Combined fluorescence and electrochemical investigation on the binding interaction between organic acid and human serum albumin

Human serum albumin （HSA） is a plasma protein responsible for the binding and transport of fatty acids and a variety of exogenous chemicals such as drugs and environmental pollutants. Such binding plays a crucial role in determining the ADME （absorption, distribution, metabolism, and excretion） and bioavailability of the pollutants. The binding interaction between HSA and acetic acid （C2）, octanoic acid （C8） and dodecanoic acid （C12） has been investigated by the combination of site-specific fluorescent probe, tryptophan intrinsic fluorescence and tyrosine electrochemistry. For the study of the fatty acid interaction with the two drug-binding sites on HSA, two fluorescent probes, dansylamide and dansyl-L-proline were employed in the displacement measurements. Intrinsic fluorescence of tryptophan in HSA was monitored upon addition of the fatty acids into HSA. Electrocatalyzed response of the tyrosine residues in HSA by a redox mediator was used to investigate the binding interaction. Qualitatively, observations from these three approaches were very similar. HSA did not show any change in the fluorescence and electrochemical experiments after mixing with C2, suggesting there is no significant interaction with the short-chain fatty acid. For C8, the measured signal dropped in a single-exponential mode, indicating an independent and non-cooperative binding. The calculated association constant and binding ratio were 3.1 × 10^6 L/mol and 1 with drug binding Site Ⅰ, 1.1 × 107 L/mol and 1 with Site Ⅱ, and 7.0× 0^4 L/mol and 4 with the tryptophan site, respectively. The measurements with C12 displayed multiple phases of fluorescence change, suggesting cooperativity and allosteric effect of the C12 binding. These results correlate well with those obtained by the established methods, and validate the new approach as a viable tool to study the interactions of environmental pollutants with biological molecules.

Exploring the phytoconstituents targeting TNF-αas potential lead compounds to treat inflammatory diseases:an in-silico approach

Objective To explore the anti-inflammatory phytoconstituents from various plant sources as tumour necrosis factor-α(TNF-α)-inhibitor,a mediator involved in the inflammatory disorder,by in silico molecular docking.Methods Based on previous findings,we performed the in silico assessment of anti-inflammatory phytoconstituents from different medicinal plants to understand their binding patterns against TNF-α(PDB ID:6OP0)using AutoDock Vina.Molecular docking was performed by setting a grid box(25×25×25)Åcentered at[-12.817×(-1.618)×19.009]A with 0.375A of grid spacing.Furthermore,Discovery Studio Client 2020 program was utilized to assess two-and three-dimensional(2D and 3D)hydrogen-bond interactions concerning an amino acid of target and ligand.Physicochemical properties were reported using the Lipinski’s rule and SwissADME database to support the in silico findings.Results From the selected medicinal plants,more than 200 phytocompounds were screened against TNF-α protein with binding scores in the range of -12.3 to -2.5 kcal/mol.Amongst them,emodin,aloe-emodin,pongamol,purpuritenin,semiglabrin,ellagic acid,imperatorin,α-tocopherol,and octanorcucurbitacin A showed good binding affinity as -10.6,-10.0,-10.5,-10.1,-11.2,-10.3,-10.1,-10.1,and -10.0 kcal/mol,respectively.Also,the absorption,distribution,metabolism,excretion,and toxicology(ADMET)profiles were well within acceptable limits.Conclusion Based on our preliminary findings,we conclude that the selected phytoconstituents have the potential to be good anti-inflammatory candidates by inhibiting the TNF-α target.These compounds can be further optimized and validated as new therapeutic components to develop more effective and safe anti-inflammatory drugs.

Engineering and finetuning expression of SerC for balanced metabolic flux in vitamin B6 production

Vitamin B6 plays a crucial role in cellular metabolism and stress response,making it an essential component for growth in all known organisms.However,achieving efficient biosynthesis of vitamin B6 faces the challenge of maintaining a balanced distribution of metabolic flux between growth and production.In this study,our focus is on addressing this challenge through the engineering of phosphoserine aminotransferase(SerC)to resolve its redundancy and promiscuity.The enzyme SerC was semi-designed and screened based on sequences and predicted kcat values,respectively.Mutants and heterologous proteins showing potential were then fine-tuned to optimize the production of vitamin B6.The resulting strain enhances the production of vitamin B6,indicating that different fluxes are distributed to the biosynthesis pathway of serine and vitamin B6.This study presents a promising strategy to address the challenge posed by multifunctional enzymes,with significant implications for enhancing biochemical production through engineering processes.

Anaerobic ammonia oxidizing bacteria： ecological distribution, metabolism, and microbial interactions

Anammox （ANaerobic AMMonia OXidation） is a newly discovered pathway in the nitrogen cycle. This discovery has increased our knowledge of the global nitrogen cycle and triggered intense interest for anammox-based applications. Anammox bacteria are almost ubiquitous in the suboxic zones of almost all types of natural ecosystems and contribute significant to the global total nitrogen loss. In this paper, their ecological distributions and contributions to the nitrogen loss in marine, wetland, terrestrial ecosystems, and even extreme environments were reviewed. The unique metabolic mechanism of anammox bacteria was well described, including the particular cellular structures and genome compositions, which indicate the special evolutionary status of anammox bacteria. Finally, the ecological interactions among anammox bacteria and other organisms were discussed based on substrate availability and spatial organizations. This review attempts to summarize the fundamental understanding of anammox, provide an up-to-date summary of the knowledge of the overall anammox status, and propose future prospects for anammox. Based on novel findings, the metagenome has become a powerful tool for the genomic analysis of communities containing anammox bacteria; the metabolic diversity and biogeochemistry in the global nitrogen budget require more comprehensive studies.

Phenolic metabolism and molecular mass distribution of polysaccharides in cellulose-deficient maize cells

As a consequence of the habituation to low levels of dichlobenil （DCB）, cultured maize cells presented an altered hemicellulose cell fate with a lower proportion of strongly wall-bound hemicelluloses and an increase in soluble extracellular polymers released into the culture medium. The aim of this study was to investigate the relative molecular mass distributions of polysaccharides as well as phenolic metabolism in cells habituated to low levels of DCB （1.5 μM）. Generally, cell wall bound hemicelluloses and sloughed polymers from habituated cells were more homogeneously sized and had a lower weight-average relative molecular mass. In addition, polysaccharides underwent massive cross-linking after being secreted into the cell wall, but this cross-linking was less pronounced in habituated cells than in non-habituated ones. However, when relativized, ferulic acid and p-coumaric acid contents were higher in this habituated cell line. Feasibly, cells habituated to low levels of DCB synthesized molecules with a lower weight-average relative molecular mass, although cross-linked, as a part of their strategy to compensate for the lack of cellulose.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.

Study of Aldo-keto Reductase 1C3 Inhibitor with Novel Framework for Treating Leukaemia Based on Virtual Screening and In vitro Biological Activity Testing

Aldo-keto reductase 1C3(AKR1C3)is a potential target for the treatment of acute myeloid leukaemia and T-cell acute lymphoblastic leukaemia.In this study,pharmacophore models,molecular docking and virtual screening of target prediction were used to find a potential AKR1C3 inhibitor.Firstly,eight bacteriocin derivatives(Z1-Z8)were selected as training sets to construct 20 pharmacophore models.The best pharmacophore model MODEL_016 was obtained by Decoy test(the enrichment degree was 21.5117,and the fitting optimisation degree was 0.9668).Secondly,MODEL_016 was used for the virtual screening of ZINC database.Thirdly,the hit 83256 molecules were docked into the AKR1C3 protein.Compared to the total scores and interactions between compounds and protein,16532 candidate compounds with higher docking scores and interactions with important residues PHE306 and TRP227 were screened.Lastly,eight compounds(A1-A8)that had good absorption,distribution,metabolism,excretion and toxicity(ADMET)properties were obtained by target prediction.Compounds A3 and A7 with high total score and good target prediction results were selected for in vitro biological activity test,whose IC_(50) values were 268.3 and 88.94µmol/L,respectively.The results provide an important foundation for the discovery of novel AKR1C3 inhibitors.The research methods used in this study can also provide important references for the research and development of new drugs.