Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Potential pharmacological mechanisms of four active compounds of Macleaya cordata extract against enteritis based on network pharmacology and molecular docking technology

Background: Macleaya cordata extract (MCE) is effective in the treatment of enteritis, but its mechanism has not been fully elucidated. Therefore, this study combined network pharmacology and molecular docking technologies to investigate the potential pharmacological mechanism of MCE in the treatment of enteritis. Methods: The information of active compounds in MCE was accessed through the literature. Furthermore, PubChem, PharmMapper, UniProt, and GeneCards databases were used to analyze the targets of MCE and enteritis. The intersection of drug and disease targets was imported into the STRING database, and the analysis results were imported into Cytoscape 3.7.1 software to construct a protein-protein interaction (PPI) network and to screen core targets. The Metascape database was used for conducting Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. AutoDock Tools software was used for the molecular docking of active compounds with the core targets. Results: MCE has four active compounds, namely, sanguinarine, chelerythrine, protopine, and allocryptopine, and a total of 269 targets after de-duplication. Furthermore, a total of 1,237 targets were associated with enteritis, 70 of which were obtained by aiding the drug-disease intersection with the aforementioned four active compound targets of MCE. Five core targets including mitogen-activated protein kinase 1 (MAPK1) and AKT serine/threonine kinase 1 (AKT1) were obtained using the PPI network, which are considered the potential targets for the four active compounds of MCE in the treatment of enteritis. The GO enrichment analysis involved 749 biological processes, 47 cellular components, and 64 molecular functions. The KEGG pathway enrichment analysis revealed 142 pathways involved in the treatment of enteritis by the four active compounds of MCE, among which PI3K-Akt and MAPK signaling pathways were the most important pathways. The results of molecular docking showed that the four active compounds demonstrated good binding properties at the five core targets. Conclusion: The pharmacological effects of the four active compounds of MCE in the treatment of enteritis involve acting on signaling pathways such as PI3K-Akt and MAPK through key targets such as AKT1 and MAPK1, thus providing new indications for further research to verify its mechanisms.

Ligand-Guided Investigation of a Series of Formamidine-Based Thiuram Disulfides as Potential Dual-Inhibitors of COX-1and COX-2

A series of thiuram disulfides 1-6 which had been previously synthesized and characterized,^[1] were studied for their potential therapeutic properties. Target-fishing analyses through HitPick and SwissTarget prediction identified COX1 and COX2, which are essential biomolecules in cancer-related inflammations, as the possible targets for compounds 1 and 4 among all the compounds tested. These two proteins have enjoyed interest as targets for treating some neoplastic cancer types such as breast, colorectal, skin, pancreatic, haematological and head cancers. The inhibitory potency of 1 and 4 as lead anticancer drug candidates with dual-target ability against COX1 and COX2 was examined through molecular docking, molecular dynamics simulation and post-MD analyses such as binding energy calculation, RMSD, RMSF, and RoG. The two compounds had better docking scores and binding energies than the known inhibitors of COX1 and COX2. Insights from the RMSD, RMSF, and RoG suggested that both 1 and 4 showed observable influence on the structural stability of these targets throughout the simulation. The reported observations of the effects of 1 and 4 on the structures of COX1 and COX2 indicate their probable inhibitory properties against these target proteins and their potential as lead anticancer drug candidates.

Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study

The Kirsten rat sarcoma viral G12C (KRAS^G12C) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRAS^G12C inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRAS^G12C inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRAS^G12C inhibitors well, with an accuracy score of validation = 0.85 and Q²_Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC₅₀) value against KRAS^G12C protein close to the KRAS^G12C inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRAS^G12C inhibitors in the KRAS^G12C protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRAS^G2C protein-ligand complex similar to the KRAS^G12C inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRAS^G12C protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRAS^G12C protein.

CAT-Site: Predicting Protein Binding Sites Using a Convolutional Neural Network

Identifying binding sites on the protein surface is an important part of computer-assisted drug design processes. Reliable prediction of binding sites not only assists with docking algorithms, but it can also explain the possible side-effects of a potential drug as well as its efficiency. In this work, we propose a novel workflow for predicting possible binding sites of a ligand on a protein surface. We use proteins from the PDBbind and sc-PDB databases, from which we combine available ligand information for similar proteins using all the possible ligands rather than only a special sub-selection to generalize the work of existing research. After performing protein clustering and merging of ligands of similar proteins, we use a three-dimensional convolutional neural network that takes into account the spatial structure of a protein. Lastly, we combine ligandability predictions for points on protein surfaces into joint binding sites. Analysis of our model's performance shows that its achieved sensitivity is 0.829, specificity is 0.98, and F1 score is 0.517, and that for 54% of larger and pharmacologically relevant binding sites, the distance between their real and predicted centers amounts to less than 4 Å.

Screening of Azadirachta indica phytoconstituents as GSK-3β inhibitor and its implication in neuroblastoma: molecular docking, molecular dynamics, MM-PBSA binding energy, and in-vitro study

Glycogen synthase kinase-3 (GSK-3), a constitutively active serine/threonine kinase, primary regulator of various cellular activities varying from glycogen metabolism to cell proliferation and regulation. GSK-3β is associated with the pathogenesis of numerous human diseases, including cancer, metabolic disorder, and Alzheimer's disease. In this study, Azadirachta indica compounds were selected and further screened on the BOILED-Egg model. The compounds showing good GIT absorption were docked with the crystal structure of GSK-3β. The compounds with high docking score were submitted for the molecular dynamic simulation (MDS) and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA). Based upon the MDS and MM-PBSA study, gedunin showed the highest binding energy throughout the MDS process. Gedunin was isolated from the Azadirachta indica, and its efficacy on GSK-3β inhibition was studied in the human neuroblastoma (SH-SY5Y) cells. Gedunin induced apoptosis and anti-proliferative activity by arresting G2/M phase, as evident by cell-cycle analysis. From immunoblot study, gedunin significantly enhanced the expression of an inhibitory form of GSK-3β (p-GSK-3β Ser9) in concentration-dependent manner. Our findings demonstrate that gedunin may act as an effective GSK-3β inhibitor suggesting that this compound may be used for the management of neuroblastoma. Further preclinical and clinical investigation is desirable.Communicated by Ramaswamy H. Sarma.

Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor

The human Guanine Protein coupled membrane Receptor 17 (hGPR17), an orphan receptor that activates uracil nucleotides and cysteinyl leukotrienes is considered as a crucial target for the neurodegenerative diseases. Yet, the detailed molecular interaction of potential synthetic ligands of GPR17 needs to be characterized. Here, we have studied a comparative analysis on the interaction specificity of GPR17-ligands with hGPR17 and human purinergic G protein-coupled receptor (hP2Y1) receptors. Previously, we have simulated the interaction stability of synthetic ligands such as T0510.3657, AC1MLNKK, and MDL29951 with hGPR17 and hP2Y1 receptor in the lipid environment. In the present work, we have comparatively studied the protein-ligand interaction of hGPR17-T0510.3657 and P2Y1-MRS2500. Sequence analysis and structural superimposition of hGPR17 and hP2Y1 receptor revealed the similarities in the structural arrangement with the local backbone root mean square deviation (RMSD) value of 1.16 Å and global backbone RMSD value of 5.30 Å. The comparative receptor-ligand interaction analysis between hGPR17 and hP2Y1 receptor exposed the distinct binding sites in terms of geometrical properties. Further, the molecular docking of T0510.3657 with the hP2Y1 receptor have shown non-specific interaction. The experimental validation also revealed that Gi-coupled activation of GPR17 by specific ligands leads to the adenylyl cyclase inhibition, while there is no inhibition upon hP2Y1 activation. Overall, the above findings suggest that T0510.3657-GPR17 binding specificity could be further explored for the treatment of numerous neuronal diseases. Communicated by Ramaswamy H. Sarma.

Anti-HIV and anti-HCV small molecule protease inhibitors in-silico repurposing against SARS-CoV-2 Mpro for the treatment of COVID-19

The COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is a global health emergency warranting development and implementation of targeted treatment. The enzyme main protease (M^pro; also known as 3C-like protease) is emerging as an attractive drug target. This enzyme plays an indispensable role in processing the translated polyproteins of viral RNA. Inhibiting the activity of M^pro would wedge viral replication. To facilitate the discovery of targeted therapy for COVID-19, we carried out the structure-assisted repurposing of existing protease inhibiting small molecules to target SARS-CoV-2 M^pro. Based on the structure of SARS-CoV-2 M^pro, here we report the small drug molecule namely saquinavir as its potent inhibitor. Findings support the premise that this promising antiviral protease inhibiting small drug molecule can be validated and implemented for the treatment and clinical management of COVID-19 pandemic disease.Communicated by Ramaswamy H. Sarma.

Accuracy and precision of binding free energy prediction for a tacrine related lead inhibitor of acetylcholinesterase with an arsenal of supercomputerized molecular modelling methods: a comparative study

Nowadays, advanced computational chemistry methods offer various strategies for revealing prospective hit structures in drug development essentially through accurate binding free energy predictions. After the era of molecular docking and quantitative structure-activity relationships, much interest has been lately oriented to perturbed molecular dynamic approaches like replica exchange with solute tempering and free energy perturbation (REST/FEP) and the potential of the mean force with adaptive biasing and accelerated weight histograms (PMF/AWH). Both of these receptor-based techniques can exploit exascale CPU&GPU supercomputers to achieve high throughput performance. In this fundamental study, we have compared the predictive power of a panel of supercomputerized molecular modelling methods to distinguish the major binding modes and the corresponding binding free energies of a promising tacrine related potential antialzheimerics in human acetylcholinesterase. The binding free energies were estimated using flexible molecular docking, molecular mechanics/generalized Born surface area/Poisson-Boltzmann surface area (MM/GBSA/PBSA), transmutation REST/FEP with 12 x 5 ns/λ windows, annihilation FEP with 20 x 5 ns/λ steps, PMF with weight histogram analysis method (WHAM) and 40 x 5 ns samples, and PMF/AWH with 10 x 100 ns replicas. Confrontation of the classical approaches such as canonical molecular dynamics and molecular docking with alchemical calculations and steered molecular dynamics enabled us to show how large errors in ΔG predictions can be expected if these in silico methods are employed in the elucidation of a common case of enzyme inhibition.Communicated by Ramaswamy H. Sarma.

Investigation of reactive properties, adsorption on fullerene, DFT, molecular dynamics simulation of an anthracene derivative targeting dihydrofolate reductase and human dUTPase

Anthracenes are aromatic compounds with flexible structure and reactivity which are of great interest to theoretical and experimental chemists. Theoretical investigations of 1,4-dihydroxy-5,8-bis[2-(2-hydroxyethylamino)ethylamino]anthracene-9,10-dione (Mitoxantrone) (DDEA) based on density functional theory, molecular dynamics and adsorption on fullerene are reported in the present research. The suitable situation for adsorption with fullerene (C60) is the cyclohex-2-ene-1,4-dione ring of DDEA. Selected quantum-molecular descriptors have been calculated to predict the most reactive sites of the DDEA molecule. Interactions of DDEA with water have been studied using MD simulations. MD simulations were also used to study solubility parameter, a significant quantity for the development of pharmaceutical formulations. The affinity of DDEA on human dihydrofolate reductase and deoxyuridine triphosphatase enzymes was investigated by MD simulation of the protein-ligand complex obtained by molecular docking study.Communicated by Ramaswamy H. Sarma.

Endophytic bacteria of Fagonia indica Burm. f revealed to harbour rich secondary antibacterial metabolites

Endophytic bacteria are the source of novel bioactive compounds, used as therapeutic agent. Molecular docking is a computational technique use frequently, to find novel drugs targets and drugs-receptors interactions. The current study was designed to isolate and identify endophytic bacteria for the extraction of bioactive compounds. Further, to characterized extracts and to explore compounds interactions with bacterial cell wall and outer membrane synthesizing proteins. Endophytes were identified using 16s rRNA amplification technique. For bioactive compounds, solvent extraction method was followed and characterized further through GC-MS analysis. To find targets and drugs-receptors interactions, molecular docking studies and biological assays were performed. The isolated endophytes belong to five different genera namely Enterobacter, Bacillus, Erwinia, Stenotrophomonas and Pantoea. In case of antibacterial assay Stenotrophomonas maltophilia extract showed significant inhibitory zones (15.11±0.11mm and 11.3±0.16) against Staphylococcus caseolyticus and Acinetobacter baumanni, with MIC 33.3 and 50μg/mL respectively. Among the characterized fifty compounds, from endophytic bacteria "antibacterial compound" N-(5-benzyl-10b-hydroxy-2-methyl-3,6-dioxooctahydro-8H-oxazolo[3,2-α] pyrrolo[2,1c] pyrazin-2-yl)-7-methyl2,3,3a,3a1,6,6a,7,8,9,10,10a,10b-dodecahydro-1H-4λ2-indolo[4,3-fg]quinoline-9-carboxamide of bacteria Stenotrophomonas maltophilia were an excellent binder with MurF ligase active site, with binding energy of -10.2 kcal/mol. Extracts of endophytic bacteria composed of various pharmacologically active ingredients such as antibacterial compounds. Molecular docking studies provide important information regarding drug-receptor interaction, thus can be used in novel drug discovery.

Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.

Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

Molecular Docking Studies on Methanolic Propolis Extracts Collected from Different Regions in Saudi Arabia as a Potential Inhibitor of Topoisomerase IIβ

Propolis is a sticky substance made by honeybees from various plant parts that is rich in biologically active substances such as flavonoids, phenolic acids, and phenolics and has a wide range of applications in the food, cosmetics, and pharmaceutical industries. The current study focused on the isolation of honeybee propolis samples from three different locations in Saudi Arabia: Al Hada, Baljurashi, and Rawdat Khuraim, and the evaluation of their anti-cancer effect against human liver cancer cell lines (HeP-G2) and human breast cancer cell lines (MCF-7). Five chemical compounds present in the methanolic extract of propolis honeybee were detected by HPLC. Furthermore, molecular modeling studies were conducted to explain the mechanism of anti-cancer activity exerted by the active compounds. The propolis samples collected from the three isolation sites had anti-cancer activity against MCF-7 and HeP-G2. Samples collected from the Rawdat Khuraim site showed the highest inhibitory activity reaching 81.5% and 83.2% against MCF-7 and HeP-G2, respectively. HPLC detected four main active compounds from propolis samples: pinobanksin, pinocembrin, galangin, and xanthomicrol. The molecular docking technique showed that galangin and pinocembrin had higher anti-cancer activity than xanthomicrol and pinobanksin as the binding affinity of galangin and pinocembrin with the active sites of the topoisomerase IIβ enzyme was much greater.

Molecular Docking and Dynamics Simulation Revealed the Potential Inhibitory Activity of New Drugs against Human Topoisomerase I Receptor

Human Topoisomerase I (hTop1p) is a ubiquitous enzyme that relaxes supercoiled DNA through a conserved mechanism involving transient breakage, rotation, and binding. Htop1p is the molecular target of the chemotherapeutic drug camptothecin (CPT). It causes the hTop1p-DNA complex to slow down the binding process and clash with the replicative machinery during the S phase of the cell cycle, forcing cells to activate the apoptotic response. This gives hTop1p a central role in cancer therapy. Recently, two artesunic acid derivatives (compounds c6 and c7) have been proposed as promising inhibitors of hTop1p with possible antitumor activity. We used several computational approaches to obtain in silico confirmations of the experimental data and to form a comprehensive dynamic description of the ligand-receptor system. We performed molecular docking analyses to verify the ability of the two new derivatives to access the enzyme-DNA interface, and a classical molecular dynamics simulation was performed to assess the capacity of the two compounds to maintain a stable binding pose over time. Finally, we calculated the noncovalent interactions between the two new derivatives and the hTop1p receptor in order to propose a possible inhibitory mechanism like that adopted by CPT.

Characterization of feruloyl esterases from Pecoramyces sp. F1 and the synergistic effect in biomass degradation

Feruloyl esterase (FAE; EC 3.1.1.73) cleaves the ester bond between ferulic acid (FA) and sugar, to assist the release of FAs and degradation of plant cell walls. In this study, two FAEs (Fae13961 and Fae16537) from the anaerobic fungus Pecoramyces sp. F1 were heterologously expressed in Pichia pastoris (P. pastoris). Compared with Fae16537, Fae13961 had higher catalytic efficiency. The optimum temperature and pH of both the FAEs were 45 ℃ and 7.0, respectively. They showed good stability-Fae16537 retained up to 80% activity after incubation at 37 ℃ for 24 h. The FAEs activity was enhanced by Ca²⁺ and reduced by Zn²⁺, Mn²⁺, Fe²⁺ and Fe³⁺. Additionally, the effect of FAEs on the hydrolytic efficiency of xylanase and cellulase was also determined. The FAE Fae13961 had synergistic effect with xylanase and it promoted the degradation of xylan substrates by xylanase, but it did not affect the degradation of cellulose substrates by cellulase. When Fae13961 was added in a mixture of xylanase and cellulase to degrade complex agricultural biomass, it significantly enhanced the mixture's ability to disintegrate complex substrates. These FAEs could serve as superior auxiliary enzymes for other lignocellulosic enzymes in the process of degradation of agricultural residues for industrial applications.

Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q² _CV) and external (Q² _EXT) validation-were found i.e., MLR1 (Q² _CV = 0.893, Q² _EXT = 0.897), RF1 (Q² _CV = 0.889, Q² _EXT = 0.907), and IBK1 (Q² _CV = 0.891, Q² _EXT = 0.907). An ensemble model was built by averaging the predicted pIC₅₀ of the three models, obtaining a Q² _EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

Anopheles gambiae Trehalase Inhibitors for Malaria Vector Control: A Molecular Docking and Molecular Dynamics Study

Trehalase inhibitors are considered safe alternatives for insecticides and fungicides. However, there are no studies testing these compounds on Anopheles gambiae, a major vector of human malaria. This study predicted the three-dimensional structure of Anopheles gambiae trehalase (AgTre) and identified potential inhibitors using molecular docking and molecular dynamics methods. Robetta server, C-I-TASSER, and I-TASSER were used to predict the protein structure, while the structural assessment was carried out using SWISS-MODEL, ERRAT, and VERIFY3D. Molecular docking and screening of 3022 compounds was carried out using AutoDock Vina in PyRx, and MD simulation was carried out using NAMD. The Robetta model outperformed all other models and was used for docking and simulation studies. After a post-screening analysis and ADMET studies, uniflorine, 67837201, 10406567, and Compound 2 were considered the best hits with binding energies of -6.9, -8.9, -9, and -8.4 kcal/mol, respectively, better than validamycin A standard (-5.4 kcal/mol). These four compounds were predicted to have no eco-toxicity, Brenk, or PAINS alerts. Similarly, they were predicted to be non-mutagenic, carcinogenic, or hepatoxic. 67837201, 10406567, and Compound 2 showed excellent stability during simulation. The study highlights uniflorine, 67837201, 10406567, and Compound 2 as good inhibitors of AgTre and possible compounds for malaria vector control.

Feruloyl Esterases Protein Engineering to Enhance Their Performance as Biocatalysts: A Review

Feruloyl esterases (FAEs) are versatile enzymes able to release hydroxycinnamic acids or synthesize their ester derivatives, both molecules with interesting biological activities such as: antioxidants, antifungals, antivirals, antifibrotic, anti-inflammatory, among others. The importance of these molecules in medicine, food or cosmetic industries provides FAEs with several biotechnological applications as key industrial biocatalysts. However, FAEs have some operational limitations that must be overcome, which can be addressed through different protein engineering approaches to enhance their thermal stability, catalytic efficiencies, and selectivity. This review aims to present a brief historical tour through the mutagenesis strategies employed to improve enzymes performance and analyze the current protein engineering strategies applied to FAEs as interesting biocatalysts. Finally, an outlook of the future of FAEs protein engineering approaches to achieve successful industrial biocatalysts is given.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

A comparative study of 5- fluorouracil, doxorubicin, methotrexate, paclitaxel for their inhibition ability for Mpro of nCoV: Molecular docking and molecular dynamics simulations

A new corona virus (nCoV) is aetiological agent responsible for the viral pneumonia epidemic. Three is no specific therapeutic medicines available for the treatment of this condition and also effective treatment choices are few. In this work author tried to investigate some repurposing drug such as 5- fluorouracil, doxorubicin, methotrexate and paclitaxel against the main protease (Mpro) of nCoV by the computational model. Molecular docking was performed to screen out the best compound and doxorubicin was found to have minimum binding energy −121.89 kcal/mol. To further study, MD simulations were performed at 300 K and the result successfully corroborate the energy obtained by molecular docking. Temperature dependent MD simulation of the best molecule that is doxorubicin obtained from docking result was performed to check the variation in structural changes in Mpro of nCoV at 290 K, 310 K, 320 K and 325 K. It is sound that doxorubicin binds effectively with Mpro of nCoV at 290 K. Further ADME properties of the 5- fluorouracil, doxorubicin, methotrexate and paclitaxel were also evaluated to understand the bioavailability.

Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding

The latest biological findings observe that the motionless "lock-and-key" theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure limits the growth of protein trajectory-related studies, thus hindering the possibility of supervised learning. A spatial-temporal pre-training method based on the modified equivariant graph matching networks, dubbed ProtMD which has two specially designed self-supervised learning tasks: atom-level prompt-based denoising generative task and conformation-level snapshot ordering task to seize the flexibility information inside molecular dynamics (MD) trajectories with very fine temporal resolutions is presented. The ProtMD can grant the encoder network the capacity to capture the time-dependent geometric mobility of conformations along MD trajectories. Two downstream tasks are chosen to verify the effectiveness of ProtMD through linear detection and task-specific fine-tuning. A huge improvement from current state-of-the-art methods, with a decrease of 4.3% in root mean square error for the binding affinity problem and an average increase of 13.8% in the area under receiver operating characteristic curve and the area under the precision-recall curve for the ligand efficacy problem is observed. The results demonstrate a strong correlation between the magnitude of conformation's motion in the 3D space and the strength with which the ligand binds with its receptor.

Identification of Novel Antimicrobial Resistance Genes Using Machine Learning, Homology Modeling, and Molecular Docking

Antimicrobial resistance (AMR) threatens the healthcare system worldwide with the rise of emerging drug resistant infectious agents. AMR may render the current therapeutics ineffective or diminish their efficacy, and its rapid dissemination can have unmitigated health and socioeconomic consequences. Just like with many other health problems, recent computational advances including developments in machine learning or artificial intelligence hold a prodigious promise in deciphering genetic factors underlying emergence and dissemination of AMR and in aiding development of therapeutics for more efficient AMR solutions. Current machine learning frameworks focus mainly on known AMR genes and are, therefore, prone to missing genes that have not been implicated in resistance yet, including many uncharacterized genes whose functions have not yet been elucidated. Furthermore, new resistance traits may evolve from these genes leading to the rise of superbugs, and therefore, these genes need to be characterized. To infer novel resistance genes, we used complete gene sets of several bacterial strains known to be susceptible or resistant to specific drugs and associated phenotypic information within a machine learning framework that enabled prioritizing genes potentially involved in resistance. Further, homology modeling of proteins encoded by prioritized genes and subsequent molecular docking studies indicated stable interactions between these proteins and the antimicrobials that the strains containing these proteins are known to be resistant to. Our study highlights the capability of a machine learning framework to uncover novel genes that have not yet been implicated in resistance to any antimicrobials and thus could spur further studies targeted at neutralizing AMR.

The Mechanism of Crocetin Targeting Cardiovascular Disease Based on Network Pharmacology Constrained by Spectral Experiments

The network pharmacology under conditions is a recent development trend. We use network pharmacology methods to analyze the mechanism of crocetin (CRO) that regulates cardiovascular diseases. In this work, the spectral experimental data of CRO-Protein interaction is first time combined with constraint conditions to solve the problems of targeting redundancy and lack of verification. CRO targets and cardiovascular disease targets were obtained by the target database. The STRING platform was used for PPI analysis. The GO and KEGG pathways of the target were analyzed using the Metascape platform; The core functional targets of CRO were screened by molecular docking techniques and the spectra of CRO and human serum albumin (HSA). Under the collaborative constraint conditions, the core targets of CRO that regulate cardiovascular diseases are ADRA1A, ADRA1B, CHRM1, CHRM2, GABRA1, and PTGS2; This study incorporates spectroscopy and molecular docking as constraints into the network pharmacological analysis, which significantly improves the credibility of network pharmacological analysis compared with unconstrained conditions. This method provides theoretical references for the in-depth study of the mechanism between active substances and protein targets for other medicines in network pharmacology.

Identification of novel natural drug candidates against BRAF mutated carcinoma; An integrative in-silico structure-based pharmacophore modeling and virtual screening process

The BRAF gene is responsible for transferring signals from outside of the cell to inside of the nucleus by converting a protein namely B-Raf through the RAS/MAPK pathway. This pathway contribute to cell division, proliferation, migration, and apoptotic cell death of human and animal. Mutation in this gene may cause the development of several cancers, including lung, skin, colon, and neuroblastoma. Currently, a few available drugs are being used that has developed by targeting the BRAF mutated protein, and due to the toxic side effects, patients suffer a lot during their treatment. Therefore this study aimed to identify potentially lead compounds that can target and block the expression of BRAF and subsequently inhibit the cancer. The hits were generated through the pharmacophore model-based virtual screening, molecular docking, pharmacohore model validation, ADME (absorption, distribution, metabolism, and excretion) analysis molecular dynamics (MD) simulation to find more suitable candidate against the overexpress BRAF gene. The pharmacophore based screening initially identified 14 k possible hits from online database which were further screened by ligand scout advance software to get hit compound. Based on molecular docking score of ZINC70454679 (-10.6 kcal/mol), ZINC253500968 (-9.4 kcal/mol), ZINC106887736 (-8.6 kcal/mol), and ZINC107434492 (-8.1 kcal/mol), pharmacophore feature and toxicity evaluation, we selected four possible lead compounds. The dynamic simulation with Schrodinger Maestro software was used to determine the stability of the potential lead candidates with target protein (PDB ID: 5VAM). The results showed that the newly obtained four compounds were more stable than the control ligand (Pub Chem ID: 90408826). The current results showed that the ZINC70454679, ZINC253500968, ZINC106887736, and ZINC107434492 compounds may be able to work against several cancers through targeting the BRAF overexpressed gene. To develop a novel drug candidate, however the evaluation of the web lab based experimental work are necessary to evaluate the efficiency of the each compound against the BRAF target gene.

Development of Novel 1,3-Disubstituted-2-Thiohydantoin Analogues with Potent Anti-Inflammatory Activity; In Vitro and In Silico Assessments

Inflammation is the main cause of several autoimmune diseases, including type I diabetes, rheumatoid arthritis, bullous pemphigoid, paraneoplastic pemphigoid, and multiple sclerosis. Currently, there is an urgent demand for the discovery of novel anti-inflammatory drugs with potent activity but also safe for long-term application. Toward this aim, the present study reported the design, synthesis, and characterization of a set of novel 1,3-disubstituted-2-thiohydantoins derivatives. The anti-inflammatory activity of synthesized compounds was assessed against murine leukemia cell line (RAW264.7) by evaluating the cytotoxicity activity and their potency to prevent nitric oxide (NO) production. The results revealed that the synthesized compounds possess a considerable cytotoxic activity together with the ability to reduce the NO production in murine leukemia cell line (RAW264.7). Among synthesized compounds, compound 7 exhibited the most potent cytotoxic activity with IC₅₀ of 197.68 μg/mL, compared to celecoxib drug (IC₅₀ value 251.2 μg/mL), and demonstrated a significant ability to diminish the NO production (six-fold reduction). Exploring the mode of action responsible for the anti-inflammatory activity revealed that compound 7 displays a significant and dose-dependent inhibitory effect on the expression of pro-inflammatory cytokines IL-1β. Furthermore, compound 7 demonstrated the ability to significantly reduce the expression of the inflammatory cytokines IL-6 and TNF-α at 50 μg/mL, as compared to Celecoxib. Finally, detailed molecular modelling studies indicated that compound 7 exhibits a substantial binding affinity toward the binding pocket of the cyclooxygenase 2 enzyme. Taken together, our study reveals that 1,3-disubstituted-2-thiohydantoin could be considered as a promising scaffold for the development of potent anti-inflammatory agents.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Metabolomic profiling of three Araucaria species, and their possible potential role against COVID-19

The COVID-19 pandemic in Egypt is a part of the worldwide global crisis of coronavirus 2 (SARS-CoV-2). The contagious life-threatening condition causes acute respiratory syndrome. The present study aimed to assess the compounds identified by LC-MS of the methanolic leaves extracts from three conifers trees cultivated in Egypt (Araucaria bidwillii, Araucaria. cunninghamii and Araucaria heterophylla) via docking technique as potential inhibitor of COVID-19 virus on multiple targets; viral main protease (Mpro, 6LU7), non-structural protein-16 which is a methyl transferase (nsp16, 6W4H) and RNA dependent RNA polymerase (nsp12, 7BV2). Among the three targets, nsp16 was the best target recognized by the tested compounds as can be deduced from docking studies. Moreover, the methanolic extract of A. cunninghamii showed the highest radical-scavenging activity using (DPPH test) with 53.7 µg/mL comparable to ascorbic acid with IC50 = 46 µg/mL The anti-inflammatory potential carried using enzyme linked immunoassay showed the highest activity for A. cunninghamii and A. bidwillii followed by A. heterophylla with IC50 = 23.20 ± 1.17 µg/mL, 82.83 ± 3.21 µg/mL and 221.13 ± 6.7 µg/mL, respectively (Celecoxib was used as a standard drug with IC50 = 141.92 ± 4.52 µg/mL). Moreover, a molecular docking study was carried for the LC-MS annotated metabolites to validate their anti-inflammatory inhibitory effect using Celecoxib as a reference compound and showed a high docking score (-7.7 kcal/mol) for Octadecyl (E) P-coumarate and (-7.3 kcal/mol) for secoisolariciresinol rhamnoside.Communicated by Ramaswamy H. Sarma.

Uncovering Streptomyces-Derived Compounds as Cosmeceuticals for the Development of Improved Skin Photoprotection Products: An In Silico Approach to Explore Multi-Targeted Agents

The search for novel photoprotective substances has become a challenge in cosmeceutical research. Streptomyces-derived compounds can serve as a promising source of photoprotective agents to formulate skin photoprotection products, such as sunscreens. This study aimed to identify specialized metabolites with the potential to modulate UV-induced cellular damage in the skin by identifying potential multi-target-directed ligands. Using a combination of ligand- and target-based virtual screening approaches, a public compound library comprising 6524 Streptomyces-derived specialized metabolites was studied for their photoprotective capability. The compounds were initially filtered by safety features and then examined for their ability to interact with key targets in the photodamage pathway by molecular docking. A set of 50 commercially available UV filters was used as the benchmark. The protein–ligand stability of selected Streptomyces-derived compounds was also studied by molecular dynamics (MD) simulations. From the compound library, 1981 compounds were found to meet the safety criteria for topically applied products, such as low skin permeability and low or non-toxicity-alerting substructures. A total of 34 compounds had promising binding scores against crucial targets involved in UV-induced photodamage, such as serotonin-receptor subtype 5-HT2A, platelet-activating factor receptor, IL-1 receptor type 1, epidermal growth factor receptor, and cyclooxygenase-2. Among these compounds, aspergilazine A and phaeochromycin F showed the highest ranked interactions with four of the five targets and triggered complex stabilization over time. Additionally, the predicted UV-absorbing profiles also suggest a UV-filtering effect. Streptomyces is an encouraging biological source of compounds for developing topical products. After in silico protein–ligand interactions, binding mode and stabilization of aspergilazine A and phaeochromycin F led to the discovery of potential candidates as photodamage multi-target inhibitors. Therefore, they can be further explored for the formulation of skin photoprotection products.

DPP-4 inhibition mediated antidiabetic potential of phytoconstituents of an aqueous fruit extract of Withania coagulans (Stocks) Dunal: in-silico, in-vitro and in-vivo assessments

The DPP-4 inhibition is an interesting target for the development of antidiabetic agents which promotes the longevity of GPL-1(Glucagon-like peptide 1). The current study was intended to assess DPP-4(Dipeptidyl Peptidase-4) inhibition mediated antidiabetic effect of phytocompounds of an aqueous fruit extract of Withania coagulans (Stocks) Dunal by in-vitro, in-silico and in-vivo approaches. The phytoconstituents screening was executed by LCMS (Liquid Chromatography with tandem mass spectrometry). The in-vitro and in-vivo, DPP-4 assays were performed by using available kits. The in-vitro DPP-4 activity was inhibited up to 68.3% by the test extract. Accordingly, in-silico determinations of molecular docking, molecular dynamics and pharmacokinetics were performed between the target enzyme DPP-4 and leading phytocompounds. The molecular dynamics authenticated the molecular docking data by crucial parameters of cytosolic milieu by the potential energy, RSMD (Root Mean Square Deviation), RSMF (Root Mean Square Fluctuation), system density, NVT (Number of particles at fixed volume, ensemble) and NPT (Number of particles at fixed pressure, ensemble). Accordingly, ADMET predictions assessed the druggability profile. Subsequently, the course of the test extract and the sitagliptin (positive control), instigated significant (p ≤ 0.001) ameliorations in HOMA indices and the equal of antioxidants in nicotinamide-streptozotocin induced type 2 diabetic animal model. Compassionately, the histopathology represented increased pancreatic cellular mass which caused in restoration of histoarchitectures. It has been concluded that phytoconstituents in W. coagulans aqueous fruit extract can regulate DPP-4, resulting in improved glucose homeostasis and enhanced endocrinal pancreatic cellular mass.Communicated by Ramaswamy H. Sarma.

Cross-linked cyclodextrin glucanotransferase aggregates from Bacillus lehensis G1 for cyclodextrin production: Molecular modeling, developmental, physicochemical, kinetic and thermodynamic properties

Type of cross-linking agents influence the stability and active cross-linked enzyme aggregates (CLEA) immobilization. The information of molecular interaction between enzyme-cross linker is not well explored thus screening wide numbers of cross-linker is crucial in CLEA development. This study combined the molecular modeling and experimental optimization to investigate the influences of different cross-linking agents in developing CLEA of cyclodextrin glucanotranferase G1 (CGTase G1) for cyclodextrins (CDs) synthesis. Seven types of cross-linkers were tested and CGTase G1 cross-linked with chitosan (CS-CGTG1-CLEA) displayed the highest activity recovery (84.6 ± 0.26%), aligning with its highest binding affinity, radius of gyration and flexibility through in-silico analysis towards CGTase G1. CS-CGTG1-CLEA was characterized and showed a longer half-life (30.06 ± 1.51 min) and retained a greater thermal stability (52.73 ± 0.93%) after 30 min incubation at optimal conditions compared to free enzyme (10.30 ± 1.34 min and 5.51 ± 2.10% respectively). CS-CGTG1-CLEA improved CDs production by 33% and yielded cumulative of 52.62 g/L CDs after five cycles for 2 h of reaction. This study reveals that abundant of hydroxyl group on chitosan interacted with CGTase G1 surface amino acid residues to form strong and stable CLEA thus can be a promising biocatalyst in CDs production.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Recognition of quinolone antibiotics by the multidrug efflux transporter MexB of Pseudomonas aeruginosa

The drug/proton antiporter MexB is the engine of the major efflux pump MexAB-OprM in Pseudomonas aeruginosa. This protein is known to transport a large variety of compounds, including antibiotics, thus conferring a multi-drug resistance phenotype. Due to the difficulty of producing co-crystals, only two X-ray structures of MexB in a complex with ligands are available to date, and mechanistic aspects are largely hypothesized based on the body of data collected for the homologous protein AcrB of Escherichia coli. In particular, a recent study (Ornik-Cha, Wilhelm, Kobylka et al., Nat. Commun., 2021, 12, 6919) reported a co-crystal structure of AcrB in a complex with levofloxacin, an antibiotic belonging to the important class of (fluoro)-quinolones. In this work, we performed a systematic ensemble docking campaign coupled to the cluster analysis and molecular-mechanics optimization of docking poses to study the interaction between 36 quinolone antibiotics and MexB. We additionally investigated surface complementarity between each molecule and the transporter and thoroughly assessed the computational protocol adopted against the known experimental data. Our study reveals different binding preferences of the investigated compounds towards the sub-sites of the large deep binding pocket of MexB, supporting the hypothesis that MexB substrates oscillate between different binding modes with similar affinity. Interestingly, small changes in the molecular structure translate into significant differences in MexB–quinolone interactions. All the predicted binding modes are available for download and visualization at the following link: https://www.dsf.unica.it/dock/mexb/quinolones.

Putative binding modes (BMs) of quinolones to the bacterial efflux transporter MexB were identified. Multiple interaction patterns are possible, supporting the hypothesis that substrates oscillate between different BMs with similar affinity.

Allostery Inhibition of BACE1 by Psychotic and Meroterpenoid Drugs in Alzheimer's Disease Therapy

In over a century since its discovery, Alzheimer's disease (AD) has continued to be a global health concern due to its incurable nature and overwhelming increase among older people. In this paper, we give an overview of the efforts of researchers towards identifying potent BACE1 exosite-binding antibodies and allosteric inhibitors. Herein, we apply computer-aided drug design (CADD) methods to unravel the interactions of some proposed psychotic and meroterpenoid BACE1 allosteric site inhibitors. This study is aimed at validating the allosteric potentials of these selected compounds targeted at BACE1 inhibition. Molecular docking, molecular dynamic (MD) simulations, and post-MD analyses are carried out on these selected compounds, which have been experimentally proven to exhibit allosteric inhibition on BACE1. The SwissDock software enabled us to identify more than five druggable pockets on the BACE1 structural surface using docking. Besides the active site region, a melatonin derivative (compound 1) previously proposed as a BACE1 allostery inhibitor showed appreciable stability at eight different subsites on BACE1. Refinement with molecular dynamic (MD) simulations shows that the identified non-catalytic sites are potential allostery sites for compound 1. The allostery and binding mechanism of the selected potent inhibitors show that the smaller the molecule, the easier the attachment to several enzyme regions. This finding hereby establishes that most of these selected compounds failed to exhibit strong allosteric binding with BACE1 except for compound 1. We hereby suggest that further studies and additional identification/validation of other BACE1 allosteric compounds be done. Furthermore, this additional allosteric site investigation will help in reducing the associated challenges with designing BACE1 inhibitors while exploring the opportunities in the design of allosteric BACE1 inhibitors.

Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel

(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.

Repurposing antiviral phytochemicals from the leaf extracts of Spondias mombin (Linn) towards the identification of potential SARSCOV-2 inhibitors

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a pneumonia-like disease with a pattern of acute respiratory symptoms, currently remains a significant public health concern causing tremendous human suffering. Although several approved vaccines exist, vaccine hesitancy, limited vaccine availability, high rate of viral mutation, and the absence of approved drugs account for the persistence of SARS-CoV-2 infections. The investigation of possibly repurposing of phytochemical compounds as therapeutic alternatives has gained momentum due to their reported affordability and minimal toxicity. This study investigated anti-viral phytochemical compounds from ethanolic leaf extracts of Spondias mombin L as potential inhibitor candidates against SARS-CoV-2. We identified Geraniin and 2-O-Caffeoyl-(+)-allohydroxycitric acid as potential SARS-CoV-2 inhibitor candidates targeting the SARS-CoV-2 RNA-dependent polymerase receptor-binding domain (RBD) of SARS-CoV-2 viral S-protein and the 3C-like main protease (3CLpro). Geraniin exhibited binding free energy (ΔGbind) of - 25.87 kcal/mol and - 21.74 kcal/mol towards SARS-CoV-2 RNA-dependent polymerase and receptor-binding domain (RBD) of SARS-CoV-2 viral S-protein respectively, whereas 2-O-Caffeoyl-(+)-allohydroxycitric acid exhibited a ΔGbind of - 32 kcal/mol towards 3CLpro. Molecular Dynamics simulations indicated a possible interference to the functioning of SARS-CoV-2 targets by the two identified inhibitors. However, further in vitro and in vivo evaluation of these potential SARS-CoV-2 therapeutic inhibitor candidates is needed.

Predicting Protein-Ligand Docking Structure with Graph Neural Network

Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.

Assessment of the CYP1A2 Inhibition-Mediated Drug Interaction Potential for Pinocembrin Using In Silico, In Vitro, and In Vivo Approaches

Pinocembrin, a bioflavonoid, is extensively used in complementary/alternative medicine. It turns out as a promising candidate against neurodegenerative diseases because of its multifaceted pharmacological action toward neuroprotection. However, literature evidence is still lacking for its inhibitory action on CYP1A2, which is responsible for xenobiotic metabolism leading to the generation of toxic metabolites and bioactivation of procarcinogens. In the present study, our aim was to evaluate the CYP1A2 inhibitory potential of pinocembrin via in silico, in vitro, and in vivo investigations. From the results of in vitro studies, pinocembrin is found to be a potent and competitive inhibitor of CYP1A2. In vitro-in vivo extrapolation results indicate the potential of pinocembrin to interact with CYP1A2 substrate drugs clinically. Molecular docking-based in silico studies demonstrate the strong interaction of pinocembrin with human CYP1A2. In in vivo investigations using a rat model, pinocembrin displayed a marked alteration in the plasma exposure of CYP1A2 substrate drugs, namely, caffeine and tacrine. In conclusion, pinocembrin has a potent CYP1A2 inhibitory action to cause drug interactions, and further confirmatory study is warranted at the clinical level.

Targeting SARS-CoV-2 non-structural protein 13 via helicase-inhibitor-repurposing and non-structural protein 16 through pharmacophore-based screening

Novel drug compound hunting was carried out for SARS-CoV-2 proteins with low mutation susceptibility. The probability of escape mutation and drug resistance is lower if conserved microbial proteins are targeted by therapeutic drugs. Mutation rate of all SARS-CoV-2 proteins were analyzed via multiple sequence alignment Non-Structural Protein 13 and Non-Structural Protein 16 were selected for the current study due to low mutation rate among viral strains and significant functionality. Cross-species mutation rate analysis for NSP13 and NSP16 showed these are well-conserved proteins among four coronaviral species. Viral helicase inhibitors, identified using literature-mining, were docked against NSP13. Pharmacophore-based screening of 11,375 natural compounds was conducted for NSP16. Stabilities of top compounds inside human body were confirmed via molecular dynamic simulation. ADME properties and LD₅₀ values of the helicase inhibitors and Ambinter natural compounds were analyzed. Compounds against NSP13 showed binding affinities between −10 and −5.9 kcal/mol whereby ivermectin and scutellarein showed highest binding energies of −10 and −9.9 kcal/mol. Docking of 18 hit compounds against NSP16 yielded binding affinities between −8.9 and −4.1 kcal/mol. Hamamelitannin and deacyltunicamycin were the top compounds with binding affinities of −8.9 kcal/mol and −8.4 kcal/mol. The top compounds showed stable ligand–protein interactions in molecular dynamics simulation. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, high binding affinity and molecular interactions. Conversion of these compounds into drugs after in vitro experimentation can become better treatment options to elevate COVID management.

In vitro and in silico evaluation of antimicrobial properties of Delphinium cashmerianum L., a medicinal herb growing in Kashmir, India

ETHNOPHARMACOLOGICAL RELEVANCE

Microorganisms are developing resistance to synthetic drugs. As a result, the search for novel antimicrobial compounds has become an urgent need. Medicinal plants are commonly used as traditional medicine and Delphinium is one of the prominent genus used in the treatment of several diseases.

AIM OF THE STUDY

The present study aimed to determine the in vitro and in silico antimicrobial activities of petroleum ether, ethyl acetate and methanol extracts from the leaf samples of plant (Delphinium cashmerianum L.) against various bacterial and fungal strains.

MATERIAL AND METHODS

Three extracts of Delphinium cashmerianum prepared and 88 bioactive compounds were analyzed through LC-MS data with the vast majority of them having therapeutic applications. These extracts have been screened for the antimicrobial activity against various bacterial (Escherichia coli, Micrococcus luteus, Klebsiella pneumoniae, Streptococcus pneumonia, Haemophilus influenzae, Neisseria mucosa) and fungal (Candida albicans, Candida glabrata, Candida paropsilosis) species through in silico molecular docking approach using autodock vina software, molecular dynamic simulation (MDS), in vitro disc diffusion and broth microdilution method for minimum inhibitory concentration (MIC) evaluation.

RESULTS

Our results demonstrated that all three extracts were active against the whole set of microorganisms. The ethyl acetate extract was the most active against S.pneumonia, K. pneumoniae and C. albicans with a minimum inhibitory concentration (MIC) value of 6.25, 25 and 50 μg/ml, respectively. The petroleum ether and methanol extracts were active against S.pneumonia and N.mucosa with MIC values of 25 and 50 μg/ml. Furthermore, we also performed the in silico virtual screening of all these compounds obtained from LC-MS data analysis against various known drug targets of bacterium and fungi. Upon analysis, we obtained 5 compounds that were efficiently binding to the drug targets. However, after performing exhaustive molecular docking and molecular dynamic simulation (MDS) analysis, it was observed that Daidzein compound is bound to drug targets more efficiently.

CONCLUSION

The results showed that these plant extracts exhibit antimicrobial activity and ethyl acetate extract proved to exhibit the most effective antibacterial and antifungal properties.

Bioactive Peptides: An Understanding from Current Screening Methodology

Bioactive peptides with high potency against numerous human disorders have been regarded as a promising therapy in disease control. These peptides could be released from various dietary protein sources through hydrolysis processing using physical conditions, chemical agents, microbial fermentation, or enzymatic digestions. Considering the diversity of the original proteins and the complexity of the multiple structural peptides that existed in the hydrolysis mixture, the screening of bioactive peptides will be a challenge task. Well-organized and well-designed methods are necessarily required to enhance the efficiency of studying the potential peptides. This article, hence, provides an overview of bioactive peptides with an emphasis on the current strategy used for screening and characterization methods. Moreover, the understanding of the biological activities of peptides, mechanism inhibitions, and the interaction of the complex of peptide–enzyme is commonly evaluated using specific in vitro assays and molecular docking analysis.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

Unravelling the Structural Mechanism of Action of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione in Dual-Targeting Tankyrase 1 and 2: A Novel Avenue in Cancer Therapy

Tankyrase (TNKS) belonging to the poly(ADPribose) polymerase family, are known for their multi-functioning capabilities, and play an essential role in the Wnt β-catenin pathway and various other cellular processes. Although showing inhibitory potential at a nanomolar level, the structural dual-inhibitory mechanism of the novel TNKS inhibitor, 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione, remains unexplored. By employing advanced molecular modeling, this study provides these insights. Results of sequence alignments of binding site residues identified conserved residues; GLY1185 and ILE1224 in TNKS-1 and PHE1035 and PRO1034 in TNKS-2 as crucial mediators of the dual binding mechanism of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione, corroborated by high per-residue energy contributions and consistent high-affinity interactions of these residues. Estimation of the binding free energy of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione showed estimated total energy of -43.88 kcal/mol and -30.79 kcal/mol towards TNKS-1 and 2, respectively, indicating favorable analogous dual binding as previously reported. Assessment of the conformational dynamics of TNKS-1 and 2 upon the binding of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione revealed similar structural changes characterized by increased flexibility and solvent assessible surface area of the residues inferring an analogous structural binding mechanism. Insights from this study show that peculiar, conserved residues are the driving force behind the dual inhibitory mechanism of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione and could aid in the design of novel dual inhibitors of TNKS-1 and 2 with improved therapeutic properties.

In silico analyses of acetylcholinesterase (AChE) and its genetic variants in interaction with the anti-Alzheimer drug Rivastigmine

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Despite causing great social and economic impact, there is currently no cure for AD. The most effective therapy to manage AD symptoms is based on acetylcholinesterase inhibitors (AChEi), from which rivastigmine presented numerous benefits. However, mutations in AChE, which affect approximately 5% of the population, can modify protein structure and function, changing the individual response to Alzheimer's treatment. In this study, we performed computer simulations of AChE wild type and variants R34Q, P135A, V333E, and H353N, identified by one or more genome-wide association studies, to evaluate their effects on protein structure and interaction with rivastigmine. The functional effects of AChE variants were predicted using eight machine learning algorithms, while the evolutionary conservation of AChE residues was analyzed using the ConSurf server. Autodock4.2.6 was used to predict the binding modes for the hAChE-rivastigmine complex, which is still unknown. Molecular dynamics (MD) simulations were performed in triplicates for the AChE wild type and mutants using the GROMACS packages. Among the analyzed variants, P135A was classified as deleterious by all the functional prediction algorithms, in addition to occurring at highly conserved positions, which may have harmful consequences on protein function. The molecular docking results suggested that rivastigmine interacts with hAChE at the upper active-site gorge, which was further confirmed by MD simulations. Our MD findings also suggested that the complex hAChE-rivastigmine remains stable over time. The essential dynamics revealed flexibility alterations at the active-site gorge upon mutations P135A, V333E, and H353N, which may lead to strong and nonintuitive consequences to hAChE binding. Nonetheless, similar binding affinities were registered in the MMPBSA analysis for the hAChE wild type and variants when complexed to rivastigmine. Finally, our findings indicated that the rivastigmine binding to hAChE is an energetically favorable process mainly driven by negatively charged amino acids.

Development of epitope-based chimeric protein as a vaccine against Lujo virus by utilizing immunoinformatic tools

Aim: Lujo is a modern zoonotic virus that is potentially fatal and spreads by bodily fluids. In this research, immunoinformatic tools are used to build a vaccine. Methodology: The epitopes of cytotoxic T-lymphocytes, helper T-lymphocytes and linear B-lymphocytes were predicted from the most antigenic protein. The designed vaccine's physiochemical properties and 3D structure have been forecasted. Low free energy and strong binding affinity estimated in molecular docking against toll-like receptor 4 (TLR4) and dynamic simulation. Furthermore, in silico cloning in the Escherichia coli K12 host system was performed for high level of expression. Conclusion: Finally, immune simulation was used to determine immune responses to the vaccine that was formulated confirming the developed vaccine as a good candidate against Lujo virus.