The discovery of isozyme-selective
histone deacetylase (HDAC) inhibitors
is critical for understanding the biological functions of individual
HDACs and for validating HDACs as drug targets. The isozyme HDAC10
contributes to chemotherapy resistance and has recently been described
to be a polyamine deacetylase, but no studies toward selective HDAC10
inhibitors have been published. Using two complementary assays, we
found Tubastatin A, an HDAC6 inhibitor, to potently bind HDAC10. We
synthesized Tubastatin A derivatives and found that a basic amine
in the cap group was required for strong HDAC10 binding. HDAC10 inhibitors
mimicked knockdown by causing dose-dependent accumulation of acidic
vesicles in a neuroblastoma cell line. Furthermore, docking into human
HDAC10 homology models indicated that a hydrogen bond between a cap
group nitrogen and the gatekeeper residue Glu272 was responsible for
potent HDAC10 binding. Taken together, our data provide an optimal
platform for the development of HDAC10-selective inhibitors, as exemplified
with the Tubastatin A scaffold.
The discovery of isozyme-selective histone deacetylase (HDAC) inhibitors is critical for understanding the biological functions of individual HDACs and for validating HDACs as clinical drug targets. The isozyme HDAC10 contributes to chemotherapy resistance via inhibition of autophagic flux and has recently been described to be a polyamine deacetylase, but no studies directed toward selective HDAC10 inhibitors have been published. Herein, we disclose that the use of two complementary ligand-displacement assays has revealed unexpectedly potent HDAC10 binding of tubastatin A, which has been previously described as a highly selective HDAC6 inhibitor. We synthesized a targeted selection of tubastatin A derivatives and found that a basic amine in the cap group was required for strong HDAC10, but not HDAC6, binding. Only potent HDAC10 binders mimicked HDAC10 knockdown by causing dose-dependent accumulation of acidic vesicles in the BE(2)-C neuroblastoma cell line. Docking of inhibitors into human HDAC10 homology models indicated that a hydrogen-bond between a basic cap group nitrogen and the HDAC10 gatekeeper residue Glu272 was responsible for potent HDAC10 binding. Taken together, the presented assays and homology models provide an optimal platform for the development of HDAC10-selective inhibitors, as exemplified with the tubastatin A scaffold.<br>
Despite reports on the pharmacological potential of Copaifera langsdorffii Desf. (Leguminosae-Caesalpinioideae) leaf extract, little is known about its chemical composition. In this work, a phytochemical study from the C. langsdorf f ii ethanol/ H 2 O 7:3 (v/v) extract was undertaken. Separation was performed by high-speed counter-current (HSCCC) and Sephadex LH-20 column chromatographies, followed by preparative HPLC. The EtOAc-and H 2 O-soluble fractions of the extract furnished the flavonoids quercitrin (1) and afzelin (2) and 3-O-(3-O-methyl-galloyl)quinic acid (3), respectively. The H 2 O-soluble fraction furnished 3,4-di-O-(3-O-methyl-galloyl)quinic acid (4), 3,5-di-O-(galloyl)-4-O-(3-O-methyl-galloyl)quinic acid (5), and 3,5-di-O-(3-O-methyl-galloyl)-4-O-(galloyl)quinic acid (6). Their chemical structures were elucidated by NMR means.
The identification
of possible targets for a known bioactive compound
is of the utmost importance for drug design and development. Molecular
docking is one possible approach for in-silico protein
target prediction, whereas a molecule is docked into several different
protein structures to identify potential targets. This reverse docking
approach is hampered by the limitation of current scoring functions
to correctly discriminate between targets and nontargets. In this
work, a development of target-specific scoring functions is described
that showed improved prediction performances for the correct target
prediction of both actives and decoys on three validation data sets.
In contrast to pure ligand-based approaches, that are in general faster
and include a greater target space, docking-based approaches can cover
also unknown chemical space that lies outside the known bioactivity
data. These target-specific scoring functions are based on known bioactivity
data retrieved from ChEMBL and supervised machine learning approaches.
Neural Networks and Support Vector Machines (SVMs) models were trained
for 20 different protein targets. Our protein–ligand interaction
fingerprint PADIF (Protein Atom Score Contributions Derived Interaction
Fingerprint) represents the input for training, whereas the PADIFs
are calculated based on docking poses of active and inactive compounds.
Different data sets of previously unseen molecules were used for the
final evaluation and analysis of the prediction performance of the
created models. For a single-target selectivity data set, the correct
target model returns in most of the cases the highest probabilities
scores for their active molecules and with statistically significant
differences from the other targets. These probability scores were
also predicted and successfully used to rank the targets for molecules
of a multitarget data set with activity data described simultaneously
for two, three, and four to seven protein targets.
The methanolic extracts of the leaves of Lippia species (L. pseudo-thea, L. hermannioides, L. alba, L. rubella, and L. sidoides) were tested for their antibacterial, antifungal, and antioxidant activity. Cytotoxicity was determined by using brine shrimp lethality bioassay. Phytochemical screening was also performed. The extracts showed a minimum inhibitory concentration (MIC) ranging from 78 to 5000 μg/mL for antibacterial activity against at least 2 species of bacteria, although none was active against Escherichia coli. Antifungal activity was found only for L. pseudo-thea (MIC, 625 μg/mL for Candida albicans) and L. sidoides (MIC, 625 μg/mL for both C. albicans and C. neoformans). The bioautography showed that flavonoids and coumarins are responsible for the antioxidant activity of the extracts and that the antimicrobial properties are due to flavonoids and terpenoids. The cytotoxic activity was stronger for L rubella extract. To our knowledge, this is the first report of the biological and chemical constituents of L. pseudo-thea, L. hermannioides, and L. rubella.
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