22 research outputs found
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 IC<sub>50</sub> 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 IC<sub>50</sub> 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