E-pharmacophore-based virtual screening to identify GSK-3β inhibitors

3-deazaadenosine: A promising novel p38γ antagonist with potential as a breast cancer therapeutic agent

Human p38γ protein kinase, or MAPK12, is a crucial signaling protein that is important in channelizing membrane signals to the nucleus in the MAPK cascade pathway, associated with breast and colorectal cancer, besides other forms of malignancies and atherosclerotic lesions too. P38γ has a significant contribution to the progression of breast carcinoma due to its multifaceted functions. Targeting p38γ for defining potent antagonists against p38γ can turn out to be an attractive and novel means of breast cancer therapeutics. Novel and potent lead molecules were designed utilizing computational drug design methodologies. Using high-throughput virtual screening, 1909 geometrically similar analogs of known inhibitors were generated, primarily using BIRB796, SB202190, ANP, CHEBI: 620708, and CHEBI: 524699. Chemical correctness was ensured using LigPrep for the standalone library, and Prep Wizard for p38γ using Maestro v.11.5. Using the Glide v5.5 flexible docking procedure on a standalone library of p38γ binding sites, we defined 18 potential leads and assessed their ADMET properties. Lead "1", among the proposed four p38γ antagonists with high-scoring and favorable interactions, was considered for 100 ns molecular dynamics simulations. Among the four proposed leads, Lead '1' displayed consistent and stable bonding interactions with p38γ throughout the 100 ns molecular dynamics (MD) simulations. Additionally, it formed water bridges, contributing to its strong association with the protein. Notably, Lead '1' (3-deazaadenosine) exhibited favorable root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) within the acceptable range of pharmacological properties. Thus, 3-deazaadenosine and its mimetic might be promising new directions for developing a novel class of antagonists for breast cancer treatment.

Drug repurposing combined with MM/PBSA based validation strategies towards MEK inhibitors screening

Emergence of oncogenic mutations in the MAPK pathway gaining more impact in the recent years. Importantly, MEK is a core element of this pathway as it is easy to inhibit and is a gatekeeper of multiple malignancies. Therefore, we performed in-silico strategy to screen repurposed candidate for MEK protein using a library of 11,808 compounds from different clusters in the DrugBank database. Glide docking, Prime-MM/GBSA and QikProp analysis were implemented to retrieve the hits with high precision. The stability of the binding mode and binding affinity of the resultant hit were explored using molecular dynamic simulations and MM/PBSA approach. The results highlight that Nebivolol (DB04861) not only achieved a stable conformation in the MEK binding pocket but also displayed highest binding affinity than the other molecules investigated in our study. Taken together, we hypothesized that Nebivolol is an excellent candidate for the inhibition of MEK in NSCLC patients in future.Communicated by Ramaswamy H. Sarma.

In silico trials to design potent inhibitors against matrilysin (MMP-7)

The study deals with structure-based rational drug design against the chief zinc-rely endopeptidase called matrilysin (MMP-7) that is involved in inflammatory and metastasis process of several carcinomas. Hyperactivated matrilysin of human was targeted, because of its hydrolytic actions on extracellular matrix (ECM) protein components constitutes fibrillar collagens, gelatins, fibronectins and it also activates zymogen forms of vital matrix metalloproteinases (gelatinase A-MMP-2 and B-MMP-9) responsible for ECM destruction in many cancers. In the present work, e-pharmacophores were generated for the respective five co-crystal structures of human matrilysin by mapping ligand's pharmacophoric features. During the lead-optimization campaign, the five e-pharmacophores-based shape screening against an in-house library of >21 million compounds created a dataset of 5000 structural analogs. The subsequent three different docking strategies, including rigid-receptor docking, quantum-polarized-ligand docking, induced-fit docking and free energy binding calculations resulted four leads as novel and potent MMP-7 binders. These four leads were observed with good pharmacological features and good receiver operating characteristics curve metrics (ROC: 0.93) in post-docking evaluations against five existing co-crystal inhibitors and 1000 decoy molecules with MMP-7. Moreover, stability and dynamics behavior of matrilysin-lead1 complex and matrilysin-cocrystal ligand (TQJ) complex were analyzed in natural physiological milieu of 1000 ns or 1 µs molecular dynamics simulations. Lead1-MMP-7 complex was found with an average Cα root-mean-square deviation (RMSD) of 2.35 Å, average ligand root-mean-square fluctuations (RMSF) of 0.66 Å and the strong metallic interactions with E220, a key residue for proteolytic action thereby hinders ECM proteolysis that in turn can halt metastatic cancerous condition.Communicated by Ramaswamy H. Sarma.

Molecular Characterization and Identification of Potential Inhibitors for 'E' Protein of Dengue Virus

Dengue is an arthropod-borne acute febrile illness caused by Dengue Virus (DENV), a member of Flaviviridae. Severity of the infection ranges from mild self-limiting illness to severe life-threatening hemorrhagic fever (DHF) and dengue shock syndrome (DSS). To date, there is no specific antiviral therapy established to treat the infection. The current study reports the epidemiology of DENV infections and potential inhibitors of DENV 'E' protein. Among the various serotypes, DENV-2 serotype was observed more frequently, followed by DENV-4, DENV-1, and DENV-3. New variants of existing genotypes were observed in DENV-1, 2, and 4 serotypes. Predominantly, the severe form of dengue was attributable to DENV-2 infections, and the incidence was more common in males and pediatric populations. Both the incidence and the disease severity were more common among the residents of non-urban environments. Due to the predominantly self-limiting nature of primary dengue infection and folk medicine practices of non-urban populations, we observed a greater number of secondary dengue cases than primary dengue cases. Hemorrhagic manifestations were more in secondary dengue in particularly in the pediatric group. Through different computational methods, ligands RGBLD1, RGBLD2, RGBLD3, and RGBLD4 are proposed as potential inhibitors in silico against DENV-1, -2, -3, and -4 serotypes.

Recent Developments in New Therapeutic Agents against Alzheimer and Parkinson Diseases: In-Silico Approaches

Neurodegenerative diseases (ND), including Alzheimer's (AD) and Parkinson's Disease (PD), are becoming increasingly more common and are recognized as a social problem in modern societies. These disorders are characterized by a progressive neurodegeneration and are considered one of the main causes of disability and mortality worldwide. Currently, there is no existing cure for AD nor PD and the clinically used drugs aim only at symptomatic relief, and are not capable of stopping neurodegeneration. Over the last years, several drug candidates reached clinical trials phases, but they were suspended, mainly because of the unsatisfactory pharmacological benefits. Recently, the number of compounds developed using in silico approaches has been increasing at a promising rate, mainly evaluating the affinity for several macromolecular targets and applying filters to exclude compounds with potentially unfavorable pharmacokinetics. Thus, in this review, an overview of the current therapeutics in use for these two ND, the main targets in drug development, and the primary studies published in the last five years that used in silico approaches to design novel drug candidates for AD and PD treatment will be presented. In addition, future perspectives for the treatment of these ND will also be briefly discussed.

On the search for COVID-19 therapeutics: identification of potential SARS-CoV-2 main protease inhibitors by virtual screening, pharmacophore modeling and molecular dynamics

COVID-19 also known as SARS-CoV-2 outbreak in late 2019 and its worldwide pandemic spread has taken the world by surprise. The minute-to-minute increasing coronavirus cases (>85 M) and progressive deaths (≈1.8 M) calls for finding a cure to this devastating pandemic. While there have been many attempts to find biologically active molecules targeting SARS-CoV-2 for treatment of this viral infection, none has found a way to the clinic yet. In this study, a 3-feature structure-based pharmacophore model was designed for SARS-CoV-2 main protease (M^Pro) that plays a vital role in the viral cellular penetration. High throughput virtual screening of the lead-like ZINC library was then performed to find a potent inhibitor employing the predesigned pharmacophore. In-silico pharmacokinetics/toxicity prediction study was subsequently applied towards the best hits. Finally, a 50 ns molecular dynamics simulation was carried out for the best hit and compared to the co-crystallized ligand where the hit compound displayed high binding and comparable interactions. The results identified new hits for SARS-CoV-2 M^Pro inhibition showing good docking score, pharmacokinetics and toxicity profile, drug-likeness, high binding energy in addition to a promising synthetic accessibility. Identifying new small compounds as potential leads for inhibiting SARS-CoV-2 is a very important step towards designing a synthesizing of promising drug candidates.Communicated by Ramaswamy H. Sarma.

Cytotoxic and pharmacokinetic studies of Indian seaweed polysaccharides for formulating raindrop synbiotic candy

Gut microbiome evidenced as the assembling mode of action facilitates the relationship of environmental factors (such as diet and lifestyle) with colorectal cancer. The cytotoxic and anticancer studies of the enzymatically extracted polysaccharides from selected Indian seaweeds (such as S. wightii, E. compressa, and A. spicifera) on Raw 264.7 macrophage and HT-29 human colon cancer cell line were investigated. E. compressa showed nitric oxide production up to a concentration of 6.99 ± 0.05 μM. The polysaccharide extract of seaweed (PES), A. spicifera (100 μg/ml) had shown the highest in-vitro cytotoxicity effect on HT-29 cells up to 52.13 ± 1.4%. Absorption, distribution, metabolism and excretion (ADME) predictions were performed for exploring the possibility of anti-cancer drug development. The formulated synbiotic candy exhibited post storage survivability of probiotic species L. plantarum NCIM 2083 up to 10⁷ CFU/ml until three weeks and it could be an aesthetic functional food for treating colon cancer.

Advances in developing novel therapeutic strategies for Alzheimer's disease

Alzheimer's Disease (AD), the most prevalent neurodegenerative disease of aging, affects one in eight older Americans. Nearly all drug treatments tested for AD today have failed to show any efficacy. There is a great need for therapies to prevent and/or slow the progression of AD. The major challenge in AD drug development is lack of clarity about the mechanisms underlying AD pathogenesis and pathophysiology. Several studies support the notion that AD is a multifactorial disease. While there is abundant evidence that amyloid plays a role in AD pathogenesis, other mechanisms have been implicated in AD such as tangle formation and spread, dysregulated protein degradation pathways, neuroinflammation, and loss of support by neurotrophic factors. Therefore, current paradigms of AD drug design have been shifted from single target approach (primarily amyloid-centric) to developing drugs targeted at multiple disease aspects, and from treating AD at later stages of disease progression to focusing on preventive strategies at early stages of disease development. Here, we summarize current strategies and new trends of AD drug development, including pre-clinical and clinical trials that target different aspects of disease (mechanism-based versus non-mechanism based, e.g. symptomatic treatments, lifestyle modifications and risk factor management).

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Changing Paradigm from one Target one Ligand Towards Multi-target Directed Ligand Design for Key Drug Targets of Alzheimer Disease: An Important Role of In Silico Methods in Multi-target Directed Ligands Design

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreo-ver, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulat-ing single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharma-cology, multi-modal therapies, multifaceted, and/or the more recently proposed term “multi-targeted designed drugs”. Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challeng-es such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summariz-ing some of the most prominent and computationally explored single targets against AD and further, we discussed a success-ful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computa-tional approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

Structure based identification and biological evaluation of novel and potent inhibitors of PCAF catalytic domain

p300/CBP Associated Factor (PCAF), a GNAT family member protein, represent a valid target for therapeutic interventions since its dysfunction has implicated in variety of diseases like cancer, diabetes, inflammatory diseases, etc. Despite its potential for therapeutics, only a small number of PCAF inhibitors were reported. Hence, in this study, the catalytic domain of PCAF was explored to screen novel, potent and cell permeable inhibitor from three small molecule databases like Life Chemical, Maybridge and Chembridge by using Structure Based Virtual Screening (SBVS) method. Further, Induced Fit Docking, Binding Free Energy calculation, Single Point Energy calculation and Molecular Dynamics Simulation were performed on selected hits. In silico results revealed that F2209-0381 has higher binding energy of -109.722 and have greater cell permeability (QPPCaco = 1456.764; QPPMDCK = 742.941) than rest of hits. Cytotoxicity effect and protein expression analysis of F2209-0381 on A549 cells reveals that it exhibited strong inhibition with IC₅₀ value of 58.31 μg/ml and significantly reduced the expression of PCAF after 72 h time point. Thus, this study warrants that F2209-0381 could become a novel, potent and cell permeable drug of PCAF thereby it could combat its mediated diseases.

Combinatorial In Silico Strategy towards Identifying Potential Hotspots during Inhibition of Structurally Identical HDAC1 and HDAC2 Enzymes for Effective Chemotherapy against Neurological Disorders

Histone deacetylases (HDACs) regulate epigenetic gene expression programs by modulating chromatin architecture and are required for neuronal development. Dysregulation of HDACs and aberrant chromatin acetylation homeostasis have been implicated in various diseases ranging from cancer to neurodegenerative disorders. Histone deacetylase inhibitors (HDACi), the small molecules interfering HDACs have shown enhanced acetylation of the genome and are gaining great attention as potent drugs for treating cancer and neurodegeneration. HDAC2 overexpression has implications in decreasing dendrite spine density, synaptic plasticity and in triggering neurodegenerative signaling. Pharmacological intervention against HDAC2 though promising also targets neuroprotective HDAC1 due to high sequence identity (94%) with former in catalytic domain, culminating in debilitating off-target effects and creating hindrance in the defined intervention. This emphasizes the need of designing HDAC2-selective inhibitors to overcome these vicious effects and for escalating the therapeutic efficacy. Here we report a top-down combinatorial in silico approach for identifying the structural variants that are substantial for interactions against HDAC1 and HDAC2 enzymes. We used extra-precision (XP)-molecular docking, Molecular Mechanics Generalized Born Surface Area (MMGBSA) for predicting affinity of inhibitors against the HDAC1 and HDAC2 enzymes. Importantly, we employed a novel in silico strategy of coupling the state-of-the-art molecular dynamics simulation (MDS) to energetically-optimized structure based pharmacophores (e-Pharmacophores) method via MDS trajectory clustering for hypothesizing the e-Pharmacophore models. Further, we performed e-Pharmacophores based virtual screening against phase database containing millions of compounds. We validated the data by performing the molecular docking and MM-GBSA studies for the selected hits among the retrieved ones. Our studies attributed inhibitor potency to the ability of forming multiple interactions and infirm potency to least interactions. Moreover, our studies delineated that a single HDAC inhibitor portrays differential features against HDAC1 and HDAC2 enzymes. The high affinity and selective HDAC2 inhibitors retrieved through e-Pharmacophores based virtual screening will play a critical role in ameliorating neurodegenerative signaling without hampering the neuroprotective isoform (HDAC1).

Virtual Screening, pharmacophore development and structure based similarity search to identify inhibitors against IdeR, a transcription factor of Mycobacterium tuberculosis

ideR, an essential gene of Mycobacterium tuberculosis, is an attractive drug target as its conditional knockout displayed attenuated growth phenotype in vitro and in vivo. To the best of our knowledge, no inhibitors of IdeR are identified. We carried out virtual screening of NCI database against the IdeR DNA binding domain followed by inhibition studies using EMSA. Nine compounds exhibited potent inhibition with NSC 281033 (I-20) and NSC 12453 (I-42) exhibiting IC₅₀ values of 2 µg/ml and 1 µg/ml, respectively. We then attempted to optimize the leads firstly by structure based similarity search resulting in a class of inhibitors based on I-42 containing benzene sulfonic acid, 4-hydroxy-3-[(2-hydroxy-1-naphthalenyl) azo] scaffold with 4 molecules exhibiting IC₅₀ ≤ 10 µg/ml. Secondly, optimization included development of energy based pharmacophore and screening of ZINC database followed by docking studies, yielding a molecule with IC₅₀ of 60 µg/ml. More importantly, a five-point pharmacophore model provided insight into the features essential for IdeR inhibition. Five molecules with promising IC₅₀ values also inhibited M. tuberculosis growth in broth culture with MIC₉₀ ranging from 17.5 µg/ml to 100 µg/ml and negligible cytotoxicity in various cell lines. We believe our work opens up avenues for further optimization studies.