Journal of Computational Biophysics and Chemistry

Breast cancer, which has been one of the most frequently diagnosed cancers worldwide for decades, continues to defy treatment. While researching a remedy to this problem, it was discovered that mTOR has a strong association with breast cancer. Uncontrolled activation of mTOR is shown in a variety of different cancer, making it a critical target for cancer treatment. Inhibition of the mTOR protein kinase can cause autophagic cell death. It is known that covalent inhibitors have become a prominent issue in drug discovery, with covalent inhibitors focusing on $\alpha ,\beta$-unsaturated carbonyl molecules. Structural modifications to $\alpha ,\beta$-unsaturated carbonyl may be one of the finest avenues for developing the best breast cancer medication. This review article discusses recent research on natural and synthetic $\alpha ,\beta$-unsaturated carbonyls and their anti-cancer properties targeting on mTOR, with SAR to showcase the efficacy of synthetic natural products compared to parental compounds using both biological assays and in silico studies.

Epidermal growth factor receptor (EGFR), a member of ErbB family of receptor tyrosine kinases, is reportedly overexpressed in various types of human malignancies. Curcumin, a derived phytochemical compound, has demonstrated antiproliferative effects in various cancer cell lines. Since curcumin degrades into different bioactive compounds, attention has been drawn to analyze if these degradation products are primarily responsible for the observed biological activity of curcumin. In the current work 11 degradation products of curcumin were selected and assessed for their drug likeness, ADME and toxicity properties using a diverse range of advanced computational methods. Binding characteristics of EGFR with these ligand molecules were examined using in silico single ligand molecular docking and multi-ligand simultaneous docking (MLSD) methods. Spiroepoxide, one intermediate product of spontaneous oxidation of curcumin, docked with minimum energy ($-$9.123kcal/mol) as compared to parent curcumin and co-crystallized Erlotinib inhibitor. Vina score for simultaneously docked autooxidation compounds at the binding site of EGFR, where one ligand was curcumin, was lower than the predicted binding energies of singly docked compounds, thus indicating that curcumin-derived compounds produced by the oxidative pathway do take part along with curcumin in inhibiting EGFR. The best ‘posed’ complex, spiroepoxide bound to EGFR, was chosen for MD simulation to examine the stability of this protein–ligand complex.

Neomorphic transformations in isocitrate dehydrogenase 1 (IDH1) are the key mutations prevalently found in various cancers including glioma. Recently identified mIDH1 specific inhibitors showed modest brain penetrating potential and dose-limiting toxicity. Herein, we elucidate virtual screening strategies to discover persuasive mIDH1 inhibitors from the approved subset of the DrugBank database consisting of 2715 molecules. Initially, a structural similarity search identified a total of 1432 lead molecules. The resultant compounds were inspected by molecular docking along with MM-GBSA and ADMET analyses. Altogether, the analyses identified Abemaciclib as the hit against mIDH1. Notably, abemaciclib was able to form hydrogen bond interaction with active site residues of mIDH1 protein. In the end, the dynamic behavior of the hit complex was also examined using molecular dynamics simulation studies. The outcome of the study culminates that the hit complex was stable throughout the simulation period of 100ns. It is worth noting that benzimidazole moiety of abemaciclib was reported to show inhibitory activity against glioma cells. Overall, these findings highlight that abemaciclib has the potential as a lead molecule against glioma. Indeed, the screened hit compound could be further explored for the development of mIDH1 inhibitor with great brain penetrating ability and low toxicity.

The SARS-CoV-2 main protease (M^pro) is one of the essential therapeutic keys of the COVID-19, which ensures the replication of the virus in the host cells, causing tissue damage, for this reason, we accelerate the research of potential natural compounds that could inhibit this enzyme or used as a scaffold to generate a series of optimized and potent inhibitors. In silico study was performed. This study was started with the generation and validation of a structure-based pharmacophore hypothesis that is used to screen a chemical library containing 99,049 natural compounds extracted, filtered, and cleaned from the Chinese universal natural products database (UNPD), the screening process using the pharmacophore hypothesis and the molecular docking yielded 15 hits. These hits were ranked depending on their binding $\mathrm{\Delta }G$, as a result, the two compounds UNPD90246 and UNPD221225 exhibited higher binding affinity to the catalytic cavity of the M^pro interacting with important amino acids which play a crucial role in the enzyme catalysis, on the other hand, the two complexes UNPD90246/5R82 and UNPD221225/5R82 subjected to the molecular dynamic simulation show very good stability along the trajectory of the simulation as revealed by the calculated RMSD, RMSF, rGyr, SASA as well as the RMSF of the protein and the potential energy. In addition, the in silico evaluation of some drug-like and pharmacokinetic (ADMET) properties, revealed in general satisfactory prediction results for almost all compounds, however, some compounds require optimization steps to improve their weakness, regarding pharmacokinetic properties such as metabolism and toxicity.

Patulin (PAT) contaminant in apple juice has attracted extensive attention. As a biosorbent, $\beta$-1,3-D-glucan shows great advantages in removing toxic contaminants. The aim of this study was to reveal the adsorption mechanisms of PAT by $\beta$-1,3-D-glucan. Four $\beta$-1,3-D-glucans with different number of side chains: curdlan, a hypothetical glucan, schizophyllan (SPG) and lentinan were simulated. Performance of molecular docking was observed to predict the binding mode and affinity. The most potential compounds were performed molecular dynamics (MD) simulation. The results show that the interaction between $\beta$-1,3-D-glucan and PAT is driven by steric complementarity, intermolecular H-bond and Pi–Sigma interaction. SPG has a large internal helical space to facilitate the insertion of PAT into the helix, and has relatively strong interaction with PAT. The side and main chains form a cavity to wrap PAT inside the helix. Furthermore, the binding free energy calculation was carried out using Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) approach. SPG showed high negative $\mathrm{\Delta }{G}_{\mathrm{\text{bind}}}$ value. Structural and energetic analyses demonstrated the adsorption characteristics of the four $\beta$-1,3-D-glucans. SPG is the most promising adsorbent for PAT. The side chain plays a major role in the adsorption process of the $\beta$-1,3-D-glucans. It provides a basis for the development of $\beta$-1,3-D-glucan as PAT adsorbent.

Alpha-Synuclein is a presynaptic neuronal protein whose masses are involved in Parkinson’s disease, a chronic and progressive neurodegenerative disorder of the central nervous system. It has been observed that upon exposure to anionic surfactants, fluorinated alcohols and membranes in vitro, $\alpha$-synuclein adopts alpha helix structure and can be fibrillated, while in aqueous solution, the protein is intrinsically disordered and the fibrillation is undesirable. Solvent is one of the most critical environmental parameters in biological studies as well as computer simulations. In this paper, we have investigated the effect of solvent composition on the conformation of $\alpha$-synuclein protein using molecular dynamics (MD) simulations. For this purpose, the effect of different combinations of water and trifluoroethanol (TFE) solvents on the secondary structure of a fragment of $\alpha$-synuclein was investigated. Moreover, thermodynamic calculations were performed through a coarse-grained (CG) model of solutions with different concentrations of $\alpha$-synuclein in the aqueous phase and in TFE medium. The potential of mean force (PMF) calculations was performed as a function of separation distance between the centers of masses of two peptides. The results indicated that in the presence of TFE, the secondary structures have a regular arrangement of amino acids stabilized by hydrogen bonding patterns. In addition, decreasing the TFE concentration reduces the stability of $\alpha$-helix structures of the protein. It seems that in diluted TFE solutions, high concentrations of water in the medium lead to the breakage of peptide–TFE hydrogen bonds. However, the hydrophobic nature of TFE and $\alpha$-synuclein molecules prevents further aggregation in the concentrated solutions.

Background: A paradigm shift towards a multifaceted natural product drug discovery approach has been observed globally. Today, many modern drugs have originated from ethnopharmacology and traditional medicine. The traditionally used medicinal plants are a rich source of biologically active phytochemicals. A paradigm shift has been observed in pharmaceutical industries towards the use of those phytochemicals for the development of drugs over synthetic compounds for minimization of side effects. Methodology: The data have been generated from the printed “Database on Medicinal Plants Used in Ayurveda” published by the Central Council for Research in Ayurvedic Sciences, Government of India. The curation of the phytochemicals and calculation of their physicochemical and drug-like properties has been done using Discovery Studio v 4.5, and the database IMPDB is designed by adhering to the principles of database design and developed using PHP as front-end and MySQL as back-end. The data in IMPDB were analyzed further and comparative analysis of physicochemical properties of IMPDB phytochemicals with other small molecule databases, followed by druggability and novelty analysis of phytochemicals of the database were conducted with wQED score and molecular fingerprinting methods. In addition to that, ADMET properties of IMPDB phytochemical were compared with FDA-approved drugs. Results: Druggability analysis of IMPDB phytochemical shows a substantial number of them are druggable. An extensive review of the existing database was conducted and the quality of data of existing databases was matched with IMPDB data, and IMPDB data was found at par with the standard databases in the same domain, in certain parameters the quality of data of IMPDB was found better. In addition to that, novelty analysis of IMPDB data in comparison to 2068 FDA-approved drugs reveals that 99.96–99.99% of the phytochemical of IMPDB is novel and to some extent better in terms of ADMET properties. The database is available from https://impdb.org.in/. Conclusion: IMPDB can be a useful database for the drug development industry and a need for the future pharmaceutical industry.

Protein–protein interaction (PPI) plays an important role in biological processes such as signal transduction, immune response and membrane transport, etc. In this work, a protein sequence-based machine learning model, SeBPPI, to predict protein–protein binding was proposed. In this model, the descriptors were generated from three pre-trained models: Unirep, ESM and TAPE. The performance of SeBPPI with these pre-trained models was evaluated on several different test datasets. The accuracy of our binary prediction model shows improvement over the existing methods. We also compared the performance of two classification heads: The Recurrent convolution neural network (RCNN) and the fully connected neural network (FNN) and found that the use of RCNN is beneficial for the overall improvement in the accuracy of the model. This study helps to improve the accuracy in sequence-based protein–protein binding predictions. The model used in this work is integrated in the web server http://www.icdrug.com/ICDrug/SeBPPI.