Water-soluble essential oil components of flowers of Paeonia × suffruticosa cultivars and in silico analysis with antidepressant targets

The present study focused on water-soluble essential oil recovered from the hydrolate of ten Paeonia × suffruticosa cultivars. Thirty-seven components, mostly oxygenated compounds (94.6-99.6%), were detected by gas chromatography-mass spectrometry (GC-MS) and GC-flame ionisation detector (GC-FID). The geranic acid chemotype was discovered (in cultivar 'Lan BaoShi'). Eight key oxygenated components were analysed in silico with antidepressant targets sodium-dependent serotonin transporter (SERT), 5-hydroxytryptamine receptor 1 A (5-HT1A), and monoamine oxidase type A (MAO-A). Geraniol, nerol, citronellol, and geranic acid presented superior docking properties. Phenylethyl alcohol and 1,3,5-trimethoxybenzene were also well docked. These molecules were bound to the active sites successfully (with partial occupancy in SERT). They might increase serotonin level or mimic its effect in central nervous system. (Z)-3-Hexen-1-ol and 1-hexanol showed weak binding. The in silico analysis revealed for the first time that the key water-soluble essential oil components of P. × suffruticosa potentially targeted antidepressant targets.

Finding the perfect pairs: A matchmaking of plant markers and primers for multi-marker eDNA metabarcoding

As the scope of plant eDNA metabarcoding diversifies, so do the primers, markers and methods. A wealth of primers exists today, but their comparative evaluation is lacking behind. Similarly, multi-marker approaches are recommended but debates persist regarding barcode complementarity and optimal combinations. After a literature compilation of used primers, we compared in silico 102 primer pairs based on amplicon size, coverage and specificity, followed by an experimental evaluation of 15 primer pairs on a mock community sample covering 268 plant species and genera, and about 100 families. The analysis was done for the four most common plant metabarcoding markers, rbcL, trnL, ITS1 and ITS2 and their complementarity was assessed based on retrieved species. By focusing on existing primers, we identify common designs, promote alternatives and enhance prior-supported primers for immediate applications. The ITS2 was the best-performing marker for flowering vascular plants and was congruent to ITS1. However, the combined taxonomic breadth of ITS2 and rbcL surpassed any other combination, highlighting their high complementarity across Streptophyta. Overall, our study underscores the significance of comprehensive primer and barcode evaluations tailored to metabarcoding applications.

Prediction of physicochemical and pharmacokinetic properties of botanical constituents by computational models

Botanicals contain complex mixtures of chemicals most of which lack pharmacokinetic data in humans. Since physicochemical and pharmacokinetic properties dictate the in vivo exposure of botanical constituents, these parameters greatly impact the pharmacological and toxicological effects of botanicals in consumer products. This study sought to use computational (i.e., in silico) models, including quantitative structure-activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, to predict properties of botanical constituents. One hundred and three major constituents (e.g., withanolides, mitragynine, and yohimbine) in 13 botanicals (e.g., ashwagandha, kratom, and yohimbe) were investigated. The predicted properties included biopharmaceutical classification system (BCS) classes based on aqueous solubility and permeability, oral absorption, liver microsomal clearance, oral bioavailability, and others. Over half of these constituents fell into BCS classes I and II at dose levels no greater than 100 mg per day, indicating high permeability and absorption (%Fa > 75%) in the gastrointestinal tract. However, some constituents such as glycosides in ashwagandha and Asian ginseng showed low bioavailability after oral administration due to poor absorption (BCS classes III and IV, %Fa < 40%). These in silico results fill data gaps for botanical constituents and could guide future safety studies. For example, the predicted human plasma concentrations may help select concentrations for in vitro toxicity testing. Additionally, the in silico data could be used in tiered or batteries of assays to assess the safety of botanical products. For example, highly absorbed botanical constituents indicate potential high exposure in the body, which could lead to toxic effects.

Enzymatic Performance of Aspergillus oryzae α-Amylase in the Presence of Organic Solvents: Activity, Stability, and Bioinformatic Studies

Enzymatic reactions can be modulated by the incorporation of organic solvents, leading to alterations in enzyme stability, activity, and reaction rates. These solvents create a favorable microenvironment that enables hydrophobic reactions, facilities enzyme-substrate complex formation, and reduces undesirable water-dependent side reactions. However, it is crucial to understand the impact of organic solvents on enzymatic activity, as they can also induce enzyme inactivation. In this study, the enzymatic performance of Aspergillus oryzae α-amylase (Taka-amylase) in various organic solvents both experimentally and computationally was investigated. The results demonstrated that ethanol and ether sustain Taka-amylase activity up to 20% to 25% of the organic solvents, with ether providing twice the stability of ethanol. Molecular dynamics simulations further revealed that Taka-amylase has a more stable structure in ether and ethanol relative to other organic solvents. In addition, the analysis showed that the loop located near the active site in the AB-domain is a vulnerable site for enzyme destabilization when exposed to organic solvents. The ability of Taka-amylase to preserve the secondary loop structure in ether and ethanol contributed to the enzyme's activity. In addition, the solvent accessibility surface area of Taka-amylase is distributed throughout all enzyme structures, thereby contributing to the instability of Taka-amylase in the presence of most organic solvents.

Phytochemical analysis and antioxidant potential of Mondia whitei and Guibourtia tessmannii against H2O2-induced cytotoxicity in PC3 cells

The management of oxidative stress-related disorders has garnered significant interest, particularly in the exploration of medicinal plants possessing potent antioxidant activities. This study was undertaken to evaluate the antioxidant activity of Mondia whitei (MW) and Guibourtia tessmannii (GT) against H2O2-induced cytotoxicity in PC3 cells. The phytochemical composition of MW and GT was determined by GC-MS analysis. Total phenolic (TP) and total flavonoid (TF) contents were quantified by Folin Ciocalteu and AlCl3 methods, respectively. The antioxidant potential of the extracts was determined using the DPPH and ABTS+ radicals scavenging method, as well as cupric and ferric reducing capacity assay. Moreover, all phytocompounds were docked against acetylcholinesterase (AChE) and glutathione S-transferase (GST) using ArgusLab, and results were analyzed using the BIOVIA Discovery Studio Visualizer 2021 client. MW and GT comprised 20 and 22 compounds, respectively. GT exhibited higher TP and TF contents (210.70 ± 12.7; 12.61 ± 1.3 GAE/g DW) compared to MW (132.59 ± 12.59; 5.53 ± 1.3 mg of GAE/g DW). Both MW and GT demonstrated substantial antioxidant activity, with GT proving to be more effective in preventing H2O2-induced cytotoxicity. For instance, MW and GT significantly (p < .001) increased the DPPH, ABTS+, and cupric activity, compared with the H2O2 group. All compounds identified in MW and GT exhibited a strong binding affinity against AChE and GST. Drug likeness and toxicity of all phytocompounds were under the acceptable norms of Lipinski's rule. In conclusion, these plants could be effective candidates for the management/treatment of oxidative stress-related disorders.Communicated by Ramaswamy H. Sarma.

Anti-Parkinson potential of hesperetin nanoparticles: in vivo and in silico investigations

Parkinson's disease (PD) is characterised by the gradual demise of dopaminergic neurons. In recent years, there has been significant interest in herbal treatments. In this study, hesperetin nanoparticles (HTN) were developed and compared their anti-PD potential with hesperetin (HT) on rotenone induced PD rats. Molecular docking was also performed to evaluate the binding affinity of hesperetin on pathological protein, i.e. D2 dopamine receptors (DR2), using Auto Dock Vina tools. The results showed a higher binding relationship of HTN on dopamine receptors (-7.2 kcal/mol) compared to L-dopa (-6.4 kcal/mol), supporting their potential as drug candidates for PD therapy. HTN was effectively synthesised using the fabrication technique and characterised by zeta potential and SEM analysis. HTN had favourable characteristics, including a size of 249.8 ± 14.9 nm and a Z-potential of -32.9 mV. After being administered orally, HTN demonstrated a notable anti-Parkinsonian effects, indicated by the significant improvement in motor function as assessed by the rota rod test (p < .001***), pole test (p < .001***), stair test (p < .01**), wood walk test (p < .01**) and an increase in substantia nigra (SN) antioxidant levels, CAT (p < .001***), SOD (p < .001***), GSH (p < .01**). Additionally, HTN led to increased dopamine levels (p < .01**) and a decrease in the oxidant system, MDA levels (p < .01**). Furthermore, histopathological examination revealed decreased SN neuronal necrosis in diseased animals treated with HTN compared to those treated with HT in a rat model of Parkinson's disease. Therefore, HTN can be regarded as a viable platform for efficient therapy of PD.

Prediction of Overall Disease Burden in (y)pN1 Breast Cancer Using Knowledge-Based Machine Learning Model

BACKGROUND

We aimed to construct an expert knowledge-based Bayesian network (BN) model for assessing the overall disease burden (ODB) in (y)pN1 breast cancer patients and compare ODB across arms of ongoing trials.

METHODS

Utilizing institutional data and expert surveys, we developed a BN model for (y)pN1 breast cancer. Expert-derived probabilities and disability weights for radiotherapy-related benefit (e.g., 7-year disease-free survival [DFS]) and toxicities were integrated into the model. ODB was defined as the sum of disability weights multiplied by probabilities. In silico predictions were conducted for Alliance A011202, PORT-N1, RAPCHEM, and RT-CHARM trials, comparing ODB, 7-year DFS, and side effects.

RESULTS

In the Alliance A011202 trial, 7-year DFS was 80.1% in both arms. Axillary lymph node dissection led to higher clinical lymphedema and ODB compared to sentinel lymph node biopsy with full regional nodal irradiation (RNI). In the PORT-N1 trial, the control arm (whole-breast irradiation [WBI] with RNI or post-mastectomy radiotherapy [PMRT]) had an ODB of 0.254, while the experimental arm (WBI alone or no PMRT) had an ODB of 0.255. In the RAPCHEM trial, the radiotherapy field did not impact the 7-year DFS in ypN1 patients. However, there was a mild ODB increase with a larger irradiation field. In the RT-CHARM trial, we identified factors associated with the major complication rate, which ranged from 18.3% to 22.1%.

CONCLUSIONS

The expert knowledge-based BN model predicted ongoing trial outcomes, validating reported results and assumptions. In addition, the model demonstrated the ODB in different arms, with an emphasis on quality of life.

Multi-level bioinformatics resources support drug target discovery of protein-protein interactions

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

Phytochemical Profile, Antioxidant, Enzyme Inhibition, Acute Toxicity, In Silico Molecular Docking and Dynamic Analysis of Apis mellifera Propolis as Antidiabetic Supplement

This study aims to identification of phytochemical profile of Apis mellifera propolis and explore potential of its anti-diabetic activity through inhibition on α-amylase (α-AE), α-glucosidase(α-GE) and finally identified the novel antidiabetic compounds from propolis. Apis mellifera propolis extract (AMPE) exhibited elevated polyphenol 33.26 ± 0.17 (mg GAE/g) and flavonoid (15.45 ± 0.13 mg RE/g), while its also indicated moderate strong antioxidant activity (EC50 793.09 ±1.94 µg/ml). This study found that AMPE displayed promising α-AE and α-GE inhibition through in vitro study. Based on LC-MS/MS screening, 18 unique AMPE compounds were identified, majorly belonging to anthraquinone and flavonoid compounds. In silico study determined that 8 compounds of AMPE compounds exhibited strong binding to α-AE, interacting to catalytic residue of ASP197. Moreover, 2 compounds exhibit potential inhibition of α-AG, by interacting to crucial amino acids of ARG315, ASP352, and ASP69. Finally, we suggested 2,7-Dihydroxy-1-(p-hydroxybenzyl)-4-methoxy-9,10-dihydrophenanthrene and 3(3-(3,4-Dihydroxybenzyl)-7-hydroxychroman-4-one as novel inhibitors of α-AE and α-GE. Notably, these compounds were initially discovered in Apis mellifera propolis, and molecular dynamic analysis confirmed their stable binding with both enzymes over 100 ns simulations. In vivo acute toxicity test reveals AMPE as a practically non-toxic product with LD50 value of 16050mg/kg.

Genomic characteristics of Salmonella Montevideo and Pomona: impact of isolation source on antibiotic resistance, virulence and metabolic capacity

Salmonella enterica is known for its disease-causing serotypes, including Montevideo and Pomona. These serotypes have been found in various environments, including river water, sediments, food, and animals. However, the global spread of these serotypes has increased, leading to many reported infections and outbreaks. The goal of this study was the genomic analysis of 48 strains of S. Montevideo and S. Pomona isolated from different sources, including clinical. Results showed that environmental strains carried more antibiotic resistance genes than the clinical strains, such as genes for resistance to aminoglycosides, chloramphenicol, and sulfonamides. Additionally, the type 4 secretion system, was only found in environmental strains. .Also many phosphotransferase transport systems were identified and the presence of genes for the alternative pathway Entner-Doudoroff. The origin of isolation may have a significant impact on the ability of Salmonella isolates to adapt and survive in different environments, leading to genomic flexibility and a selection advantage.

Comprehensive evaluation of nucleic acid amplification methods widely used for generic detection of sandfly-borne phleboviruses

Sandfly-borne phleboviruses (SBPs), which cause sandfly fever, aseptic meningitis, encephalitis, and meningoencephalitis, are emerging pathogens of major public health concern. Virus nucleic acid testing is essential for SBP diagnosis, especially in the early stages of infection, and for the discovery of novel SBPs. The efficacy of utilizing generic primers that target conserved nucleotide sequences for the detection of both known and novel SBPs has not been extensively evaluated. We aimed to compare and evaluate the performance of five generic primer sets, widely used to detect S- and L-segments of arthropod-borne phleboviruses and designed as singleplex (n = 3) and nested (n = 2) formats, including both well-known and recently characterized 15 Old World virus strains. Furthermore, we performed in silico analysis to assess the detection capabilities of these generic primer sets. The initial evaluation of previously published generic primer sets for SBP detection yielded two singleplex primer sets with the potential to be adapted for use in real-time or high-throughput detection settings. Studies are ongoing to develop and further optimize a preliminary assay and test various hosts and vectors to assess their capacity to detect known and novel viruses.

IMPORTANCE

Virus nucleic acid testing is the primary diagnostic method, particularly in the early stages of illness. Virus-specific or syndromic tests are widely used for this purpose. The use of generic primers has had a considerable impact on the discovery, identification, and detection of Old World sandfly-borne phleboviruses (OWSBP). The study is significant because it is the first to carry out a comparative evaluation of all published OWSBP generic primer sets.

OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues

Familial hypercholesterolemia (FH) is an inherited metabolic disease affecting cholesterol metabolism, with 90% of cases caused by mutations in the LDL receptor gene (LDLR), primarily missense mutations. This study aims to integrate six commonly used predictive software to create a new model for predicting LDLR mutation pathogenicity and mapping hot spot residues. Six predictive-software are selected: Polyphen-2, SIFT, MutationTaster, REVEL, VARITY, and MLb-LDLr. Software accuracy is tested with the characterized variants annotated in ClinVar and, by bioinformatic and machine learning techniques all models are integrated into a more accurate one. The resulting optimized model presents a specificity of 96.71% and a sensitivity of 98.36%. Hot spot residues with high potential of pathogenicity appear across all domains except for the signal peptide and the O-linked domain. In addition, translating this information into 3D structure of the LDLr highlights potentially pathogenic clusters within the different domains, which may be related to specific biological function. The results of this work provide a powerful tool to classify LDLR pathogenic variants. Moreover, an open-access guide user interface (OptiMo-LDLr) is provided to the scientific community. This study shows that combination of several predictive software results in a more accurate prediction to help clinicians in FH diagnosis.

In Vitro Human Dermal Absorption Studies on Pesticides in Complex Mixtures: Investigation of Guidance Criteria and Possible Impact Parameters

Pesticides must not pose unacceptable risks to human health, so risk assessments are conducted before products are authorised. Dermal exposure is often the main route of intake, so estimating realistic and trustworthy dermal absorption values is crucial for risk assessment. Although there are agreed test guidelines for in vitro dermal absorption studies, not every product is tested due to cost reasons. The present dataset consists of 945 individual in vitro experiments on the dermal absorption of human skin with 179 active substances of pesticides in 353 different mixtures, including concentrates and dilutions. The dataset was evaluated to identify the possible impacts of experimental conditions and physico-chemical properties on dermal absorption. The dataset was also analysed to assess the appropriateness of the pro rata correction for untested dilutions, and the set concentration cut-off to decide on the dilution status for choosing a default value on dermal absorption. The study found that the implementation of specific guidelines improved the harmonisation of study conduct, with support for approaches such as pro rata correction and default values. Further analysis of the specific co-formulants may identify influencing factors that may be more important than the experimental variables.

BEERS2: RNA-Seq simulation through high fidelity in silico modeling

Simulation of RNA-seq reads is critical in the assessment, comparison, benchmarking and development of bioinformatics tools. Yet the field of RNA-seq simulators has progressed little in the last decade. To address this need we have developed BEERS2, which combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline. BEERS2 takes input transcripts (typically fully length messenger RNA transcripts with polyA tails) from either customizable input or from CAMPAREE simulated RNA samples. It produces realistic reads of these transcripts as FASTQ, SAM or BAM formats with the SAM or BAM formats containing the true alignment to the reference genome. It also produces true transcript-level quantification values. BEERS2 combines a flexible and highly configurable design with detailed simulation of the entire library preparation and sequencing pipeline and is designed to include the effects of polyA selection and RiboZero for ribosomal depletion, hexamer priming sequence biases, GC-content biases in polymerase chain reaction (PCR) amplification, barcode read errors and errors during PCR amplification. These characteristics combine to make BEERS2 the most complete simulation of RNA-seq to date. Finally, we demonstrate the use of BEERS2 by measuring the effect of several settings on the popular Salmon pseudoalignment algorithm.

In situtumor model for longitudinal in silico imaging trials

Objective.In this article, we introduce a computational model for simulating the growth of breast cancer lesions accounting for the stiffness of surrounding anatomical structures.Approach.In our model, ligaments are classified as the most rigid structures while the softer parts of the breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of the breast's soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure. We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by Graffet alwith a resolution of 50μm generated with the open-source and readily available Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) imaging pipeline. To visualize and validate the realism of the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the siemens mammomat inspiration mammography system in a digital format. This system was instrumental in generating digital mammogram (DM) images for each breast model containing the simulated lesions.Results.By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the lesions as they integrated with the anatomical backgrounds. Our research also involved a reader study that compared 25 simulated DM regions of interest (ROIs) with inserted lesions from our models with DM ROIs from the DDSM dataset containing real manifestations of breast cancer. In general the simulation time for the lesions was approximately 2.5 hours, but it varied depending on the lesion's local environment.Significance.The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the progression of breast cancer.

Influence of posterior cruciate ligament tension on tibiofemoral and patellofemoral joint contact mechanics in cruciate-retaining total knee replacement: a combined musculoskeletal multibody and finite-element simulation

The influence of posterior cruciate ligament (PCL) tension on the clinical outcome of cruciate-retaining total knee replacement (CR-TKR) remains controversial. Various numerical approaches have been used to study this influence systematically, but the models used are limited by certain assumptions and simplifications. Therefore, the objective of this computational study was to develop a combined musculoskeletal multibody and finite-element simulation during a squat motion to 90° knee flexion with a CR-TKR design to overcome previous limitations regarding model inputs. In addition, different PCL tensions (tight, lax, resected) were modeled and the influence on tibiofemoral and resurfaced patellofemoral joint dynamics and contact stresses was evaluated. The effect of the PCL on knee joint dynamics and contact stresses was more pronounced at higher flexion angles. Tibiofemoral joint dynamics were influenced and a tight PCL induced increased posterior femoral translation during flexion. The maximum contact stress in the tibial insert increased from 20.6 MPa to 22.5 MPa for the resected and tightest PCL at 90° knee flexion. Patellofemoral joint dynamics were only slightly affected by PCL tension. However, the maximum contact stress in the patellar component decreased from 58.0 MPa to 53.7 MPa for the resected and tightest PCL at 90° knee flexion. The combination of musculoskeletal multibody and finite-element simulation is a sufficient method to comprehensively investigate knee joint dynamics and contact stresses in CR-TKR. The PCL tension after CR-TKR affects joint dynamics and contact stresses at the articulating implant surfaces.

A novel method to derive a human safety limit for PFOA by gene expression profiling and modelling

Perfluorooctanoic acid (PFOA) is a persistent environmental contaminant that can accumulate in the human body due to its long half-life. This substance has been associated with liver, pancreatic, testicular and breast cancers, liver steatosis and endocrine disruption. PFOA is a member of a large group of substances also known as "forever chemicals" and the vast majority of substances of this group lack toxicological data that would enable their effective risk assessment in terms of human health hazards. This study aimed to derive a health-based guidance value for PFOA intake (ng/kg BW/day) from in vitro transcriptomics data. To this end, we developed an in silico workflow comprising five components: (i) sourcing in vitro hepatic transcriptomics concentration-response data; (ii) deriving molecular points of departure using BMDExpress3 and performing pathway analysis using gene set enrichment analysis (GSEA) to identify the most sensitive molecular pathways to PFOA exposure; (iii) estimating freely-dissolved PFOA concentrations in vitro using a mass balance model; (iv) estimating in vivo doses by reverse dosimetry using a PBK model for PFOA as part of a quantitative in vitro to in vivo extrapolation (QIVIVE) algorithm; and (v) calculating a tolerable daily intake (TDI) for PFOA. Fourteen percent of interrogated genes exhibited in vitro concentration-response relationships. GSEA pathway enrichment analysis revealed that "fatty acid metabolism" was the most sensitive pathway to PFOA exposure. In vitro free PFOA concentrations were calculated to be 2.9% of the nominal applied concentrations, and these free concentrations were input into the QIVIVE workflow. Exposure doses for a virtual population of 3,000 individuals were estimated, from which a TDI of 0.15 ng/kg BW/day for PFOA was calculated using the benchmark dose modelling software, PROAST. This TDI is comparable to previously published values of 1.16, 0.69, and 0.86 ng/kg BW/day by the European Food Safety Authority. In conclusion, this study demonstrates the combined utility of an "omics"-derived molecular point of departure and in silico QIVIVE workflow for setting health-based guidance values in anticipation of the acceptance of in vitro concentration-response molecular measurements in chemical risk assessment.

Preparation, identification, and molecular docking of novel angiotensin-converting enzyme inhibitory peptides derived from rice-based distillers' spent cakes

BACKGROUND

Rice-based distillers' spent cake (RDSC), a by-product of the Chinese liquor (Baijiu) industry, is a potential source of ACE inhibitory peptide. Since angiotensin-converting enzyme (ACE) plays a crucial role in controlling hypertension, inhibition of ACE has been widely emphasized. The ACE inhibitory active peptide derived from by-products of food has been recognized as safer and cheaper inhibitors.

RESULTS

Aimed to discover ACE-inhibiting active peptides in RDSC. Hydrolysis of RDSC by alcalase for 4 h followed by ultrafiltration yielded low-molecular-weight (< 3 kDa) fractions. Subsequently, a comprehensive method that using a combination of LC-Q-TOF-MS and LC-Q Exactive-MS to identify the novel short- (3-5 amino acids residues; n = 7) and medium-sized peptides (more than 6 amino acids residues; n = 6). In vitro activity assay showed that the peptides KPFFPGL, GFPRPLL, GPPGVF, and VGK exhibited the highest activity with IC50 of 11.63, 12.34, 19.55, and 33.54 μM. Molecular docking reveal that the active and inactive sites (Glu123, Asp121, Arg522, and Lys118) play important roles in enhancing the ACE inhibitory activity of peptides.

CONCLUSION

Here we report a comprehensive method that effectively extracted and identified the bioactive peptides from RDSC. Four highly active novel peptides may be the most promising candidates for functional foods to against hypertension, provide significant information for enhancing value of rice-based distilled by-products. This article is protected by copyright. All rights reserved.

Enhancing the Catalytic Efficiency of D-lactonohydrolase through the Synergy of Tunnel Engineering, Evolutionary Analysis, and Force-Field Calculations

Computational design advances enzyme evolution and their use in biocatalysis in a faster and more efficient manner. In this study, a synergistic approach integrating tunnel engineering, evolutionary analysis, and force-field calculations has been employed to enhance the catalytic activity of D-lactonohydrolase (D-Lac), which is a pivotal enzyme involved in the resolution of racemic pantolactone during the production of vitamin B5. The best mutant, N96S/A271E/F274Y/F308G (M3), was obtained and its catalytic efficiency (kcat/KM) was nearly 23-fold higher than that of the wild-type. The M3 whole-cell converted 20 % of DL-pantolactone into D-pantoic acid (D-PA, >99 % e.e.) with a conversion rate of 47 % and space-time yield of 107.1 g L-1 h-1, demonstrating its great potential for industrial-scale D-pantothenic acid production. Molecular dynamics (MD) simulations revealed that the reduction in the steric hindrance within the substrate tunnel and conformational reconstruction of the distal loop resulted in a more favourable"catalytic" conformation, making it easier for the substrate and enzyme to enter their pre-reaction state. This study illustrates the potential of the distal residue on the pivotal loop at the entrance of the D-Lac substrate tunnel as a novel modification hotspot capable of reshaping energy patterns and consequently influencing the enzymatic activity.

SKping cell cycle regulation: role of ubiquitin ligase SKP2 in hematological malignancies

SKP2 (S-phase kinase-associated protein 2) is a member of the F-box family of substrate-recognition subunits in the SCF ubiquitin-protein ligase complexes. It is associated with ubiquitin-mediated degradation in the mammalian cell cycle components and other target proteins involved in cell cycle progression, signal transduction, and transcription. Being an oncogene in solid tumors and hematological malignancies, it is frequently associated with drug resistance and poor disease outcomes. In the current review, we discussed the novel role of SKP2 in different hematological malignancies. Further, we performed a limited in-silico analysis to establish the involvement of SKP2 in a few publicly available cancer datasets. Interestingly, our study identified Skp2 expression to be altered in a cancer-specific manner. While it was found to be overexpressed in several cancer types, few cancer showed a down-regulation in SKP2. Our review provides evidence for developing novel SKP2 inhibitors in hematological malignancies. We also investigated the effect of SKP2 status on survival and disease progression. In addition, the role of miRNA and its associated families in regulating Skp2 expression was explored. Subsequently, we predicted common miRNAs against Skp2 genes by using miRNA-predication tools. Finally, we discussed current approaches and future prospective approaches to target the Skp2 gene by using different drugs and miRNA-based therapeutics applications in translational research.

Expression and functional analysis of a recombinant aquaporin Z from Antarctic Pseudomonas sp. AMS3

Aquaporin (AQP) is a water channel protein from the family of transmembrane proteins which facilitates the movement of water across the cell membrane. It is ubiquitous in nature, however the understanding of the water transport mechanism, especially for AQPs in microbes adapted to low temperatures, remains limited. AQP also has been recognized for its ability to be used for water filtration, but knowledge of the biochemical features necessary for its potential applications in industrial processes has been lacking. Therefore, this research was conducted to express, extract, solubilize, purify, and study the functional adaptations of the aquaporin Z family from Pseudomonas sp. AMS3 via molecular approaches. In this study, AqpZ1 AMS3 was successfully subcloned and expressed in E. coli BL21 (DE3) as a recombinant protein. The AqpZ1 AMS3 gene was expressed under optimized conditions and the best optimized condition for the AQP was in 0.5 mM IPTG incubated at 25°C for 20 h induction time. A zwitterionic mild detergent [(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate was the suitable surfactant for the protein solubilization. The protein was then purified via affinity chromatography. Liposome and proteoliposome was reconstituted to determine the particle size using dynamic light scattering. This information obtained from this psychrophilic AQP identified provides new insights into the structural adaptation of this protein at low temperatures and could be useful for low temperature application and molecular engineering purposes in the future.

In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations

In silico modeling offers an opportunity to supplement and accelerate cardiac safety testing. With in silico modeling, computational simulation methods are used to predict electrophysiological interactions and pharmacological effects of novel drugs on critical physiological processes. The O'Hara-Rudy's model was developed to predict the response to different ion channel inhibition levels on cardiac action potential duration (APD) which is known to directly correlate with the QT interval. APD data at 30% 60% and 90% inhibition were derived from the model to delineate possible ventricular arrhythmia scenarios and the marginal contribution of each ion channel to the model. Action potential values were calculated for epicardial, myocardial, and endocardial cells, with action potential curve modeling. This study assessed cardiac ion channel inhibition data combinations to consider when undertaking in silico modeling of proarrhythmic effects as stipulated in the Comprehensive in Vitro Proarrhythmia Assay (CiPA). As expected, our data highlight the importance of the delayed rectifier potassium channel (IKr) as the most impactful channel for APD prolongation. The impact of the transient outward potassium channel (Ito) inhibition on APD was minimal while the inward rectifier (IK1) and slow component of the delayed rectifier potassium channel (IKs) also had limited APD effects. In contrast, the contribution of fast sodium channel (INa) and/or L-type calcium channel (ICa) inhibition resulted in substantial APD alterations supporting the pharmacological relevance of in silico modeling using input from a limited number of cardiac ion channels including IKr, INa, and ICa, at least at an early stage of drug development.

IL-18 and CD14 variants in chronic HBV predisposition: a case-control study with in silico analyses focused on transcription and splicing

Hepatitis B virus (HBV), a vaccine-avoidable infection, is a health concern worldwide, leading to liver disorders such as acute self-constraint and chronic hepatitis, liver failure, hepatic cirrhosis, and even hepatocellular carcinoma if untreated. 'Immunogeneticprofiling', genetic variations of the pro- and anti-inflammatory cytokines responsible for regulating the immune responses, cause person-to-person differences and impact the clinical manifestation of the disease. The current experimental-bioinformatics research was conducted to examine whether promoteric IL-18-rs187238 C > G and -rs1946518 T > G and intronic CD14-rs2569190 A > G variations are associated with chronic HBV. A total of 400 individuals (200 in each case and control group) participated in the study and were genotyped using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. The data was also assessed bioinformatics-wise for conservation, genomic transcription and splicing, and protein interactions. Findings proposed that unlike the IL-18-rs1946518 T > G and CD14-rs2569190 A > G, the IL-18-rs187238 C > G is a protector against chronic HBV (odds ratio [OR] = 0.62, 95% confidence intervals [CI]: 0.46-0.83, and p = 0.002). The TG/CC/AA, TG/CC/AG, TT/CC/AG, and GG/CC/AA combined genotypes significantly increased chronic HBV risk (p < 0.05), while the IL-18 G/T and G/G haplotypes lessened it (p < 0.05). Moreover, IL-18-rs1946518 T > G is in the protected genomic regions across mammalian species. In contrast to the IL-18-rs1946518 T > G, IL-18-rs187238 C > G is likely to create novel binding sites for transcription factors, and the CD14-rs2569190 A > G presumably changed the ribonucleic acid splicing pattern. More research on larger populations and other ethnicities is required to authenticate these results.

Integrating network pharmacology with molecular docking to rationalize the ethnomedicinal use of Alchornea laxiflora (Benth.) Pax & K. Hoffm. for efficient treatment of depression

Background: Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) has been indicated in traditional medicine to treat depression. However, scientific rationalization is still lacking. Hence, this study aimed to investigate the antidepressant potential of A. laxiflora using network pharmacology and molecular docking analysis. Materials and methods: The active compounds and potential targets of A. laxiflora and depression-related targets were retrieved from public databases, such as PubMed, PubChem, DisGeNET, GeneCards, OMIM, SwissTargetprediction, BindingDB, STRING, and DAVID. Essential bioactive compounds, potential targets, and signaling pathways were predicted using in silico analysis, including BA-TAR, PPI, BA-TAR-PATH network construction, and GO and KEGG pathway enrichment analysis. Later on, with molecular docking analysis, the interaction of essential bioactive compounds of A. laxiflora and predicted core targets of depression were verified. Results: The network pharmacology approach identified 15 active compounds, a total of 219 compound-related targets, and 14,574 depression-related targets with 200 intersecting targets between them. SRC, EGFR, PIK3R1, AKT1, and MAPK1 were the core targets, whereas 3-acetyloleanolic acid and 3-acetylursolic acid were the most active compounds of A. laxiflora with anti-depressant potential. GO functional enrichment analysis revealed 129 GO terms, including 82 biological processes, 14 cellular components, and 34 molecular function terms. KEGG pathway enrichment analysis yielded significantly enriched 108 signaling pathways. Out of them, PI3K-Akt and MAPK signaling pathways might have a key role in treating depression. Molecular docking analysis results exhibited that core targets of depression, such as SRC, EGFR, PIK3R1, AKT1, and MAPK1, bind stably with the analyzed bioactive compounds of A. laxiflora. Conclusion: The present study elucidates the bioactive compounds, potential targets, and pertinent mechanism of action of A. laxiflora in treating depression. A. laxiflora might exert an antidepressant effect by regulating PI3K-Akt and MAPK signaling pathways. However, further investigations are required to validate.

Biodirected Screening and Preparation of Larimichthys crocea Angiotensin-I-Converting Enzyme-Inhibitory Peptides by a Combined In Vitro and In Silico Approach

Food-derived angiotensin-I-converting enzyme (ACE)-inhibitory peptides have gained attention for their potent and safe treatment of hypertensive disorders. However, there are some limitations of conventional methods for preparing ACE-inhibitory peptides. In this study, in silico hydrolysis, the quantitative structure-activity relationship (QSAR) model, LC-MS/MS, inhibition kinetics, and molecular docking were used to investigate the stability, hydrolyzability, in vitro activity, and inhibition mechanism of bioactive peptides during the actual hydrolysis process. Six novel ACE-inhibitory peptides were screened from the Larimichthys crocea protein (LCP) and had low IC50 values (from 0.63 ± 0.09 µM to 10.26 ± 0.21 µM), which were close to the results of the QSAR model. After in vitro gastrointestinal simulated digestion activity of IPYADFK, FYEPFM and NWPWMK were found to remain almost unchanged, whereas LYDHLGK, INEMLDTK, and IHFGTTGK were affected by gastrointestinal digestion. Meanwhile, the inhibition kinetics and molecular docking results were consistent in that ACE-inhibitory peptides of different inhibition forms could effectively bind to the active or non-central active centers of ACE through hydrogen bonding. Our proposed method has better reproducibility, accuracy, and higher directivity than previous methods. This study can provide new approaches for the deep processing, identification, and preparation of Larimichthys crocea.

Phytochemical profiling of the essential oils from three Curcuma species and their in vitro and in silico dengue protease inhibition activity

The chemical compositions, in vitro and in silico anti-dengue activity of the essential oils of the rhizomes of Curcuma longa Linn., C. aeruginosa Roxb., and C. xanthorrhiza Roxb. had been investigated. The C. longa oil was mainly composed of ar-turmerone (54.0%) and curlone (17.7%), while the C. aeruginosa oil was rich in curzerenone (23.4%), 1,8-cineole (21.2%), and camphor (7.1%). Xanthorrhizol (21.6%), β-curcumene (19.5%), ar-curcumene (14.2%), and camphor (9.2%) were the major compounds in the C. xanthorrhiza oil. Among the oils, the C. longa oil was found to be the most active NSB-NS3 protease inhibitor (IC50 1.98 μg/mL). PLS biplot disclosed that the essential oils were classified into three separated clusters based on their characteristic chemical compositions, with C. longa positioned closest to the in vitro anti-dengue activity. Four compounds from the C. longa oil have both hydrogen and hydrophobic bonds that could be responsible for the DENV-2 NS2B-NS3 inhibitory effect.

Synthesis, in silico screening, and biological evaluation of novel pyridine congeners as anti-epileptic agents targeting AMPA (α-amino-3-hydroxy-5-methylisoxazole) receptors

The research involves the synthesis of a series of new pyridine analogs 5(i-x) and their evaluation for anti-epileptic potential using in silico and in vivo models. Synthesis of the compounds was accomplished by using the Vilsmeier-Haack reaction principle. AutoDock 4.2 was used for their in silico screening against AMPA (-amino-3-hydroxy-5-methylisoxazole) receptor (PDB ID:3m3f). For in vivo testing, the maximal electroshock seizure (MES) model was used. The physicochemical, pharmacokinetic, drug-like, and drug-score features of all synthesized compounds were assessed using the online Swiss ADME and Protein Plus software. The in silico results showed that all the synthesized compounds 5(i-x) had 1-3 interactions and affinities ranging from -6.5 to -8.0 kJ/mol with the targeted receptor compared to the binding affinities of the standard drug phenytoin and the original ligand of the target (P99), which were -7.6 and -6.8 kJ/mol, respectively. In vivo study results showed that the compound 5-Carbamoyl-2-formyl-1-[2-(4-nitrophenyl)-2-oxo-ethyl]-pyridinium gave 60% protection against epileptic seizures compared to 59% protection afforded by regular phenytoin. All of them met Lipinski's rule of five and had drug-likeness and drug score values of 0.55 and 0.8, respectively, making them chemically and functionally like phenytoin. According to the findings of the studies, the synthesized derivatives have the potential to be employed as a stepping stone in the development of novel anti-epileptic drugs.

Chemico-pharmacological evaluation of the methanolic leaf extract of Catharanthus ovalis: GC-MS/MS, in vivo, in vitro, and in silico approaches

Introduction: Natural plant-based medicines have gained popularity recently as a major source of inventive, risk-free, and more potent secondary bioactive compounds with medicinal potential. Catharanthus ovalis is a perennial shrub containing various indole alkaloids cultivated extensively for local medical purposes. Methods: This research is conducted to identify the phytocompounds present in the leaves of C. ovalis and its central and peripheral analgesic, thrombolytic, and membrane-stabilizing activities through tail immersion, acetic acid-induced writhing, human blood clot lysis, and erythrocyte lysis by heat and hypotonic solution methods, respectively. Results and discussion: A total of 39 compounds were identified using GC-MS/MS techniques, including hexadecanoic acid, methyl ester (56.749%), methyl stearate (29.782%), carvacrol and its TBDMS derivative (12.586%), and 9-octadecenoic acid, methyl ester, (E)-] (9.297%) presented in high quantity. The highest tail immersion latency was observed for the 600 mg/kg extract of C. ovalis crude extract. Both 400 and 600 mg/kg doses of C. ovalis crude extract exhibited prominent peripheral analgesic activity. The maximum thrombolytic effect was observed by DCM soluble fraction extract by inhibiting 54.87% of the clot. However, the aqueous-soluble fraction of this extract manifested an excellent membrane-stabilizing effect by showing 73.98% and 87.51% hemolysis against heat- and hypotonic-induced hemolysis, respectively. Some of the compounds were identified as active agents against different receptors related to these diseases, which supported the findings of in vitro and in vivo tests. Conclusion: Further investigation needs to be conducted to specify and identify the exact mechanism of action of these compounds.

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer

Despite progress made with immune checkpoint inhibitors and targeted therapies, skin cancer remains a significant public health concern in the United States. The intricacies of the disease, encompassing genetics, immune responses, and external factors, call for a comprehensive approach. Techniques in systems genetics, including transcriptional correlation analysis, functional pathway enrichment analysis, and protein-protein interaction network analysis, prove valuable in deciphering intricate molecular mechanisms and identifying potential diagnostic and therapeutic targets for skin cancer. Recent studies demonstrate the efficacy of these techniques in uncovering molecular processes and pinpointing diagnostic markers for various skin cancer types, highlighting the potential of systems genetics in advancing innovative therapies. While certain limitations exist, such as generalizability and contextualization of external factors, the ongoing progress in AI technologies provides hope in overcoming these challenges. By providing protocols and a practical example involving Braf, we aim to inspire early-career experimental dermatologists to adopt these tools and seamlessly integrate these techniques into their skin cancer research, positioning them at the forefront of innovative approaches in combating this devastating disease.

Comparative Analysis of the Structure and Pharmacological Properties of Some Piperidines and Host-Guest Complexes of β-Cyclodextrin

Pain and anesthesia are a problem for all physicians. Scientists from different countries are constantly searching for new anesthetic agents and methods of general anesthesia. In anesthesiology, the role and importance of local anesthesia always remain topical. In the present work, a comparative analysis of the results of pharmacological studies on models of the conduction and terminal anesthesia, as well as acute toxicity studies of the inclusion complex of 1-methyl-4-ethynyl-4-hydroxypiperidine (MEP) with β-cyclodextrin, was carried out. A virtual screening and comparative analysis of pharmacological activity were also performed on a number of the prepared piperidine derivatives and their host-guest complexes of β-cyclodextrin to identify the structure-activity relationship. Various programs were used to study biological activity in silico. For comparative analysis of chemical and pharmacological properties, data from previous works were used. For some piperidine derivatives, new dosage forms were prepared as beta-cyclodextrin host-guest complexes. Some compounds were recognized as promising local anesthetics. Pharmacological studies have shown that KFCD-7 is more active than reference drugs in terms of local anesthetic activity and acute toxicity but is less active than host-guest complexes, based on other piperidines. This fact is in good agreement with the predicted results of biological activity.

Quassinoids from Eurycoma longifolia as Potential Dihydrofolate Reductase Inhibitors: A Computational Study

BACKGROUND

Quassinoids are degraded triterpene compounds that can be obtained from various species of the Simaroubaceae plant family, including Eurycoma longifolia. Quassinoids are the major compounds in E. longifolia, and they are known to have various medicinal potentials, such as anticancer and antimalarial properties. Dihydrofolate reductase (DHFR) was reported to be one of the important targets for certain anticancer and antimalarial drugs. Twelve quassinoids from E. longifolia were identified to have anticancer effects based on their IC50 values. This study aimed to evaluate the interactions of these twelve quassinoids with DHFR via Autodock 4.2 software and Biovia Discovery Studio Visualiser.

METHODS

Twelve quassinoids from E. longifolia and their interactions with DHFR were evaluated via Autodock 4.2 software and Biovia Discovery Studio Visualiser. Their drug-likeness and pharmacokinetic properties were also assessed using the ADMETlab 2.0 program.

RESULTS

The molecular docking results showed that eleven quassinoids showed better docking scores than methotrexate, in which the binding energy (BE) of these quassinoids ranged from - 7.87 to -9.58 kcal/mol. Their inhibition constant (Ki) ranged from 0.095 to 1.71 μM. At the same time, the BE and Ki values for methotrexate were -7.80 kcal/mol and 1.64 μM, respectively.

CONCLUSION

From the analysis, 6-dehydrolongilactone and eurycomalide B are among the twelve compounds that showed great potential as hit-to-lead compounds based on the docking score on DHFR, drug-likeness, and ADMET properties. These results suggest a great potential to pursue validation studies via in vitro and in vivo models.

Oncopig bladder cancer cells recapitulate human bladder cancer treatment responses in vitro

Introduction

Bladder cancer is a common neoplasia of the urinary tract that holds the highest cost of lifelong treatment per patient, highlighting the need for a continuous search for new therapies for the disease. Current bladder cancer models are either imperfect in their ability to translate results to clinical practice (mouse models), or rare and not inducible (canine models). Swine models are an attractive alternative to model the disease due to their similarities with humans on several levels. The Oncopig Cancer Model has been shown to develop tumors that closely resemble human tumors. However, urothelial carcinoma has not yet been studied in this platform.

Methods

We aimed to develop novel Oncopig bladder cancer cell line (BCCL) and investigate whether these urothelial swine cells mimic human bladder cancer cell line (5637 and T24) treatment-responses to cisplatin, doxorubicin, and gemcitabine in vitro.

Results

Results demonstrated consistent treatment responses between Oncopig and human cells in most concentrations tested (p>0.05). Overall, Oncopig cells were more predictive of T24 than 5637 cell therapeutic responses. Microarray analysis also demonstrated similar alterations in expression of apoptotic (GADD45B and TP53INP1) and cytoskeleton-related genes (ZMYM6 and RND1) following gemcitabine exposure between 5637 (human) and Oncopig BCCL cells, indicating apoptosis may be triggered through similar signaling pathways. Molecular docking results indicated that swine and humans had similar Dg values between the chemotherapeutics and their target proteins.

Discussion

Taken together, these results suggest the Oncopig could be an attractive animal to model urothelial carcinoma due to similarities in in vitro therapeutic responses compared to human cells.

Short Fragmented Peptides from Pardachirus Marmoratus Exhibit Stronger Anticancer Activities in In Silico Residue Replacement and Analyses

BACKGROUND

Cancer is a worldwide issue. It has been observed that conventional therapies face many problems, such as side effects and drug resistance. Recent research reportedly used marine-derived products to treat various diseases and explored their potential in treating cancers.

OBJECTIVE

This study aims to discover short-length anticancer peptides derived from pardaxin 6 through an in silico approach.

METHODS

Fragmented peptides ranging from 5 to 15 amino acids were derived from the pardaxin 6 parental peptide. These peptides were further replaced with one residue and, along with the original fragmented peptides, were predicted for their SVM scores and physicochemical properties. The top 5 derivative peptides were further examined for their toxicity, hemolytic probability, peptide structures, docking models, and energy scores using various web servers. The trend of in silico analysis outputs across 5 to 15 amino acid fragments was further analyzed.

RESULTS

Results showed that when the amino acids were increased, SVM scores of the original fragmented peptides were also increased. Designed peptides had increased SVM scores, which was aligned with previous studies where the single residue replacement transformed the non-anticancer peptide into an anticancer agent. Moreover, in vitro studies validated that the designed peptides retained or enhanced anticancer effects against different cancer cell lines. Interestingly, a decreasing trend was observed in those fragmented derivative peptides.

CONCLUSION

Single residue replacement in fragmented pardaxin 6 was found to produce stronger anticancer agents through in silico predictions. Through bioinformatics tools, fragmented peptides improved the efficiency of marine-derived drugs with higher efficacy and lower hemolytic effects in treating cancers.

In silico assessment of a natural small molecule as an inhibitor of programmed death ligand 1 for cancer immunotherapy: a computational approach

Programmed cell death ligand 1 (PD-L1) is a crucial target for cancer therapy. Here, an in silico study investigates PD-L1 to inhibit its interaction with PD1, thereby promoting an immune response to eliminate cancer cells. The study employed machine learning (ML) -based QSAR to detect PDL1 inhibitors. Morgan's fingerprint with docking score showed a 0.83 correlation with the experimental IC50, enabling the screening of 3200 natural compounds. The top three compounds, considered 2819, 2821 and 3188, were selected from the ML-based QSAR and subjected to molecular docking and simulation. The binding scores for 2819, 2821 and 3188 were -7.0, -9.0 and -8.9 kcal/mol, respectively. The stability of the ligands during a 100 ns simulation was assessed using RMSD, showing that 2819 and 2821 maintained stable patterns comparable to the control inhibitor. Notably, 2819 exhibited a consistent stable pattern throughout the simulation, while 2821 showed stability in the last 40 ns. The control compound showed the highest number of hydrogen bonds with proteins, whereas compounds 2819 and 2821 formed continuous H-bonds. 3188 was separated from the protein in later phases and is not regarded as a potential PD-L1-binding molecule. MMGBSA binding free energy for complexes was computed. Control had the lowest binding free energy, while 2819 and 2821 also had lower binding energies. In contrast, 3188 showed poor binding free energy, causing protein separation. Principal component analysis showed a loss of entropy and reduced protein conformational variation. Overall, 2819 and 2821 are potential binders for PD-L1 inhibition and immune response triggering.Communicated by Ramaswamy H. Sarma.

In Silico Screening of Bacteriocin Gene Clusters within a Set of Marine Bacillota Genomes

Due to their potential application as an alternative to antibiotics, bacteriocins, which are ribosomally synthesized antimicrobial peptides produced by bacteria, have received much attention in recent years. To identify bacteriocins within marine bacteria, most of the studies employed a culture-based method, which is more time-consuming than the in silico approach. For that, the aim of this study was to identify potential bacteriocin gene clusters and their potential producers in 51 marine Bacillota (formerly Firmicutes) genomes, using BAGEL4, a bacteriocin genome mining tool. As a result, we found out that a majority of selected Bacillota (60.78%) are potential bacteriocin producers, and we identified 77 bacteriocin gene clusters, most of which belong to class I bacteriocins known as RiPPs (ribosomally synthesized and post-translationally modified peptides). The identified putative bacteriocin gene clusters are an attractive target for further in vitro research, such as the production of bacteriocins using a heterologous expression system.

N-Phenylnorbornenesuccinimide derivatives, agricultural defensive, and enzymatic target selection

BACKGROUND

Faced with the need to develop new herbicides with modes of action different to those observed for existing agrochemicals, one of the most promising strategies employed by synthetic chemists involves the structural modification of molecules found in natural products. Molecules containing amides, imides, and epoxides as functional groups are prevalent in nature and find extensive application in synthesizing more intricate compounds due to their biological properties. In this context, this paper delineates the synthesis of N-phenylnorbornenesuccinimide derivatives, conducts biological assays, and carries out in silico investigation of the protein target associated with the most potent compound in plant organisms. The phytotoxic effects of the synthesized compounds (2-29) were evaluated on Allium cepa, Bidens pilosa, Cucumis sativus, Sorghum bicolor, and Solanum lycopersicum.

RESULTS

Reaction of endo-bicyclo[2.2.1]hept-5-ene-3a,7a-dicarboxylic anhydride (1) with aromatic amines led to the N-phenylnorbornenesuccinic acids (2-11) with yields ranging from 75% to 90%. Cyclization of compounds (2-11) in the presence of acetic anhydride and sodium acetate afforded N-phenylnorbornenesuccinimides (12-20) with yields varying from 65% to 89%. Those imides were then subjected to epoxidation reaction to afford N-phenylepoxynorbornanesuccimides (21-29) with yields from 60% to 90%. All compounds inhibited the growth of seedlings of the plants evaluated. Substance 23 was the most active against the plants tested, inhibiting 100% the growth of all species in all concentrations. Cyclophilin was found to be the enzymatic target of compound 23.

CONCLUSION

These findings suggest that derivatives of N-phenylnorbornenesuccinimide are promising compounds in the quest for more selective and stable agrochemicals. This perspective reinforces the significance of these derivatives as potential innovative herbicides and emphasizes the importance of further exploring their biological activity on weeds. © 2024 Society of Chemical Industry.

Immunoinformatic Identification of Multiple Epitopes of gp120 Protein of HIV-1 to Enhance the Immune Response against HIV-1 Infection

Acquired Immunodeficiency Syndrome is caused by the Human Immunodeficiency Virus (HIV), and a significant number of fatalities occur annually. There is a dire need to develop an effective vaccine against HIV-1. Understanding the structural proteins of viruses helps in designing a vaccine based on immunogenic peptides. In the current experiment, we identified gp120 epitopes using bioinformatic epitope prediction tools, molecular docking, and MD simulations. The Gb-1 peptide was considered an adjuvant. Consecutive sequences of GTG, GSG, GGTGG, and GGGGS linkers were used to bind the B cell, Cytotoxic T Lymphocytes (CTL), and Helper T Lymphocytes (HTL) epitopes. The final vaccine construct consisted of 315 amino acids and is expected to be a recombinant protein of approximately 35.49 kDa. Based on docking experiments, molecular dynamics simulations, and tertiary structure validation, the analysis of the modeled protein indicates that it possesses a stable structure and can interact with Toll-like receptors. The analysis demonstrates that the proposed vaccine can provoke an immunological response by activating T and B cells, as well as stimulating the release of IgA and IgG antibodies. This vaccine shows potential for HIV-1 prophylaxis. The in-silico design suggests that multiple-epitope constructs can be used as potentially effective immunogens for HIV-1 vaccine development.

In silico identification of human microRNAs pointing centrin genes in Leishmania donovani: Considering the RNAi-mediated gene control

Leishmaniasis, a parasitic disease caused by different species of the protozoa parasite Leishmania, is a neglected tropical human disease that is endemic in about a hundred countries worldwide. According to the World Health Organization (WHO), the annual incidence of cutaneous leishmaniasis (CL) is estimated to be 0.7-1.2 million cases globally, whereas the annual incidence of visceral leishmaniasis is estimated to be 0.2-0.4 million cases. In many eukaryotic organisms, including human beings and protozoan parasites, centrin genes encode proteins that play essential roles within the centrosome or basal body. Human microRNAs (miRNAs) have been linked to several infectious and non-infectious diseases associated with pathogen-host interactions, and they play the emphatic roles as gene expression regulators. In this study, we used the MirTarget bioinformatics tool, which is a machine learning-based approach implemented in miRDB, to predict the target of human miRNAs in Leishmania donovani centrin genes. For cross-validation, we utilized additional prediction algorithms, namely, RNA22 and RNAhybrid, targeting all five centrin isotypes. The centrin-3 (LDBPK_342160) and putative centrin-5 (NC_018236.1) genes in L. donovani were targeted by eight and twelve human miRNAs, respectively, among 2,635 known miRNAs (miRBase). hsa-miR-5193 consistently targeted both genes. Using TargetScan, TarBase, miRecords, and miRTarBase, we identified miRNA targets and off-targets in human homologs of centrin, inflammation, and immune-responsive genes. Significant targets were screened based on GO terminologies and KEGG pathway-enrichment analysis (Log10 p-value >0.0001). In silico tools that predict the biological roles of human miRNAs as primary gene regulators in pathogen-host interactions help unravel the regulatory patterns of these miRNAs, particularly in the early stages of inflammatory responses. It is also noted that these miRNAs played an important role in the late phase of adaptive immune response, inclusively their impacts on the immune system's response to L. donovani.

Predicting sulfanilamide solubility in the binary mixtures using a reference solvent approach

BACKGROUND

Solubility is a fundamental physicochemical property of active pharmaceutical ingredients. The optimization of a dissolution medium aims not only to increase solubility and other aspects are to be included such as environmental impact, toxicity degree, availability, and costs. Obtaining comprehensive solubility characteristics of chemical compounds is a non-trivial and demanding process. Therefore, support from theoretical approaches is of practical importance.

OBJECTIVES

This study aims to examine the accuracy of the reference solubility approach in the case of sulfanilamide dissolution in a variety of binary solvents. This pharmaceutically active substance has been extensively studied, and a substantial amount of solubility data is available. Unfortunately, using this set of data directly for theoretical modeling is impeded by noticeable inconsistencies in the published solubility data. Hence, this aspect is addressed by data curation using theoretical and experimental confirmations.

MATERIAL AND METHODS

In the experimental part of our study, the popular shake-flask method combined with ultraviolet (UV) spectrophotometric measurements was applied for solubility determination. The computational phase utilized the conductor-like screening model for real solvents (COSMO-RS) approach.

RESULTS

The analysis of the results of solubility calculations for sulfonamide in binary solvents revealed abnormally high error values for acetone-ethyl acetate mixtures, which were further confirmed with experimental measurements. Additional confirmation was obtained by extending the solubility measurements to a series of homologous acetate esters.

CONCLUSIONS

Our study addresses the crucial issue of coherence of solubility data used for many theoretical inquiries, including parameter fitting of semi-empirical models, in-depth thermodynamic interpretations and application of machine learning protocols. The effectiveness of the proposed methodology for dataset curation was demonstrated for sulfanilamide solubility in binary mixtures. This approach enabled not only the formulation of a consistent dataset of sulfanilamide solubility binary solvent mixtures, but also its implementation as a qualitative tool guiding rationale solvent selection for experimental solubility screening.

An In Silico Model for Predicting the Efficacy of Edge-to-Edge Repair for Mitral Regurgitation

In recent years, transcatheter edge-to-edge repair (TEER) has been widely adopted as an effective treatment for mitral regurgitation (MR). The aim of this study is to develop a personalized in silico model to predict the effect of edge-to-edge repair in advance to the procedure for each individual patient. For this purpose, we propose a combination of a valve deformation model for computing the mitral valve (MV) orifice area (MVOA) and a lumped parameter model for the hemodynamics, specifically mitral regurgitation volume (RVol). Although we cannot obtain detailed information on the three-dimensional flow field near the mitral valve, we can rapidly simulate the important medical parameters for the clinical decision support. In the present method, we construct the patient-specific pre-operative models by using the parameter optimization and then simulate the postoperative state by applying the additional clipping condition. The computed preclip MVOAs show good agreement with the clinical measurements, and the correlation coefficient takes 0.998. In addition, the MR grade in terms of RVol also has good correlation with the grade by ground truth MVOA. Finally, we try to investigate the applicability for the predicting the postclip state. The simulated valve shapes clearly show the well-known double orifice and the improvement of the MVOA, compared with the preclip state. Similarly, we confirmed the improved reverse flow and MR grade in terms of RVol. A total computational time is approximately 8 h by using general-purpose PC. These results obviously indicate that the present in silico model has good capability for the assessment of edge-to-edge repair.

Simulating photodynamic therapy for the treatment of glioblastoma using Monte Carlo radiative transport

Significance

Glioblastoma (GBM) is a rare but deadly form of brain tumor with a low median survival rate of 14.6 months, due to its resistance to treatment. An independent simulation of the INtraoperative photoDYnamic therapy for GliOblastoma (INDYGO) trial, a clinical trial aiming to treat the GBM resection cavity with photodynamic therapy (PDT) via a laser coupled balloon device, is demonstrated.

Aim

To develop a framework providing increased understanding for the PDT treatment, its parameters, and their impact on the clinical outcome.

Approach

We use Monte Carlo radiative transport techniques within a computational brain model containing a GBM to simulate light path and PDT effects. Treatment parameters (laser power, photosensitizer concentration, and irradiation time) are considered, as well as PDT's impact on brain tissue temperature.

Results

The simulation suggests that 39% of post-resection GBM cells are killed at the end of treatment when using the standard INDYGO trial protocol (light fluence = 200    J / cm 2 at balloon wall) and assuming an initial photosensitizer concentration of 5    μ M . Increases in treatment time and light power (light fluence = 400    J / cm 2 at balloon wall) result in further cell kill but increase brain cell temperature, which potentially affects treatment safety. Increasing the p hotosensitizer concentration produces the most significant increase in cell kill, with 61% of GBM cells killed when doubling concentration to 10    μ M and keeping the treatment time and power the same. According to these simulations, the standard trial protocol is reasonably well optimized with improvements in cell kill difficult to achieve without potentially dangerous increases in temperature. To improve treatment outcome, focus should be placed on improving the photosensitizer.

Conclusions

With further development and optimization, the simulation could have potential clinical benefit and be used to help plan and optimize intraoperative PDT treatment for GBM.

Computational identification and analysis of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene: a structural and functional impact

Cytochrome P450 oxidoreductase (POR) protein is essential for steroidogenesis, and POR gene mutations are frequently associated with P450 Oxidoreductase Deficiency (PORD), a disorder of hormone production. To our knowledge, no previous attempt has been made to identify and analyze the deleterious/pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene through an extensive computational approach. Computational algorithms and tools were employed to identify, characterize, and validate the pathogenic SNPs associated with certain diseases. To begin with, all the high-confidence SNPs were collected, and their structural and functional impacts on the protein structures were explored. The results of various in silico analyses affirm that the A287P and R457H variants of POR could destabilize the interactions between the amino acids and the hydrogen bond networks, resulting in functional deviations of POR. The literature study further confirms that the pathogenic mutations (A287P and R457H) are associated with the onset of PORD. Molecular dynamics simulations (MDS) and essential dynamics (ED) studies characterized the structural consequences of prioritized deleterious mutations, representing the structural destabilization that might disrupt POR biological function. The identified deleterious mutations at the cofactor's binding domains might interfere with the essential interactions between the protein and cofactors, thus inhibiting POR catalytic activity. The consolidated insights from the computational analyses can be used to predict potential deleterious mutants and understand the disease's pathological basis and the molecular mechanism of drug metabolism for the application of personalized medication. HIGHLIGHTSNADPH cytochrome P450 oxidoreductase (POR) mutations are associated with a broad spectrum of human diseasesIdentified and analyzed the most deleterious nsSNPs of POR through the sequence and structure-based prediction toolsInvestigated the structural and functional impacts of the most significant mutations (A287P and R457H) associated with PORDMolecular dynamics and PCA-based FEL analysis were utilized to probe the mutation-induced structural alterations in PORCommunicated by Ramaswamy H. Sarma.

Drug discovery: In silico dry data can bypass biological wet data?

The recent and extraordinary increase in computer power, along with the availability of efficient algorithms based on artificial intelligence, has prompted a large number of inexperienced scientists to challenge the complex and yet competitive world of drug discovery, by pretending to identify new hits through the sole use of computer aided drug design (CADD). Does the golden era of dry data run the risk of overshadowing the importance of wet data and, in doing so, forget that in silico and biological data need each other in successful preclinical drug discovery programmes?

Network pharmacology based pharmacokinetic assessment and evaluation of the therapeutic potential of catechin derivatives as a potential myostatin inhibitor: A special view on Sarcopenic Obesity

Sarcopenic obesity has become a significant age-related metabolic problem. Catechins are flavanol, derivatives which poses a strong antioxidant activity. The major components of catechin derivatives. were identified through our physicochemical and pharmacokinetic parameters estimation. Therefore, in this study, network pharmacology was used to explore the multiple targets related to Sarcopenia, Metabolic syndrome, and obesity. The targets were identified from network analysis. The catechin derivatives were screened using Lipinski's rule of five, Veber scale, Egan scale, and Muegge scale. From this drugglikness property catechin and Epicatechin was selected which were docked towards the myostatin inhibition PDB ID: 3HH2. Furthermore, the computational docking method on Catechin and Epicatechin with the stronger interaction towards myostatin inhibition receptor with the binding energy of -6.90 kcal/mol. and -7.0 kcal/mol from autodock software, respectively, for catechin and Epicatechin. Higher binding energy confirms the pharmacotherapeutic activity of Catechin and Epicatechin toward the myostatin inhibitor target.

Beauv

Imperata cylindrica (L.) P. Beauv. is an invasive species widely used in treatment of several diseases associated with pain and inflammation in different countries including Madagascar. This work aims to report the isolation of the antioxidant, analgesic and anti-nflammatory compounds from the methanol extract of I. cylindrica. The bio-guided method was used to isolate its bioactive compounds by combining chromatographic methods, writhing test in mice and antioxidant assays. Stigmast-4-en-3-one was isolated as one among the compounds responsible for the analgesic and anti-inflammatory properties and isovanillin as one among the antioxidant compounds from the extract. Stigmast-4-en-3-one showed a good oral pharmacokinetic profile and good binding affinities with some pro-inflammatory targets. It did not show any mutagenic effect, nor a carcinogenic one and had a low risk to be a cardiotoxic agent. All of our results provide scientific justification for its traditional medicinal use in the management of pain and inflammatory related diseases.

Molecular docking, dynamics and in vitro analysis of multi-target inhibitors for Clostridioides difficile

The opportunistic pathogen, Clostridioides difficile owes its extreme pathogenicity for its ability to develop antibiotic resistance and recurrent infections. The current antibiotics used for the treatment are showing declining sensitivity and rising antibiotic resistance. Therefore, it is of interest to develop the anti-clostridial drugs to overcome these issues. Hence, we have explored ZINC library to find the suitable lead compounds against five target proteins of C. difficile. Multistep virtual screening is performed to find the suitable compounds that are checked for their stability using molecular dynamics and are validated in vitro against C. difficile. In our study, five compounds viz., ZINC64969876, ZINC13641164, ZINC13691348, ZINC5554596 and ZINC3894278 that inhibit HisC, Spo0A, PdcA, DAHP synthase and cyclic-di GMP proteins, respectively have been identified. Further, these compounds were tested in vitro against four different isolates of C. difficile and all of them were found to inhibit the pathogen. However, to use these compounds as anti-clostridial drugs, further testing needs to be done. The selected compounds from our study are reported for the first time as antimicrobial agents against C. difficile.

Development and application of consensus in silico models for advancing high-throughput toxicological predictions

Computational toxicology models have been successfully implemented to prioritize and screen chemicals. There are numerous in silico (quantitative) structure-activity relationship ([Q]SAR) models for the prediction of a range of human-relevant toxicological endpoints, but for a given endpoint and chemical, not all predictions are identical due to differences in their training sets, algorithms, and methodology. This poses an issue for high-throughput screening of a large chemical inventory as it necessitates several models to cover diverse chemistries but will then generate data conflicts. To address this challenge, we developed a consensus modeling strategy to combine predictions obtained from different existing in silico (Q)SAR models into a single predictive value while also expanding chemical space coverage. This study developed consensus models for nine toxicological endpoints relating to estrogen receptor (ER) and androgen receptor (AR) interactions (i.e., binding, agonism, and antagonism) and genotoxicity (i.e., bacterial mutation, in vitro chromosomal aberration, and in vivo micronucleus). Consensus models were created by combining different (Q)SAR models using various weighting schemes. As a multi-objective optimization problem, there is no single best consensus model, and therefore, Pareto fronts were determined for each endpoint to identify the consensus models that optimize the multiple-criterion decisions simultaneously. Accordingly, this work presents sets of solutions for each endpoint that contain the optimal combination, regardless of the trade-off, with the results demonstrating that the consensus models improved both the predictive power and chemical space coverage. These solutions were further analyzed to find trends between the best consensus models and their components. Here, we demonstrate the development of a flexible and adaptable approach for in silico consensus modeling and its application across nine toxicological endpoints related to ER activity, AR activity, and genotoxicity. These consensus models are developed to be integrated into a larger multi-tier NAM-based framework to prioritize chemicals for further investigation and support the transition to a non-animal approach to risk assessment in Canada.

Synergistic Differential DNA Demethylation Activity of Danshensu (Salvia miltiorrhiza) Associated with Different Probiotics in Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a major hepatic disorder occurring in non-alcohol-drinking individuals. Salvianic acid A or Danshensu (DSS, 3-(3, 4-dihydroxyphenyl)-(2R)-lactic acid), derived from the root of Danshen (Salvia miltiorrhiza), has demonstrated heart and liver protective properties. In this work, we investigated the antioxidant activity and hepatoprotective activity of Danshensu alone and in combination with different agents, such as probiotic bacteria (Lactobacillus casei and Lactobacillus acidophilus), against several assays. The inhibition mechanism of the methylation gene biomarkers, such as DNMT-1, MS, STAT-3, and TET-1, against DSS was evaluated by molecular docking and RT-PCR techniques. The physicochemical and pharmacokinetic ADMET properties of DSS were determined by SwissADME and pkCSM. The results indicated that all lipid blood test profiles, including cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), were reduced after the oral administration of Danshensu combined with probiotics (L. casei and L. acidophilus) that demonstrated good, efficient free radical scavenging activity, measured using anti-oxidant assays. ADMET and drug-likeness properties certify that the DSS could be utilized as a feasible drug since DSS showed satisfactory physicochemical and pharmacokinetic ADMET properties.

High-throughput computational screening for identification of potential hits against bacterial Acriflavine resistance protein B (AcrB) efflux pump

Antibiotic resistance is a pressing global health challenge, driven in part by the remarkable efflux capabilities of efflux pump in AcrB (Acriflavine Resistance Protein B) protein in Gram-negative bacteria. In this study, a multi-approached computational screening strategy encompassing molecular docking, In silico absorption, distribution, metabolism, excretion and toxicity (ADMET) analysis, druglikeness assessment, molecular dynamics simulations and density functional theory studies was employed to identify novel hits capable of acting against AcrB-mediated antibiotic resistance. Ligand library was acquired from the COCONUT database. Performed computational analyses unveiled four promising hit molecules (CNP0298667, CNP0399927, CNP0321542 and CNP0269513). Notably, CNP0298667 exhibited the highest negative binding affinity of -11.5 kcal/mol, indicating a possibility of strong potential to disrupt AcrB function. Importantly, all four hits met stringent druglikeness criteria and demonstrated favorable in silico ADMET profiles, underscoring their potential for further development. MD simulations over 100 ns revealed that the CNP0321542-4DX5 and CNP0269513-4DX5 complexes formed robust and stable interactions with the AcrB efflux pump. The identified hits represent a promising starting point for the design and optimization of novel therapeutics aimed at combating AcrB-mediated antibiotic resistance in Gram-negative bacteria.Communicated by Ramaswamy H. Sarma.

Revealing Propolis Potential Activity on Inhibiting Estrogen Receptor and Heat Shock Protein 90 Overexpressed in Breast Cancer by Bioinformatics Approaches

Breast cancer is the most commonly diagnosed cancer globally, with the highest incidence of breast cancer occurring in Asian countries including Indonesia. Among the types of breast cancer, the estrogen receptor (ER)-positive subtype which is prominent with estrogen receptor alpha (ERα) and heat shock protein 90 (HSP90) overexpression genes becomes the most prevalent than the others, approximately 75% of all breast cancer cases. ERα and HSP90 play a role in breast cancer activities including breast tumor growth, invasion, and metastasis mechanism. Propolis, a natural bee product, has been explored for its anticancer activity. However, there is lack of studies that evaluated the potential inhibitor from propolis compounds to the ERα and HSP90 proteins. Therefore, this article focuses on examining the correlation between ERα and HSP90's role in breast cancer and investigating the potential of 93 unique propolis compositions in inhibiting these genes in breast cancer using in silico approaches. This study revealed the positive correlation between ERα and HSP90 genes in breast cancer disease development. Furthermore, we also found novel potential bioactive compounds of propolis against breast cancer through binding with ERα and HSP90; they were 3',4',7-trihydroxyisoflavone and baicalein-7-O-β-D glucopyranoside, respectively. Further research on these compounds is needed to elucidate deeper mechanisms and activity in the real biological system to develop new breast cancer drug treatments.