Pharmacophore modelling,virtual screening and molecular docking studies on PLD1 inhibitors

Lipid metabolism plays a significant role in influenza virus replication and subsequent infection. The regulatory mechanism governing lipid metabolism and viral replication is not properly understood to date, but both Phospholipase D (PLD1 and PLD2) activities are stimulated in viral infection. In vitro studies indicate that chemical inhibition of PLD1 delays viral entry and reduction of viral loads. The current study reports a three-dimensional pharmacophore model based on 35 known PLD1 inhibitors. A sub-set of 25 compounds was selected as the training set and the remaining 10 compounds were kept in the test set. One hundred and twelve pharmacophore models were generated; a six-featured pharmacophore model (AADDHR.57) with survival score (2.69) produced a statistically significant three-dimensional quantitative structure–activity relationship model with r² = 0.97 (internal training set), r² = 0.71 (internal test set) and Q² = 0.64. The predictive power of the pharmacophore model was validated with an external test set (r² = 0.73) and a systematic virtual screening work-flow was employed showing an enrichment factor of 23.68 at the top 2% of the dataset (active and decoys). Finally, the model was used for screening of the filtered PubChem database to fetch molecules which can be proposed as potential PLD1 inhibitors for blocking influenza infection.     

Identification of potential Gly/NMDA receptor antagonists by cheminformatics approach: a combination of pharmacophore modelling,virtual screening and molecular docking studies

The Gly/NMDA receptor has become known as potential target for the management of neurodegenerative diseases. Discovery of Gly/NMDA antagonists has thus attracted much attention in recent years. In the present research, a cheminformatics approach has been used to determine structural requirements for Gly/NMDA antagonism and to identify potential antagonists. Here, 37 quinoxaline derivatives were selected to develop a significant pharmacophore model with good certainty. The selected model was validated by leave-one-out cross-validation, an external test set, decoy set and Y-randomization test. Applicability domain was verified by the standardization approach. The validated 3D-QSAR model was used to screen virtual hits from the ZINC database by pharmacophore mapping. Molecular docking was used for assessment of receptor–ligand binding modes and binding affinities. The GlideScore and molecular interactions with critical amino acids were considered as crucial features to identify final hits. Furthermore, hits were analysed for in silico pharmacokinetic parameters and Lipinski’s rule of five, demonstrating their potential as drug-like candidates. The PubChem and SciFinder search tools were used to authenticate the novelty of leads retrieved. Finally, five different leads have been suggested as putative novel candidates for the exploration of potent Gly/NMDA receptor antagonists.     

Pharmacophore modelling,molecular docking and virtual screening for EGFR (HER 1) tyrosine kinase inhibitors

A pharmacophore model has been developed using diverse classes of epidermal growth factor receptor (EGFR) tyrosine kinase (TK) inhibitors useful in the treatment of human tumours. Among the top 10 generated hypotheses, the second hypothesis, with one hydrogen bond acceptor, one ring aromatic and three hydrophobic features, was found to be the best on the basis of Cat Scramble validation as well as test set prediction (r _training?=?0.89, r _test?=?0.82). The model also maps well to the external test set molecules as well as clinically active molecules and corroborates the docking studies. Finally, 10 hits were identified as potential leads after virtual screening of ZINC database for EGFR TK inhibition. The study may facilitate the designing and discovery of novel EGFR TK inhibitors.     

Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling,and molecular docking approach

Upregulation of store-operated Ca²⁺ influx via ORAI1, an integral component of the CRAC channel, is responsible for abnormal cytokine release in active rheumatoid arthritis, and therefore ORAI1 has been proposed as an attractive molecular target. In this study, we attempted to predict the mechanical insights of ORAI1 inhibitors through pharmacophore modelling, 3D-QSAR, molecular docking and free energy analysis. Various hypotheses of pharmacophores were generated and from that, a pharmacophore hypothesis with two hydrogen bond acceptors, one hydrogen bond donor and two aromatic rings (AADRR) resulted in a statistically significant 3D-QSAR model (r² = 0.84 and q² = 0.74). We believe that the obtained statistical model is a reliable QSAR model for the diverse dataset of inhibitors against the IL-2 production assay. The visualization of contours in active and inactive compounds generated from the 3D-QSAR models and molecular docking studies revealed major interaction with GLN108, HIS113 and ASP114, and interestingly, these residues are located near the Ca²⁺ selectivity filter region. Free energy binding analysis revealed that Coulomb energy, van der Waals energy and non-polar solvation terms are more favourable for ligand binding. Thus, the present study provides the physical and chemical requirements for the development of novel ORAI1 inhibitors with improved biological activity.     

Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies

HIV-1 integrase (IN) is a retroviral enzyme that catalyses integration of the reverse-transcribed viral DNA into the host genome, which is necessary for efficient viral replication. In this study, we have performed an in silico virtual screening for the identification of potential HIV-1 IN strand transfer (ST) inhibitors. Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to 3-Hydroxypyrimidine-2,4-diones. Based on the ligand-based pharmacophore model, we obtained a five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features. The pharmacophore hypothesis AADHR was used as a 3D query in a sequential virtual screening study to filter small molecule databases Maybridge, ChemBridge and Asinex. Hits matching with pharmacophore hypothesis AADHR were retrieved and passed progressively through Lipinski’s rule of five filtering, molecular docking and hierarchical clustering. The five compounds with best hits with novel and diverse chemotypes were subjected to QM/MM docking, which showed improved docking accuracy. We further performed molecular dynamics simulation and found three compounds that form stable interactions with key residues. These compounds could be used as a leads for further drug development and rational design of HIV-1 IN inhibitors.     

Glutamine: fructose-6-phosphate amidotransferase (GFAT): homology modelling and designing of new inhibitors using pharmacophore and docking based hierarchical virtual screening protocol

Glutamine: fructose-6-phosphate amidotransferase (GFAT), also termed GFPT1 and GFAT1, catalyzes the first committed step of the hexosamine biosynthesis pathway in mammals and consequently plays an important role in type 2 diabetes. In the present study, a combination of pharmacophore modelling, homology modelling, and molecular docking analysis was performed to design new glutamine competitive inhibitors of human GFAT, and to investigate important interaction details of inhibitor molecules. A pharmacophore model of GFAT inhibitors was developed, subsequently validated, and utilized for the screening by the PHASE database to identify new molecules. Afterwards, homology modelling was performed to construct the glutamine-binding site of the GFAT protein. The modelled active site was utilized to dock the studied molecules to investigate important receptor-ligand interactions and to scrutinize database-screened molecules on the basis of essential interactions. This systematic in silico protocol helped us to identify new molecules that would be explored for the treatment of type 2 diabetes and its complications.     

基于药效团的BRD4靶向小分子抑制剂虚拟筛选

BRD4靶点和多种肿瘤密切相关,是具有良好成药性的热门靶点.本文选取活性较好且结构差异较大的BRD4小分子抑制剂作为训练集分子,基于配体小分子共同特征(HipHop)方法使用Discovery Studio 3.0分子模拟软件构建了药效团.药效团通过测试集验证、ROC曲线验证(SE(sensitivity)=0.937...     

Identification of potential inhibitors of human methionine aminopeptidase (type II) for cancer therapy: Structure-based virtual screening,ADMET prediction and molecular dynamics studies

Methionine Aminopeptidases MetAPs are divalent-cofactor dependent enzymes that are responsible for the cleavage of the initiator Methionine from the nascent polypeptides. MetAPs are classified into two isoforms: namely, MetAP1 and MetAP2. Several studies have revealed that MetAP2 is upregulated in various cancers, and its inhibition has shown to suppress abnormal or excessive blood vessel formation and tumor growth in model organisms. Clinical studies show that the natural product fumagillin, and its analogs are potential inhibitors of MetAP2. However, due to their poor pharmacokinetic properties and neurotoxicities in clinical studies, their further developments have received a great setback. Here, we apply structure-based virtual screening and molecular dynamics methods to identify a new class of potential inhibitors for MetAP2. We screened Otava’s Chemical Library, which consists of about 3 200 000 tangible-chemical compounds, and meticulously selected the top 10 of these compounds based on their inhibitory potentials against MetAP2. The top hit compounds subjected to ADMET predictor using 3 independent ADMET prediction programs, were found to be drug-like. To examine the stability of ligand binding mode, and efficacy, the unbound form of MetAP2, its complexes with fumagillin, spiroepoxytriazole, and the best promising compounds compound-3369841 and compound-3368818 were submitted to 100 ns molecular dynamics simulation. Like fumagillin, spiroepoxytriazole, and both compound-3369841 and compound-3368818 showed stable binding mode over time during the simulations. Taken together, these uninherited-fumagillin compounds may serve as new class of inhibitors or provide scaffolds for further optimization towards the design of more potent MetAP2 inhibitors -development of such inhibitors would be essential strategy against various cancer types.     

Sub-structure-based screening and molecular docking studies of potential enteroviruses inhibitors

Rhinoviruses (RV), especially Human rhinovirus (HRVs) have been accepted as the most common cause for upper respiratory tract infections (URTIs). Pleconaril, a broad spectrum anti-rhinoviral compound, has been used as a drug of choice for URTIs for over a decade. Unfortunately, for various complications associated with this drug, it was rejected, and a replacement is highly desirable. In silico screening and prediction methods such as sub-structure search and molecular docking have been widely used to identify alternative compounds. In our study, we have utilised sub-structure search to narrow down our quest in finding relevant chemical compounds. Molecular docking studies were then used to study their binding interaction at the molecular level. Interestingly, we have identified 3 residues that is worth further investigation in upcoming molecular dynamics simulation systems of their contribution in stable interaction.     

Identification of natural compounds targeting SARS-CoV-2 Mpro by virtual screening and molecular dynamics simulations

The SARS-CoV-2 main protease (Mpro) has been chosen as a conserved molecular target to develop broad-spectrum antiviral drugs. Using molecular docking and molecular dynamics (MD) simulations, a total of 5600 natural compounds available for virtual screening were tested to identify potential inhibitors of SARS-CoV-2 Mpro. As a result, three natural compounds (pentagalloylglucose, malonylawobanin and gnetin E dihydride) were found to be potential inhibitors of SARS-CoV-2, which confirms the theoretical and practical significance of this approach for the design of SARS-CoV-2 inhibitors.     

Binding mode of inhibitors and Cryptosporidium parvum IMP dehydrogenase: A combined ligand- and receptor-based study

A combined ligand- and target-based approach was used to analyse the interaction models of Cryptosporidium parvum inosine 5’-monophosphate dehydrogenase (CpIMPDH) with selective inhibitors. First, a ligand-based pharmacophore model was generated from 20 NAD⁺ competitive CpIMPDH inhibitors with the HipHop module. The characteristic of the NAD⁺ binding site of CpIMPDH was then described, and the binding modes of the representative inhibitors were studied by molecular docking. The combination of the pharmacophore model and the docking results allowed us to evaluate the pharmacophore features and structural information of the NAD⁺ binding site of CpIMPDH. This research supports the proposal of an interaction model inside the NAD⁺ binding site of CpIMPDH, consisting of four key interaction points: two hydrophobic-aromatic groups, a hydrophobic-aliphatic group and a hydrogen bond donor. This study also provides guidance for the design of more potent CpIMPDH inhibitors for the treatment of Cryptosporidium infections.     

Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

In this study, a combination of virtual screening methods were utilized to identify novel potential indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. A series of IDO1 potential inhibitors were identified by a combination of following steps: Lipinski's Rule of Five, Veber rules filter, molecular docking, HipHop pharmacophores, 3D-Quantitative structure activity relationship (3D-QSAR) studies and Pan-assay Interference Compounds (PAINS) filter. Three known categories of IDO1 inhibitors were used to constructed pharmacophores and 3D-QSAR models. Four point pharmacophores (RHDA) of IDO1 inhibitors were generated from the training set. The 3D-QSAR models were obtained using partial least squares (PLS) analyze based on the docking conformation alignment from the training set. The leave-one-out correlation (q²) and non-cross-validated correlation coefficient (r²_pred) of the best CoMFA model were 0.601 and 0.546, and the ones from the best CoMSIA model were 0.506 and 0.541, respectively. Six hits from Specs database were identified and analyzed to confirm their binding modes and key interactions to the amino acid residues in the protein. This work may provide novel backbones for new generation of inhibitors of IDO1.     

New leads for selective GSK-3 inhibition: pharmacophore mapping and virtual screening studies

Summary Glycogen Synthase Kinase-3 is a regulatory serine/threonine kinase, which is being targeted for the treatment of a number of human diseases including type-2 diabetes mellitus, neurodegenerative diseases, cancer and chronic inflammation. Selective GSK-3 inhibition is an important requirement owing to the possibility of side effects arising from other kinases. A pharmacophore mapping strategy is employed in this work to identify new leads for selective GSK-3 inhibition. Ligands known to show selective GSK-3 inhibition were employed in generating a pharmacophore map using distance comparison method (DISCO). The derived pharmacophore map was validated using (i) important interactions involved in selective GSK-3 inhibitions, and (ii) an in-house database containing different classes of GSK-3 selective, non-selective and inactive molecules. New Lead identification was carried out by performing virtual screening using validated pharmacophoric query and three chemical databases namely NCI, Maybridge and Leadquest. Further data reduction was carried out by employing virtual filters based on (i) Lipinski’s rule of 5 (ii) van der Waals bumps and (iii) restricting the number of rotatable bonds to seven. Final screening was carried out using FlexX based molecular docking study.     

Identification of potential inhibitors against nuclear Dam1 complex subunit Ask1 of Candida albicans using virtual screening and MD simulations

Identification of hit compounds against specific target form the starting point for a drug discovery program. A consistent decline of new chemical entities (NCEs) in recent years prompted a challenge to explore newer approaches to discover potential hit compounds that in turn can be converted into leads, and ultimately drug with desired therapeutic efficacy. The vast amount of omics and activity data available in public databases offers an opportunity to identify novel targets and their potential inhibitors. State of the art in silico methods viz., clustering of compounds, virtual screening, molecular docking, MD simulations and MMPBSA calculations were employed in a pipeline to identify potential ‘hits’ against those targets as well whose structures, as of now, could only predict through threading approaches. In the present work, we have started from scratch, amino acid sequence of target and compounds retrieved from PubChem compound database, modeled it in such a way that led to the identification of possible inhibitors of Dam1 complex subunit Ask1 of Candida albicans. We also propose a ligand based binding site determination approach. We have identified potential inhibitors of Ask1 subunit of a Dam1 complex of C. albicans, which is required to prevent precocious spindle elongation in pre-mitotic phases. The proposed scheme may aid to find virtually potential inhibitors of other unique targets against candida.     

Synthesis and molecular docking study of novel COVID-19 inhibitors

In 2020, the world tried to combat the corona virus (COVID-19) pandemic. A proven treatment method specific to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is still not found. In this study, seven new antiviral compounds were designed for COVID-19 treatment. The ability of these compounds to inhibit COVID-19’s RNA processing was calculated by the molecular docking study. It has been observed that the compounds can have high binding affinities especially against NSP12 (between -9.06 and -8.00 kcal/mol). The molecular dynamics simulation of NSP12-ZG 7 complex proved the stability of interaction. The synthesis of two most active molecules was performed by one-pot reaction and characterized by FT-IR, ¹H-NMR, ¹³C-NMR, and mass spectroscopy. The compounds presented with their synthesis are inhibitory core structures against SARS-CoV-2 infection.     

The identification of new ATAD2 bromodomain inhibitors: the application of combined ligand and structure-based virtual screening

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches were used to identify new inhibitors for ATAD2 bromodomain. The LBVS approach was used to search 23,129,083 clean compounds to identify compounds similar to an active compound with reported pIC₅₀ equal to 7.2. Based on LBVS results, 19 compounds were selected. To perform SBVS, by applying nine filters on 23,129,083 clean compounds, 1,057,060 compounds were selected. After performing SBVS on these selected compounds with idock software, 16 compounds with the lowest binding energies were selected. More accurate molecular docking analysis was performed on these 35 selected compounds by using iGEMDOCK software and six of them with the lowest binding energies were selected as hit compounds. These compounds were zinc36647229, zinc77969074, zinc13637358, zinc77971540, zinc12991296 and zinc19374204.     

Cluster‐based molecular docking study for in silico identification of novel 6‐fluoroquinolones as potential inhibitors against Mycobacterium tuberculosis

A classical protein sequence alignment and homology modeling strategy were used for building three Mycobacterium tuberculosis‐DNA gyrase protein models using the available topoII‐DNA‐6FQ crystal structure complexes originating from different organisms. The recently determined M. tuberculosis‐DNA gyrase apoprotein structures and topoII‐DNA‐6FQ complexes were used for defining the 6‐fluoroquinolones (6‐FQs) binding pockets. The quality of the generated models was initially validated by docking of the cocrystallized ligands into their binding site, and subsequently by quantitative evaluation of their discriminatory performances (identification of active/inactive 6‐FQs) for a set of 145 6‐FQs with known biological activity values. The M. tuberculosis‐DNA gyrase model with the highest estimated discriminatory power was selected and used afterwards in an additional molecular docking experiment on a mixed combinatorial set of 427 drug‐like 6‐FQ analogs for which the biological activity values were predicted using a prebuilt counter‐propagation artificial neural network model. A novel three‐level Boolean‐based [T/F (true/false)] clustering algorithm was used to assess the generated binding poses: Level 1 (geometry properties assessment), Level 2 (score‐based clustering and selection of the (T)‐signed highly scored Level 1 poses), and Level 3 (activity‐based clustering and selection of the most “active” (T)‐signed Level 2 hits). The frequency analysis of occurrence of the fragments attached at R₁ and R₇ position of the (T)‐signed 6‐FQs selected in Level 3 revealed several novel attractive fragments and confirmed some previous findings. We believe that this methodology could be successfully used in establishing novel possible structure‐activity relationship recommendations in the 6‐FQs optimization, which could be of great importance in the current antimycobacterial hit‐to‐lead processes. © 2012 Wiley Periodicals, Inc.     

Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

This study was performed to find the selective chemical features for Aurora kinase-B inhibitors using the potent methods like Hip-Hop, virtual screening, homology modeling, molecular dynamics and docking. The best hypothesis, Hypo1 was validated toward a wide range of test set containing the selective inhibitors of Aurora kinase-B. Homology modeling and molecular dynamics studies were carried out to perform the molecular docking studies. The best hypothesis Hypo1 was used as a 3D query to screen the chemical databases. The screened molecules from the databases were sorted based on ADME and drug like properties. The selective hit compounds were docked and the hydrogen bond interactions with the critical amino acids present in Aurora kinase-B were compared with the chemical features present in the Hypo1. Finally, we suggest that the chemical features present in the Hypo1 are vital for a molecule to inhibit the Aurora kinase-B activity.