mètodes computacionals basats en Camps d’Interacció Molecular. Així, es van establir i interpretar models de Relacions Quantitatives Estructura-Activitat (QSAR) emprant descriptors independents de l’alineament (GRIND) i, amb col•laboració amb la Universitat de Perugia, aquest criteri de cerca es va ampliar amb l’aplicació de descriptors FLAP de nova generació. Una altra estratègia es va basar en intentar substituir l’esquelet esteroide dels brassinoesteroides per una estructura equivalent, fixant com a cadena lateral el grup (R)-hexahidromandelil. S’han aplicat dos criteris: mètodes computacionals basats en models QSAR establerts amb descriptors GRIND i també en la metodologia SHOP (scaffold hopping), i, per altra banda, anàlegs proposats racionalment a partir d’un estudi efectuat sobre disruptors endocrins no esteroïdals. Sobre les estructures trobades s’hi va unir la cadena lateral comercial esmentada per via sintètica, en la qual s’ha hagut de fer un èmfasi especial en grups protectors. En total, 49 estructures es proposen per a ser obtingudes sintèticament. També s’ha treballat en l’obtenció un agonista derivat de l’hipotètic antagonista KM-01. Totes les molècules candidates, ja siguin comercials o obtingudes sintèticament, estant sent avaluades en el test d’inclinació de la làmina d’arròs (RLIT).

Comparison of indices proposed as criteria for assigning skin notation

OBJECTIVES: Skin notations are used as a hazard identification tool to flag chemicals associated with a potential risk related to transdermal penetration. The transparency and rigorousness of the skin notation assignment process have recently been questioned. We compared different approaches proposed as criteria for these notations as a starting point for improving and systematizing current practice. METHODS: In this study, skin notations, dermal acute lethal dose 50 in mammals (LD(50)s) and two dermal risk indices derived from previously published work were compared using the lists of Swiss maximum allowable concentrations (MACs) and threshold limit values (TLVs) from the American Conference of Governmental Industrial Hygienists (ACGIH). The indices were both based on quantitative structure-activity relationship (QSAR) estimation of transdermal fluxes. One index compared the cumulative dose received through skin given specific exposure surface and duration to that received through lungs following inhalation 8 h at the MAC or TLV. The other index estimated the blood level increase caused by adding skin exposure to the inhalation route at kinetic steady state. Dermal-to-other route ratios of LD(50) were calculated as secondary indices of dermal penetrability. RESULTS: The working data set included 364 substances. Depending on the subdataset, agreement between the Swiss and ACGIH skin notations varied between 82 and 87%. Chemicals with a skin notation were more likely to have higher dermal risk indices and lower dermal LD(50) than chemicals without a notation (probabilities between 60 and 70%). The risk indices, based on cumulative dose and kinetic steady state, respectively, appeared proportional up to a constant independent of chemical-specific properties. They agreed well with dermal LD(50)s (Spearman correlation coefficients -0.42 to -0.43). Dermal-to-other routes LD(50) ratios were moderately associated with QSAR-based transdermal fluxes (Spearman correlation coefficients -0.2 to -0.3). CONCLUSIONS: The plausible but variable relationship between current skin notations and the different approaches tested confirm the need to improve current skin notations. QSAR-based risk indices and dermal toxicity data might be successfully integrated in a systematic alternative to current skin notations for detecting chemicals associated with potential dermal risk in the workplace. [Authors]