Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation

Investigation of the Inhibitory Activity of β-Arbutin and its Analogues on Tyrosinase Based on Molecular Docking and Enzyme Inhibition Kinetics

β-Arbutin, a natural glucoside hydroquinone derivative known for its skin-whitening properties through tyrosinase inhibition in melanin synthesis, may pose potential risks of allergy and carcinogenicity due to the release of hydroquinone during use. This study explores the inhibitory effects of phenyl-β-D-pyranoglucoside (compound 1), 4-methoxyphenyl-β-D-pyranoglucoside (compound 2), 4-hydroxymethylphenyl-β-D-pyranoglucoside (compound 3), and β-arbutin (compound 4) on tyrosinase using enzyme kinetics, molecular docking, and molecular dynamics simulations. Results show compounds 1, 3, and 4 exhibit competitive inhibition, while compound 2 shows mixed inhibition. Docking analysis reveals phenyl rings of all compounds interact with the enzyme's active site, with compound 3 forming a metal bond with copper ions. MD simulations indicate high stability for compounds 2, 3, and 4, with compound 3 showing the lowest RMSD and compact Rg, suggesting stronger binding. Compound 1 is less stable and less inhibitory. These insights are valuable for designing effective tyrosinase inhibitors.

The clinical application of artificial intelligence in cancer precision treatment

BACKGROUND

Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking.

OBSERVATIONS

By collecting and integrating large volumes of data and applying it to clinical tasks across various algorithms and models, artificial intelligence plays a significant role in cancer precision medicine. Here, we describe the general principles of artificial intelligence, including machine learning and deep learning. We further summarize the latest developments in artificial intelligence applications in cancer precision medicine. In tumor precision treatment, artificial intelligence plays a crucial role in individualizing both conventional and emerging therapies. In specific fields, including target prediction, targeted drug generation, immunotherapy response prediction, neoantigen prediction, and identification of long non-coding RNA, artificial intelligence offers promising perspectives. Finally, we outline the current challenges and ethical issues in the field.

CONCLUSIONS

Recent clinical studies demonstrate that artificial intelligence is involved in cancer precision medicine and has the potential to benefit cancer healthcare, particularly by optimizing conventional therapies, emerging targeted therapies, and individual immunotherapies. This review aims to provide valuable resources to clinicians and researchers and encourage further investigation in this field.

Impact of pH and protein/polysaccharide ratio on phycocyanin-okra polysaccharides complex

Phycocyanin is a natural blue pigment that tends to denature and lose its color in acidic solutions. In response to this problem, the complexes of phycocyanin (PC) with okra polysaccharides (OP) were prepared by ultrasonic processing at different pH conditions, and the molecular interactions of the complexes were characterized. The results showed that there were significant differences in the color, functional groups, and surface morphology of the complexes formed at different pH conditions. By colorimetry and particle size tester, it was demonstrated that the complex solution showed a steady blue color at pH 3.4. The highest fluorescence intensity (1.55 × 107 a.u.) and the significant red-shift of the complexes were observed at 0.4 % m/v polysaccharides addition. Infrared spectroscopy test further demonstrated that OP induced the formation of higher-order trimers of PC, which kept the color stable. Molecular dynamics simulations showed that the binding energy of PC/OP complex was -42.21 ± 2.61 kcal/mol, indicating that the binding affinity was very strong. Overall, this study suggests that this complex stabilizes the structure of PC, which in turn exerts a biological effect and will facilitate the use of PC as an artificial color substitute in food or beverage applications.

Investigate the binding of pesticides with the TLR4 receptor protein found in mammals and zebrafish using molecular docking and molecular dynamics simulations

The widespread use of pesticides poses significant threats to both environmental and human health, primarily due to their potential toxic effects. The study investigated the cardiovascular toxicity of selected pesticides, focusing on their interactions with Toll-like receptor 4 (TLR4), an important part of the innate immune system. Using computational tools such as molecular docking, molecular dynamics (MD) simulations, principal component analysis (PCA), density functional theory (DFT) calculations, and ADME analysis, this study identified C160 as having the lowest binding affinity (-8.2 kcal/mol), followed by C107 and C165 (-8.0 kcal/mol). RMSD, RMSF, Rg, and hydrogen bond metrics indicated the formation of stable complexes between specific pesticides and TLR4. PCA revealed significant structural changes upon ligand binding, affecting stability and flexibility, while DFT calculations provided information about the stability, reactivity, and polarity of the compounds. ADME studies highlighted the solubility, permeability, and metabolic stability of C107, C160, and C165, suggesting their potential for bioavailability and impact on cardiovascular toxicity. C107 and C165 exhibit higher bioactivity scores, indicating favourable absorption, metabolism, and distribution properties. C165 also violated rule where molecular weight is greater than 500 g/mol. Further, DFT and NCI analysis of post MD conformations confirmed the binding of ligands at the binding pocket. The analysis shed light on the molecular mechanisms of pesticide-induced cardiovascular toxicity, aiding in the development of strategies to mitigate their harmful effects on human health.

Phytochemical profiling and evaluation of the antidiabetic potential of Ichnocarpus frutescens (Krishna Sariva): kinetic study, molecular modelling, and free energy approach

This research explored novel antidiabetic drugs from natural sources using the Ayurvedic Rasayana herb Ichnocarpus frutescens through invitro enzyme assay, kinetics study, and computational approaches. Invitro enzyme inhibition assay demonstrated the promising inhibitory activity of root extract against alpha-amylase (α-A) and alpha-glucosidase (α-G) enzyme with IC50 value 7.34 ± 0.22 mg/ml and 4.40 ± 0.25 mg/ml respectively. Enzyme kinetic study revealed the competitive inhibition of both proteins by Ichnocarpus frutescens extract. High-Resolution Liquid Chromatography Mass Spectrometer and Docking study revealed the better binding energy of phytoconstituents 23-Acetoxysoladulcidine, Atrovirinone, Bismurrayaquinone A, Lamprolobine, Zygadenine, and Gambiriin A3 than standard drug acarbose. Molecular modelling showed stable protein-ligands binding interaction during the 100 ns simulation. It revealed comparable Root Mean Square Deviation, Radius of Gyration, and Solvent Accessible Surface Area of these compounds with acarbose. The active site residues of both proteins remained stable and showed significantly less Root Mean Square Fluctuation. Molecular Mechanics with Generalised Bonn Surface Area analysis has illustrated the similar inhibitory activity of Zygadenine for α-A, 23-Acetoxysoladulcidine, and Gambiriin A3 for α-G protein, compared to the FDA-approved drug acarbose. Thus, the study suggested that the root of Ichnocarpus frutescens can be used as α-A and α-G inhibitors and be considered a compelling lead for the medication of type 2 diabetes.Communicated by Ramaswamy H. Sarma.

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines

With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy cancer cells. However, enhancing the efficacy of cancer vaccines remains a challenge. Adjuvants, which enhance the immune response to antigens and improve vaccine effectiveness, have faced limitations in recent years, resulting in few novel adjuvants being identified. The advancement of artificial intelligence (AI) technology in drug development has provided a foundation for adjuvant screening and application, leading to a diversification of adjuvants. This article reviews the significant role of tumor vaccines in basic research and clinical treatment and explores the use of AI technology to screen novel adjuvants from databases. The findings of this review offer valuable insights for the development of new adjuvants for next-generation vaccines.

6'-O-Caffeoylarbutin from Quezui Tea: A Highly Effective and Safe Tyrosinase Inhibitor

Tyrosinase is vital in fruit and vegetable browning and melanin synthesis, crucial for food preservation and pharmaceuticals. We investigated 6'-O-caffeoylarbutin's inhibition, safety, and preservation on tyrosinase. Using HPLC, we analyzed its effect on mushroom tyrosinase and confirmed reversible competitive inhibition. UV_vis and fluorescence spectroscopy revealed a stable complex formation with specific binding, causing enzyme conformational changes. Molecular docking and simulations highlighted strong binding, enabled by hydrogen bonds and hydrophobic interactions. Cellular tests showed growth reduction of A375 cells with mild HaCaT cell toxicity, indicating favorable safety. Animal experiments demonstrated slight toxicity within safe doses. Preservation trials on apple juice showcased 6'-O-caffeoylarbutin's potential in reducing browning. In essence, this study reveals intricate mechanisms and applications of 6'-O-caffeoylarbutin as an effective tyrosinase inhibitor, emphasizing its importance in food preservation and pharmaceuticals. Our research enhances understanding in this field, laying a solid foundation for future exploration.

Dronabinol as an answer to flavivirus infections: an in-silico investigation

Flavivirus infections are common in several parts of the world. Two major types of flaviviruses are dengue and zika viruses. Both these two viral infections have caused many fatalities around the world. There is an absence of a vaccine and an effective medication against these viruses. In this study, we analyzed the ability of dronabinol to act as a potential cure against these viral infections. We performed the docking of dronabinol with several viral proteins followed by molecular dynamics simulation, MM/PBSA and PCA analysis. We checked the ability of the polyphenol dronabinol to interfere with the binding of viral helicases to their cellular targets. We performed 2 D-QSAR studies, drug likeliness, ADMET and target prediction studies. From our study, we observed that dronabinol had the best docking ability against the helicase proteins of dengue and zika. Molecular dynamics simulation and MM/PBSA investigation confirmed the stability of the binding while PCA investigation showed a lowering of molecular motions in response to dronabinol docking to the helicases. Dronabinol interfered in the binding of the helicases to RNA. 2 D QSAR studies revealed a low IC50 value for dronabinol. Dronabinol showed favorable drug-likeness, ADMET properties and target prediction results. Thus we propose dronabinol be further investigated in-vitro as a cure against dengue and zika virus infections.Communicated by Ramaswamy H. Sarma.

Identification of common genes in obesity and cancer through network interaction and targeting those genes by virtual screening approach

Obesity may have an effect on cancer outcomes, resulting in global inequalities in cancer survival and death. Microarray data analysis was done to identify differentially expressed genes (DEGs) in obese and cancer patients. Total 1977 differentially expressed genes among obesity and gastric cancer, breast cancer, pancreatic cancer, and colorectal cancer were used to build a gene interaction network, which was then analyzed by using Cytoscape software. It has been identified that JUN, CXCL12, and LEP genes show a higher degree and stress, and play an important role in obesity and cancer progression. Further, CXCL12 and LEP were taken for virtual screening study with coumarin and its derivatives to develop a drug against obesity and cancer. The interactions of CXCL12 and LEP with coumarins were studied by molecular docking and it shows good interaction as well as docking score as compared to the standard one. The ADME properties were predicted to check the drug-likeness activity of coumarins and the most of the drug-likeness activities are in admire range. The Binding free energy of the docked complex was calculated by performing MM-GBSA. The molecular docking, ADME properties prediction, and MM-GBSA was performed on Maestro 12.6. The top docked score compounds were further subjected to molecular dynamic simulation to check the stability by using GROMACS. The MM-PBSA study was performed to calculate the binding energy components as well as the energy contributions of specific amino acids. The resultant compounds could be a potent anti-obesity and anti-cancer drug.Communicated by Ramaswamy H. Sarma.

Revolutionizing antiretroviral therapy for human immunodeficiency virus/AIDS: A computational approach using molecular docking, virtual screening, and 3D pharmacophore building to address therapeutic failure and propose highly effective candidates

OBJECTIVES

In the context of human immunodeficiency virus (HIV) treatment, the emergence of therapeutic failures with existing antiretroviral drugs presents a significant challenge. This study aims to employ advanced molecular modeling techniques to identify potential alternatives to current antiretroviral agents.

METHODS

The study focuses on three essential classes of antiretroviral drugs: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs). Computational analyses were performed on a database of 3,343,652 chemical molecules to evaluate their binding affinities, pharmacokinetic properties, and interactions with viral reverse transcriptase and protease enzymes. Molecular docking, virtual screening, and 3D pharmacophore modeling were utilized to identify promising candidates.

RESULTS

Molecular docking revealed compounds with high binding energies and strong interactions at the active sites of target enzymes. Virtual screening narrowed down potential candidates with favorable pharmacological profiles. 3D pharmacophore modeling identified crucial structural features for effective binding. Overall, two molecules for class 1, 7 molecules for class 2, and 2 molecules for class 3 were selected. These compounds exhibited robust binding affinities, interactions with target enzymes, and improved pharmacokinetic properties, showing promise for more effective HIV treatments in cases of therapeutic failures.

CONCLUSION

The combination of molecular docking, virtual screening, and 3D pharmacophore modeling yielded lead compounds that hold potential for addressing HIV therapeutic failures. Further experimental investigations are essential to validate the efficacy and safety of these compounds, with the ultimate goal of advancing toward clinical applications in HIV management.

Can polyoxometalates (POMs) prevent of coronavirus 2019-nCoV cell entry? Interaction of POMs with TMPRSS2 and spike receptor domain complexed with ACE2 (ACE2-RBD): Virtual screening approaches

The unexpected appearance and global spread of COVID-19 create significant difficulties for healthcare systems and present an unusual challenge for the fast discovery of medicines to combat this fatal disease. Screening metallodrugs libraries from the medicinal inorganic chemistry society may expand the studied ‘chemical space’ and improve the probability of discovering effective anti-COVID drugs, including polyoxometalates. POMs are an oxygen-rich family of inorganic cluster systems that have previously been tested for antiviral action against different types of viruses. Human angiotensin-converting enzyme 2 (ACE2), human transmembrane protease serine 2 (TMPRSS2), and the SARS-CoV-2 spike glycoprotein are required for host cell-mediated viral entrance. Targeting these proteins demonstrates potential possibilities for preventing infections and transmissions in the initial stage. As a result, POMs with known antiviral effects were investigated for this purpose using molecular docking and dynamic simulations. This research shows that POMs can prevent SARS CoV-2 from entering cells by blocking TMPRSS2, which SARS-CoV-2 uses for spike glycoprotein priming. They may also engage with ACE2 and the spike glycoprotein and disrupt their binding by blocking the active sites. We think that a thorough investigation of POMs as possible anti-COVID-19 drugs will provide significant opportunities.