Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors

Cannabinoids: Potential for Modulation and Enhancement When Combined with Vitamin B12 in Case of Neurodegenerative Disorders

The enduring relationship between humanity and the cannabis plant has witnessed significant transformations, particularly with the widespread legalization of medical cannabis. This has led to the recognition of diverse pharmacological formulations of medical cannabis, containing 545 identified natural compounds, including 144 phytocannabinoids like Δ9-THC and CBD. Cannabinoids exert distinct regulatory effects on physiological processes, prompting their investigation in neurodegenerative diseases. Recent research highlights their potential in modulating protein aggregation and mitochondrial dysfunction, crucial factors in conditions such as Alzheimer's Disease, multiple sclerosis, or Parkinson's disease. The discussion emphasizes the importance of maintaining homeodynamics in neurodegenerative disorders and explores innovative therapeutic approaches such as nanoparticles and RNA aptamers. Moreover, cannabinoids, particularly CBD, demonstrate anti-inflammatory effects through the modulation of microglial activity, offering multifaceted neuroprotection including mitigating aggregation. Additionally, the potential integration of cannabinoids with vitamin B12 presents a holistic framework for addressing neurodegeneration, considering their roles in homeodynamics and nervous system functioning including the hippocampal neurogenesis. The potential synergistic therapeutic benefits of combining CBD with vitamin B12 underscore a promising avenue for advancing treatment strategies in neurodegenerative diseases. However, further research is imperative to fully elucidate their effects and potential applications, emphasizing the dynamic nature of this field and its potential to reshape neurodegenerative disease treatment paradigms.

Phylogeny and evolution of SARS-CoV-2 during Delta and Omicron variant waves in India

SARS-CoV-2 evolution has continued to generate variants, responsible for new pandemic waves locally and globally. Varying disease presentation and severity has been ascribed to inherent variant characteristics and vaccine immunity. This study analyzed genomic data from 305 whole genome sequences from SARS-CoV-2 patients before and through the third wave in India. Delta variant was reported in patients without comorbidity (97%), while Omicron BA.2 was reported in patients with comorbidity (77%). Tissue adaptation studies brought forth higher propensity of Omicron variants to bronchial tissue than lung, contrary to observation in Delta variants from Delhi. Study of codon usage pattern distinguished the prevalent variants, clustering them separately, Omicron BA.2 isolated in February grouped away from December strains, and all BA.2 after December acquired a new mutation S959P in ORF1b (44.3% of BA.2 in the study) indicating ongoing evolution. Loss of critical spike mutations in Omicron BA.2 and gain of immune evasion mutations including G142D, reported in Delta but absent in BA.1, and S371F instead of S371L in BA.1 could explain very brief period of BA.1 in December 2021, followed by complete replacement by BA.2. Higher propensity of Omicron variants to bronchial tissue, probably ensured increased transmission while Omicron BA.2 became the prevalent variant possibly due to evolutionary trade-off. Virus evolution continues to shape the epidemic and its culmination.Communicated by Ramaswamy H. Sarma.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

Assessing the role of residue Phe108 of cytochrome P450 3A4 in allosteric effects of midazolam metabolism

Cytochrome P450 3A4 (CYP3A4) is involved in the metabolism of more drugs in clinical use than any other xenobiotic-metabolizing enzyme. CYP3A4-mediated drug metabolism is usually allosterically modulated by substrate concentration (homotropic allostery) and other drugs (heterotropic allostery), exhibiting unusual kinetic profiles and regiospecific metabolism. Recent studies suggest that residue Phe108 (F108) of CYP3A4 may have an important role in drug metabolism. In this work, residue mutations were coupled with well-tempered metadynamics simulations to assess the importance of F108 in the allosteric effects of midazolam metabolism. Comparing the simulation results of the wild-type and mutation systems, we identify that the π-π interaction and steric effect between the F108 side chain and midazolam is favorable for the stable binding of substrate in the active site. F108 also plays an important role in the transition of substrate binding mode, which mainly induces the transition of substrate binding mode by forming π-π interactions with multiple aromatic rings of the substrate. Moreover, the side chain of F108 is closely related to the radius and depth of the 2a and 2f channels, and F108 may further regulate drug metabolism by affecting the pathway, orientation, or time of substrate entry into the CYP3A4 active site or product egress from the active site. Altogether, we suggest that F108 affects drug metabolism and regulatory mechanisms by affecting substrate binding stability, binding mode transition, and channel characteristics of CYP3A4. Our findings could promote the understanding of complicated allosteric mechanisms in CYP3A4-mediated drug metabolism, and the knowledge could be used for drug development and disease treatment.

Unraveling the allosteric inhibition mechanism of PARP-1 CAT and the D766/770A mutation effects via Gaussian accelerated molecular dynamics and Markov state model

PARP-1 (Poly (ADP-ribose) polymerase 1) is a nuclear enzyme and plays a key role in many cellular functions, such as DNA repair, modulation of chromatin structure, and recombination. Developing the PARP-1 inhibitors has emerged as an effective therapeutic strategy for a growing list of cancers. The catalytic structural domain (CAT) of PARP-1 upon binding the inhibitor allosterically regulates the conformational changes of helix domain (HD), affecting its identification with the damaged DNA. The typical type I (EB47) and III (veliparib) inhibitors were able to lengthening or shortening the retention time of this enzyme on DNA damage and thus regulating the cytotoxicity. Nonetheless, the basis underlying allosteric inhibition is unclear, which limits the development of novel PARP-1 inhibitors. Here, to investigate the distinct allosteric changes of EB47 and veliparib against PARP-1 CAT, each complex was simulated via classical and Gaussian accelerated molecular dynamics (cMD and GaMD). To study the reverse allosteric basis and mutation effects, the complexes PARP-1 with UKTT15 and PARP-1 D766/770A mutant with EB47 were also simulated. Importantly, the markov state models were built to identify the transition pathways of crucial substates of allosteric communication and the induction basis of PARP-1 reverse allostery. The conformational change differences of PARP-1 CAT regulated by allosteric inhibitors were concerned with to their interaction at the active site. Energy calculations suggested the energy advantage of EB47 in inhibiting the wild-type PARP-1, compared with D766/770A PARP-1. Secondary structure results showed the change of two key loops (αB-αD and αE-αF) in different systems. This work reported the basis of PARP-1 allostery from both thermodynamic and kinetic views, providing the guidance for the discovery and design of more innovative PARP-1 allosteric inhibitors.

Investigation of the mutated antimicrobial peptides to inhibit ACE2, TMPRSS2 and GRP78 receptors of SARS-CoV-2 and angiotensin II type 1 receptor (AT1R) as well as controlling COVID-19 disease

SARS-CoV-2 is a global problem nowadays. Based on studies, some human receptors are involved in binding to SARS-CoV-2. Thus, the inhibition of these receptors can be effective in the treatment of Covid-19. Because of the proven benefits of antimicrobial peptides (AMPs) and the side effects of chemical drugs, they can be known as an alternative to recent medicines. RCSB PDB to obtain PDB id, StraPep and PhytAMP to acquire Bio-AMPs information and 3-D structure, and AlgPred, Toxinpred, TargetAntiAngio, IL-4pred, IL-6pred, ACPred and Hemopred databases were used to find the best score peptide features. HADDOCK 2.2 was used for molecular docking analysis, and UCSF Chimera software version 1.15, SWISS-MODEL and BIOVIA Discovery Studio Visualizer4.5 were used for mutation and structure modeling. Furthermore, MD simulation results were achieved from GROMACS 4.6.5. Based on the obtained results, the Moricin peptide was found to have the best affinity for ACE2. Moreover, Bacteriocin leucocin-A had the highest affinity for GRP78, Cathelicidin-6 had the best affinity for AT1R, and Bacteriocin PlnK had the best binding affinity for TMPRSS2. Additionally, Bacteriocin glycocin F, Bacteriocin lactococcin-G subunit beta and Cathelicidin-6 peptides were the most common compounds among the four receptors. However, these peptides also have some side effects. Consequently, the mutation eliminated the side effects, and MD simulation results indicated that the mutation proved the result of the docking analysis. The effect of AMPs on ACE2, GRP78, TMPRSS2 and AT1R receptors can be a novel treatment for Covid-19.Communicated by Ramaswamy H. Sarma.

Targeting the omicron variant of SARS-CoV-2 with phytochemicals from Saudi medicinal plants: molecular docking combined with molecular dynamics investigations

The new health crises caused by SARS-CoV-2 have resulted in millions of deaths worldwide. First discovered in November 2021, the omicron variant is more transmissible and is able to evade the immune system better than other previously identified SARS-CoV-2 variants, leading to a spike in cases. Great efforts have been made to discover inhibitors against SARS-CoV-2. Main protease (Mpro) inhibitors are considered promising anti-SARS-CoV-2 agents. The U.S. FDA has issued an Emergency Use Authorization for ritonavir-boosted nirmatrelvir. Nirmatrelvir is the first orally bioavailable inhibitor of SARS-CoV-2 Mpro. There is an urgent need to monitor the mutations and solve the problem of resistance, especially omicron Mpro, which contains one mutation - P132H. In the present study, 132,57 phytochemicals from 80 medicinal plants grown in Saudi Arabia were docked into the active site of Mpro omicron variant. Free binding energies were also calculated. This led to the discovery of five phytochemicals that showed better docking scores than the bound ligand nirmatrelvir. In addition, these molecules exhibited favorable free binding energies. The stability of compounds 1-5 with the protein was studied using molecular dynamics (MD) simulations. These compounds showed acceptable ADMET properties. The results were compared with the wild type. These candidates could be envisioned as new hits against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Possible molecular exploration of herbal pair Haizao-Kunbu in the treatment of Graves' disease by network pharmacology, molecular docking, and molecular dynamic analysis

Objective

To promote the development and therapeutic application of new medications, it is crucial to conduct a thorough investigation into the mechanism by which the traditional Chinese herb pair of Haizao-Kunbu (HK) treats Graves' disease (GD).

Materials and methods

Chemical ingredients of HK, putative target genes, and GD-associated genes were retrieved from online public databases. Using Cytoscape 3.9.1, a compound-gene target network was established to explore the association between prosperous ingredients and targets. STRING, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway analyses visualized core targets and disease pathways. Additionally, we conducted a refined analysis of the binding interactions between active ingredients and their respective targets. To visualize these findings, we employed precise molecular docking techniques. Furthermore, we carried out molecular dynamics simulations to gain insights into the formation of more tightly bound complexes.

Results

We found that there were nine key active ingredients in HK, which mainly acted on 21 targets. These targets primarily regulated several biological processes such as cell population proliferation, protein phosphorylation, and regulation of kinase activity, and acted on PI3K-AKT and MAPK pathways to treat GD. Analysis of the molecular interaction simulation under computer technology revealed that the key targets exhibited strong binding activity to active ingredients, and Fucosterol-AKT1 and Isofucosterol-AKT1 complexes were highly stable in humans.

Conclusion

This study demonstrates that HK exerts therapeutic effects on GD in a multi-component, multi-target, and multi-pathway manner by regulating cell proliferation, differentiation, inflammation, and immunomodulatory-related targets. This study provides a theoretical foundation for further investigation into GD.

Computational Insights into Natural Antischistosomal Metabolites as SmHDAC8 Inhibitors: Molecular Docking, ADMET Profiling, and Molecular Dynamics Simulation

Schistosomiasis is a neglected tropical disease with a significant socioeconomic impact. It is caused by several species of blood trematodes from the genus Schistosoma, with S. mansoni being the most prevalent. Praziquantel (PZQ) is the only drug available for treatment, but it is vulnerable to drug resistance and ineffective in the juvenile stage. Therefore, identifying new treatments is crucial. SmHDAC8 is a promising therapeutic target, and a new allosteric site was discovered, providing the opportunity for the identification of a new class of inhibitors. In this study, molecular docking was used to screen 13,257 phytochemicals from 80 Saudi medicinal plants for inhibitory activity on the SmHDAC8 allosteric site. Nine compounds with better docking scores than the reference were identified, and four of them (LTS0233470, LTS0020703, LTS0033093, and LTS0028823) exhibited promising results in ADMET analysis and molecular dynamics simulation. These compounds should be further explored experimentally as potential allosteric inhibitors of SmHDAC8.

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (Mpro) using molecular docking and deep learning methods

Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (Mpro) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of Mpro, we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus Mpro dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from -8.0 to -8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against Mpro. Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients.

Structure based virtual screening, molecular dynamic simulation to identify the oxadiazole derivatives as inhibitors of Enterococcus D-Ala-D-Ser ligase for combating vancomycin resistance

Vancomycin resistance in enterococci mainly arises due to alteration in terminal peptidoglycan dipeptide. A comprehensive structural analysis for substrate specificity of dipeptide modifying d-Alanine: d-Serine ligase (Ddls) is essential to screen its inhibitors for combating vancomycin resistance. In this study modeled 3D structure of EgDdls from E. gallinarum was used for structure based virtual screening (SBVS) of oxadiazole derivatives. Initially, fifteen oxadiazole derivatives were identified as inhibitors at the active site of EgDdls from PubChem database. Further, four EgDdls inhibitors were evaluated using pharmacokinetic profile and molecular docking. The results of molecular docking showed that oxadiazole inhibitors could bind preferentially at ATP binding pocket with the lowest binding energy. Further, molecular dynamics simulation results showed stable behavior of EgDdls in complex with screened inhibitors. The residues Phe172, Lys174, Glu217, Phe292, and Asn302 of EgDdls were mainly involved in interactions with screened inhibitors. Furthermore, MM-PBSA calculation showed electrostatic and van der Waals interactions mainly contribute to overall binding energy. The PCA analysis showed motion of central domain and omega loop of EgDdls. This is involved in the formation of native dipeptide and stabilized after binding of 2-(1-(Ethylsulfonyl) piperidin-4-yl)-5-(furan-2-yl)-1,3,4-oxadiazole, which could be reason for the inhibition of EgDdls. Hence, in this study we have screened inhibitors of EgDdls which could be useful to alleviate the vancomycin resistance problem in enterococci, involved in hospital-acquired infections, especially urinary tract infections (UTI).

"Identification of alkaloid compounds as potent inhibitors of Mycobacterium tuberculosis NadD using computational strategies"

Mycobacterium tuberculosis is leading cause of death worldwide. NAD participates in a host of redox reactions in energy landscape of organisms. Several studies implicate surrogate energy pathways involving NAD pools as important in survival of active as well as dormant mycobacteria. One of the NAD metabolic pathway enzyme, nicotinate mononucleotide adenylyltransferase (NadD) is indispensable in mycobacterial NAD metabolism and is perceived as an attractive drug target in pathogen. In this study, we have employed in silico screening, simulation and MM-PBSA strategies to identify potentially important alkaloid compounds against mycobacterial NadD for structure-based inhibitor development. We have performed an exhaustive structure-based virtual screening of an alkaloid library, ADMET, DFT profiling followed by Molecular Dynamics (MD) simulation, and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation to identify 10 compounds which exhibit favourable drug like properties and interactions. Interaction energies of these 10 alkaloid molecules range between -190 kJ/mol and -250 kJ/mol. These compounds could be promising starting point in the development of selective inhibitors against Mycobacterium tuberculosis.

Virtual screening, molecular simulations and bioassays: Discovering novel microsomal prostaglandin E Synthase-1 (mPGES-1) inhibitors

Microsomal prostaglandin E synthase-1 (mPGES-1) is an inducible prostaglandin E synthase expressed following exposure to pro-inflammatory stimuli. The mPGES-1 enzyme represents a new target for the therapeutic treatment of acute and chronic inflammatory disorders and cancer. In the present study, compounds from the ZINC15 database with an indole scaffold were docked at the mPGES-1 binding site using Glide (high-throughput virtual screening [HTVS], standard precision [SP] and extra precision [XP]), and the stabilities of the complexes were determined by molecular simulation studies. Following HTVS, the top 10% compounds were retained and further screened by SP. Again, the top 10% of these compounds were retained. Finally, the Glide XP scores of the compounds were determined, 20% were analyzed, and the Prime MM-GBSA total free binding energies of the compounds were calculated. The molecular simulations (100 ns) of the reference ligand, LVJ, and the two best-scoring compounds were performed with the Desmond program to analyze the dynamics of the target protein-ligand complexes. In human lung cells treated with the hit compounds, cell viability by colorimetric method and PGE₂ levels by immunoassay method were determined. These in vitro experiments demonstrated that the two indole-containing hit compounds are potential novel inhibitors of mPGES-1 and are, therefore, potential therapeutic agents for cancer/inflammation therapies. Moreover, the compounds are promising lead mPGES-1 inhibitors for novel molecule design.

Sub-pocket-focused designing of tacrine derivatives as potential acetylcholinesterase inhibitors

Human acetylcholinesterase (hAChE) has a potential role in the management of acetylcholine, one of the neurotransmitters that modulate the overall activity of cholinergic system, AChE inhibitors have a greater impact in the therapeutics. Though the atomic structure of hAChE has been extensively studied, the precise active site geometry upon binding to different ligands are yet to be explored. In the present study, an extensive structural analysis of our recently reported hAChE-tacrine complex has carried out and revealed the presence of two prominent sub-pockets located at the vicinity of the hAChE active site. Structural bioinformatics assisted studies designed 132 putative sub-pockets focused tacrine derivatives (SPFTDs), their molecular docking, free energy estimations revealed that they are stronger than tacrine in terms of binding affinity. Our in vitro studies also supported the in silico findings, all these SPFTDs are having better potencies than tacrine. Cytotoxic nature of these SPFTDs on HepG2 and Neuro-2a cell lines, diminishes the possibilities for future in vivo studies. However, the identification of these sub pockets and the SPFTDs paved a new way to the future drug discovery especially since AChE is one of the promising and approved drug targets in treatment of AD drug discovery.

In Vitro and In Silico Studies of Kinase Inhibitor of MAPK3 Protein to Determine Leishmania martiniquensis Treatment

PURPOSE

Leishmaniasis is a parasitic disease transmitted by the bite of the phlebotomine female sand fly. Currently, no reported effective vaccines are available for the treatment of leishmaniasis; consequently, restricting this disease completely depends on controlling its transmission. Mitogen-activated protein (MAP) kinases have been reported to be involved in the regulation of the flagellum length and hence play an important role in disease transmission, especially the MAPK3 protein. Therefore, the current work focused on identifying approved drugs that can inhibit the MAPK3 protein.

METHODS

First, the recombinant plasmid (pET28b( +) MAPK3) was cloned into E. coli strain BL21 using the heatshock method. Afterward, E. coli was induced using IPTG, and cells were harvested for protein purification in the next step. After that, the MAPK3 protein was purified using Ni-NTA column. Then, the inhibition kinase activity of the purified MAPK3 protein was performed using an ADP-Glo^™ Kinase Assay kit. Furthermore, the cytotoxicity of Leishmania cells were detected by alamarBlue^™ Cell Viability Reagent. Finally, the binding affinity within the binding site of MAPK3 protein was performed by computational methods.

RESULTS

Purification of the MAPK3 protein was done using an Ni-NTA column and a protein band was identified at the expected 44 kDa molecular weight. Afterward, the ability of commercial drugs (afatinib and lapatinib) to inhibit the purified MAPK3 kinase activity was performed using an ADP-Glo^™ Kinase Assay kit. The half-maximal inhibitory concentrations (IC₅₀) of two drugs inhibited the MAPK3 protein within the same range of IC₅₀ values (3.27 and 2.22 µM for afatinib and lapatinib, respectively). Furthermore, the cytotoxicity assay of compounds toward the extracellular promastigote and intracellular amastigote stages was investigated using alamarBlue^™ Cell Viability Reagent. The results showed that both drugs were more efficient against extracellular promastigotes and intracellular amastigotes of both Leishmania donovani and Leishmania martiniquensis. Finally, the molecular dynamics simulation (MD) was performed to study the intermolecular interactions of both drugs with MAPK3 protein. From 100 ns molecular dynamics simulation, the structural stability of both drugs in a complex with MAPK3 was quite stable.

CONCLUSION

This work was suggesting that afatinib and lapatinib act as MAPK3 inhibitors and might be developed for leishmaniasis treatment.

In silico analysis of a novel protein in CAR T- cell therapy for the treatment of hematologic cancer through molecular modelling, docking, and dynamics approach

Cellular therapy has emerged as a key tool in the treatment of hematological malignancies. An advanced cell therapy known as chimeric antigen receptor T cell therapy (CAR T-cell therapy) has been approved by the United States Food and Drug Administration (FDA) as KYMRIAH by Novartis and YESCARTA by Gilead/Kite pharma in the year 2017. A chimeric receptor is composed of an extracellular antigen recognition site along with some co-stimulating and signalling domains. On the whole, it turns out to be one of the most potent receptors on T cells targeting a specific type of cancer cell based on its antigenic marker. CD19 CAR T-cell therapy is the first clinically approved therapy for lymphoma with remarkable results in complete remission of B cell lymphoblastic leukemia up to 90%. The high rate of effectiveness of the CAR T-cell therapy against B-ALL justifies the investigation and application of this therapy for fatal diseases like all types of hematological malignancies. The most critical aspect of chimeric receptor therapy is designing and building an artificial receptor that is specific to a given type of cancer. For this reason, the in silico technique is an appropriate model to investigate the integrity and effectiveness of the engineered chimeric receptor prior to commencing in vitro experiments followed by clinical trials. This computerized experimental study aids in predicting the molecular mechanism of chimeric protein and how it interacts with both ligands. We have anticipated various features of the chimeric protein in terms of qualitative analysis (structure, protein modelling, physiological properties) and functional analysis (antigenicity, allergenicity, its receptor-ligand binding ability, involving signalling pathways). Furthermore, the reliability and validation of the binding mode of the chimeric protein against receptors were performed through a complex molecular dynamics simulation for a 100 ns timeframe in an aqueous environment. The obtained simulation study showed that CD30 was a better approachable marker as compared to CD20 due to its better binding energy score and also binding conformations stability.

Explore the mechanism and substance basis of Mahuang FuziXixin Decoction for the treatment of lung cancer based on network pharmacology and molecular docking

BACKGROUND

Mahuang FuziXixin Decoction (MFXD) is a classic Chinese herbal formula for the treatment of lung cancer. However, its mechanisms of action are unclear. In present study, network pharmacology and molecular docking technology were employed to investigate the molecular mechanism and substance basis of MFXD for the treatment of lung cancer.

METHOD

The active compounds and corresponding targets of MFXD were collected through the TCMSP database. OMIM and GeneCards databases were applied to filter the targets of lung cancer. The protein-protein interaction (PPI) were acquired through the STRING platform. Metascape and the Bioinformatics server were used for the visualization of GO and KEGG analysis. The tissue and organ distribution of targets was evaluated based on the BioGPS database. The binding affinity between potential targets and active compounds was evaluated by molecular docking.

RESULT

A total of 51 active compounds and 118 targets of MFXD were collected. The target with a higher degree were identified through the PPI network, namely AR, RELA, NCOA1, EGFR, FOS, CCND1, ESR1 and HSP90AA1. GO and KEGG analysis suggested that MFXD treatment of lung cancer mainly involves hormone and response to inorganic substance, transcription regular complex, transcription factor binding and Pathways in cancer. Experimental validation showed that MFXD treatment inhibited the proliferation of NSCLC cells through downregulation the expression of EGFR, HIF1A, NCOA1 and RELA. Moreover, molecular docking revealed that hydrogen bond and hydrophobic interaction contribute to the binding of the compounds to targets.

CONCLUSION

Our findings comprehensively elucidated the actives, potential targets, and molecular mechanisms of MFXD against lung cancer, providing a promising strategy for the scientific basis and therapeutic mechanism of traditional Chinese medicine prescriptions for the treatment of the disease.

Molecular dynamics simulations highlight the altered binding landscape at the spike-ACE2 interface between the Delta and Omicron variants compared to the SARS-CoV-2 original strain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 variant (Omicron), represents a significant deviation in genetic makeup and function compared to previous variants. Following the BA.1 sublineage, the BA.2 and BA.3 Omicron subvariants became dominant, and currently the BA.4 and BA.5, which are quite distinct variants, have emerged. Using molecular dynamics simulations, we investigated the binding characteristics of the Delta and Omicron (BA.1) variants in comparison to wild-type (WT) at the interface of the spike protein receptor binding domain (RBD) and human angiotensin converting enzyme-2 (ACE2) ectodomain. The primary aim was to compare our molecular modelling systems with previously published observations, to determine the robustness of our approach for rapid prediction of emerging future variants. Delta and Omicron were found to bind to ACE2 with similar affinities (-39.4 and -43.3 kcal/mol, respectively) and stronger than WT (-33.5 kcal/mol). In line with previously published observations, the energy contributions of the non-mutated residues at the interface were largely retained between WT and the variants, with F456, F486, and Y489 having the strongest energy contributions to ACE2 binding. Further, residues N440K, Q498R, and N501Y were predicted to be energetically favourable in Omicron. In contrast to Omicron, which had the E484A and K417N mutations, intermolecular bonds were detected for the residue pairs E484:K31 and K417:D30 in WT and Delta, in accordance with previously published findings. Overall, our simplified molecular modelling approach represents a step towards predictive model systems for rapidly analysing arising variants of concern.

Structural dynamics and kinase inhibitory activity of three generations of tyrosine kinase inhibitors against wild-type, L858R/T790M, and L858R/T790M/C797S forms of EGFR

Mutations in the tyrosine kinase domain of epidermal growth factor receptor (EGFR), including L858R/T790M double and L858R/T790M/C797S triple mutations, are major causes of acquired resistance towards EGFR targeted drugs. In this work, a combination of comprehensive molecular modeling and in vitro kinase inhibition assay was used to unravel the mutational effects of EGFR on the susceptibility of three generations of EGFR tyrosine kinase inhibitors (erlotinib, gefitinib, afatinib, dacomitinib, and osimertinib) in comparison with the wild-type EGFR. The binding affinity of all studied inhibitors towards the double and triple EGFR mutations was in good agreement with the experimental data, ranked in the order of osimertinib > afatinib > dacomitinib > erlotinib > gefitinib. Three hot-spot residues at the hinge region (M790, M793, and C797) were involved in the binding of osimertinib and afatinib, enhancing their inhibitory activity towards mutated EGFRs. Both double and triple EGFR mutations causing erlotinib and gefitinib resistance are mainly caused by the low number of H-bond occupations, the low number of surrounding atoms, and the high number of water molecules accessible to the enzyme active site. According to principal component analysis, the molecular complexation of osimertinib against the two mutated EGFRs was in a closed conformation, whereas that against wild-type EGFR was in an open conformation, resulting in drug resistance. This work paves the way for further design of the novel EGFR inhibitors to overcome drug resistance mechanisms.

Curcumin inhibits spike protein of new SARS-CoV-2 variant of concern (VOC) Omicron, an in silico study

BACKGROUND

Omicron (B.1.1.529), a variant of SARS-CoV-2 is currently spreading globally as a dominant strain. Due to multiple mutations at its Spike protein, including 15 amino acid substitutions at the receptor binding domain (RBD), Omicron is a variant of concern (VOC) and capable of escaping vaccine generated immunity. So far, no specific treatment regime is suggested for this VOC.

METHODS

The three-dimensional structure of the Spike RBD domain of Omicron variant was constructed by incorporating 15 amino acid substitutions to the Native Spike (S) structure and structural changes were compared that of the Native S. Seven phytochemicals namely Allicin, Capsaicin, Cinnamaldehyde, Curcumin, Gingerol, Piperine, and Zingeberene were docked with Omicron S protein and Omicron S-hACE2 complex. Further, molecular dynamic simulation was performed between Crcumin and Omicron S protein to evaluate the structural stability of the complex in the physiological environment and compared with that of the control drug Chloroquine.

RESULTS

Curcumin, among seven phytochemicals, was found to have the most substantial inhibitory potential with Omicron S protein. Further, it was found that curcumin could disrupt the Omicron S-hACE2 complex. The molecular dynamic simulation demonstrated that Curcumin could form a stable structure with Omicron S in the physiological environment.

CONCLUSION

To conclude, Curcumin can be considered as a potential therapeutic agent against the highly infectious Omicron variant of SARS-CoV-2.

Targeting the Type 5 Metabotropic Glutamate Receptor: A Potential Therapeutic Strategy for Neurodegenerative Diseases?

The type 5 metabotropic glutamate receptor, mGlu₅, has been proposed as a potential therapeutic target for the treatment of several neurodegenerative diseases. In preclinical neurodegenerative disease models, novel allosteric modulators have been shown to improve cognitive performance and reduce disease-related pathology. A common pathological hallmark of neurodegenerative diseases is a chronic neuroinflammatory response, involving glial cells such as astrocytes and microglia. Since mGlu₅ is expressed in astrocytes, targeting this receptor could provide a potential mechanism by which neuroinflammatory processes in neurodegenerative disease may be modulated. This review will discuss current evidence that highlights the potential of mGlu₅ allosteric modulators to treat neurodegenerative diseases, including Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Furthermore, this review will explore the role of mGlu₅ in neuroinflammatory responses, and the potential for this G protein-coupled receptor to modulate neuroinflammation.

3-Arylsulfonylquinolines from N-Propargylamines via Cascaded Oxidative Sulfonylation Using DABSO

We report a cascaded oxidative sulfonylation of N-propargylamine via a three-component coupling reaction using DABCO·(SO₂)₂ (DABSO). 3-Arylsulfonylquinolines were obtained by mixing diazonium tetrafluoroborate, N-propargylamine, and DABSO under argon atmosphere in dichloroethane (DCE) for 1 h. In a radical pathway, DABSO was utilized as the sulfone source and an oxidant in this radical-mediated cascaded reaction.

Generative machine learning for de novo drug discovery: A systematic review

Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have revolutionized de novo drug discovery, rendering the explorative process more efficient. Several model frameworks and input formats have been proposed to enhance the performance of intelligent algorithms in generative molecular design. In this systematic literature review of experimental articles and reviews over the last five years, machine learning models, challenges associated with computational molecule design along with proposed solutions, and molecular encoding methods are discussed. A query-based search of the PubMed, ScienceDirect, Springer, Wiley Online Library, arXiv, MDPI, bioRxiv, and IEEE Xplore databases yielded 87 studies. Twelve additional studies were identified via citation searching. Of the articles in which machine learning was implemented, six prominent algorithms were identified: long short-term memory recurrent neural networks (LSTM-RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), adversarial autoencoders (AAEs), evolutionary algorithms, and gated recurrent unit (GRU-RNNs). Furthermore, eight central challenges were designated: homogeneity of generated molecular libraries, deficient synthesizability, limited assay data, model interpretability, incapacity for multi-property optimization, incomparability, restricted molecule size, and uncertainty in model evaluation. Molecules were encoded either as strings, which were occasionally augmented using randomization, as 2D graphs, or as 3D graphs. Statistical analysis and visualization are performed to illustrate how approaches to machine learning in de novo drug design have evolved over the past five years. Finally, future opportunities and reservations are discussed.

VARIDT 2.0: structural variability of drug transporter

The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs' variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT (https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation

The triple reuptake inhibitors (TRIs) class is a class of effective inhibitors of human monoamine transporters (hMATs), which includes dopamine, norepinephrine, and serotonin transporters (hDATs, hNETs, and hSERTs). Due to the high degree of structural homology of the binding sites of those transporters, it is a great challenge to design potent TRIs with fine-tuned binding profiles. The molecular determinants responsible for the binding selectivity of TRIs to hDATs, hNETs, and hSERTs remain elusive. In this study, the solved X-ray crystallographic structure of hSERT in complex with escitalopram was used as a basis for modeling nine complexes of three representative TRIs (SEP225289, NS2359, and EB1020) bound to their corresponding targets. Molecular dynamics (MD) and effective post-trajectory analysis were performed to estimate the drug binding free energies and characterize the selective profiles of each TRI to hMATs. The common binding mode of studied TRIs to hMATs was revealed by hierarchical clustering analysis of the per-residue energy. Furthermore, the combined protein-ligand interaction fingerprint and residue energy contribution analysis indicated that several conserved and nonconserved "Warm Spots" such as S149, V328, and M427 in hDAT, F317, F323, and V325 in hNET and F335, F341, and V343 in hSERT were responsible for the TRI-binding selectivity. These findings provided important information for rational design of a single drug with better polypharmacological profiles through modulating multiple targets.