In-silico Experiments Research Articles

In Silico Hybridization and Molecular Dynamics Simulations for the Identification of Candidate Human MicroRNAs for Inhibition of Virulent Proteins' Expression in Staphylococcus aureus.

Staphylococcus aureus is a major threat to human health, causing infections that range in severity from moderate to fatal. The rising rates of antibiotic resistance highlight the critical need for new therapeutic techniques to combat this infection. It has been recently discovered that microRNAs (miRNAs) are essential for cross-kingdom communication, especially when it comes to host-pathogen interactions. It has been demonstrated that these short noncoding RNAs control gene expression in the gut microbiota, maintaining homeostasis; dysbiosis in this system has been linked to several diseases, including cancer. Our research attempts to use this understanding to target specific bacterial species and prevent severe diseases. In particular, we look for putative human miRNAs that can attach to virulent bacterial proteins' mRNA and prevent them from being expressed. In-silico hybridization experiments were performed between 100 human miRNA sequences with varied expression levels in gram-positive bacterial infections and five virulence factor genes. In addition, these miRNAs' binding properties were investigated using molecular dynamics (MD) simulations. Our findings demonstrate that human miRNAs can target and inhibit the expression of bacterial virulent genes, thereby opening up new paths for developing innovative miRNA-based therapeutics. The implementation of MD simulations in our study not only improves the validity of our findings but also proposes a new method for constructing miRNA-based therapies against life-threatening bacterial infections.

A workflow for the hybrid modelling and simulation of multi-timescale biological systems

With the steady advance of in-silico biological experimentation, model construction and simulation becomes a ubiquitous tool to understand and predict the behaviour of many biological systems. However, biological processes may contain components from different types of reaction networks, resulting in models with different (e.g., slow and fast) timescales. Hybrid simulation is one approach which can be employed to efficiently execute multi-timescale models. In this paper, we present a methodology and workflow utilizing (coloured) hybrid Petri nets to construct smaller and more complicated hybrid models. The presented workflow integrates algorithms and ideas from hybrid simulation of biochemical reaction networks as well as Petri nets. We also construct multi-timescale hybrid models and then show how these models can be efficiently executed using three different advanced hybrid simulation algorithms.

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems.

Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and to predict the effects of cell-specific perturbations on neural activity or behavior. To overcome these limitations, we introduce the "cell-type dynamical systems" (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for both cell types, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that the animal's choice can be decoded from both E and I latents and thus is not restricted to a single cell-class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the experimentally observed effects of different perturbations on behavior, whereas a standard LDS model-which does not differentiate between cell types-did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.

Inhibitory effects of wild Origanum elongatum extracts on Fusarium oxysporum mycelium growth and spores germination: Evidence from in-vitro, in-planta, and in-silico experiments

Fusarium wilt, caused by Fusarium oxysporum, is one of the most devastating diseases to crops including potatoes. This fungus is widespread and dispersed via resistant spores. In this study, organic (methanol, hexane, and chloroform) and aqueous extracts of the aerial parts of wild Origanum elongatum were tested against mycelium and spores of F. oxysporum in both in-vitro (using the poisoned food technique) and in-planta assays. Further, the chemical composition of the extract was identified via HPLC-PDA-MS/MS. Then, molecular modeling was used to clarify the antifungal activity of identified biomolecules. The obtained results showed that organic and aqueous extracts showed significant antifungal and anti-sporulation activities with different effectiveness depending on the extract, concentration, and application test. In in-vitro assays of the aqueous extract, the highest antifungal activity was recorded at concentrations of 10 mg mL−1, while the highest anti-sporulation activity (91.79 %) was observed at 50 mg mL−1. In the organic extracts, methanol was the most effective against fungus with 100 % inhibition of mycelium at 5 mg mL −1 and the highest anti-sporulation effect (73.58 %) at 20 mg mL−1. In in-planta assays, the methanolic extract at 20 mg mL−1 achieved 0 % disease severity, while the aqueous extract at 50 mg mL−1 reduced severity to 7 %, both significantly more effective than the untreated control. Therefore, methanol was the most effective and these biological properties are supported by a wide range of bioactive molecules, including 56 molecules compared to only 28 molecules in the aqueous extract. These include glycosides of apigenin and kaempferol and salvianolic acid b. However, more research is needed to clarify the inhibition action of the individual components and their synergetic actions in controlled and field conditions.

Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas

To conduct a comprehensive investigation of the sarcoma immune cell infiltration (ImmCI) patterns and tumoral microenvironment (TME). We utilized transcriptomic, clinical, and mutation data of sarcoma patients (training cohort) obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) server. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were applied to decipher the immune cell infiltration landscape and TME profiles of sarcomas. An unsupervised clustering method was utilized for classifying ImmCI clusters (initial clustering) and ImmCI-based differentially expressed gene-driven clusters (secondary clustering). Mortality rates and immune checkpoint gene levels was analyzed among the identified clusters. We calculated the ImmCI score through principal component analysis. The tumor immune dysfunction evaluation (TIDE) score was also employed to quantify immunotherapy efficacy between two ImmCI score groups. We further validated the biomarkers for ImmCI and gene-driven clusters via experimental verification and the accuracy of the ImmCI score in predicting survival outcomes and immunotherapy efficacy by external validation cohorts (testing cohort). We demonstrated that ImmCI cluster A and gene-driven cluster A, were beneficial prognostic biomarkers and indicators of immune checkpoint blockade response in sarcomas via in-silico and laboratory experiments. Additionally, the ImmCI score exhibited independent prognostic significance and was predictive of immunotherapy response. Our research underscores the clinical significance of ImmCI scores in identifying sarcoma patients likely to respond to immunotherapy.

Development, calibration and validation of impact-specific cervical spine models: A novel approach using hybrid multibody and finite-element methods

Background and Objective:Spinal cord injuries can have a severe impact on athletes’ or patients’ lives. High axial impact scenarios like tackling and scrummaging can cause hyperflexion and buckling of the cervical spine, which is often connected with bilateral facet dislocation. Typically, finite-element (FE) or musculoskeletal models are applied to investigate these scenarios, however, they have the drawbacks of high computational cost and lack of soft tissue information, respectively. Moreover, material properties of the involved tissues are commonly tested in quasi-static conditions, which do not accurately capture the mechanical behavior during impact scenarios. Thus, the aim of this study was to develop, calibrate and validate an approach for the creation of impact-specific hybrid, rigid body - finite-element spine models for high-dynamic axial impact scenarios. Methods:Five porcine cervical spine models were used to replicate in-vitro experiments to calibrate stiffness and damping parameters of the intervertebral joints by matching the kinematics of the in-vitro with the in-silico experiments. Afterwards, a five-fold cross-validation was conducted. Additionally, the von Mises stress of the lumped FE-discs was investigated during impact. Results:The results of the calibration and validation of our hybrid approach agree well with the in-vitro experiments. The stress maps of the lumped FE-discs showed that the highest stress of the most superior lumped disc was located anterior while the remaining lumped discs had their maximum in the posterior portion. Conclusion:Our hybrid method demonstrated the importance of impact-specific modeling. Overall, our hybrid modeling approach enhances the possibilities of identifying spine injury mechanisms by facilitating dynamic, impact-specific computational models.

Comparison and development of cross-study normalization methods for inter-species transcriptional analysis.

Performing joint analysis of gene expression datasets from different experiments can present challenges brought on by multiple factors-differences in equipment, protocols, climate etc. "Cross-study normalization" is a general term for transformations aimed at eliminating such effects, thus making datasets more comparable. However, joint analysis of datasets from different species is rarely done, and there are no dedicated normalization methods for such inter-species analysis. In order to test the usefulness of cross-studies normalization methods for inter-species analysis, we first applied three cross-study normalization methods, EB, DWD and XPN, to RNA sequencing datasets from different species. We then developed a new approach to evaluate the performance of cross-study normalization in eliminating experimental effects, while also maintaining the biologically significant differences between species and conditions. Our results indicate that all normalization methods performed relatively well in the cross-species setting. We found XPN to be better at reducing experimental differences, and found EB to be better at preserving biological differences. Still, according to our in-silico experiments, in all methods it is not possible to enforce the preservation of the biological differences in the normalization process. In addition to the study above, in this work we propose a new dedicated cross-studies and cross-species normalization method. Our aim is to address the shortcoming mentioned above: in the normalization process, we wish to reduce the experimental differences while preserving the biological differences. We term our method as CSN, and base it on the performance evaluation criteria mentioned above. Repeating the same experiments, the CSN method obtained a better and more balanced conservation of biological differences within the datasets compared to existing methods. To summarize, we demonstrate the usefulness of cross-study normalization methods in the inter-species settings, and suggest a dedicated cross-study cross-species normalization method that will hopefully open the way to the development of improved normalization methods for the inter-species settings.

In-silico heart model phantom to validate cardiac strain imaging

The quantification of cardiac strains as structural indices of cardiac function has a growing prevalence in clinical diagnosis. However, the highly heterogeneous four-dimensional (4D) cardiac motion challenges accurate “regional” strain quantification and leads to sizable differences in the estimated strains depending on the imaging modality and post-processing algorithm, limiting the translational potential of strains as incremental biomarkers of cardiac dysfunction. There remains a crucial need for a feasible benchmark that successfully replicates complex 4D cardiac kinematics to determine the reliability of strain calculation algorithms. In this study, we propose an in-silico heart phantom derived from finite element (FE) simulations to validate the quantification of 4D regional strains. First, as a proof-of-concept exercise, we created synthetic magnetic resonance (MR) images for a hollow thick-walled cylinder under pure torsion with an exact solution and demonstrated that “ground-truth” values can be recovered for the twist angle, which is also a key kinematic index in the heart. Next, we used mouse-specific FE simulations of cardiac kinematics to synthesize dynamic MR images by sampling various sectional planes of the left ventricle (LV). Strains were calculated using our recently developed non-rigid image registration (NRIR) framework in both problems. Moreover, we studied the effects of image quality on distorting regional strain calculations by conducting in-silico experiments for various LV configurations. Our studies offer a rigorous and feasible tool to standardize regional strain calculations to improve their clinical impact as incremental biomarkers.

Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments.

Navigation through tortuous and deformable vessels using catheters with limited steering capability underscores the need for reliable path planning. State-of-the-art path planners do not fully account for the deformable nature of the environment. This work proposes a robust path planner via a learning from demonstrations method, named Curriculum Generative Adversarial Imitation Learning (C-GAIL). This path planning framework takes into account the interaction between steerable catheters and vessel walls and the deformable property of vessels. In-silico comparative experiments show that the proposed network achieves a 38% higher success rate in static environments and 17% higher in dynamic environments compared to a state-of-the-art approach based on GAIL. In-vitro validation experiments indicate that the path generated by the proposed C-GAIL path planner achieves a targeting error of 1.26 ±0.55mm and a tracking error of 5.18 ±3.48mm. These results represent improvements of 41% and 40% over the conventional centerline-following technique for targeting error and tracking error, respectively. The proposed C-GAIL path planner outperforms the state-of-the-art GAIL approach. The in-vitro validation experiments demonstrate that the path generated by the proposed C-GAIL path planner aligns better with the actual steering capability of the pneumatic artificial muscle-driven catheter utilized in this study. Therefore, the proposed approach can provide enhanced support to the user in navigating the catheter towards the target with greater accuracy, effectively meeting clinical accuracy requirements. The proposed path planning framework exhibits superior performance in managing uncertainty associated with vessel deformation, thereby resulting in lower tracking errors.

FateNet: an integration of dynamical systems and deep learning for cell fate prediction.

Understanding cellular decision-making, particularly its timing and impact on the biological system such as tissue health and function, is a fundamental challenge in biology and medicine. Existing methods for inferring fate decisions and cellular state dynamics from single-cell RNA sequencing data lack precision regarding decision points and broader tissue implications. Addressing this gap, we present FateNet, a computational approach integrating dynamical systems theory and deep learning to probe the cell decision-making process using scRNA-seq data. By leveraging information about normal forms and scaling behavior near bifurcations common to many dynamical systems, FateNet predicts cell decision occurrence with higher accuracy than conventional methods and offers qualitative insights into the new state of the biological system. Also, through in-silico perturbation experiments, FateNet identifies key genes and pathways governing the differentiation process in hematopoiesis. Validated using different scRNA-seq data, FateNet emerges as a user-friendly and valuable tool for predicting critical points in biological processes, providing insights into complex trajectories. github.com/ThomasMBury/fatenet.

MetDecode: methylation-based deconvolution of cell-free DNA for noninvasive multi-cancer typing.

Circulating-cell free DNA (cfDNA) is widely explored as a noninvasive biomarker for cancer screening and diagnosis. The ability to decode the cells of origin in cfDNA would provide biological insights into pathophysiological mechanisms, aiding in cancer characterization and directing clinical management and follow-up. We developed a DNA methylation signature-based deconvolution algorithm, MetDecode, for cancer tissue origin identification. We built a reference atlas exploiting de novo and published whole-genome methylation sequencing data for colorectal, breast, ovarian, and cervical cancer, and blood-cell-derived entities. MetDecode models the contributors absent in the atlas with methylation patterns learnt on-the-fly from the input cfDNA methylation profiles. In addition, our model accounts for the coverage of each marker region to alleviate potential sources of noise. In-silico experiments showed a limit of detection down to 2.88% of tumor tissue contribution in cfDNA. MetDecode produced Pearson correlation coefficients above 0.95 and outperformed other methods in simulations (P < 0.001; T-test; one-sided). In plasma cfDNA profiles from cancer patients, MetDecode assigned the correct tissue-of-origin in 84.2% of cases. In conclusion, MetDecode can unravel alterations in the cfDNA pool components by accurately estimating the contribution of multiple tissues, while supplied with an imperfect reference atlas. MetDecode is available at https://github.com/JorisVermeeschLab/MetDecode.

In-silico heart model phantom to validate cardiac strain imaging.

The quantification of cardiac strains as structural indices of cardiac function has a growing prevalence in clinical diagnosis. However, the highly heterogeneous four-dimensional (4D) cardiac motion challenges accurate "regional" strain quantification and leads to sizable differences in the estimated strains depending on the imaging modality and post-processing algorithm, limiting the translational potential of strains as incremental biomarkers of cardiac dysfunction. There remains a crucial need for a feasible benchmark that successfully replicates complex 4D cardiac kinematics to determine the reliability of strain calculation algorithms. In this study, we propose an in-silico heart phantom derived from finite element (FE) simulations to validate the quantification of 4D regional strains. First, as a proof-of-concept exercise, we created synthetic magnetic resonance (MR) images for a hollow thick-walled cylinder under pure torsion with an exact solution and demonstrated that "ground-truth" values can be recovered for the twist angle, which is also a key kinematic index in the heart. Next, we used mouse-specific FE simulations of cardiac kinematics to synthesize dynamic MR images by sampling various sectional planes of the left ventricle (LV). Strains were calculated using our recently developed non-rigid image registration (NRIR) framework in both problems. Moreover, we studied the effects of image quality on distorting regional strain calculations by conducting in-silico experiments for various LV configurations. Our studies offer a rigorous and feasible tool to standardize regional strain calculations to improve their clinical impact as incremental biomarkers.

Evaluating the role of soil EPS in modifying the toxicity potential of the mixture of polystyrene nanoplastics and xenoestrogen, Bisphenol A (BPA) in Allium cepa L.

The coexistence of emerging pollutants like nanoplastics and xenoestrogen chemicals such as Bisphenol A (BPA) raises significant environmental concerns. While the individual impacts of BPA and polystyrene nanoplastics (PSNPs) on plants have been studied, their combined effects are not well understood. This study examines the interactions between eco-corona formation, physicochemical properties, and cyto-genotoxic effects of PSNPs and BPA on onion (Allium cepa) root tip cells. Eco-corona formation was induced by exposing BPA-PSNP mixtures to soil extracellular polymeric substances (EPS), and changes were analyzed using 3D-EEM, TEM, FTIR, hydrodynamic diameter, and contact angle measurements. Onion roots were treated with BPA (2.5, 5, and 10 mgL-1) combined with plain, aminated, and carboxylated PSNPs (100 mgL-1), with and without EPS interaction. Toxicity was assessed via cell viability, oxidative stress markers (superoxide radical, total ROS, hydroxyl radical), lipid peroxidation, SOD and catalase activity, mitotic index, and chromosomal abnormalities. BPA alone increased cytotoxic and genotoxic parameters in a dose-dependent manner. BPA with aminated PSNPs exhibited the highest toxicity among the pristine mixtures, revealing increased chromosomal abnormalities, oxidative stress, and cell mortality with rising BPA concentrations. In-silico experiments demonstrated the relationship between superoxide dismutase (SOD), catalase enzymes, PSNPs, BPA, and their mixtures. EPS adsorption notably reduced cyto-genotoxic effects, lipid peroxidation, and ROS levels, mitigating the toxicity of BPA-PSNP mixtures.

Computational investigation of the anticancer potential of Sorghum bicolor and Setaria italica phytochemicals against dihydrofolate reductase (DHFR) enzyme

Breast and prostate cancer holds the position of foremost contributors to mortality. Dietary therapies for accompanied by medication are widely recognized as a potential method to successfully tackle cancer. Millet grains are the most ancient food, a perfect combination of proteins, carbohydrates, fiber, macronutrients, micronutrients, and vitamins. This study aims to examine the anticancer potential of Sorghum bicolor (Sorghum) and Setaria italica (Foxtail) phytochemicals. The 50 phytochemicals of sorghum and foxtail millets were retrieved through a literature survey and docked to the Dihydrofolate reductase (DHFR), an enzyme essential for cell growth and proliferation. The top-scoring phytochemicals were filtered and further investigated with active-site residue interaction, drug-likeness, and pharmacokinetics analysis. The ligand stability with the DHFR was evaluated through density functional theory (DFT) based HOMO and LUMO calculations. The results show that caffeic acid, ferulic acid, hesperetic acid, stigmasterol, Cis-p-Coumaric acid, and luteolinidin attained greater stability within the active site of DHFR. These phytochemicals showed a docking score of − 6.4 kcal/mol, − 6.4 kcal/mol, − 6.1 kcal/mol, − 6.4 kcal/mol, − 5.4 kcal/mol, and − 6.7 kcal/mol with DHFR (PDB ID:1BOZ) and flutamide and capecitabine have docking score of − 7.5 and − 8.1 for 1BOZ and − 7.4 and − 7.1 with DHFR (PDB ID:1OHK) respectively. The dynamic interaction at the molecular level validated the stability of these phytochemicals against both DHFR target proteins along with excellent drug-likeness and pharmacokinetic properties. However, the current findings were proven and validated through in-silico experiments to validate above identified phytochemicals as DHFR inhibitors, so millets are used as therapeutics for breast and prostate cancer.

A multi-region recurrent circuit for evidence accumulation in rats.

Decision-making based on noisy evidence requires accumulating evidence and categorizing it to form a choice. Here we evaluate a proposed feedforward and modular mapping of this process in rats: evidence accumulated in anterodorsal striatum (ADS) is categorized in prefrontal cortex (frontal orienting fields, FOF). Contrary to this, we show that both regions appear to be indistinguishable in their encoding/decoding of accumulator value and communicate this information bidirectionally. Consistent with a role for FOF in accumulation, silencing FOF to ADS projections impacted behavior throughout the accumulation period, even while nonselective FOF silencing did not. We synthesize these findings into a multi-region recurrent neural network trained with a novel approach. In-silico experiments reveal that multiple scales of recurrence in the cortico-striatal circuit rescue computation upon nonselective FOF perturbations. These results suggest that ADS and FOF accumulate evidence in a recurrent and distributed manner, yielding redundant representations and robustness to certain perturbations.

Ultrasound-assisted ring opening of epoxides in HFIP: THF: Synthesis, characterization, computational studies and molecular docking of novel 2‑hydroxy dithiocarbamates

Under the influence of ultrasonic irradiation, pyridazinone, triazinone, or phthalimide containing 2‑hydroxy dithiocarbamates, a biologically relevant novel organo-sulfur compound, was synthesized. Detailed characterization, computational, and molecular docking studies are being investigated. Molecular interactions were studied using 3D Hirshfeld surfaces and corresponding 2D fingerprint plots. Theoretical (DFT) studies on the molecular structure, HOMO, LUMO, and quantum chemical descriptors were performed at the B3LYP/6–311++G(d,p) level of theory. At the same time, the interaction energy was computed using the B3LYP/6–31G(d,p) level of theory. The FMO study revealed that molecules 4a and 4p in the gas phase have 3.545 eV and 3.263 eV HOMO-LUMO energy gaps, respectively, and they are hence kinetically stable. Quantum chemical calculations confirm the electrophilic character of compounds 4a and 4p, as the molecule is stable and highly electrophilic. The interactions of 2‑hydroxy dithiocarbamate derivatives (4a-4t) with the ligand-binding site of the target COX-2 (cyclooxygenase-2) enzyme were investigated using in-silico molecular docking experiments. Compared to the standard medicine celecoxib, the results showed that most synthesized derivatives had better glide scores and interaction. The docking study of all the synthesized compounds revealed that compounds 4a, 4e, and 4o interact well with the COX-2 enzyme as anti-inflammatory drugs. Molecular dynamic simulation was utilized to validate the docking study and explore the stable binding site and interaction of compound 4o, which is the most potent. The findings indicated that compound 4o exhibited better stability and interaction when compared to the reference drug.

Phytochemical profiling, antiviral activities, molecular docking, and dynamic simulations of selected Ruellia species extracts

The antiviral properties of the flowering aerial extracts of Ruellia tuberosa and Ruellia patula were investigated through phytochemical profiling via LC–MS/MS and HPLC techniques. Qualitative LC–MS/MS analyses identified seventy-seven metabolites from both Ruellia species. R. tuberosa had the highest phenolic content (49.3%), whereas R. patula had the highest flavonoid content (57.8%). Additionally, quantitative HPLC investigations of the compounds identified by LC–MS/MS were performed using the available standard compounds. The main constituents in the R. tuberosa extract was found to be catechin (5321.63 µg/g), gallic acid (2878.71 µg/g), and ellagic acid (2530.79 µg/g), whereas the major compounds in the R. patula extract was found to be rutin (11,074.19 µg/g) and chlorogenic acid (3157.35 µg/g). Furthermore, the antiviral activities of both Ruellia species against HAdV-40, herpes simplex type 2 and H1N1 were evaluated. These findings demonstrated that R. tuberosa was more active than R. patula against all tested viruses, except for the HSV-2 virus, against which R. patula showed greater activity than R. tuberosa, with IC50 values of 20, 65, 22.59, and 13.13 µg/ml for R. tuberosa flowering aerial parts and 32.26, 11.66, and 23.03 µg/ml for R. patula flowering aerial parts, respectively for HAdV-40, herpes simplex type 2, and H1N1. Additionally, computational docking and molecular dynamics simulations were used to assess the molecular interactions between the bioactive compounds and specific viral targets. The combined findings from the in-vitro and in-silico experiments comprehensively evaluated the antiviral activities of both Ruellia species extracts.

Hydrothiolation of Triisopropylsilyl Acetylene Sulfur Pentafluoride - Charting the Chemical Space of β-SF5 Vinyl Sulfides.

Recently, we suggested liquid and high-boiling TIPS-CC-SF5 (TASP) as a versatile reagent to access so far elusive SF5-containing building blocks by less specialized laboratories under bench-top conditions. The synthesis of non-aromatic SF5 building blocks generally requires on-site fluorination or pentafluorosulfanylation steps employing toxic and/or gaseous reagents. Herein, we underline the versatility of this reagent by reporting a benign bench-top protocol for the synthesis of Z-configured β-pentafluorosulfanylated vinyl sulfides in good to excellent yields (up to 99 %) with exclusive (Z)-diasteroselectivity and broad functional group tolerance. This method exploits an in-situ protodesilylation-hydrothiolation sequence. This so far uncharted class of compounds was characterized by means of NMR-spectroscopy as well as SC-XRD. Furthermore, we suggest the reaction to proceed via a kinetically controlled closed-shell reaction pathway, corroborated by in-silico experiments.

Synthesis of 2,2-dimethyl-chroman-based stereochemically flexible and constrained anti-breast cancer agents

Receptors are proteinous macromolecules which remain in the apo form under normal/unliganded conditions. As the ligand approaches, there are specific stereo-chemical changes in the apo form of the receptor as per the stereochemistry of a ligand. Accordingly, a series of substituted dimethyl-chroman-based stereochemically flexible and constrained Tamoxifen analogs were synthesized as anti-breast cancer agents. The synthesized compounds 19a-e, 20a-e, 21, and 22a-e, showed significant antiproliferative activity against estrogen receptor-positive (ER+, MCF-7) and negative (ER-, MDA MB-231) cells within IC50 value 8.5–25.0 µM. Amongst all, four potential molecules viz 19b, 19e, 22a, and 22c, were evaluated for their effect on the cell division cycle and apoptosis of ER+ and ER- cancer cells (MCF-7 & MDA MB-231cells), which showed that these compounds possessed antiproliferative activity through triggering apoptosis. In-silico docking experiments elucidated the possible affinity of compounds with estrogen receptors-α and -β.

Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides

Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with <10% errors. First, we utilize 4-mercaptophenylboronic acid to selectively capture the epimers at molecular sites of isomerism to form epimer-specific SERS fingerprints. Corroborating with in-silico experiments, we establish five spectral features, each corresponding to a structural characteristic: (1) presence/absence of epimers, (2) monosaccharide/cerebroside, (3) saturated/unsaturated cerebroside, (4) glucosyl/galactosyl, and (5) GlcCer or GalCer’s carbon chain lengths. Leveraging these insights, we create a fully generalizable framework to identify and quantify cerebrosides at concentrations between 10−4 to 10−10 M and achieve multiplex quantification of binary mixtures containing biomarkers GlcCer24:1, and GalCer24:1 using their untrained spectra in the models.