1,721,039 research outputs found
INVESTIGATING THE NR2F2 STRUCTURE FOR DRUG REPURPOSING
No full textNuclear receptors (NRs) are transcription factors which play a crucial role in regulating various physiological and developmental processes. Within this superfamily, NR2Fs, also known as Chicken Ovalbumin Upstream Promoter Transcription Factor (COUP-TF), is a family of nuclear orphan receptors, due to the lack of endogenous ligands. The NR2Fs are composed of three members: NR2F1 (COUP-TFI), NR2F2 (COUP-TFII) and NR2F6 (COUP-TFIII). Structurally, the two most important regions, but independently from each other, of NRs are the DNA binding domain (DBD) and ligand binding domain (LBD) which is homologous between all three family members. The variable N-terminal is less conserved, the C-terminal region does not exist in all receptors, and its function is not well understood. Due to their central role in cell growth, they are promising candidates as novel therapeutic targets for cancer therapy [T Sajinovic, G Baier. doi: 10.31083/j.fbl2801013]. In this context, we performed a computational analysis on the X-ray crystal structure of the human NR2F2 ligand binding domain (PDBID: 3CJW) to comprehend the potential hotspot binding sites and predict their druggability. The two identified binding sites (Figure) were then used in a virtual screening protocol, including pharmacophore models and docking studies, of large libraries such as Drugbank, FDA, and commercial libraries for drug repurposing. The results of this investigation will be reported and discussed
Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics
Full text linkTo date, computational approaches have been recognized as a key component in drug design and discovery workflows. Developed to help researchers save time and reduce costs, several computational tools have been developed and implemented in the last twenty years. At present, they are routinely used to identify a therapeutic target, understand ligand–protein and protein–protein interactions, and identify orthosteric and allosteric binding sites, but their primary use remains the identification of hits through ligand-based and structure-based virtual screening and the optimization of lead compounds, followed by the estimation of the binding free energy. The repurposing of an old drug for the treatment of new diseases, helped by in silico tools, has also gained a prominent role in virtual screening campaigns.
Moreover, the availability and the decreasing cost of hardware and software, together with the development of several web servers on which to upload and download computational data, have contributed to the success of computer-assisted drug design. These improved, accurate, and reliable methods should help to add new and more potent molecules to the paraphernalia of approved drugs. Nevertheless, the ease of access of computational tools in drug design (software, databases, libraries, and web servers) should not encourage users with little or almost no knowledge of the underlying physical basis of the methods used, who could compromise the interpretation of the results. The figure of the computational (medicinal) chemist should be recognized and included in all research groups. These considerations led us to promote a volume collecting some original contributions regarding all aspects of the computational approaches, such as docking, induced-fit docking, molecular dynamics simulations, free energy calculations, and reverse modeling. We also include ligand-based approaches, such as molecular similarity fingerprints, shape methods, pharmacophore modeling, and QSAR. Drug design and the development process strive to predict the metabolic fate of a drug candidate to establish a relationship between the pharmacodynamics and pharmacokinetics and highlight the potential toxicity of the drug candidate. Even though the use of computational approaches is often combined, we tried to identify which of these play a central role in each manuscript.
In this Special Issue, the use of molecular dynamics simulations, both unbiased and biased, cover a major part of the contributions. Non-covalent inhibition of the immunoproteasome was investigated in-depth through MD-binding and binding pose metadynamics [1]. MD simulations provided insight into the structural features of hTSPO (Translocator Protein) and the previously unknown interplay between PK11195, a molecule routinely used in positron emission tomography (PET) for the imaging of neuroinflammatory sites, and cholesterol [2]. The interaction of certain endogen substrates, drug substrates, and inhibitors with wild-type MRP4 (WT-MRP4) and its variants G187W and Y556C were studied to determine differences in the intermolecular interactions and affinity related to SNPs using several approaches, but particularly all-atom, coarse-grained, and umbrella sampling molecular dynamics simulations (AA-MDS and CG-MDS, respectively) [3]. Natural sodium–glucose co-transporter 2 (SGLT2) inhibitors were selected to explore their potential against an emerging uropathogenic bacterial therapeutic target, i.e., FimH, which plays a critical role in the colonization of uropathogenic bacteria on the urinary tract surface, and molecular dynamics simulations were carried out to study the potential interactions [4]. Doxorubicin encapsulation in carbon nanotubes with haeckelite or Stone–Wales defects as drug carriers were investigated using a molecular dynamics approach [5]. The combined use of different approaches has been reported in a series of papers associated with the virtual screening of libraries. Almeelebia and co. screened 224,205 natural compounds from the ZINC database against the catalytic site of the Mtb proteasome [6]. Pharmacophore-based virtual screening and molecular docking were carried out to identify potential Src inhibitors starting from a total of 891 molecules. Finally, MD simulations identified two molecules as potential lead compounds against Src kinase [7]. An in silico study identified a methotrexate analog as a potential inhibitor of drug-resistant human dihydrofolate reductase for cancer therapeutics [8]. A structure-based method for high-throughput virtual screening aimed to identify new dual-target hit molecules for acetylcholinesterase, and the α7 nicotinic acetylcholine receptor was reported and confirmed in vitro [9]. A new complementary computational analysis called “dock binning” evaluates antibody–antigen docking models to identify why and where they might compete in terms of possible binding sites on the antigen [10]. Interesting drug repurposing strategies have been reported. Hudson and Samudrala presented a computational analysis of a novel drug opportunities (CANDO) platform for shotgun multitarget repurposing. It implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures [11]. Qi and co. data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarities between gene expression signature (GES) profiles from drugs and their indicated diseases for GES-guided drug-repositioning approaches [12]. In late 2019, the SARS-CoV-2 pandemic focused the attention of many researchers intending to find not only vaccines but also new antiviral drugs. These reasons boosted the use of computational approaches to explore large libraries of natural compounds, already approved drugs, and in-house and commercial compounds [13,14]. In this issue, Baig and co. studied the efficacy of the Mpro inhibitor PF-00835231 against Mpro and its reported mutants in clinical trials. Several in silico approaches were used to investigate and compare the efficacy of PF-00835231 and five drugs previously documented to inhibit Mpro [15]. Li and co. computationally investigated the MPD3 phytochemical database along with the pool of reported natural antiviral compounds to be used against SARS-CoV-2 [16]. Pedretti and co., exploiting the availability of resolved structures, designed a structure-based computational approach. The innovative idea of their study was to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns [17]. In the context of antiviral drugs, Regad and co. investigated the emergence of HIV-2 resistance. They proposed a structural analysis of 31 drug-resistant mutants of HIV-2 protease (PR2), an important target against HIV-2 infection [18]. A wide series of contributions regarding the use of QSAR, machine learning, and deep learning has reported interesting outcomes. A multiple-molecule drug design based on systems biology approaches and a deep neural network to mitigate human skin aging was developed by Yeh and co. With the proposed systems medicine design procedure, they not only shed light on the skin-aging molecular progression mechanisms, but they also suggested two multiple-molecule drugs to mitigate human skin aging [19]. The construction of quantitative structure–activity relationship (QSAR) models was used to predict the biological activities of the compounds obtained with virtual screening and identify new selective chemical entities for the COX-2 enzyme [20]. The three-dimensional QSAR model, employing a common-features pharmacophore as an alignment rule, was built on 20 highly active/selective HDAC1 inhibitors. The predictive power of the 3D QSAR model represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity [21]. Different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints were evaluated to develop quantitative structure–activity relationship (QSAR) models, which are important for hERG potassium channel blocker prediction [22].
Throughout this Special Issue, all the recent aspects of the computational approaches applied to several research fields are reported. We express our deep gratitude to all the contributors to this Special Issue for their commitment, hard work, and outstanding papers. We also thank all the reviewers involved in the manuscript revisions for their unpaid contributions to improve any aspects of the submitted works. Last but not least, we deeply thank Mrs. Jessie Zhang for her assistance during the period in which we served as guest editors
A comprehensive computational analysis of NR2F2/6 receptors for drug repurposing
No full textNuclear Receptors (ORFs) are a small family of transcription factors (15 members)
playing a crucial role in regulating various physiological and developmental processes.
Within this superfamily, NR2Fs, also known as Chicken Ovalbumin Upstream Promoter
Transcription Factor (COUP-TF), is a family of nuclear orphan receptors, due to the lack of
endogenous ligands. The NR2Fs are composed of three members: NR2F1 (COUP-TFI, EAR-
3), NR2F2 (COUP-TFII, ARP-1) and NR2F6 (COUP-TFIII, EAR-2). Due to the pivotal functions
of NR2Fs in cell growth, they are regarded as promising candidates for the development
of novel therapeutic targets in cancer treatment [1]. In the context of the PNRR project,
"HEAL ITALIA", a comprehensive computational analysis was conducted on the X-ray
crystal structures of the human ligand binding domain of NR2F2 (PDB ID: 3CJW) and the
NR2F6 (PDB ID: 8C5L). To date, only compound CIA1 has been identified as an inhibitor of
NR2F2 in prostate cancer cell lines (IC50 1.2-7.6 μM). To this aim, the ligand binding
domain was mapped identifying potential binding sites, designated as site 1 and site 2.
Followed by classic docking with CIA1, molecular dynamics (MD), Binding Pose
Metadynamics (BPMD), and Molecular Mechanics-Generalized Born Surface Area
continuum solvation (MM-GBSA) were conducted to assess the stability of the complexes
NR2F2-CIA in the two sites. For NR2F6 no inhibitor has been identified in the literature.
Potential hotspot binding sites were identified and their potential for drug use was
predicted. Subsequently, the identified binding sites for NR2F2 and NR2F6 were then
used to perform a virtual screening protocol involving pharmacophore models and
docking studies on extensive libraries, such as Drugbank, FDA and commercial libraries
for drug repurposing
Non-covalent immunoproteasome inhibitors: virtual screening and in vitro test on β1i /β5i subunits
No full textImmunoproteasome inhibition is a challenging strategy for the treatment of hematological malignancies, autoimmune and inflammatory diseases [1,2]. The search for non-covalent inhibitors of the immunoproteasome β1i/β5i catalytic subunits could be a new strategy to avoid the drawbacks of the known covalent inhibitors. Here, we report the biological evaluation of thirty-four compounds selected from commercial libraries. A virtual screening strategy including a dynamic pharmacophore modeling approach onto the β1i subunit and a pharmacophore/docking approach onto the β5i subunit aided the identification of these hits [3]. Compound 3 is the most active onto β1i subunit with Ki = 11.84±1.63 μM, compound 17 showed Ki = 12.50±0.77 μM onto β5i subunit. Compound 2 showed inhibitory activity on both subunits (Ki = 12.53±0.18 Ki = 31.95±0.81 onto β1i subunit and β5i subunit, respectively). The hit compounds identified represent an interesting starting point for further optimization
Uno studio comparativo in silico sui possibili target di Ataluren e analoghi farmaci promotori di readthrough di codoni di stop prematuri
Full text linkE’ noto in letteratura che Ataluren (acido 5-(fluorofenil)-1,2,4-ossadiazolil-benzoico) sia in grado di sopprimere le mutazioni non senso favorendo il readthrough dei codoni di stop prematuri, anche se il suo meccanismo di azione non risulta ancora chiaro. La probabile interazione tra Ataluren e CTFR-mRNA è stata precedentemente studiata mediante dinamica molecolare. In questo studio1, abbiamo esteso il modeling del probabile meccanismo di azione di Ataluren mediante approcci computazionali completementari, quali Induced Fit Docking (IFD), Quantum Polarized Ligand Docking (QPLD), metodi MM-GBSA e mutagenesi computazionale. Oltre a considerare il CTFR-mRNA, sono stati presi in considerazione altri target implicati nel processo di traduzione, quali la subunità 16S dell’rRNA batterico e la subunità 18S dell’rRNA eucariotico, che sono target comprovati di molti aminoglicosidi noti per la loro capacità di sopprimere l’attività di correzione svolta normalmente dal ribosoma; il fattore di rilascio eucariotico eRF1, per valutare la potenziale influenza di Ataluren sulla fine del processo di traduzione. Inoltre, è stato effettuato un confronto tra Ataluren, un suo nuovo promettente analogo NV2445 (acido 4-(5-(o-tolil)-1,3,4-ossadiazol-2-il)benzoico)2 e una serie di antibiotici aminoglicosidici. I risultati hanno confermato che mRNA è il più probabile target per Ataluren e i suoi derivati. I calcoli di energia libera di legame effettuati in seguito alla mutagenesi computazionale, hanno mostrato che il legame tra Ataluren e il codone di stop prematuro è fortemente influenzato dalla presenza di nucleotidi ausiliari nell’intorno genico
Exploring the new non-covalent immunoproteasome inhibitors of β1i /β5i subunits: Virtual screening and in vitro test
No full textReverse screening on indicaxanthin from Opuntia ficus-indica as natural chemoactive and chemopreventive agent
No full textIndicaxanthin is a bioactive and bioavailable betalain pigment extracted from Opuntia ficus indica fruits. Indicaxanthin has pharmacokinetic proprieties, rarely found in other phytochemicals, and it has been demonstrated that it provides a broad-spectrum of pharmaceutical activity, exerting anti-proliferative, anti-inflammatory, and neuromodulator effects. The discovery of the Indicaxanthin physiological targets plays an important role in understanding the biochemical mechanism. In this study, combined reverse pharmacophore mapping, reverse docking, and text-based database search identified Inositol Trisphosphate 3-Kinase (ITP3K-A), Glutamate carboxypeptidase II (GCPII), Leukotriene-A4 hydrolase (LTA4H), Phosphoserine phosphatase (HPSP), Phosphodiesterase 4D (PDE4D), AMPA receptor (GluA3 and GluA2 subunits) and Kainate receptor (GluK1 isoform) as potential targets for Indicaxanthin. These targets are implicated in neuromodulation, and inflammatory regulation, normally expressed mostly in the CNS, and expressed (or overexpressed) in cancer tissues (i.e. breast, thyroid, and prostate cancer cells). Moreover, this study provides qualitative and quantitative information about dynamic interactions of Indicaxanthin at the binding site of target proteins, through molecular dynamics simulations and MM-GBSA
Immuno-oncological treatment of Non-Small-Cell Lung Cancer (NSCLC) in advanced stage with Nivolumab
Full text linkImmuno-oncology marked a therapeutic revolution in the treatment of cancer. Thanks to the new strategy that aims to awaken the immune system to fight cancer cells, there has been a change in the clinical course in the treatment of advanced Non-Small Cell Lung Cancer (NSCLC). Our study aimed to evaluate the therapeutic efficacy of nivolumab monotherapy in the treatment of patients with advanced stage IIIB/IV non-small cell lung cancer beyond the second line. The results showed a progression-free survival of 7.35 months and an improvement in the quality of life of patients compared to other treatments. In addition, no type 3 and type 4 adverse reactions were detected in
patients treated with Nivolumab. We hope that these results, already promising, will lead to an increase in overall survival in the future
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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