Molecular simulation approach integrated with deep learning model for the discovery of Tankyrase inhibitors

Tankyrases belong to the poly (ADP-ribose) polymerase family epitomized as a novel group of medicinal targets with various prospective diseased conditions and it is appraised to be a challenging drug target for the intervention of multiple cancers. Thus, the principal objective of our study is to explore the dual-site selective tankyrase 1 inhibitor by employing the pharmacophore strategy. Initially, the ligand-based pharmacophore study generated five featured pharmacophore hypothesis, which was then employed for the database screening. The screened molecules were scrutinized through docking, MM/GBSA calculations and molecular simulation alongside pharmacokinetics properties. The analysis yielded potent dual-site tankyrase 1 inhibitors such as nebivolol and ondansetron from the DrugBank repository. Notably, the recognized lead molecules were perceived to have higher XP GScore and binding energy scores. Subsequently, simulation studies were executed to validate the structural stability of the lead molecules. It is worth mentioning that the existence of benzopyran and carbazole scaffolds in the lead molecules displayed anti-neoplastic activity and also facilitate the effective binding with tankyrase 1 protein. Ultimately, the IC50 values of the lead molecules were examined against the NCI-H596 cell line using a deep learning model. Indeed, these results are of immense importance and provide a clue to the experimental biologist in developing a potent tankyrase 1 dual-site inhibitor.

Perceiving SARS-CoV2 Mpro and PLpro dual inhibitors from pool of recognized antiviral compounds of endophytic microbes: an in-silico simulation study

The enormous impact of SARS-CoV2 continues and scientific community is seeking to discover the tactics to impede the spread of virus. The essential result is attenuated, and genetically engineered vaccines are being driven into the market with the general effectiveness being around 80%. Therefore, vaccination is not the sole answer for combat this pandemic. The substitute methodology is adapted to target on this virus with a medication in blend with existing vaccines. Papain like protease (nsp-3; nonstructural protein) and Mpro (nsp-5; nonstructural protein) of novel corona virus are the ideal target to develop drugs as they play different roles that are essential for viral development and replication. Utilizing computational methodology, we plan to distinguish a plausible microbial metabolite as analogue of GRL0617 (the well-established inhibitor of PLpro) and X77 (the well-established inhibitor of Mpro) from the pool of known antiviral compounds of endophytic microbes to interact and inhibit PLpro and Mpro as dual inhibitors. In the wake of collecting known antiviral compounds of endophytic microbes and screened them through pharmacophore hypothesis, molecular docking, and dynamics simulation, we perceive Cytonic acid A and Cytonic acid B to be seen as the potent PLpro and Mpro dual inhibitors using rigorous computational methods.

Discovery of structural prospects of imidazo[1,5-a]pyrazine derivatives as BTK inhibitors against cancer: A computational study

Background: Bruton’s tyrosine kinase (BTK) plays an important role in cell development and proliferation. BTK inhibitors are encouraging novel agents against B-cell malignancies and autoimmune diseases. Although BTK inhibitors have been approved by the FDA, but to lower off-target effects and to reduce emerging resistances, it is necessary to develop novel BTK inhibitors with better outcomes and minimum side effects. Objective: The present study includes pharmacophore hypothesis, 3D QSAR, virtual screening, docking, ADME analysis and screening of potential imidazo[1,5-a]pyrazine derivatives as BTK inhibitors. Methods: Generation of pharmacophore hypothesis, virtual screening, 3D QSAR, molecular docking and ADME analysis. Methods: Generation of pharmacophore hypothesis, virtual screening, 3D QSAR, molecular docking and ADME analysis. Results: Pharmacophore study generated 20 pharmacophore hypotheses as BTK inhibitor. The five-point hypothesis DPRRR_1 were selected, consist one hydrogen bond donor, one positive ionic, and three ring aromatic features. 3D QSAR study of the compounds provided the best model with high Q2 (0.8683), R2 (0.983) and R2CV (0.5338) values. The developed pharmacophore model was further taken for screening of ZINC database ligands for evaluation of docking interaction and physiochemical properties. Potent compounds of the series 15, 27, 8n and 38 showed good docking scores -8.567, -7.465, -6.922, -6.137, respectively. Conclusion: All the pharmacokinetic parameters analysed, including human oral absorption of active compounds of the series were found to be within the permissible range. The present geometry and features included in pharmacophore hypothesis can be used for the development of novel BTK inhibitors as anticancer agents.

Benchmarks for interpretation of QSAR models

Interpretation of QSAR models is useful to understand the complex nature of biological or physicochemical processes, guide structural optimization or perform knowledge-based validation of QSAR models. Highly predictive models are usually complex and their interpretation is non-trivial. This is particularly true for modern neural networks. Various approaches to interpretation of these models exist. However, it is difficult to evaluate and compare performance and applicability of these ever-emerging methods. Herein, we developed several benchmark data sets with end-points determined by pre-defined patterns. These data sets are purposed for evaluation of the ability of interpretation approaches to retrieve these patterns. They represent tasks with different complexity levels: from simple atom-based additive properties to pharmacophore hypothesis. We proposed several quantitative metrics of interpretation performance. Applicability of benchmarks and metrics was demonstrated on a set of conventional models and end-to-end graph convolutional neural networks, interpreted by the previously suggested universal ML-agnostic approach for structural interpretation. We anticipate these benchmarks to be useful in evaluation of new interpretation approaches and investigation of decision making of complex “black box” models.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

In silico screening of phytoconstituents with antiviral activities against SARS-COV-2 main protease, Nsp12 polymerase and Nsp13 helicase proteins

Aims: The present study aim was to analyse the molecular interactions of the phytoconstituents known for their antiviral activity with the SARS-CoV-2 nonstructural proteins such as main protease (6LU7), Nsp12 polymerase (6M71), and Nsp13 helicase (6JYT). The applied in silico methodologies was molecular docking and pharmacophore modeling using Schrodinger software. Methods: The phytoconstituents were taken from PubChem, and SARS-CoV-2 proteins were downloaded from the protein data bank. The molecular interactions, binding energy, ADMET properties and pharmacophoric features were analysed by glide XP, prime MM-GBSA, qikprop and phase application of Schrodinger respectively. The antiviral activity of the selected phytoconstituents was carried out by PASS predictor, online tools. Results: The docking score analysis showed that quercetin 3-rhamnoside (-8.77 kcal/mol) and quercetin 3-rhamnoside (-7.89 kcal/mol) as excellent products to bind with their respective targets such as 6LU7, 6M71 and 6JYT. The generated pharmacophore hypothesis model validated the docking results, confirming the hydrogen bonding interactions of the amino acids. The PASS online tool predicted constituent's antiviral potentials. Conclusion: The docked phytoconstituents showed excellent interactions with the SARS-CoV-2 proteins, and on the outset, quercetin 3-rhamnoside and quercetin 7-rhamnoside have well-interacted with all the three proteins, and these belong to the plant Houttuynia cordata. The pharmacophore hypothesis has revealed the characteristic features responsible for their interactions, and PASS prediction data has supported their antiviral activities. Thus, these natural compounds could be developed as lead molecules for antiviral treatment against SARS-CoV-2. Further in-vitro and in-vivo studies could be carried out to provide better drug therapy.

Molecular simulation strategies for the discovery of selective inhibitors of β-catenin

Tumor dissemination and relapse in lung cancer were found to be due to the existence of cancer stem cells. In particular, the [Formula: see text]-catenin pathway is found to be one of the crucial pathways in maintaining the stem-like properties of the cells. Thus, targeting the [Formula: see text]-catenin family of proteins is a significant therapeutic route in the treatment of lung cancer. Therefore, in the present study, a pharmacophore-based drug repurposing approach was accomplished to pinpoint potent [Formula: see text]-catenin inhibitors from the DrugBank database. Primarily, ligand-based pharmacophore hypothesis (AAHHR) was generated using existing [Formula: see text]-catenin inhibitors available in the literature and utilized for library screening. Subsequently, the inhibitory activity of the screened compounds was examined by the hierarchical docking process and the Prime MM-GBSA algorithm. Moreover, quantum chemical calculations and molecular dynamics simulations were executed to analyze the inhibitory effects of the screened hit molecule. The results indicate that hit molecule, DB08047 was found to possess better binding free energy, favorable ligand strain energy, satisfactory pharmacokinetic properties and superior free energy landscape profile. Eventually, the pIC[Formula: see text] values of the lead compounds were predicted by the AutoQSAR algorithm. It is noteworthy to mention that DB08047 was found to possess pyrazole scaffolds which could downregulate [Formula: see text]-catenin pathway and thus facilitate the controlled cell growth/inhibit tumor growth.

The search for peptide deformylase inhibitor from Indonesian Medicinal Plant Database: an in-silico investigation

The peptide deformylase protein (PDF) has emerged as a promising target for the discovery of novel antibiotics with a novel mechanism of action. The current investigation was aimed at identifying potential inhibitor of PDF by using structure-based pharmacophore modelling. The pharmacophore hypothesis consisted of one hydrophobic, one negative ionizable, and one hydrogen bond donor features which were built using the structure of cognate ligand of PDF (BB2). Further, the pharmacophore model was validated and used to screen hit molecule against Indonesian Medicinal Plant Database and retrieved 32 hit molecules. All hit molecules were docked to PDF and four best molecules were subjected for 50-ns molecular dynamics (MD) simulation. MD simulation confirmed the docked poses of ligand as indicated by the RMSD and RMSF values. Prediction of affinities employing Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) method revealed that quercetin 3-(6''-malonylneohesperidoside) had a comparable affinity with that of BB2, which indicated its potential as a novel herbal-based PDF inhibitor.

In silico studies on smoothened human receptor and its antagonists in search of anticancer effects

Lately, the cancers related with abnormal hedgehog (Hh) signalling pathway are targeted by smoothened (SMO) receptor inhibitors that are rapidly developing. Still, the problems of known inhibitors such as severe side effects, weak potency against solid tumors or even the acquired resistance need to be overcome by developing new suitable inhibitors. To explore the structural requirements of antagonists needed for SMO receptor inhibition, pharmacophore mapping, 3D-QSAR models, database screening and docking studies were performed. The best selected pharmacophore hypothesis based on which statistically significant atom-based 3D-QSAR model was developed (R2 = = 0.856, Q2 = 0.611 and Pearson-R = 0.817), was further subjected to dataset screening in order to evaluate its ability to prioritize active compounds over decoys. The efficiency of one four-points pharmacophore hypothesis (AAHR.524) was observed based on good evaluation metrics such as the area under the curve (0.795), and weighted average precision (0.835), suggesting that the model is trustworthy in predicting novel inhibitors against SMO receptor.