1,721,057 research outputs found
Thirty Years of Structural Studies on Cyclin-Dependent Kinases: Implications for Drug Discovery
No full textCyclin-dependent Kinases (CDKs) (EC 2.7.11.22) comprise a group of Serine/Threonine kinases [1, 2] involved
in several cellular processes ranging from cell cycle progression to differentiation of nerve cells. CDKs also have
roles in regulating transcription and senescence. In the human genome, 20 CDKs have been identified [3]. Due to its
pivotal function in controlling the cell cycle progression in eukaryotes, CDK2 has been the subject of functional and
structural studies in the last decades [4]. The critical role of CDK2 in cancer motivated extensive crystallographic
analyses to support Structure-Based Drug Design (SBDD). In 1993, the high-resolution crystal structures of CDK2,
both in its apo form and in complex with ATP, provided the first detailed view of this important therapeutic target
Treatment of Complex Regional Pain Syndrome (Crps): New Perspectives in the Use of Sulfonamides as Modulators of P2x Receptors
Full text linkComputational 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
Sulfonamide moiety as "molecular chimera" in the design of new drugs
No full textThe -SO2NH- group is of great significance in modern pharmaceutical use since in sulfa-drugs it is possible to introduce easily chemical modifications, and even small changes may lead to an improved version of an already existing drug
Natural products as non-covalent and covalent modulators of the KEAP1/NRF2 pathway exerting antioxidant effects
Full text linkBy controlling several antioxidant and detoxifying genes at the transcriptional level, including NAD(P)H quinone oxidoreductase 1 (NQO1), multidrug resistance-associated proteins (MRPs), UDP-glucuronosyltransferase (UGT), glutamate-cysteine ligase catalytic (GCLC) and modifier (GCLM) subunits, glutathione S-transferase (GST), sulfiredoxin1 (SRXN1), and heme-oxygenase-1 (HMOX1), the KEAP1/NRF2 pathway plays a crucial role in the oxidative stress response. Accordingly, the discovery of modulators of this pathway, activating cellular signaling through NRF2, and targeting the antioxidant response element (ARE) genes is pivotal for the development of effective antioxidant agents. In this context, natural products could represent promising drug candidates for supplementation to provide antioxidant capacity to human cells. In recent decades, by coupling in silico and experimental methods, several natural products have been characterized to exert antioxidant effects by targeting the KEAP1/NRF2 pathway. In this review article, we analyze several natural products that were investigated experimentally and in silico for their ability to modulate KEAP1/NRF2 by non-covalent and covalent mechanisms. These latter represent the two main sections of this article. For each class of inhibitors, we reviewed their antioxidant effects and potential therapeutic applications, and where possible, we analyzed the structure-activity relationship (SAR). Moreover, the main computational techniques used for the most promising identified compounds are detailed in this survey, providing an updated view on the development of natural products as antioxidant agents
Reverse 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
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
Exploring the non-covalent ligand-binding mechanism on immunoproteasome by enhanced Molecular Dynamics
No full textSelective inhibition of immunoproteasome is a valuable strategy to treat autoimmune and
inflammatory diseases, and hematologic malignancies. In particular, non-covalent inhibition
is strongly desirable because it is free of the drawbacks and side effects associated with
covalent inhibition. Recently, a new series of amide derivatives with Ki values in the low/submicromolar ranges toward the β1i subunit have been identified as non-covalent inhibitors
1
. We
investigated the binding mechanism of the most potent and selective inhibitor (1) to elucidate
the steps from the ligand entrance into the binding pocket to the ligand-induced
conformational changes. We carried out a total of 400ns of MD-binding analysis, followed by
200ns of plain MD. The trajectories clustering allowed identifying three representative poses
evidencing new key interactions with Phe31 and Lys33 together to a flipped orientation of a
representative pose. Further, Binding pose metadynamics (BPMD) studies have been performed
to evaluate the binding affinity, comparing
(1) with other four inhibitors of β1i subunit (2, 3, 4, and 5). Results are consistent with
experimental values of inhibition, confirming (1) as a lead compound of this series. The
adopted methods provided a full dynamic description of the binding events and the information
obtained could be exploited for the rational design of new and more active inhibitor
Deciphering the Nonsense Readthrough Mechanism of Action of Ataluren: An in Silico Compared Study
Full text linkAtaluren was reported to suppress nonsense mutations by promoting the readthrough of premature stop codons, although its mechanism of action (MOA) is still debated. The likely interaction of Ataluren with CFTR-mRNA has been previously studied by molecular dynamics. In this work we extended the modeling of Ataluren's MOA by complementary computational approaches such as induced fit docking (IFD), quantum polarized ligand docking (QPLD), MM-GBSA free-energy calculations, and computational mutagenesis. In addition to CFTR-mRNA, this study considered other model targets implicated in the translation process, such as eukaryotic rRNA 18S, prokaryotic rRNA 16S, and eukaryotic Release Factor 1 (eRF1), and we performed a comparison with a new promising Ataluren analogue (NV2445) and with a series of aminoglycosides, known to suppress the normal proofreading function of the ribosome. Results confirmed mRNA as the most likely candidate target for Ataluren and its analogue, and binding energies calculated after computational mutagenesis highlighted how Ataluren's interaction with the premature stop codon could be affected by ancillary nucleotides in the genetic context
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