Interplay of precision therapeutics and MD study: Calocybe indica's potentials against cervical cancer and its interaction with VEGF via octadecanoic acid.

The evolving landscape of personalized medicine necessitates a shift from traditional therapeutic interventions towards precision-driven approaches. Embracing this paradigm, our research probes the therapeutic efficacy of the aqueous crude extract (ACE) of Calocybe indica in cervical cancer treatment, merging botanical insights with advanced molecular research. We observed that ACE exerts significant influences on nuclear morphology and cell cycle modulation, further inducing early apoptosis and showcasing prebiotic attributes. Characterization of ACE have identified several phytochemicals including significant presence of octadeconoic acid. Simultaneously, utilizing advanced Molecular Dynamics (MD) simulations, we deciphered the intricate molecular interactions between Vascular Endothelial Growth Factor (VEGF) and Octadecanoic acid to establish C.indica's role as an anticancer agent. Our study delineates Octadecanoic acid's potential as a robust binding partner for VEGF, with comprehensive analyses from RMSD and RMSF profiles highlighting the stability and adaptability of the protein-ligand interactions. Further in-depth thermodynamic explorations via MM-GBSA calculations reveal the binding landscape of the VEGF-Octadecanoic acid complex. Emerging therapeutic innovations, encompassing proteolysis-targeting chimeras (PROTACs) and avant-garde nanocarriers, are discussed in the context of their synergy with compounds like Calocybe indica P&C. This convergence underscores the profound therapeutic potential awaiting clinical exploration. This study offers a holistic perspective on the promising therapeutic avenues facilitated by C. indica against cervical cancer, intricately woven with advanced molecular interactions and the prospective integration of precision therapeutics in modern oncology.

In silico study of alkaloids with quercetin nucleus for inhibition of SARS-CoV-2 protease and receptor cell protease.

Covid-19 disease caused by the deadly SARS-CoV-2 virus is a serious and threatening global health issue declared by the WHO as an epidemic. Researchers are studying the design and discovery of drugs to inhibit the SARS-CoV-2 virus due to its high mortality rate. The main Covid-19 virus protease (Mpro) and human transmembrane protease, serine 2 (TMPRSS2) are attractive targets for the study of antiviral drugs against SARS-2 coronavirus. Increasing consumption of herbal medicines in the community and a serious approach to these drugs have increased the demand for effective herbal substances. Alkaloids are one of the most important active ingredients in medicinal plants that have wide applications in the pharmaceutical industry. In this study, seven alkaloid ligands with Quercetin nucleus for the inhibition of Mpro and TMPRSS2 were studied using computational drug design including molecular docking and molecular dynamics simulation (MD). Auto Dock software was used to evaluate molecular binding energy. Three ligands with the most negative docking score were selected to be entered into the MD simulation procedure. To evaluate the protein conformational changes induced by tested ligands and calculate the binding energy between the ligands and target proteins, GROMACS software based on AMBER03 force field was used. The MD results showed that Phyllospadine and Dracocephin-A form stable complexes with Mpro and TMPRSS2. Prolinalin-A indicated an acceptable inhibitory effect on Mpro, whereas it resulted in some structural instability of TMPRSS2. The total binding energies between three ligands, Prolinalin-A, Phyllospadine and Dracocephin-A and two proteins MPro and TMRPSS2 are (-111.235 ± 15.877, - 75.422 ± 11.140), (-107.033 ± 9.072, -84.939 ± 10.155) and (-102.941 ± 9.477, - 92.451 ± 10.539), respectively. Since the binding energies are at a minimum, this indicates confirmation of the proper binding of the ligands to the proteins. Regardless of some Prolinalin-A-induced TMPRSS2 conformational changes, it may properly bind to TMPRSS2 binding site due to its acceptable binding energy. Therefore, these three ligands can be promising candidates for the development of drugs to treat infections caused by the SARS-CoV-2 virus.

Identifying the temporal contributors and their interactions during dynamic formation of black tea cream.

Tea cream formed in hot and strong tea infusion while cooling deteriorates quality and health benefits of tea. However, the interactions among temporal contributors during dynamic formation of tea cream are still elusive. Here, by deletional recombination experiments and molecular dynamics simulation, it was found that proteins, caffeine (CAF), and phenolics played a dominant role throughout the cream formation, and the contribution of amino acids was highlighted in the early stage. Furthermore, CAF was prominent due to its extensive binding capacity and the filling complex voids property, and caffeine-theaflavins (TFs) complexation may be the core skeleton of the growing particles in black tea infusion. In addition to TFs, the unidentified phenolic oxidation-derived products (PODP) were confirmed to contribute greatly to the cream formation.

Mulberry Leaf Compounds and Gut Microbiota in Alzheimer's Disease and Diabetes: A Study Using Network Pharmacology, Molecular Dynamics Simulation, and Cellular Assays.

Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer's disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases' treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 µM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food-medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research.

Dissecting the CRISPR Cas1-Cas2 Protospacer Binding and Selection Mechanism by Using Molecular Dynamics Simulations.

Cas1 and Cas2 are highly conserved proteins among the clustered regularly interspaced short palindromic repeat Cas (CRISPR-Cas) systems and play a crucial role in protospacer selection and integration. According to the double-forked CRISPR Cas1-Cas2 complex, we conducted extensive all-atom molecular dynamics simulations to investigate the protospacer DNA binding and recognition. Our findings revealed that single-point amino acid mutations in Cas1 or in Cas2 had little impact on the binding of the protospacer, both in the binding and precatalytic states. In contrast, multiple-point amino acid mutations, particularly G74A, P80L, and V89A mutations on Cas2 and Cas2' proteins (m-multiple1 system), significantly affected the protospacer binding and selection. Notably, mutations on Cas2 and Cas2' led to an increased number of hydrogen bonds (#HBs) between Cas2&Cas2' and the dsDNA in the m-multiple1 system compared with the wild-type system. And the strong electrostatic interactions between Cas1-Cas2 and the protospacer DNA (psDNA) in the m-multiple1 system again suggested the increase in the binding affinity of protospacer acquisition. Specifically, mutations in Cas2 and Cas2' can remotely make the protospacer adjacent motif complementary (PAMc) sequences better in recognition by the two active sites, while multiple mutations K211E, P202Q, P212L, R138L, V134A, A286T, P282H, and P294H on Cas1a/Cas1b&Cas1a'/Cas1b' (m-multiple2 system) decrease the #HBs and the electrostatic interactions and make the PAMc worse in recognition compared with the wild-type system.

Leveraging shape screening and molecular dynamics simulations to optimize PARP1-Specific chemo/radio-potentiators for antitumor drug design.

PARP1 plays a pivotal role in DNA repair within the base excision pathway, making it a promising therapeutic target for cancers involving BRCA mutations. Current study is focused on the discovery of PARP inhibitors with enhanced selectivity for PARP1. Concurrent inhibition of PARP1 with PARP2 and PARP3 affects cellular functions, potentially causing DNA damage accumulation and disrupting immune responses. In step 1, a virtual library of 593 million compounds has been screened using a shape-based screening approach to narrow down the promising scaffolds. In step 2, hierarchical docking approach embedded in Schrödinger suite was employed to select compounds with good dock score, drug-likeness and MMGBSA score. Analysis supplemented with decomposition energy, molecular dynamics (MD) simulations and hydrogen bond frequency analysis, pinpointed that active site residues; H862, G863, R878, M890, Y896 and F897 are crucial for specific binding of ZINC001258189808 and ZINC000092332196 with PARP1 as compared to PARP2 and PARP3. The binding of ZINC000656130962, ZINC000762230673, ZINC001332491123, and ZINC000579446675 also revealed interaction involving two additional active site residues of PARP1, namely N767 and E988. Weaker or no interaction was observed for these residues with PARP2 and PARP3. This approach advances our understanding of PARP-1 specific inhibitors and their mechanisms of action, facilitating the development of targeted therapeutics.

Bioactive compounds from Ocimum tenuiflorum and Poria cocos: A novel natural Compound for insomnia treatment based on A computational approach.

Insomnia, a widespread public health issue, is associated with substantial distress and daytime functionality impairments and can predispose to depression and cardiovascular disease. Cognitive Behavioral Anti-insomnia therapies including benzodiazepines often face limitations due to patient adherence or potential adverse effects. This study focused on identifying novel bioactive compounds from medicinal plants, aiming to discover and develop new therapeutic agents with low risk-to-benefit ratios using computational drug discovery methods. Through a systematic framework involving compound library preparation, evaluation of drug-likeness and pharmacokinetics, toxicity prediction, molecular docking, and molecular dynamic simulations, two natural compounds such as 2-(4-hydroxy-3-methoxyphenyl)-8-methoxy-6-prop-2-enyl-3,4-dihydro-2H-chromen-3-ol from Ocimum tenuiflorum and 7-(2-hydroxypropan-2-yl)-1,4a-dimethyl-9-oxo-3,4,10,10a-tetrahydro-2H-phenanthrene-1-carboxylic acid from Poria cocos exhibited high binding affinity with orexin receptor type 1 (OX1R) and type 2 (OX2R), surpassing commercial drugs used in insomnia treatment. Additionally, they showed interactions with critical amino acid residues within the receptors that play crucial roles in competitive inhibitor activity, like commercial drugs such as Suvorexant, Lemborexant, and Daridorexant. Further, molecular dynamics simulations of the protein-ligand complexes under conditions that mimic the in vivo environment revealed both compounds' sustained and robust interactions with the OX1R and OX2R, reinforcing their potential as effective therapeutic candidates. Furthermore, upon evaluating both compounds' drug-likeness, pharmacokinetics, and toxicity profiles, it was discerned that they displayed considerable drug-like properties and favorable pharmacokinetics, along with diminished toxicity. The research provides a solid foundation for further exploring and validating these compounds as potential anti-insomnia therapeutics.

Potential of Gut Microbial Metabolites in Treating Osteoporosis and Obesity: A Network Pharmacology and Bioinformatics Approach.

BACKGROUND The gut microbial metabolites demonstrate significant activity against metabolic diseases including osteoporosis (OP) and obesity, but active compounds, targets, and mechanisms have not been fully identified. Hence, the current investigation explored the mechanisms of active metabolites and targets against OP and obesity by using network pharmacology approaches. MATERIAL AND METHODS The gutMGene database was used to collect gut microbial targets-associated metabolites; DisGeNET and OMIM databases were used to identify targets relevant to OP and obesity. A total of 63 and 89 overlapped targets were considered the final OP and obesity targets after creating a Venn diagram of metabolites-related targets and disease-related targets. Furthermore, the top 20% of degrees, betweenness, and closeness were used to form the sub-network of protein-protein interaction of these targets. Finally, the biotransformation-increased receptors and biological mechanisms were identified and validated using ADMET properties analysis, molecular docking, and molecular dynamic simulation. RESULTS GO, KEGG pathway analysis, and protein-protein interactions were performed to establish metabolites and target networks. According to the enrichment analysis, OP and obesity are highly linked to the lipid and atherosclerosis pathways. Moreover, ADMET analysis depicts that the major metabolites have drug-likeliness activity and no or less toxicity. Following that, the molecular docking studies showed that compound K and TP53 target have a remarkable negative affinity (-8.0 kcal/mol) among all metabolites and targets for both diseases. Finally, the conformity of compound K against the targeted protein TP53 was validated by 250ns MD simulation. CONCLUSIONS Therefore, we summarized that compound K can regulate TP53 and could be developed as a therapy option for OP and obesity.

Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer.

Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.

Identification of Phytochemicals from Arabian Peninsula Medicinal Plants as Strong Binders to SARS-CoV-2 Proteases (3CLPro and PLPro) by Molecular Docking and Dynamic Simulation Studies.

We provide promising computational (in silico) data on phytochemicals (compounds 1-10) from Arabian Peninsula medicinal plants as strong binders, targeting 3-chymotrypsin-like protease (3CLPro) and papain-like proteases (PLPro) of SARS-CoV-2. Compounds 1-10 followed the Lipinski rules of five (RO5) and ADMET analysis, exhibiting drug-like characters. Non-covalent (reversible) docking of compounds 1-10 demonstrated their binding with the catalytic dyad (CYS145 and HIS41) of 3CLPro and catalytic triad (CYS111, HIS272, and ASP286) of PLPro. Moreover, the implementation of the covalent (irreversible) docking protocol revealed that only compounds 7, 8, and 9 possess covalent warheads, which allowed the formation of the covalent bond with the catalytic dyad (CYS145) in 3CLPro and the catalytic triad (CYS111) in PLPro. Root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and radius of gyration (Rg) analysis from molecular dynamic (MD) simulations revealed that complexation between ligands (compounds 7, 8, and 9) and 3CLPro and PLPro was stable, and there was less deviation of ligands. Overall, the in silico data on the inherent properties of the above phytochemicals unravel the fact that they can act as reversible inhibitors for 3CLPro and PLPro. Moreover, compounds 7, 8, and 9 also showed their novel properties to inhibit dual targets by irreversible inhibition, indicating their effectiveness for possibly developing future drugs against SARS-CoV-2. Nonetheless, to confirm the theoretical findings here, the effectiveness of the above compounds as inhibitors of 3CLPro and PLPro warrants future investigations using suitable in vitro and in vivo tests.

Evaluating the Therapeutic Potential of Curcumin and Synthetic Derivatives: A Computational Approach to Anti-Obesity Treatments.

Natural compounds such as curcumin, a polyphenolic compound derived from the rhizome of turmeric, have gathered remarkable scientific interest due to their diverse metabolic benefits including anti-obesity potential. However, curcumin faces challenges stemming from its unfavorable pharmacokinetic profile. To address this issue, synthetic curcumin derivatives aimed at enhancing the biological efficacy of curcumin have previously been developed. In silico modelling techniques have gained significant recognition in screening synthetic compounds as drug candidates. Therefore, the primary objective of this study was to assess the pharmacokinetic and pharmacodynamic characteristics of three synthetic derivatives of curcumin. This evaluation was conducted in comparison to curcumin, with a specific emphasis on examining their impact on adipogenesis, inflammation, and lipid metabolism as potential therapeutic targets of obesity mechanisms. In this study, predictive toxicity screening confirmed the safety of curcumin, with the curcumin derivatives demonstrating a safe profile based on their LD50 values. The synthetic curcumin derivative 1A8 exhibited inactivity across all selected toxicity endpoints. Furthermore, these compounds were deemed viable candidate drugs as they adhered to Lipinski's rules and exhibited favorable metabolic profiles. Molecular docking studies revealed that both curcumin and its synthetic derivatives exhibited favorable binding scores, whilst molecular dynamic simulations showed stable binding with peroxisome proliferator-activated receptor gamma (PPARγ), csyclooxygenase-2 (COX2), and fatty acid synthase (FAS) proteins. The binding free energy calculations indicated that curcumin displayed potential as a strong regulator of PPARγ (-60.2 ± 0.4 kcal/mol) and FAS (-37.9 ± 0.3 kcal/mol), whereas 1A8 demonstrated robust binding affinity with COX2 (-64.9 ± 0.2 kcal/mol). In conclusion, the results from this study suggest that the three synthetic curcumin derivatives have similar molecular interactions to curcumin with selected biological targets. However, in vitro and in vivo experimental studies are recommended to validate these findings.

Identification of novel AKT1 inhibitors from Sapria himalayana bioactive compounds using structure-based virtual screening and molecular dynamics simulations.

Through the experimental and computational analyses, the present study sought to elucidate the chemical composition and anticancer potential of Sapria himalayana plant extract (SHPE). An in vitro analysis of the plant extract was carried out to determine the anticancer potential. Further, network pharmacology, molecular docking, and molecular dynamic simulation were employed to evaluate the potential phytochemical compounds for cervical cancer (CC) drug formulations. The SHPE exhibited anti-cancerous potential through inhibition properties against cancer cell lines. The LC-MS profiling showed the presence of 14 compounds in SHPE. Using network pharmacology analysis, AKT1 (AKT serine/threonine kinase 1) is identified as the possible potential target, and EGFR (Epidermal Growth Factor Receptor) is identified as the possible key signal pathway. The major targets were determined to be AKT1, EGFR by topological analysis and molecular docking. An in silico interaction of phytoconstituents employing molecular docking demonstrated a high binding inclination of ergoloid mesylate and Ergosta-5,7,9(11),22-tetraen-3-ol, (3.beta.,22E)- with binding affinities of -15.5 kcal/mol, and -11.3 kcal/mol respectively. Further, MD simulation and PCA analyses showed that the phytochemicals possessed significant binding efficacy with CC protein. These results point the way for more investigation into SHPE compound's potential as CC treatment.

Structural characterization, molecular dynamic simulation, and conformational visualization of a water-soluble glucan with high molecular weight from Gastrodia elata Blume.

Polysaccharides have been widely used in the development of natural drugs and health food. However, polysaccharide characterization lags due to inherently complicated features and the limitations of existing detection approaches. We aimed to provide new insight into the fine structure and conformational visualization of polysaccharides from Gastrodia elata Blume, a medicinal and edible plant. A water-soluble polysaccharide (GEP2-6) with the high molecular weight of 2.7 × 106 Da was first obtained, and its purity reached 99.2 %. Chemical and spectroscopic analyses jointly revealed that GEP2-6 was a glucan linked by α-(1 â†’ 4) and α-(1 â†’ 6) glycosidic bonds. After enzymolysis, the local structure of GEP2-6 included α-1,4-Glcp, α-1,6-Glcp, α-1,4,6-Glcp, and α-1-Glcp at a molar ratio of 31.27∶1.32∶1.08∶0.93. The glycosidic linkage pattern of repeating units was further simulated by a glycan database and spatial examination software. The good dissolution performance was interpreted by dynamics simulation and practical molecular characteristics. Spherical flexible chains and the porous stable conformation were corroborated using atomic force microscopy. In addition, GEP2-6 could effectively scavenge DPPH and hydroxyl radicals as a promising natural antioxidant. These efforts will contribute to the expansion of clinical applications of this G. elata polysaccharide and the structural elucidation for macromolecular polysaccharides combined with traditional and modern analysis techniques.

Raman spectroscopy study of 7,8-dihydrofolate inhibition on the Wuhan strain SARS-CoV-2 binding to human ACE2 receptor.

Emerging evidence suggests that elevated levels of folic acid in the bloodstream may confer protection against Wuhan-SARS-CoV-2 infection and mitigate its associated symptoms. Notably, two comprehensive studies of COVID-19 patients in Israel and UK uncovered a remarkable trend, wherein individuals with heightened folic acid levels exhibited only mild symptoms and necessitated no ventilatory support. In parallel, research has underscored the potential connection between decreased folic acid levels and the severity of Covid-19 among hospitalized patients. Yet, the underlying mechanisms governing this intriguing inhibition remain elusive. In a quest to elucidate these mechanisms, we conducted a molecular dynamics simulation approach followed by a Raman spectroscopy study to delve into the intricate interplay between the folic acid metabolite, 7,8-dihydrofolate (DHF), and the angiotensin-converting enzyme ACE2 receptor, coupled with its interaction with the receptor-binding domain (RBD) of the Wuhan strain of SARS-CoV-2. Through a meticulous exploration, we scrutinized the transformation of the ACE2 + RBD complex, allowing these reactants to form bonds. This was juxtaposed with a similar investigation where ACE2 was initially permitted to react with DHF, followed by the exposure of the ACE2 + DHF complex to RBD. We find that DHF, when bonded to ACE2, functions as a physical barrier, effectively inhibiting the binding of the Wuhan strain RBD. This physicochemical process offers a cogent explanation for the observed inhibition of host cell infection in subjects receiving supplementary folic acid doses, as epidemiologically substantiated in multiple studies. This study not only sheds light on a potential avenue for mitigating SARS-CoV-2 infection but also underscores the crucial role of folic acid metabolites in host-virus interactions. This research paves the way for novel therapeutic strategies in the battle against COVID-19 and reinforces the significance of investigating the molecular mechanisms underlying the protective effects of folic acid in the context of viral infections.

Integrated study reveals mechanism of Tripterygium Wilfordii against cholangiocarcinoma based on bioinformatics approaches and molecular dynamics simulation.

BACKGROUND: Tripterygium wilfordii Hook. f. (TW) shows anticancer activity, and no study has comprehensively investigated the effects of TW in treating cholangiocarcinoma (CHOL). This study was designed to identify the therapeutic role and the mechanism of TW against CHOL to obtain anti-CHOL candidate components and targets. METHODS: Ingredients of TW were collected from the Traditional Chinese Medicine System Pharmacology Database and literature. Limma package and weighted gene co-expression network analysis were used to identify the genes related to CHOL. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) was performed by R package Cluster-Profiler and Metascape, respectively. Protein-Protein Interaction (PPI) network was used to select core genes in the treatment of CHOL by TW, followed by GEPIA2, UALCAN database, and ROC curves to assess their diagnostic and prognostic capability. Molecular docking and molecular dynamics simulation were applied to explore the binding affinity and stability of the complex between the bioactive ingredients in TW and core targets. RESULTS: A total of 67 ingredients in TW were collected, and 495 genes were obtained as genes of CHOL. 55 common TW-CHOL targets were identified. 171 biological process terms and 100 KEGG pathways were enriched. 12 genes were regarded as core genes through PPI analysis, such as CYP3A4, CES1, GC, and PLG, whose good diagnostic and prognostic capability were identified. Ten ingredients were selected through the construction of Herb-Components-Targets-Disease network. Molecular docking and molecular dynamics simulation both confirmed the good binding affinity and stability of the ligand-protein complexes. CONCLUSION: This study identified the therapeutic role and predicted the mechanism of TW against CHOL, where TW may combat CHOL through the regulation of metabolic conditions of the body, bile acid secretion, xenobiotics metabolism, and the inflammatory response. Celastrol, triptonide, triptolide and wilforlide A emerged as promising anti-CHOL candidates. So, this study offered a reference for the treatment of CHOL and the development of anti-CHOL drugs.

A computational tumor growth model experience based on molecular dynamics point of view using deep cellular automata.

Cancer, as identified by the World Health Organization, stands as the second leading cause of death globally. Its intricate nature makes it challenging to study solely based on biological knowledge, often leading to expensive research endeavors. While tremendous strides have been made in understanding cancer, gaps remain, especially in predicting tumor behavior across various stages. The integration of artificial intelligence in oncology research has accelerated our insights into tumor behavior, right from its genesis to metastasis. Nevertheless, there's a pressing need for a holistic understanding of the interactions between cancer cells, their microenvironment, and their subsequent interplay with the broader body environment. In this landscape, deep learning emerges as a potent tool with its multifaceted applications in diverse scientific challenges. Motivated by this, our study presents a novel approach to modeling cancer tumor growth from a molecular dynamics' perspective, harnessing the capabilities of deep-learning cellular automata. This not only facilitates a microscopic examination of tumor behavior and growth but also delves deeper into its overarching behavioral patterns. Our work primarily focused on evaluating the developed tumor growth model through the proposed network, followed by a rigorous compatibility check with traditional mathematical tumor growth models using R and Matlab software. The outcomes notably aligned with the Gompertz growth model, accentuating the robustness of our approach. Our validated model stands out by offering adaptability to diverse tumor growth datasets, positioning itself as a valuable tool for predictions and further research.

Structure-Based Discovery of the SARS-CoV-2 Main Protease Noncovalent Inhibitors from Traditional Chinese Medicine.

Traditional Chinese medicine (TCM) has been extensively employed for the treatment of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there is demand for discovering more SARS-CoV-2 Mpro inhibitors with diverse scaffolds to optimize anti-SARS-CoV-2 lead compounds. In this study, comprehensive in silico and in vitro assays were utilized to determine the potential inhibitors from TCM compounds against SARS-CoV-2 Mpro, which is an important therapeutic target for SARS-CoV-2. The ensemble docking analysis of 18263 TCM compounds against 15 SARS-CoV-2 Mpro conformations identified 19 TCM compounds as promising candidates. Further in vitro testing validated three compounds as inhibitors of SARS-CoV-2 Mpro and showed IC50 values of 4.64 ± 0.11, 7.56 ± 0.78, and 11.16 ± 0.26 µM, with EC50 values of 12.25 ± 1.68, 15.58 ± 0.77, and 29.32 ± 1.25 µM, respectively. Molecular dynamics (MD) simulations indicated that the three complexes remained stable over the last 100 ns of production run. An analysis of the binding mode revealed that the active compounds occupy different subsites (S1, S2, S3, and S4) of the active site of SARS-CoV-2 Mpro via specific poses through noncovalent interactions with key amino acids (e.g., HIS 41, ASN 142, GLY 143, MET 165, GLU 166, or GLN 189). Overall, this study provides evidence indicating that the three natural products obtained from TCM could be further used for anti-COVID-19 research, justifying the investigation of Chinese herbal medicinal ingredients as bioactive constituents for therapeutic targets.

Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors.

In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

Evaluation of the inhibitory potential of bioactive compounds against SARS-CoV-2 by in silico approach.

CONTEXT: The COVID-19 (coronavirus disease 19) pandemic brought on by the SARS-CoV-2 outbreak (severe acute respiratory syndrome coronavirus 2) has stimulated the exploration of various available chemical compounds that could be used to treat the infection. This has driven numerous researchers to investigate the antiviral potential of several bioactive compounds from medicinal plants due to their reduced adverse effects compared to chemicals. Some of the bioactive compounds used in folklore treatment strategies are reported as effective inhibitors against the proliferative and infective cycles of SARS-CoV-2. The secondary metabolites from plants are generally used to treat various diseases due to their intact medicinal properties. The present study analyzes the inhibitory potential of phytochemicals from medicinal plants like Sphaeranthus indicus, Lantana camara, and Nelumbo nucifera against SARS-CoV-2 by molecular docking. METHODS: Ten druggable protein targets from SARS-CoV-2 are docked against the phytochemicals from the selected medicinal plants. The phytocompounds astragalin, isoquercetin, and 5-hydroxy-7-methoxy-6-c-glycosy flavone were found to have lower binding energy depicting their inhibitive potential compared with the reported inhibitors that are used in the treatment of SARS-CoV-2 infection. The phytocompounds found to have the least binding energy were selected for further analyses. To assess the compounds' potential as drugs, their ADMET characteristics were also examined. Sphaeranthus indicus, Lantana camara, and Nelumbo nucifera six possible compounds were separately screened for ADME and toxicity characteristics; then, the results were analyzed. To assess the impact of the phytocompound binding on the dynamics of SARS-CoV-2 ribonuclease protein NSP15, microsecond-level all atomistic molecular dynamics simulations were performed, and their dynamics were analyzed. Microsecond-level molecular dynamics simulations of both the ligands complexed with NSP15 revealed that the ligand induces allosteric effects on NSP15, which could lead to destabilization of NSP15 hexameric interface and loss of RNA binding. The low binding energy exhibited by the phytochemicals from Lantana camera, Sphaeranthus indicus, and Nelumbo nucifera against the protein targets of SARS-CoV-2 showed inhibitory potential by the selected molecules. Their predicted interference of the enzymes involved in the molecular mechanisms aiding the proliferation of SARS-CoV-2 indicated the inhibitive ability of the phytochemicals.

Proline-Tyrosine Ring Interactions in Black Widow Dragline Silk Revealed by Solid-State Nuclear Magnetic Resonance and Molecular Dynamics Simulations.

Selective one-dimensional 13C-13C spin-diffusion solid-state nuclear magnetic resonance (SSNMR) provides evidence for CH/π ring packing interactions between Pro and Tyr residues in 13C-enriched Latrodectus hesperus dragline silk. The secondary structure of Pro-containing motifs in dragline spider silks consistently points to an elastin-like type II ß-turn conformation based on 13C chemical shift analysis. 13C-13C spin diffusion measurements as a function of mixing times allow for the measurement of spatial proximity between the Pro and Tyr rings to be ∼0.5-1 nm, supporting strong Pro-Tyr ring interactions that likely occur through a CH/π mechanism. These results are supported by molecular dynamics (MD) simulations and analysis and reveals new insights into the secondary structure and Pro-Tyr ring stacking interactions for one of nature's toughest biomaterials.