Discovery of Potent Natural-Product-Derived SIRT2 Inhibitors Using Structure-Based Exploration of SIRT2 Pharmacophoric Space Coupled With QSAR Analyses

BACKGROUND

SIRT2 belongs to a class III of histone deacetylase (HDAC) and has crucial roles in neurodegeneration and malignancy.

OBJECTIVE

Discover structurally novel natural-product-derived SIRT2 inhibitors.

METHODS

Structure-based pharmacophore modeling integrated with validated QSAR analysis were implemented to discover structurally novel SIRT2 inhibitors from natural products database. The targeted QSAR model combined molecular descriptors with structure-based pharmacophore capable of explaining bioactivity variation of structurally diverse SIRT2 inhibitors. Manually built pharmacophore model, validated with receiver operating characteristic curve, and selected using the statistically optimum QSAR equation, was applied as a 3D-search query to mine AnalytiCon Discovery database of natural products.

RESULTS

Experimental in vitro testing of highest-ranked hits identified asperphenamate and salvianolic acid B as active SIRT2 inhibitors with IC50 values in low micromolar range.

CONCLUSION

New chemical scaffolds of SIRT2 inhibitors have been identified that could serve as a starting point for lead-structure optimization.

In silico approaches using pharmacophore model combined with molecular docking for discovery of novel ULK1 inhibitors

Background: Discovery of effective autophagy-initiating kinase ULK1 inhibitors has attracted more and more attention in cancer treatment. Methodology & results: The present study describes the application of a pharmacophore-based virtual screening and structure-based docking approach guided drug design. Compound U-2 exhibited a nanomolar range of IC₅₀ against the ULK1 target. Molecular dynamics simulation was used to assess the quality of docking studies. The determinants of binding affinity were investigated, and a different binding pattern was observed. Subsequently, prediction properties of ADMET (absorption, distribution, metabolism, excretion and toxicity) and hepatotoxicity in vitro studies indicated that U-2 possessed good drug-like properties. Moreover, western blot analysis indicated that the compound inhibited autophagic flux in cells. Conclusion: The present study provides an appropriate guideline for discovering novel ULK1 inhibitors. The novel compound may serve as a good starting point for further development and optimizations.

In Silico Drug-designing Studies on Sulforaphane Analogues: Pharmacophore Mapping, Molecular Docking and QSAR Modeling

AIMS

In the presented work we successfully discovered several novel NQO1 inducers using the computational approaches.

BACKGROUND

The phytochemical sulforaphane (SFN) is a potent inducer of carcinogen detoxication enzymes like NAD(P)H:quinone oxidoreductase 1 (NQO1) through the Kelch-like erythroid cellderived protein with CNC homology[ECH]-associated protein 1 (Keap1)-[NF-E2]-related factor 2 (Nrf2) signaling pathway.

OBJECTIVE

In this paper, we report the first QSAR and pharmacophore modeling study of sulforaphane analogues as NQO1 inducers. The pharmacophore model and understanding the relationships between the structures and activities of the known inducers will give useful information on the structural basis for NQO1 enzymatic activity and lead optimization for future rational design of new sulforaphane analogues as potent NQO1 inducers.

METHODS

In this study, a combination of QSAR modeling, pharmacophore generation, virtual screening and molecular docking was performed on a series of sulforaphane analogues as NQO1 inducers.

RESULTS

In deriving the QSAR model, the stepwise multiple linear regression established a reliable model with the training set (N: 43, R: 0.971, RMSE: 0.216) and test set (N: 14, R: 0.870, RMSE: 0.324, Q2: 0.80) molecules. The best ligand-based pharmacophore model comprised two hydrophobic (HY), one ring aromatic (RA) and three hydrogen bond acceptor (HBA) sites. The model was validated by a testing set and the decoys set, Güner-Henry (GH) scoring methods, etc. The enrichment of model was assessed by the sensitivity (0.92) and specificity (0.95). Moreover, the values of enrichment factor (EF) and the area under the receiver operating characteristics curve (AUC) were 12 and 0.94, respectively. This well-validated model was applied to screen two Asinex libraries for the novel NQO1 inducers. The hits were subsequently subjected to molecular docking after being filtering by Lipinski's, MDDR-like, and Veber rules as well as evaluating their interaction with three major drugmetabolizing P450 enzymes, CYP2C9, CYP2D6 and CYP3A4. Ultimately, 12 hits filtered by molecular docking were subjected to validated QSAR model for calculating their inducer potencies and were introduced as potential NQO1 inducers for further investing action.

CONCLUSION

Conclusively, the validated QSAR model was applied on the hits to calculate their inducer potencies and these 12 hits were introduced as potential NQO1 inducers for further investigations.

Biased Ligands Differentially Shape the Conformation of the Extracellular Loop Region in 5-HT2B Receptors

G protein-coupled receptors are linked to various intracellular transducers, each pathway associated with different physiological effects. Biased ligands, capable of activating one pathway over another, are gaining attention for their therapeutic potential, as they could selectively activate beneficial pathways whilst avoiding those responsible for adverse effects. We performed molecular dynamics simulations with known β-arrestin-biased ligands like lysergic acid diethylamide and ergotamine in complex with the 5-HT_2B receptor and discovered that the extent of ligand bias is directly connected with the degree of closure of the extracellular loop region. Given a loose allosteric coupling of extracellular and intracellular receptor regions, we delineate a concept for biased signaling at serotonin receptors, by which conformational interference with binding pocket closure restricts the signaling repertoire of the receptor. Molecular docking studies of biased ligands gathered from the BiasDB demonstrate that larger ligands only show plausible docking poses in the ergotamine-bound structure, highlighting the conformational constraints associated with bias. This emphasizes the importance of selecting the appropriate receptor conformation on which to base virtual screening workflows in structure-based drug design of biased ligands. As this mechanism of ligand bias has also been observed for muscarinic receptors, our studies provide a general mechanism of signaling bias transferable between aminergic receptors.

Identification of potent human carbonic anhydrase IX inhibitors: a combination of pharmacophore modeling, 3D-QSAR, virtual screening and molecular dynamics simulations

Human carbonic anhydrase IX (hCA IX) is a promising target for the development of potential anticancer agents. In the current study, pharmacophore and 3D-QSAR models have been developed using SLC-0111 derivatives. The developed models have been further utilized for the virtual screening process to develop potent hCA IX inhibitors. Thirteen different models have been developed by employing various combinations of training and test set molecules. Based on this, a model, AADDR.135, comprising two H-bond acceptors, two H-bond donors and one aromatic ring, has been found as the best QSAR model. The proposed model exhibits high robustness (R² = 0.9789), with good predictive ability (Q² = 0.6872). An external library of drug-like compounds (∼10000 molecules) imported from the ZINC15 database has been screened over the model AADDR.135. In total, 1601 compounds were obtained as hits. Molecular docking studies and molecular dynamics simulations have been performed on the obtained hits and, based on these computations, two unique molecules have been identified as potential hCA IX inhibitors. These show higher binding energies compared to the parent molecule and its most potent analogue.Communicated by Ramaswamy H. Sarma.

Parameters for Irreversible Inactivation of Monoamine Oxidase

The irreversible inhibitors of monoamine oxidases (MAO) slow neurotransmitter metabolism in depression and neurodegenerative diseases. After oxidation by MAO, hydrazines, cyclopropylamines and propargylamines form a covalent adduct with the flavin cofactor. To assist the design of new compounds to combat neurodegeneration, we have updated the kinetic parameters defining the interaction of these established drugs with human MAO-A and MAO-B and analyzed the required features. The K_i values for binding to MAO-A and molecular models show that selectivity is determined by the initial reversible binding. Common to all the irreversible inhibitor classes, the non-covalent 3D-chemical interactions depend on a H-bond donor and hydrophobic-aromatic features within 5.7 angstroms apart and an ionizable amine. Increasing hydrophobic interactions with the aromatic cage through aryl halogenation is important for stabilizing ligands in the binding site for transformation. Good and poor inactivators were investigated using visible spectroscopy and molecular dynamics. The initial binding, close and correctly oriented to the FAD, is important for the oxidation, specifically at the carbon adjacent to the propargyl group. The molecular dynamics study also provides evidence that retention of the allenyl imine product oriented towards FADH⁻ influences the formation of the covalent adduct essential for effective inactivation of MAO.

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB

Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.

Structure-based discovery of interleukin-33 inhibitors: a pharmacophore modelling, molecular docking, and molecular dynamics simulation approach

Interleukin (IL)-33 is a new cytokine of the IL-1 family that is related to several inflammatory and autoimmune diseases. IL-33 binds to its ST2 receptor and leads to biological responses thereof. Currently, no drugs have been approved for the treatment of IL-33 related diseases. The aim of this study was to search for small molecules that inhibit the protein-protein interaction between IL-33 and ST2. A virtual screening was first performed to identify potential molecules that can bind IL-33. By analysing the interactions between key residues in the complex of IL-33/ST2, two pharmacophore hypotheses were then generated based on the 'mimicry' and 'pair-rule' principles. From a database of 62,074 compounds, 60 molecules satisfying the pharmacophore models were identified and docked to IL-33. Among 35 compounds successfully docked into the protein, 9 potential ligands in complex with IL-33 were selected for further analysis by molecular dynamics simulations. Based on the stability of the complexes and the interactions of each ligand with the key residues of IL-33, two compounds DB00158 and DB00642 were identified as the most potential inhibitors that can be further investigated as promising novel IL-33 inhibitory drugs.

In silico modeling studies of N9-substituted harmine derivatives as potential anticancer agents: combination of ligand-based and structure-based approaches

A computational study was carried out to develop quantitative-structure activity relationship (QSAR), pharmacophore, molecular docking and molecular dynamics simulations of a series of N9-substituted harmine derivatives in order to investigate the structural factors involved in the cytotoxic activity and thus design new active derivatives. A valid 3 D-QSAR (R²= 0.89, q²=0.67, R²pred = 0.72) and 2 D-QSAR (R²= 0.81, q²=0.69, R²pred = 0.76) models were obtained correlating the cytotoxic activity with hydrophobic and hydrogen bond acceptor (HBA) features for 3 D-QSAR and SlogP and a_acc descriptors for 2 D-QSAR. Analysis of the selected descriptors for both models highlighted that lipophilicity and hydrogen bonding acceptor atoms remain the crucial properties and those on which cytotoxic activity depends. Also, these findings are in agreement with the characteristics of the generated pharmacophore. Furthermore, molecular docking revealed that the binding energy (-9.74 kcal/mol) and inhibition constant (0.071 µmol) correlate with the activity of the most active compound that forms hydrophobic interactions and two hydrogen bonds with the the dual specificity tyrosine phosphorylation regulated kinase 1 A (DYRK1A). The molecular dynamics simulations revealed that the protein-ligand equilibrium is stable after 100000 fs of trajectories. Based on these results, we designed new N⁹-substituted harmine derivatives with improved properties: predicted cytotoxic activities, estimated binding energies, estimated inhibition constants and interaction modes with amino acid residues of DYRK1A, compared to the best compound in the studied dataset. Additionally, these newly designed inhibitors showed promising results in the preliminary in silico Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) evaluations.Communicated by Ramaswamy H. Sarma.

Novel Small-Molecule Scaffolds as Candidates against the SARS Coronavirus 2 Main Protease: A Fragment-Guided in Silico Approach

The ongoing pandemic caused by the novel coronavirus has been the greatest global health crisis since the Spanish flu pandemic of 1918. Thus far, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in over 1 million deaths, and there is no cure or vaccine to date. The recently solved crystal structure of the SARS-CoV-2 main protease has been a major focus for drug-discovery efforts. Here, we present a fragment-guided approach using ZINCPharmer, where 17 active fragments known to bind to the catalytic centre of the SARS-CoV-2 main protease (SARS-CoV-2 M^pro) were used as pharmacophore queries to search the ZINC databases of natural compounds and natural derivatives. This search yielded 134 hits that were then subjected to multiple rounds of in silico analyses, including blind and focused docking against the 3D structure of the main protease. We scrutinised the poses, scores, and protein-ligand interactions of 15 hits and selected 7. The scaffolds of the seven hits were structurally distinct from known inhibitor scaffolds, thus indicating scaffold novelty. Our work presents several novel scaffolds as potential candidates for experimental validation against SARS-CoV-2 M^pro.

Discovery and Design of Novel Small Molecule GSK-3 Inhibitors Targeting the Substrate Binding Site

The serine/threonine kinase, GSK-3, is a promising drug discovery target for treating multiple pathological disorders. Most GSK-3 inhibitors that were developed function as ATP competitive inhibitors, with typical limitations in specificity, safety and drug-induced resistance. In contrast, substrate competitive inhibitors (SCIs), are considered highly selective, and more suitable for clinical practice. The development of SCIs has been largely neglected in the past because the ambiguous, undefined nature of the substrate-binding site makes them difficult to design. In this study, we used our previously described structural models of GSK-3 bound to SCI peptides, to design a pharmacophore model and to virtually screen the “drug-like” Zinc database (~6.3 million compounds). We identified leading hits that interact with critical binding elements in the GSK-3 substrate binding site and are chemically distinct from known GSK-3 inhibitors. Accordingly, novel GSK-3 SCI compounds were designed and synthesized with IC₅₀ values of~1–4 μM. Biological activity of the SCI compound was confirmed in cells and in primary neurons that showed increased β-catenin levels and reduced tau phosphorylation in response to compound treatment. We have generated a new type of small molecule GSK-3 inhibitors and propose to use this strategy to further develop SCIs for other protein kinases.

Exploring the selectivity profile of sigma receptor ligands by molecular docking and pharmacophore analyses

BACKGROUND

Sigma receptors (σRs), initially classified as an additional class of opioid receptors, are now recognized as a unique entity with no homology to opioid receptors divided into two distinct subtypes namely σ1R and σ2R. 1Rtargeting ligands have been conceived and explored for the treatment of various neurodegenerative disorders and neuropathic pain. Activation of the σ2R appears to be involved in the regulation of cellular proliferation and cell death.

OBJECTIVE

Up to now, the rational design of novel σ1R ligands was efficiently guided by computational methods, especially relying on homology modeling studies. Conversely, the limited number of in silico studies was applied in the search of σ2Rtargeting compounds. Herein we explored by computational methods several series of 1R ligands featuring variable selectivity profile towards σ1R and σR in order to gain useful information guiding the rational design of more selective ligands.

METHODS

Based on the recent X-ray crystallographic structure of the human σ1R, deepening molecular docking studies on different series of σR ligands have been performed. These calculations have been followed by molecular dynamic simulations (MD) and by two pharmacophore analyses, taking into account the activity levels towards σ1R and σR.

RESULTS

Structure-based studies revealed key contacts to be achieved in order to guide selectivity of σ1R-targeting compounds while the two pharmacophore models described the main features turning in effective σ1R or σ2R ligands.

CONCLUSION

The applied computational approach allowed a more comprehensive exploration of the structure-activity relationship (SAR) within the herein analyzed R ligands, deriving useful guidelines for the rational design of more selective compo.

Computational screening of natural and natural-like compounds to identify novel ligands for sigma-2 receptor

Sigma-2 (σ₂) receptor is a transmembrane protein shown to be linked with neurodegenerative diseases and cancer development. Thus, it emerges as a potential biological target for the advancement of anticancer and anti-Alzheimer's agents. The current study was aimed to identify potential σ₂ receptor ligands using integrated computational approaches including homology modelling, combined pharmacophore- and docking-based virtual screening, and molecular dynamics (MD) simulation. Pharmacophore-based screening was conducted against a database composed of 20,523 small natural and natural-like products. In total, 1200 structures were found to satisfy the required pharmacophore features and were then exposed to docking-based screening against the generated homology model of σ₂ receptor. On the basis of the pharmacophore fit scores, docking scores, and mechanism of binding interaction, 20 potential hits were retained. Five promising candidates were selected (SR84, SR823, SR300, SR413, and SR530) on the basis of their binding score and interaction. Further, in silico ADMET profiling of these compounds showed that the selected compounds possess favourable ADME properties with low toxicity risk. The mechanism of interaction of these compounds with σ₂ receptor as well as their binding stability were characterized by MD simulation.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

A multi-stage virtual screening of FDA-approved drugs reveals potential inhibitors of SARS-CoV-2 main protease

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global health emergency. Repurposing of approved pharmaceutical drugs for COVID-19 treatment represents an attractive approach to quickly identify promising drug candidates. SARS-CoV-2 main protease (M^pro) is responsible for the maturation of viral functional proteins making it a key antiviral target. Based on the recently revealed crystal structures of SARS-CoV-2 M^pro, we herein describe a multi-stage virtual screening protocol including pharmacophore screening, molecular docking and protein-ligand interaction fingerprints (PLIF) post-docking filtration for efficient enrichment of potent SARS-CoV-2 M^pro inhibitors. Potential hits, along with a cocrystallized control were further studied via molecular dynamics. A 150-ns production trajectory was followed by RMSD, free energy calculation, and H-bond analysis for each compound. The applied virtual screening protocol led to identification of five FDA-approved drugs with promising binding modes to key subsites of the substrate-binding pocket of SARS-CoV-2 M^pro. The identified compounds belong to different pharmaceutical classes, including several protease inhibitors, antineoplastic agents and a natural flavonoid. The drug candidates discovered in this study present a potential extension of the recently reported SARS-CoV-2 M^pro inhibitors that have been identified using other virtual screening protocols and may be repurposed for COVID-19 treatment.

Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors

Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors.

An in silico approach to identify potential medicinal plants for treating Alzheimer disease: a case study with acetylcholinesterase

Alzheimer's disease (AD) is a progressive neurological disorder affecting an estimated 10 million people worldwide. There is no cure for AD, and only a handful of drugs are known to provide some relief of the symptoms. The prescription drug donepezil has been widely used to treat to slow the progression and onset of the disease; however, the unpleasant side effects have paved the way to find alternative medicines. Many herbs are known to improve brain function, but evidence of medicinal plants that can treat AD is limited due to the lack of concrete rational evidences. Moreover, the traditional method of randomly screening plant extract against AD targets takes time and resources. In this study, a receptor-based in silico method has been implemented which serves to accelerate the process of identification of medicinal plants useful for treatment of AD. A database of natural compounds was compiled to identify hits against acetylcholinesterase (AChE). Receptor-based pharmacophore screening was performed, and selected hits were subjected to docking and molecular dynamics simulations. Molecular Mechanics/Generalized Born surface area (MM/GBSA) calculations were carried out to identify the best scoring hits further. In vitro assays were done for the plant extracts containing the top-scoring hits against AChE. Three plant extracts showed favorable inhibitory activity.Communicated by Ramaswamy H. Sarma.

Pharmacophore based virtual screening, molecular docking, molecular dynamics and MM-GBSA approach for identification of prospective SARS-CoV-2 inhibitor from natural product databases

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily appeared in Wuhan, China, in December 2019. At present, no proper therapy and vaccinations are available for the disease, and it is increasing day by day with a high mortality rate. Pharmacophore based virtual screening of the selected natural product databases followed by Glide molecular docking and dynamics studies against SARS-CoV-2 main protease was investigated to identify potential ligands that may act as inhibitors. The molecules SN00293542 and SN00382835 revealed the highest docking score of −14.57 and −12.42 kcal/mol, respectively, when compared with the co-crystal ligands of PDB-6Y2F (O6K) and 6W63 (X77) of the SARS-CoV-2 M^pro. To further validate the interactions of top scored molecules SN00293542 and SN00382835, molecular dynamics study of 100 ns was carried out. This indicated that the protein-ligand complex was stable throughout the simulation period, and minimal backbone fluctuations have ensued in the system. Post-MM-GBSA analysis of molecular dynamics data showed free binding energy-71.7004 +/− 7.98, −56.81+/− 7.54 kcal/mol, respectively. The computational study identified several ligands that may act as potential inhibitors of SARS-CoV-2 M^pro. The top-ranked molecules SN00293542, and SN00382835 occupied the active site of the target, the main protease like that of the co-crystal ligand. These molecules may emerge as a promising ligands against SARS-CoV-2 and thus needs further detailed investigations.

Communicated by Ramaswamy H. Sarma

In-silico Studies and Biological Activity of Potential BACE-1 Inhibitors

BACKGROUND

Alzheimer's disease is a neurological condition causing cognitive inability and dementia. The pathological lesions and neuronal damage in the brain are caused by self-aggregated fragments of mutated Amyloidal precursor protein (APP).

OBJECTIVE

The controlled APP processing by inhibition of secretase is the strategy to reduce Aβ load to treat Alzheimer's disease.

METHODS

A QSAR study was performed on 55 Pyrrolidine based ligands as BACE-1 inhibitors with an activity magnitude greater than 4 of compounds.

RESULTS

In the advent of designing new BACE-1 inhibitors, the pharmacophore model with correlation (r = 0.90) and root mean square deviation (RMSD) of 0.87 was developed and validated. Further, the hits retrieved by the in-silico approach were evaluated by docking interactions.

CONCLUSION

Two structurally diverse compounds exhibited Asp32 and Thr232 binding with the BACE-1 receptor. The aryl-substituted carbamate compound exhibited the highest fit value and docking score. The biological activity evaluation by in-vitro assay was found to be >0.1μM.

Targeting SARS-CoV-2 RBD Interface: a Supervised Computational Data-Driven Approach to Identify Potential Modulators

Coronavirus disease 2019 (COVID-19) has spread out as a pandemic threat affecting over 2 million people. The infectious process initiates via binding of SARS-CoV-2 Spike (S) glycoprotein to host angiotensin-converting enzyme 2 (ACE2). The interaction is mediated by the receptor-binding domain (RBD) of S glycoprotein, promoting host receptor recognition and binding to ACE2 peptidase domain (PD), thus representing a promising target for therapeutic intervention. Herein, we present a computational study aimed at identifying small molecules potentially able to target RBD. Although targeting PPI remains a challenge in drug discovery, our investigation highlights that interaction between SARS-CoV-2 RBD and ACE2 PD might be prone to small molecule modulation, due to the hydrophilic nature of the bi-molecular recognition process and the presence of druggable hot spots. The fundamental objective is to identify, and provide to the international scientific community, hit molecules potentially suitable to enter the drug discovery process, preclinical validation and development.

Antidepressant Drug Design on TCAs and Phenoxyphenylpropylamines utilizing QSAR and Pharmacophore Modeling

BACKGROUND

Depression is a mental illness caused by the imbalance of important neurotransmitters such as serotonin (5-HT) and norepinephrine (NE). It is a serious neurological disorder that could be treated by antidepressant drugs.

OBJECTIVE

There are two major classes such as TCAs and phenoxyphenylpropylamines which have been proven to be broad-spectrum antidepressant compounds. Several attempts were made to design, synthesize and discover potent antidepressant compounds having the least toxicity and most selectivity towards serotonin and norepinephrine transporters. But there is hardly any drug design based on quantitative structure-activity relationship (QSAR) and pharmacophore modeling attempted yet.

METHOD

In the present study, many TCAs (dibenzoazepine) and phenoxyphenylpropylamine derivatives are taken into consideration for pharmacophore feature generation followed by pharmacophoric distant related descriptors based QSAR modeling. Further, several five new congeners have been designed which are subjected to the prediction of biological activities in terms of serotonin receptor affinity utilizing validated QSAR models developed by us.

RESULTS

An important pharmacophoric feature point C followed by the generation of a topography of the TCAs and phenoxyphenylpropylamine has been predicted. The developed pharmacophoric feature-based QSAR can explain 64.2% of the variances of 5-HT receptor antagonism. The best training model has been statistically validated by the prediction of test set compounds. This training model has been used for the prediction of some newly designed congeneric compounds which are comparable with the existed drugs.

CONCLUSION

The newly designed compounds may be proposed for further synthesis and biological screening as antidepressant agents.

Combined Ligand-Based and Structure-Based Virtual Screening Approach for Identification of New Dipeptidyl Peptidase 4 Inhibitors

BACKGROUND

Dipeptidyl Peptidase 4 (DPP 4) enzyme cleaves an incretin-based glucoregulatory hormone Glucagon Like Peptide -1 from N-terminal where penultimate amino acid is either alanine or proline. Several DPP 4 inhibitors, "gliptins", are approved for the management of Type 2 Diabetes or are under clinical trial. In the present study, combined pharmacophore and docking-based virtual screening protocol were used for the identification of new hits from the Specs Database, which would inhibit DPP 4.

METHODS

The entire computational studies were performed using the Discovery Studio v. 4.1 software package, Pipeline Pilot v. 9.2 (Accelrys Inc.) and FRED v. 2.2.5 (OpenEye Scientific Software). Common feature pharmacophore model was generated from known DPP 4 inhibitors and validated by Receiver Operating curve analysis and GH-scoring method. Database search of Specs commercial database was performed using validated pharmacophore. Hits obtained from pharmacophore search were further docked into the binding site of DPP 4. Based on the analysis of docked poses of hits, 10 compounds were selected for in- vitro DPP 4 enzyme inhibition assay.

RESULTS

Based on docking studies, virtual hits were predicted to form interaction with essential amino acid residues of DPP 4 and have an almost similar binding orientation as that of the reference molecule. Three compounds having Specs database ID- AN-465/42837213, AP-064/42049348 and AN- 465/43369427 were found to inhibit DPP 4 enzyme moderately.

CONCLUSION

The present study demonstrates a successful utilization of in-silico tools in the identification of new DPP 4 inhibitor, which can serve as a starting point for the development of novel DPP 4 inhibitors.

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing compounds (PTCSC) have been developed in order to restore protein translation by allowing the incorporation of an amino acid in place of a stop codon. However, limitations exist in terms of efficacy and toxicity. To identify new compounds that have PTC-suppressing ability, we selected and clustered existing PTCSC, allowing for the construction of a common pharmacophore model. Machine learning (ML) and deep learning (DL) models were developed for prediction of new PTCSC based on known compounds. We conducted a search of the NCI compounds database using the pharmacophore-based model and a search of the DrugBank database using pharmacophore-based, ML and DL models. Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, and DL searches. Our results suggest notable correspondence of the pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing compounds.

Identification of the natural compound inhibitors against Plasmodium falciparum plasmepsin-II via common feature based screening and molecular dynamics simulations

Malaria is counted amongst the deadly disease caused by Plasmodium falciparum. Recently, plasmepsin-II enzyme has gained much importance as an attractive drug target for the exploration of antimalarials. Therefore, the common feature pharmacophore models were generated from the crystallized complexes of the plasmepsin-II proteome. These models were subjected to a series of validation procedures, i.e. test set and Güner Henry studies to enlist the representative models. The selected representative hypotheses incorporating the most essential chemical features (common ZHHA) were screened against the natural product database to retrieve the potential candidates. To ensure the selection of the drug-like candidates, prior to screening, filtering steps (Drug-likeness and ADMET filters) were employed on the selected database. To study the interaction pattern of the candidates within the protein, these molecules were advanced to the molecular docking studies. Subsequently, based on the selected cut-off criteria obtained via redocking of the reference (4Z22), 15 compounds showed higher docking score (> -16.05 kcal/mol), and displayed the presence of hydrogen bonding with the crucial amino acids, i.e. Asp34 and Asp214. Further, the stability of the docked molecules was scrutinized via molecular dynamics simulations, and the results were compared with the reference compound 4Z22. All the docked compounds showed stable dynamics behaviour. Thus, in the present contribution, the combination of screening and stability procedures resulted in the identification of 15 hits that can serve as a new chemical space in the designing of the novel antimalarials.Communicated by Ramaswamy H. Sarma.

Structural insight into TNF-α inhibitors through combining pharmacophore-based virtual screening and molecular dynamic simulation

Tumor Necrosis Factor-alpha (TNF-α), a multifunctional cytokine responsible for providing resistance against infections, inflammation, and cancers. TNF-α has emerged as a promising drug target against several autoimmune and inflammatory disorders. Several synthetic antibodies (Infliximab, Etanercept, and Adalimumab) are available, but their potential to cause severe side effects has prompted them to develop alternative small molecules-based therapies for inhibition of TNF-α. In the present study, combined in silico approaches based on pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics studies were employed to understand significant direct interactions between TNF-α protein and small molecule inhibitors. Initially, four different small molecule libraries (∼17.5 million molecules) were virtually screened against the selected pharmacophore model. The identified hits were further subjected to molecular docking studies. The three potent lead compounds (ZINC05848961, ZINC09402309, ZINC04502991) were further subjected to 100 ns molecular dynamic studies to examine their stability. Our docking and molecular dynamic analysis revealed that the selected lead compounds target the TNF receptor (TNFR) and efficiently block the production of TNF. Moreover, in silico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) analysis revealed that all the predicted compounds have good pharmacokinetic properties with high gastrointestinal absorption and a decent bioavailability score. Furthermore, toxicity profiles further evidenced that these compounds have no risk of being mutagenic, tumorigenic, reproductive and irritant except ZINC11915498. In conclusion, the present study could serve as the starting point to develop new therapeutic regimens to treat various TNF- related diseases. Communicated by Ramaswamy H. Sarma.