Defluorination of Organofluorine Compounds Using Dehalogenase Enzymes from Delftia acidovorans (D4B).

Organofluorine compounds have been widely used as pharmaceuticals, agricultural pesticides, and water-resistant coatings for decades; however, these compounds are recognized as environmental pollutants. The capability of microorganisms and enzymes to defluorinate organofluorine compounds is both rare and highly desirable to facilitate environmental remediation efforts. Recently, a strain of Delftia acidovorans (D4B) was identified with potential biodegradation activity toward perfluoroalkyl substances (PFAS) and other organofluorine compounds. Genomic analysis found haloacid and fluoroacetate dehalogenases as enzymes associated with Delftia acidovorans. Here, defluorination activity of these enzymes toward different fluorinated substrates was investigated after their recombinant expression and purification from E. coli. Using an electrochemical fluoride probe, 19F NMR, and mass spectrometry to monitor defluorination, we identified two dehalogenases, DeHa2 (a haloacid dehalogenase) and DeHa4 (a fluoroacetate dehalogenase), with activity toward mono- and difluoroacetate. Of the two dehalogenases, DeHa4 demonstrated a low pH optimum compared to DeHa2, which lost catalytic activity under acidic conditions. DeHa2 and DeHa4 are relatively small proteins, operate under aerobic conditions, and remain active for days in the presence of substrates. Significantly, while there have been many reports on dehalogenation of monofluoroacetate by dehalogenases, this study adds to the relatively small list of enzymes reported to carry out enzymatic defluorination of the more recalcitrant disubstituted carbon in an organofluorine compound. Thus, DeHa2 and DeHa4 represent organofluorine dehalogenases that may be used in the future to design and engineer robust defluorination agents for environmental remediation efforts.

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein.

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

Enhanced humification of full-scale apple wood and cow manure by promoting lignocellulose degradation via biomass pretreatments.

Agroforestry waste and cow manure pollute the environment, of which, agroforestry waste is difficult to degrade. Compost is an effective way to dispose agroforestry waste; however, the low degradation efficiency of lignocellulose in agroforestry waste affects the process of composting humification. This study investigated lignocellulose degradation and composting humification in full-size apple wood and cow manure composting processes by applying different pretreatments (acidic, alkaline, and high-temperature) to apple wood. Simultaneously, physicochemical characterization and metagenome sequencing were combined to analyze the function of carbohydrate-active enzymes database (CAZy). Therefore, microbial communities and functions were linked during the composting process and the lignocellulose degradation mechanism was elaborated. The results showed that the addition of apple wood increased the compost humus (HS) yield, and pretreatment of apple wood enhanced the lignocellulose degradation during composting processes. In addition, pretreatment improved the physicochemical properties, such as temperature, pH, electric conductivity (EC), ammonium nitrogen (NH4+), and nitrate nitrogen (NO3-) in the compost, of which, acid treated apple wood compost (AcAWC) achieved the highest temperature of 58.4 °C, effectively promoting nitrification with NO3- ultimately reaching 0.127 g/kg. In all composts, microbial networks constructed a high proportion of positively correlated connections, and microorganisms promoted the composting process through cooperation. The proportions of glycosyltransferase (GT) and glycoside hydrolase (GH) promoted the separation and degradation of lignocellulose during composting to form HS. Notably, the adverse effects of the alkali-treated apple wood compost on bacteria were greater. AcAWC showed significant correlations between bacterial and fungal communities and both lignin and hemicellulose, and had more biomarkers associated with lignocellulose degradation and humification. The lignin degradation rate was 24.57 % and the HS yield increased by 27.49 %. Therefore, AcAWC has been confirmed to enhance lignocellulose degradation and promote compost humification by altering the properties of the apple wood and establishing a richer microbial community.

Accurate prediction by AlphaFold2 for ligand binding in a reductive dehalogenase and implications for PFAS (per- and polyfluoroalkyl substance) biodegradation.

Despite the success of AlphaFold2 (AF2), it is unclear how AF2 models accommodate for ligand binding. Here, we start with a protein sequence from Acidimicrobiaceae TMED77 (T7RdhA) with potential for catalyzing the degradation of per- and polyfluoroalkyl substances (PFASs). AF2 models and experiments identified T7RdhA as a corrinoid iron-sulfur protein (CoFeSP) which uses a norpseudo-cobalamin (BVQ) cofactor and two Fe4S4 iron-sulfur clusters for catalysis. Docking and molecular dynamics simulations suggest that T7RdhA uses perfluorooctanoic acetate (PFOA) as a substrate, supporting the reported defluorination activity of its homolog, A6RdhA. We showed that AF2 provides processual (dynamic) predictions for the binding pockets of ligands (cofactors and/or substrates). Because the pLDDT scores provided by AF2 reflect the protein native states in complex with ligands as the evolutionary constraints, the Evoformer network of AF2 predicts protein structures and residue flexibility in complex with the ligands, i.e., in their native states. Therefore, an apo-protein predicted by AF2 is actually a holo-protein awaiting ligands.

Biochar-goethite composites inhibited/enhanced degradation of triphenyl phosphate by activating persulfate: Insights on the mechanism.

In this study, the biochar-goethite composites (MBC@FH) were synthesized through co-ball milling and the degradation of triphenyl phosphate (TPhP) was compared in persulfate (PDS) alone system and MBC@FH&PDS systems. The results showed that TPhP can be effectively degraded in PDS alone system and degradation efficiency reached up to 90 % within reaction of 8 h, at a PDS concentration of 10 mM, a reaction temperature of 30 °C and a system pH of 6.12. The obvious degradation can be ascribed to the reactive oxygen species (ROS) generated by self-decompose of PDS, among which 1O2, ∙OH and O2∙- play a major role in the degradation process. Although 350 °C biochar-goethite composites (MBC35@FH) and 800 °C biochar-goethite composites (MBC80@FH) facilitated PDS activation to produce more ROS, the catalytic degradation of TPhP was different in their systems. The degradation of TPhP was inhibited by MBC35@FH due to its stronger adsorption for TPhP, while MBC80@FH promoted TPhP degradation and degradation efficiency was up to 100 % within 6 h. 1O2 and SO4∙- played a stronger degradation role than ∙OH and O2∙- in above systems. The transformation of Fe species, functional groups (oxygen-containing functional groups, pyrrolic nitrogen) and persistent free radicals (PFRs) on the MBC@FH were involved in the PDS activation to produce ROS. Furthermore, MBC80@FH was more capable of activating PDS than MBC35@FH due to its abundant defect sites, larger specific surface area, more PFRs, higher Fe content and stronger electron transfer capability. In addition, seven possible TPhP intermediates were identified and possible degradation pathways of TPhP were proposed accordingly. This study illustrated that not all metallic carbon catalysts are necessarily beneficial for organic contaminants degradation.

Agave REVEILLE1 regulates the onset and release of seasonal dormancy in Populus.

Deciduous woody plants like poplar (Populus spp.) have seasonal bud dormancy. It has been challenging to simultaneously delay the onset of bud dormancy in the fall and advance bud break in the spring, as bud dormancy, and bud break were thought to be controlled by different genetic factors. Here, we demonstrate that heterologous expression of the REVEILLE1 gene (named AaRVE1) from Agave (Agave americana) not only delays the onset of bud dormancy but also accelerates bud break in poplar in field trials. AaRVE1 heterologous expression increases poplar biomass yield by 166% in the greenhouse. Furthermore, we reveal that heterologous expression of AaRVE1 increases cytokinin contents, represses multiple dormancy-related genes, and up-regulates bud break-related genes, and that AaRVE1 functions as a transcriptional repressor and regulates the activity of the DORMANCY-ASSOCIATED PROTEIN 1 (DRM1) promoter. Our findings demonstrate that AaRVE1 appears to function as a regulator of bud dormancy and bud break, which has important implications for extending the growing season of deciduous trees in frost-free temperate and subtropical regions to increase crop yield.

Enhanced removal of tetrabromobisphenol A by Burkholderia cepacian Y17 immobilized on biochar.

Biochar-immobilized bacteria have been widely used to remove organic pollutants; however, the enhanced effect of biochar-immobilized bacteria on tetrabromobisphenol A (TBBPA) removal has not been fully investigated and the removal mechanism remains unclear. In this study, a bacterial strain with high TBBPA degradation ability, Burkholderia cepacian Y17, was isolated from an e-waste disassembly area, immobilized with biochar, and used for the removal of TBBPA. Comparisons were performed of the factors affecting the immobilization and TBBPA removal efficiency, including the biochar preparation temperature, immobilization temperature, and pH. The highest 7-day TBBPA removal efficiency by immobilized bacteria was observed with the most suitable biochar preparation temperature (BC500) and an immobilization pH and temperature of 7 and 35 °C, respectively. The TBBPA removal efficiency reached 59.37%, which was increased by 30.23% and 15.88% compared to that of free and inactivated immobilized Y17, respectively. The suitable biochar preparation temperature BC500, immobilization temperature of 35 °C, and neutral pH of 7 increased the bacterial population and extracellular polymer concentration, which facilitated bacterial immobilization on biochar and promoted TBBPA removal. In this case, the high immobilized bacteria concentration (4.62 × 108 cfu∙g-1) and protein and polysaccharide contents (28.43 and 16.16 mg·g-1) contributed to the removal of TBBPA by facilitating TBBPA degradation. The main TBBPA degradation processes by BC500-immobilized Y17 involved debromination, ß-scission, demethylation, O-methylation, hydroxylation, and hydroxyl oxidation. This study proposes a method for preparing immobilized bacteria for TBBPA removal and enriches the microbial degradation technology for TBBPA.

TCA and SSRI Antidepressants Exert Selection Pressure for Efflux-Dependent Antibiotic Resistance Mechanisms in Escherichia coli.

Microbial diversity is reduced in the gut microbiota of animals and humans treated with selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs). The mechanisms driving the changes in microbial composition, while largely unknown, is critical to understand considering that the gut microbiota plays important roles in drug metabolism and brain function. Using Escherichia coli, we show that the SSRI fluoxetine and the TCA amitriptyline exert strong selection pressure for enhanced efflux activity of the AcrAB-TolC pump, a member of the resistance-nodulation-cell division (RND) superfamily of transporters. Sequencing spontaneous fluoxetine- and amitriptyline-resistant mutants revealed mutations in marR and lon, negative regulators of AcrAB-TolC expression. In line with the broad specificity of AcrAB-TolC pumps these mutants conferred resistance to several classes of antibiotics. We show that the converse also occurs, as spontaneous chloramphenicol-resistant mutants displayed cross-resistance to SSRIs and TCAs. Chemical-genomic screens identified deletions in marR and lon, confirming the results observed for the spontaneous resistant mutants. In addition, deletions in 35 genes with no known role in drug resistance were identified that conferred cross-resistance to antibiotics and several displayed enhanced efflux activities. These results indicate that combinations of specific antidepressants and antibiotics may have important effects when both are used simultaneously or successively as they can impose selection for common mechanisms of resistance. Our work suggests that selection for enhanced efflux activities is an important factor to consider in understanding the microbial diversity changes associated with antidepressant treatments. IMPORTANCE Antidepressants are prescribed broadly for psychiatric conditions to alter neuronal levels of synaptic neurotransmitters such as serotonin and norepinephrine. Two categories of antidepressants are selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs); both are among the most prescribed drugs in the United States. While it is well-established that antidepressants inhibit reuptake of neurotransmitters there is evidence that they also impact microbial diversity in the gastrointestinal tract. However, the mechanisms and therefore biological and clinical effects remain obscure. We demonstrate antidepressants may influence microbial diversity through strong selection for mutant bacteria with increased AcrAB-TolC activity, an efflux pump that removes antibiotics from cells. Furthermore, we identify a new group of genes that contribute to cross-resistance between antidepressants and antibiotics, several act by regulating efflux activity, underscoring overlapping mechanisms. Overall, this work provides new insights into bacterial responses to antidepressants important for understanding antidepressant treatment effects.

Highly stable cathodes for proton exchange membrane fuel cells: Novel carbon supported Au@PtNiAu concave octahedral core-shell nanocatalyst.

Despite the remarkable research efforts, the lack of ideal activity and state-of-the-art electrocatalysts remains a substantial challenge for the global application of fuel cell technology. Herein, is reported the synthesis of Au@PtNiAu concave octahedral core-shell nanocatalysts (Au@PtNiAu-COCS) via solvothermal synthesis modification and optimization approach. The special structure generating a large number of step atoms, enhancing the oxygen reduction reaction (ORR) and methanol oxidation reaction (MOR) activity and stability. The superior ORR mass activity of the Au@PtNiAu-COCS is 11.22 times than the exhibited of Pt/C initially by Pt loading, and 5.11 times by Pt + Au loading. After 30 k cycles the mass activity remains 78.8% (8.83 times the initial Pt/C activity) and the half-wave potential only shifts 12 mV. Au@PtNiAu-COCS has superior half-cell activity and gives ideal membrane electrode assemblies. Furthermore, for MOR the Au@PtNiAu-COCS show enhanced anti-toxic (tolerant) ability in CO. This work provides a new strategy to develop core-shell structure nanomaterials for electrocatalysis.

Fe3O4 loaded on ball milling biochar enhanced bisphenol a removal by activating persulfate: Performance and activating mechanism.

In this study, pristine biochar (BC), ball milling biochar (MBC), Fe3O4 modified BC (Fe3O4@BC), and Fe3O4 modified MBC (Fe3O4@MBC) were prepared to compare the Bisphenol A (BPA) removal efficiency by activating persulfate (PDS). All catalysts exhibited excellent degradation rather than adsorption in the PDS system, and Fe3O4@MBC800 had the best BPA removal efficiency, with 96.73% degradation and negligible 1.43% adsorption due to the synergistic effect between MBC800 and Fe3O4 particles. Radical quenching experiments and electron paramagnetic resonance analysis indicated radical pathways, namely, SO4∙- and ∙OH, O2∙-, and non-radical pathway (1O2) involving BPA degradation. The abundant oxygen-containing groups, increased graphitization and mesopores of MBC800, and Fe3+/Fe2+ conversion of Fe3O4 particles facilitated PDS activation to produce reactive oxygen species. In addition, the superior electrochemical performance accelerated the electron transfer between the catalyst and PDS, promoting BPA degradation in the Fe3O4@MBC800/PDS system. More importantly, Fe3O4@MBC800 is resistant to environmental interference, including pH, anions, cations, and humic acid, and has good catalytic reusability and stability, which fulfills the requirements of engineering applications. Therefore, Fe3O4 loaded on ball-milled biochar provides a convenient strategy for preparing environmentally friendly, economical, and efficient carbon-based catalysts to remove organic contaminants.

AlphaFold2 models indicate that protein sequence determines both structure and dynamics.

AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of [0, 1]. We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics.

A pH-Based Single-Sensor Array for Discriminating Metal Ions in Water.

Human activities, such as mining and manufacturing, expose society and the natural environment to harmful levels of metal ions. Recently, optical sensor arrays for metal ion detection have become popular owing to their favourable features, such as facile sample preparation and the requirement of less expensive instrumentation compared to traditional, spectrometry-based analysis techniques. Sensor arrays usually consist of numerous optical probes that are used in combination to generate unique analyte responses. In contrast, here we present an array that comprises a single fluorescent sensor, Coum4-DPA, that produces unique responses to metal ions in different pH environments. With this simple sensing platform, we were able to classify 10 metal ions in different water sources and quantify Pb2+ in tap water using just one fluorescent sensor, a few pH buffers and two sets of spectral data. This novel approach significantly decreases time and costs associated with probe synthesis and data collection, making it highly transferrable to real-world metal sensing applications.

A quantitative yeast aging proteomics analysis reveals novel aging regulators.

Calorie restriction (CR) is the most robust longevity intervention, extending lifespan from yeast to mammals. Numerous conserved pathways regulating aging and mediating CR have been identified; however, the overall proteomic changes during these conditions remain largely unexplored. We compared proteomes between young and replicatively aged yeast cells under normal and CR conditions using the Stable-Isotope Labeling by Amino acids in Cell culture (SILAC) quantitative proteomics and discovered distinct signatures in the aging proteome. We found remarkable proteomic similarities between aged and CR cells, including induction of stress response pathways, providing evidence that CR pathways are engaged in aged cells. These observations also uncovered aberrant changes in mitochondria membrane proteins as well as a proteolytic cellular state in old cells. These proteomics analyses help identify potential genes and pathways that have causal effects on longevity.

Overexpression of an Agave Phosphoenolpyruvate Carboxylase Improves Plant Growth and Stress Tolerance.

It has been challenging to simultaneously improve photosynthesis and stress tolerance in plants. Crassulacean acid metabolism (CAM) is a CO2-concentrating mechanism that facilitates plant adaptation to water-limited environments. We hypothesized that the ectopic expression of a CAM-specific phosphoenolpyruvate carboxylase (PEPC), an enzyme that catalyzes primary CO2 fixation in CAM plants, would enhance both photosynthesis and abiotic stress tolerance. To test this hypothesis, we engineered a CAM-specific PEPC gene (named AaPEPC1) from Agave americana into tobacco. In comparison with wild-type and empty vector controls, transgenic tobacco plants constitutively expressing AaPEPC1 showed a higher photosynthetic rate and biomass production under normal conditions, along with significant carbon metabolism changes in malate accumulation, the carbon isotope ratio δ13C, and the expression of multiple orthologs of CAM-related genes. Furthermore, AaPEPC1 overexpression enhanced proline biosynthesis, and improved salt and drought tolerance in the transgenic plants. Under salt and drought stress conditions, the dry weight of transgenic tobacco plants overexpressing AaPEPC1 was increased by up to 81.8% and 37.2%, respectively, in comparison with wild-type plants. Our findings open a new door to the simultaneous improvement of photosynthesis and stress tolerance in plants.

Protein interaction potential landscapes for yeast replicative aging.

We proposed a novel interaction potential landscape approach to map the systems-level profile changes of gene networks during replicative aging in Saccharomyces cerevisiae. This approach enabled us to apply quasi-potentials, the negative logarithm of the probabilities, to calibrate the elevation of the interaction landscapes with young cells as a reference state. Our approach detected opposite landscape changes based on protein abundances from transcript levels, especially for intra-essential gene interactions. We showed that essential proteins play different roles from hub proteins on the age-dependent interaction potential landscapes. We verified that hub proteins tend to avoid other hub proteins, but essential proteins prefer to interact with other essential proteins. Overall, we showed that the interaction potential landscape is promising for inferring network profile change during aging and that the essential hub proteins may play an important role in the uncoupling between protein and transcript levels during replicative aging.

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.

Association study based on topological constraints of protein-protein interaction networks.

The non-random interaction pattern of a protein-protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) method that gauges the observed interactions between two sets of genes based on the comparison between permutation null models and the empirical networks. This enables NetPAS to evaluate relationships, constrained by network topology, between gene sets related to different phenotypes. We demonstrated the utility of NetPAS in 50 well-curated gene sets and comparison of association studies using Z-scores, modified Z'-scores, p-values and Jaccard indices. Using NetPAS, a weighted human disease network was generated from the association scores of 19 gene sets from OMIM. We also applied NetPAS in gene sets derived from gene ontology and pathway annotations and showed that NetPAS uncovered functional terms missed by DAVID and WebGestalt. Overall, we show that NetPAS can take topological constraints of molecular networks into account and offer new perspectives than existing methods.

Atomistic probing of aptameric binding of CD19 outer membrane domain reveals an "aptamer walking" mechanism.

We propose an integrated structural approach to search potential aptamer molecules for targeting cancer receptor proteins. We used the outer cellular domain of the B-lymphocyte antigen, CD19, as the target for this study. First, using available protein-aptamer structures deposited in the protein data bank as resources, structural annotation was performed to seek the most probable binding aptamer and its potential initial configuration to the CD19 structure. Using this initial structure, molecular dynamics (MD) simulations were performed for adjustment of the aptamer-binding. During this process, we observed an "aptamer walking" mechanism of the binding of the single-stranded RNA-aptamer to CD19: the aptamer molecule gradually adjusts its configurations and shifts toward favorable binding positions. However, the target molecule CD19 maintained a relatively stable conformation during this process. The interface area between the RNA-aptamer and CD19 increased from less than 8 nm2 to over 12 nm2 during a 2-µs MD simulation. Using a stable binding pose as the starting structure, we manually mutated the RNA-aptamer to a DNA-aptamer and found that the interface area was further increased to over 16 nm2 , indicating a stronger affinity compared to the RNA-aptamer. The RNA- and DNA-aptamers and their stable binding-poses to the CD19 molecule may be used as templates in designing potential aptamer molecules that target the B-cell marker molecule CD19 with enhanced specificity and stability.

Advancing Aptamers as Molecular Probes for Cancer Theranostic Applications-The Role of Molecular Dynamics Simulation.

Theranostics cover emerging technologies for cell biomarking for disease diagnosis and targeted introduction of drug ingredients to specific malignant sites. Theranostics development has become a significant biomedical research endeavor for effective diagnosis and treatment of diseases, especially cancer. An efficient biomarking and targeted delivery strategy for theranostic applications requires effective molecular coupling of binding ligands with high affinities to specific receptors on the cancer cell surface. Bioaffinity offers a unique mechanism to bind specific target and receptor molecules from a range of non-targets. The binding efficacy depends on the specificity of the affinity ligand toward the target molecule even at low concentrations. Aptamers are fragments of genetic materials, peptides, or oligonucleotides which possess enhanced specificity in targeting desired cell surface receptor molecules. Aptamer-target binding results from several inter-molecular interactions including hydrogen bond formation, aromatic stacking of flat moieties, hydrophobic interaction, electrostatic, and van der Waals interactions. Advancements in Systematic Evolution of Ligands by Exponential Enrichment (SELEX) assay has created the opportunity to artificially generate aptamers that specifically bind to desired cancer and tumor surface receptors with high affinities. This article discusses the potential application of molecular dynamics (MD) simulation to advance aptamer-mediated receptor targeting in targeted cancer therapy. MD simulation offers real-time analysis of the molecular drivers of the aptamer-receptor binding and generate optimal receptor binding conditions for theranostic applications. The article also provides an overview of different cancer types with focus on receptor biomarking and targeted treatment approaches, conventional molecular probes, and aptamers that have been explored for cancer cells targeting.

Characterisation of aptamer-anchored poly(EDMA-co-GMA) monolith for high throughput affinity binding.

Immobilisation of aptameric ligands on solid stationary supports for effective binding of target molecules requires understanding of the relationship between aptamer-polymer interactions and the conditions governing the mass transfer of the binding process. Herein, key process parameters affecting the molecular anchoring of a thrombin-binding aptamer (TBA) onto polymethacrylate monolith pore surface, and the binding characteristics of the resulting macroporous aptasensor were investigated. Molecular dynamics (MD) simulations of the TBA-thrombin binding indicated enhanced Guanine 4 (G4) structural stability of TBA upon interaction with thrombin in an ionic environment. Fourier-transform infrared spectroscopy and thermogravimetric analyses were used to characterise the available functional groups and thermo-molecular stability of the immobilised polymer generated with Schiff-base activation and immobilisation scheme. The initial degradation temperature of the polymethacrylate stationary support increased with each step of the Schiff-base process: poly(Ethylene glycol Dimethacrylate-co-Glycidyl methacrylate) or poly(EDMA-co-GMA) [196.0 °C (±1.8)]; poly(EDMA-co-GMA)-Ethylenediamine [235.9 °C (±6.1)]; poly(EDMA-co-GMA)-Ethylenediamine-Glutaraldehyde [255.4 °C (±2.7)]; and aptamer-modified monolith [273.7 °C (±2.5)]. These initial temperature increments reflected in the associated endothermic energies were determined with differential scanning calorimetry. The aptameric ligand density obtained after immobilisation was 480 pmol/µL. Increase in pH and ionic concentration affected the surface charge distribution and the binding characteristics of the aptamer-modified disk-monoliths, resulting in the optimum binding pH and ionic concentration of 8.0 and 5 mM Mg2+, respectively. These results are critical in understanding and setting parametric constraints indispensable to develop and enhance the performance of aptasensors.