There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.
G protein-coupled receptor (GPCR) oligomerization, while contentious, continues to attract the attention of researchers. Numerous experimental investigations have validated the presence of GPCR dimers, and the relevance of dimerization in the effectuation of physiological functions intensifies the attractiveness of this concept as a potential therapeutic target. GPCRs, as a single entity, have been the main source of scrutiny for drug design objectives for multiple diseases such as cancer, inflammation, cardiac, and respiratory diseases. The existence of dimers broadens the research scope of GPCR functions, revealing new signaling pathways that can be targeted for disease pathogenesis that have not previously been reported when GPCRs were only viewed in their monomeric form. This review will highlight several aspects of GPCR dimerization, which include a summary of the structural elucidation of the allosteric modulation of class C GPCR activation offered through recent solutions to the three-dimensional, full-length structures of metabotropic glutamate receptor and γ-aminobutyric acid B receptor as well as the role of dimerization in the modification of GPCR function and allostery. With the growing influence of computational methods in the study of GPCRs, we will also be reviewing recent computational tools that have been utilized to map protein–protein interactions (PPI).
Peroxisome proliferator-activated receptors (PPARs) are
associated
with the regulation of metabolic homeostasis. Based on a previous
report that 1′-homologated 4′-thionucleoside acts as
a dual PPARγ/δ modulator, carbocyclic nucleosides 2–5 with various sugar conformations were
synthesized to determine whether sugar puckering affects binding to
PPARs. (S)-conformer 2 was synthesized
using Charette asymmetric cyclopropanation, whereas (N)-conformer 3 was synthesized using stereoselective
Simmons–Smith cyclopropanation. All synthesized nucleosides
did not exhibit binding affinity to PPARα but exhibited significant
binding affinities to PPARγ/δ. The binding affinity of
final nucleosides to PPARγ did not differ significantly based
on their conformation, but their affinity to PPARδ depended
greatly on their conformation, correlated with adiponectin production.
(N)-conformer 3h was discovered to be
the most potent PPARδ antagonist with good adiponectin production,
which exhibited the most effective activity in inhibiting the mRNA
levels of LPS-induced IL-1β expression in RAW 264.7 macrophages,
implicating its anti-inflammatory activity.
This study presents the results of surface modification of zirconium dioxide (ZrO2) nanoparticles by 3-(trimethoxysilyl) propyl methacrylate silane coupling agent by assessing some characteristics and properties of modified ZrO2 nanoparticles by infrared spectroscopy, thermogravimetric analysis, size distribution, zeta potential, and field emission scanning electron microscopy methods. The modified and unmodified ZrO2 nanoparticles have been used as nanoadditives for organic coatings based on acrylic emulsion resin. The abrasion resistance of acrylic coating was evaluated according to ASTM E968-15. The obtained results show that ZrO2 nanoparticles were functionalized successfully with 3-(trimethoxysilyl) propyl methacrylate silane. The modified ZrO2 nanoparticles exhibit a positive effectiveness in the enhancement of the abrasion resistance of acrylic resin coating compared to unmodified ZrO2 nanoparticles.
A series of fexaramine analogs were
synthesized and evaluated to
develop an intestine-selective/specific FXR partial agonist. Introduction
of both a CN substituent at the C-2 in the biphenyl ring and a fluorine
at the C-5 in the aniline ring in fexaramine markedly increased FXR
agonistic activity. 27c showed 53 ± 3% maximum efficacy
relative to GW4064 in an FXR agonist assay. A substantial amount of 27c was absorbed in the intestine after oral administration
in rats, and then it was rapidly metabolized to inactive carboxylic
acid 44 by serum esterases. In CDAHFD-fed mice, oral
administration of 27c strongly induced multiple intestinal
FXR target genes, FGF15, SHP, IBABP, and OST-α, but failed to
activate SHP in the liver. 27c significantly reduced
the liver fibrogenesis area, hepatic fibrosis markers, and serum level
of AST. Rational optimization of fexaramine has led to the identification
of an intestine-specific FXR partial agonist 27c.
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