Bioprospecting Fluorescent Plant Growth Regulators from Arabidopsis to Vegetable Crops

The phytohormone auxin is involved in almost every process of a plant’s life, from germination to plant development. Nowadays, auxin research connects synthetic chemistry, plant biology and computational chemistry in order to develop innovative and safe compounds to be used in sustainable agricultural practice. In this framework, we developed new fluorescent compounds, ethanolammonium p-aminobenzoate (HEA-pABA) and p-nitrobenzoate (HEA-pNBA), and investigated their auxin-like behavior on two main commercial vegetables cultivated in Europe, cucumber (Cucumis sativus) and tomato (Solanumlycopersicum), in comparison to the model plant Arabidopsis (Arabidopsis thaliana). Moreover, the binding modes and affinities of two organic salts in relation to the natural auxin indole-3-acetic acid (IAA) into TIR1 auxin receptor were investigated by computational approaches (homology modeling and molecular docking). Both experimental and theoretical results highlight HEA-pABA as a fluorescent compound with auxin-like activity both in Arabidopsis and the commercial cucumber and tomato. Therefore, alkanolammonium benzoates have a great potential as promising sustainable plant growth stimulators to be efficiently used in vegetable crops

Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: Meeting new challenges

© 2014 Elsevier Ltd All rights reserved. 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

FRET Detection of Lymphocyte Function-Associated Antigen-1 Conformational Extension

Lymphocyte function-associated antigen 1 (LFA-1, CD11a/CD18, αLβ2-integrin) and its ligands are essential for adhesion between T-cells and antigen-presenting cells, formation of the immunological synapse, and other immune cell interactions. LFA-1 function is regulated through conformational changes that include the modulation of ligand binding affinity and molecular extension. However, the relationship between molecular conformation and function is unclear. Here fluorescence resonance energy transfer (FRET) with new LFA-1-specific fluorescent probes showed that triggering of the pathway used for T-cell activation induced rapid unquenching of the FRET signal consistent with extension of the molecule. Analysis of the FRET quenching at rest revealed an unexpected result that can be interpreted as a previously unknown LFA-1 conformation

Modeling of ligand binding to dopamine D2 receptor

The dopaminic receptors have been for long time the major targets for developing new small molecules with high affinity and selectivity to treat psychiatric disorders, neurodegeneration, drug abuse, and other therapeutic areas. In the absence of a 3D structure for the human D2 dopamine (HDD2) receptor, the efforts for discovery and design of new potential drugs rely on comparative models generation, docking and pharmacophore development studies. To get a better understanding of the HDD2 receptor binding site and the ligand-receptor interactions a homology model of HDD2 receptor based on the X-ray structure of β2-adrenergic receptor has been built and used to dock a set of partial agonists of HDD2 receptor. The main characteristics of the binding mode for the HDD2 partial agonists set are given by the ligand particular folding and a complex network of contacts represented by stacking interactions, salt bridge and hydrogen bond formation. The characterization of the partial agonist binding mode at HDD2 receptor provide the needed information to generate pharmacophore models which represent essential information in the future virtual screening studies in order to identify new potential HDD2 partial agonists

Porphyrin Hetero-Trimer Involving a Hydrophilic and a Hydrophobic Structure with Application in the Fluorescent Detection of Toluidine Blue

The combination of a metallated porphyrin, Pt(II)-5,10,15,20-tetrakis-(4-allyloxyphenyl)-porphyrin (Pt-allyloxyPP), and a water-soluble porphyrin, 5,10,15,20-tetrakis(4-sulfonatophenyl)-porphyrin (TSPP), leads to the formation of a porphyrin hetero-trimer. The hetero-trimer, consisting of two TSPP molecules linked via oxygen atoms axially to the platinum atom in the Pt-allyloxyPP molecule, was characterized by UV–Vis, FT-IR, fluorescence, and 1H-NMR spectroscopy, and the proposed structure was confirmed. The new porphyrin hetero-trimer offers both the advantage of enhanced fluorescence and the presence of multiple sites for the detection of toluidine blue, due to its high affinity for acidic binding sites. This work brings attention to the purposely designed fluorescent sensor for toluidine blue, in the biologically relevant concentration domain of 1.9 × 10−6–6.39 × 10−5 M, with a very good accuracy

Modeling Kinase Inhibition Using Highly Confident Data Sets

Protein kinases form a consistent class of promising drug targets, and several efforts have been made to predict the activities of small molecules against a representative part of the kinome. This study continues our previous work (Bora, A.; Avram, S.; Ciucanu, I.; Raica, M.; Avram, S. Predictive Models for Fast and Effective Profiling of Kinase Inhibitors. J. Chem. Inf. Model. 2016, 56, 895−905; www.chembioinf.ro) aiming to build and measure the performance of ligand-based kinase inhibitor prediction models. Here we analyzed kinase–inhibitor pairs with multiple activity points extracted from the ChEMBL database and identified the main sources of inconsistency. Our results indicate that lower IC₅₀ values are usually less affected by errors and reflect more accurately the structure–activity relationship of the molecules against the target, ideally for quantitative structure–activity relationship studies. Further, we modeled the activities of 104 kinases using unbiased target-specific activity points. The performance of predictors built on extended connectivity fingerprints (ECFP4) and two-dimensional pharmacophore fingerprints (PFPs) are compared by means of tolerance intervals (TIs) (95%/95%) in virtual screening (VS) and classification tasks using external random (<i>RandSets</i>) and diversity-based (<i>DivSets</i>) test sets. We found that the two encodings perform superior to each other on different kinases in VS and that PFP models perform consistently better in classifying actives (higher sensitivity). Next, we combined the two encodings into a single one (PFPECFP) and demonstrated that especially in VS (as indicated by the exponential receiver operating curve enrichment metric (eROCE)), for the vast majority of kinases the model performance increased compared with the individual fingerprint models. These findings are highlighted in the more challenging <i>DivSets</i> compared with <i>RandSets</i>. The current paper explores the boundaries of inhibitor predictors for individual kinases to enhance VS and ultimately aid the discovery of novel compounds with desirable polypharmacology

Modeling Kinase Inhibition Using Highly Confident Data Sets

Protein kinases form a consistent class of promising drug targets, and several efforts have been made to predict the activities of small molecules against a representative part of the kinome. This study continues our previous work (Bora, A.; Avram, S.; Ciucanu, I.; Raica, M.; Avram, S. Predictive Models for Fast and Effective Profiling of Kinase Inhibitors. J. Chem. Inf. Model. 2016, 56, 895−905; www.chembioinf.ro) aiming to build and measure the performance of ligand-based kinase inhibitor prediction models. Here we analyzed kinase–inhibitor pairs with multiple activity points extracted from the ChEMBL database and identified the main sources of inconsistency. Our results indicate that lower IC₅₀ values are usually less affected by errors and reflect more accurately the structure–activity relationship of the molecules against the target, ideally for quantitative structure–activity relationship studies. Further, we modeled the activities of 104 kinases using unbiased target-specific activity points. The performance of predictors built on extended connectivity fingerprints (ECFP4) and two-dimensional pharmacophore fingerprints (PFPs) are compared by means of tolerance intervals (TIs) (95%/95%) in virtual screening (VS) and classification tasks using external random (<i>RandSets</i>) and diversity-based (<i>DivSets</i>) test sets. We found that the two encodings perform superior to each other on different kinases in VS and that PFP models perform consistently better in classifying actives (higher sensitivity). Next, we combined the two encodings into a single one (PFPECFP) and demonstrated that especially in VS (as indicated by the exponential receiver operating curve enrichment metric (eROCE)), for the vast majority of kinases the model performance increased compared with the individual fingerprint models. These findings are highlighted in the more challenging <i>DivSets</i> compared with <i>RandSets</i>. The current paper explores the boundaries of inhibitor predictors for individual kinases to enhance VS and ultimately aid the discovery of novel compounds with desirable polypharmacology