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What is the method for Predicting Aptamer-Protein Interactions?

Aptamers are single-stranded DNA or RNA oligos that can bind to a variety of targets with high specificity and selectivity and thus are widely used in the field of biosensing and disease therapies. Aptamers are generated by SELEX, which is a time-consuming procedure. In this study, using in silico and computational tools, we attempt to predict whether an aptamer can interact with a specific protein target. We present multiple data representations of protein and aptamer pairs and multiple machine-learning-based models to predict aptamer-protein interactions with a fair degree of selectivity. One of our models showed 96.5% accuracy and 97% precision, which are significantly better than those of the previously reported models. Additionally, we used molecular docking and SPR binding assays for two aptamers and the predicted targets as examples to exhibit the robustness of the APIPred algorithm. This reported model can be used for the high throughput screening of aptamer-protein pairs for targeting cancer and rapidly evolving viral epidemics.

What is the deep neural network for predicting aptamer-protein interaction using pretrained encoders.

BACKGROUND: Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures. RESULTS: In this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates. CONCLUSION: The results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans .

How to use molecular dynamics and 3D structure evaluation to select RNA aptamers?

Aptamers are single-stranded DNA or RNA that bind to specific targets such as proteins, thus having similar characteristics to antibodies. It can be synthesized at a lower cost, with no batch-to-batch variations, and is easier to modify chemically than antibodies, thus potentially being used as therapeutic and biosensing agents. The current method for RNA aptamer identification in vitro uses the SELEX method, which is considered inefficient due to its complex process. Computational models of aptamers have been used to predict and study the molecular interaction of modified aptamers to improve affinity. In this study, we generated three-dimensional models of five RNA aptamers from their sequence using mFold, RNAComposer web server, and molecular dynamics simulation. The model structures were then evaluated and compared with the experimentally determined structures. This study showed that the combination of mFold, RNAComposer, and molecular dynamics simulation could generate 14-16, 28, or 29 nucleotides length of 3D RNA aptamer with similar geometry and topology to the experimentally determined structures. The non-canonical basepair structure of the aptamer loop was formed through the MD simulation, which also improved the three-dimensional RNA aptamers model. Clustering analysis was recommended to choose the more representative model.

Do ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors compete and identified by in silico?

The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein is of paramount importance. Recently, two DNA aptamers were designed with the aim to inhibit the interaction between the ACE2 receptor and the spike protein of SARS-CoV-2. Indeed, the two molecules interact with the ACE2 receptor in the region around the K353 residue, preventing its binding of the spike protein. If on the one hand this inhibition process hinders the entry of the virus into the host cell, it could lead to a series of side effects, both in physiological and pathological conditions, preventing the correct functioning of the ACE2 receptor. Here, we discuss through a computational study the possible effect of these two very promising DNA aptamers, investigating all possible interactions between ACE2 and its experimentally known molecular partners. Our in silico predictions show that some of the 10 known molecular partners of ACE2 could interact, physiologically or pathologically, in a region adjacent to the K353 residue. Thus, the curative action of the proposed DNA aptamers could recruit ACE2 from its biological functions.

Can bioinformatics approaches for aptamers be used for pathogen detection platform with high sensitivity?

Foodborne outbreaks urge public health domain to upgrade diagnosis by means of simpler, quicker, and more affordable pathogen detection methods. A molecular recognition probe against an analyte of interest makes up a biosensor, along with a method for turning the recognition event into a quantifiable signal. Single-stranded DNA or RNA aptamers are promising bio-recognition molecules for a range of targets, including a wide range of non-nucleic acid targets with which they are highly specific and affine. In the proposed study, 40 DNA aptamers were screened and analyzed interactions using in-silico SELEX procedures, which can selectively interact with active sites at the extracellular region of the Outer membrane Protein W (OmpW) of Vibrio Cholerae. Multiple modeling techniques, like protein structural prediction with I-TASSER, aptamer structural modeling using M-fold, RNA composer, protein-DNA docking using HADDOCK, and large-scale (500 ns) molecular dynamics simulations through GROMACS have been employed. Out of 40, six aptamers having lowest free energy were docked against the predicted active site at the extracellular region of OmpW. VBAPT4-OmpW and VBAPT17-OmpW, the two highest-scoring Aptamer-Protein complexes, were chosen for molecular dynamics simulations. VBAPT4-OmpW is quite unable to attain its structural local minima after 500 ns. But VBAPT17-OmpW is showing great stability and is not destructive even after 500 ns. RMSF, DSSP, PCA, and Essential Dynamics all provided additional confirmation. Current findings, combined with the fabrication of biosensor devices, could pave the way for an innovative pathogen detection platform with high sensitivity, along with an effective and low-impact curative strategy for corresponding diseases.Communicated by Ramaswamy H. Sarma.

What is RNA-DraCALA and how can it be used for predicting RNA binding sites on proteins?

Ligand-binding RNAs (RNA aptamers) are widespread in the three domains of life, serving as sensors of metabolites and other small molecules. When aptamers are embedded within RNA transcripts as components of riboswitches, they can regulate gene expression upon binding their ligands. Previous methods for biochemical validation of computationally predicted aptamers are not well-suited for rapid screening of large numbers of RNA aptamers. Therefore, we utilized DRaCALA (Differential Radial Capillary Action of Ligand Assay), a technique designed originally to study protein-ligand interactions, to examine RNA-ligand binding, permitting rapid screening of dozens of RNA aptamer candidates concurrently. Using this method, which we call RNA-DRaCALA, we screened 30 ykkC family subtype 2a RNA aptamers that were computationally predicted to bind (p)ppGpp. Most of the aptamers bound both ppGpp and pppGpp, but some strongly favored only ppGpp or pppGpp, and some bound neither. Expansion of the number of biochemically verified sites allowed construction of more accurate secondary structure models and prediction of key features in the aptamers that distinguish a ppGpp from a pppGpp binding site. To demonstrate that the method works with other ligands, we also used RNA DRaCALA to analyze aptamer binding by thiamine pyrophosphate.

How are aptamers selected for bacterial toxins detection through in silico methods?

The most commonly used toxins in biological warfare are staphylococcal enterotoxin B (3SEB), cholera toxin (1XTC), and botulinum toxin (3BTA). Uncovering novel strategies for identifying these toxins is paramount; therefore, aptamers are used for this purpose. Aptamers are single-stranded DNA or RNA oligonucleotides selected via Systematic Evolution of Ligands by Exponential Enrichment (SELEX) with high binding affinity and specificity against target molecules. However, SELEX in vitro is tedious; hence, adopting alternative in silico molecular docking approaches is necessary. We aimed to conduct molecular docking with accessible tools and obtain RNA aptamers. First, 4,820,095 sequences obtained from an initial library of 9.5 x 10(9) Python script sequences were used. The GraphClust program was used to create representative groups or clusters, and the DoGSiteScorer (https://proteins.plus/) was used to conduct binding site detection of the proteins: 5DO4 (thrombin), 3SEB, 1XTC, and 3BTA. rDock, HDock, and PatchDock were adopted, combining different docking program results (consensus scoring), to improve receptor-ligand prediction. An analysis of the poses and root mean square deviation (RMSD) was performed, and 468 structurally different aptamers were obtained. The DoGSiteScorer program predicted the binding site of each protein to direct the interaction with the aptamer. Candidate aptamers for 3SEB, 1XTC, and 3BTA were selected according to the pose value considering the closeness of the interaction with a lower mean of 45.923 A, 45.854 A, and 72.490 A, respectively.Communicated by Ramaswamy H. Sarma.

Are there any RNA aptamer 3D-structural modeling database?

RNA aptamers are oligonucleotides with high binding affinity and specificity for target molecules and are expected to be a new generation of therapeutic molecules and targeted delivery materials. The tertiary structure of RNA molecules and RNA-protein interaction sites are increasingly important as potential targets for new drugs. The pathological mechanisms of diseases must be understood in detail to guide drug design. In developing RNA aptamers as drugs, information about the interaction mechanisms and structures of RNA aptamer-target protein complexes are useful. We constructed a database, RNA aptamer 3D-structural modeling (RNAapt3D), consisting of RNA aptamer data that are potential drug candidates. The database includes RNA sequences and computationally predicted RNA tertiary structures based on secondary structures and implements methods that can be used to predict unknown structures of RNA aptamer-target molecule complexes. RNAapt3D should enable the design of RNA aptamers for target molecules and improve the efficiency and productivity of candidate drug selection. RNAapt3D can be accessed at https://rnaapt3d.medals.jp.

How can aptamer-small-molecule interactions be predicted using metastable states from multiple independent molecular dynamics simulations?

Understanding aptamer-ligand interactions is necessary to rationally design aptamer-based systems. Commonly used in silico tools have proven to be accurate to predict RNA and DNA oligonucleotide tertiary structures. However, given the complexity of nucleic acids, the most thermodynamically stable conformation is not necessarily the one with the highest affinity for a specific ligand. Because many metastable states may coexist, it remains challenging to predict binding sites through molecular docking simulations using available computational pipelines. In this study, we used independent simulations to broaden the conformational diversity sampled from DNA initial models of distinct stability and assessed the binding affinity of selected metastable representative structures. In our results, utilizing multiple metastable conformations for molecular docking analysis helped identify structures favorable for ligand binding and accurately predict the binding sites. Our workflow was able to correctly identify the binding sites of the characterized adenosine monophosphate and l-argininamide aptamers. Additionally, we demonstrated that our pipeline can be used to aid the design of competition assays that are conducive to aptasensing strategies using an uncharacterized aflatoxin B1 aptamer. We foresee that this approach may help rationally design effective and truncated aptamer sequences interacting with protein biomarkers or small molecules of interest for drug design and sensor applications.

What role do selectively scaled molecular dynamics simulations play in assessing ligand poses within RNA aptamers?

Predicting the structure (or pose) of RNA-ligand complexes is an important problem in RNA structural biology. Although one could use computational docking to rapidly sample putative poses of RNA-ligand complexes, accurately discriminating the native-like poses from non-native, decoy poses remains a formidable challenge. Here, we started from the assumption that native-like RNA-ligand poses are less likely to dissociate during molecular dynamics simulations, and then we used enhanced simulations to promote ligand unbinding for diverse poses of a handful of RNA aptamer-ligand complexes. By fitting unbinding profiles obtained from the simulations to a single exponential, we identified the characteristic decay time (tau) as particularly effective at resolving native poses from decoys. We also found that a simple regression model trained to predict the simulation-derived parameters directly from structure could also discriminate ligand poses for similar RNA aptamers. Characterizing the unbinding properties of individual poses may thus be an effective strategy for enhancing pose prediction for ligands interacting with RNA aptamers. A similar strategy might be applicable to other ligandable RNAs; however, further analysis will be required to confirm this hypothesis.

How does modelling aptamers using nucleic acid mimics progress from sequence to three-dimensional docking?

Aptamers are single-stranded oligonucleotides, formerly evolved by Systematic Evolution of Ligands by EXponential enrichment (SELEX), that fold into functional three-dimensional structures. Such conformation is crucial for aptamers' ability to bind to a target with high affinity and specificity. Unnatural nucleotides have been used to develop nucleic acid mimic (NAM) aptamers with increased performance, such as biological stability. Prior knowledge of aptamer-target interactions is critical for applying post-SELEX modifications with unnatural nucleotides since it can affect aptamers' structure and performance. Here, we describe an easy-to-apply in silico workflow using free available software / web servers to predict the tertiary conformation of NAM, DNA and RNA aptamers, as well as the docking with the target molecule. Representative 2'-O-methyl (2'OMe), locked nucleic acid (LNA), DNA and RNA aptamers, with experimental data deposited in Protein Data Bank, were selected to validate the workflow. All aptamers' tertiary structure and docking models were successfully predicted with good structural similarity to the experimental data. Thus, this workflow will boost the development of aptamers, particularly NAM aptamers, by assisting in the rational modification of specific nucleotides and avoiding trial-and-error approaches.

What is being investigated regarding the selection and characterization of DNA aptamers targeting hLCN6 protein for sperm capture?

It is an urgent and difficult task to establish a simple and efficient method for identifying and isolating sperm cells from mixed stains in forensic science. In this project, we developed a DNA aptamer-based system for sperm separation and purification from mixed stain samples by targeting sperm surface proteins. Human lipocalin 6 (hLCN6) is an epididymal secreted protein that binds to the head and tail of sperm cells and associated with sperm maturation. Using systematic evolution of ligands by exponential enrichment (SELEX) technology, aptamers that bind with high affinity and specificity to hLCN6 were screened from a random single-stranded DNA (ssDNA) library using magnetic bead-bound hLCN6 as target. The enriched library was obtained after 15 SELEX rounds. Of hLCN6-binding aptamer variants, 19 were further classified into one of the four groups based on their N60 random sequence regions, wherein one representative from each group was characterized. Prediction analysis of the secondary structure suggested discrete features with typical loop and stem motifs. Binding capability of selected aptamers was investigated by quantitative PCR, and aptamer H2 was found to be the most specific aptamer to sperm cells. The dissociation constant (K(d)) of H2 aptamer was calculated as 3.21 +/- 0.75 nM. Furthermore, H2 aptamer-coupled magnetic beads can recognize and capture sperm cells, which establishes the foundation of an approach for rapidly isolating sperm cells from mixed stains based on nucleic acid-protein interaction.

How can RNAi be specifically delivered using Spike's aptamer-functionalized lipid nanoparticles for targeting SARS-CoV-2 in a clinical case study?

Coronavirus (SARS-CoV-2) as a global pandemic has attracted the attention of many scientific centers to find the right treatment. We expressed and purified the recombinant receptor-binding domain (RBD) of the SARS-CoV-2 spike (S) protein, and specific RBD aptamers were designed using SELEX method. RNAi targeting nucleocapsid phosphoprotein was synthesized and human lung cells were inoculated with aptamer-functionalized lipid nanoparticles (LNPs) containing RNAi. The results demonstrated that RBD aptamer having K(D) values of 0.290 nm possessed good affinity. Based on molecular docking and efficacy prediction analysis, siRNA molecule was showed the best action. LNPs were appropriately functionalized by aptamer and contained RNAi molecules. Antiviral assay using q-PCR and ELISA demonstrated that LNP functionalized with 35 microm Apt and containing 30 nm RNAi/ml of cell culture had the best antiviral activity compared to other concentrations. Applied aptamer in the nanocarrier has two important functions. First, it can deliver the drug (RNAi) to the surface of epithelial cells. Second, by binding to the SARS-CoV-2 spike protein, it inhibits the virus entrance into cells. Our data reveal an interaction between the aptamer and the virus, and RNAi targeted the virus RNA. CT scan and the clinical laboratory tests in a clinical case study, a 36-year old man who presented with severe SARS-CoV-2, demonstrated that inhalation of 10 mg Apt-LNPs-RNAi nebulized/day for six days resulted in an improvement in consolidation and ground-glass opacity in lungs on the sixth day of treatment. Our findings suggest the treatment of SARS-CoV-2 infection through inhalation of Aptamer-LNPs-RNAi.

How can TFIDF-Random Forest be utilized for predicting aptamer-protein interacting pairs?

Aptamers are short, single-stranded oligonucleotides or peptides generated from in vitro selection to selectively bind with various molecules. Due to their molecular recognition capability for proteins, aptamers are becoming promising reagents in new drug development. Aptamers can fold into specific spatial configuration that bind to certain targets with extremely high specificity. The ability of aptamers to reversibly bind proteins has generated increasing interest in using them to facilitate controlled release of therapeutic biomolecules. In-vitro selection experiments to produce the aptamer-protein binding pairs is very complex and MD/MM in-silico experiments can be computationally expensive. In this study, we introduce a natural language processing approach for data-driven computational selection. We compared our method to the sequential model with the embedding layer, applied in the literature. We transformed the DNA/RNA and protein sequences into text format using a sliding window approach. This methodology showed that efficiency was notably higher than those observed from the literature. This indicates that our preliminary model has marked improvement over previous models which brings us closer to a data-driven computational selection method.

What information can be gained from high-throughput analysis of interactions between viral proteins and host cell RNAs?

RNA-protein interactions of a virus play a major role in the replication of RNA viruses. The replication and transcription of these viruses take place in the cytoplasm of the host cell; hence, there is a probability for the host RNA-viral protein and viral RNA-host protein interactions. The current study applies a high-throughput computational approach, including feature extraction and machine learning methods, to predict the affinity of protein sequences of ten viruses to three categories of RNA sequences. These categories include RNAs involved in the protein-RNA complexes stored in the RCSB database, the human miRNAs deposited at the mirBase database, and the lncRNA deposited in the LNCipedia database. The results show that evolution not only tries to conserve key viral proteins involved in the replication and transcription but also prunes their interaction capability. These proteins with specific interactions do not perturb the host cell through undesired interactions. On the other hand, the hypermutation rate of NSP3 is related to its affinity to host cell RNAs. The Gene Ontology (GO) analysis of the miRNA with affiliation to NSP3 suggests that these miRNAs show strongly significantly enriched GO terms related to the known symptoms of COVID-19. Docking and MD simulation study of the obtained miRNA through high-throughput analysis suggest a non-coding RNA (an RNA antitoxin, ToxI) as a natural aptamer drug candidate for NSP5 inhibition. Finally, a significant interplay of the host RNA-viral protein in the host cell can disrupt the host cell's system by influencing the RNA-dependent processes of the host cells, such as a differential expression in RNA. Furthermore, our results are useful to identify the side effects of mRNA-based vaccines, many of which are caused by the off-label interactions with the human lncRNAs.

How can de novo ssRNA aptamers against SARS-CoV-2's main protease be designed and validated through molecular dynamics simulation?

Herein, we have generated ssRNA aptamers to inhibit SARS-CoV-2 M(pro), a protease necessary for the SARS-CoV-2 coronavirus replication. Because there is no aptamer 3D structure currently available in the databanks for this protein, first, we modeled an ssRNA aptamer using an entropic fragment-based strategy. We refined the initial sequence and 3D structure by using two sequential approaches, consisting of an elitist genetic algorithm and an RNA inverse process. We identified three specific aptamers against SARS-CoV-2 M(pro), called MApta(pro), MApta(pro)-IR1, and MApta(pro)-IR2, with similar 3D conformations and that fall in the dimerization region of the SARS-CoV-2 M(pro) necessary for the enzymatic activity. Through the molecular dynamic simulation and binding free energy calculation, the interaction between the MApta(pro)-IR1 aptamer and the SARS-CoV-2 M(pro) enzyme resulted in the strongest and the highest stable complex; therefore, the ssRNA MApta(pro)-IR1 aptamer was selected as the best potential candidate for the inhibition of SARS-CoV-2 M(pro) and a perspective therapeutic drug for the COVID-19 disease.

How can aptamer sequences that interact with target proteins be predicted using an aptamer-protein interaction classifier and Monte Carlo tree search approach?

Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis and treatment and are materials that can be produced through relatively simple experiments. Systematic evolution of ligands by exponential enrichment (SELEX) is one of the most widely used experimental methods for generating aptamers; however, it is highly expensive and time-consuming. To reduce the related costs, recent studies have used in silico approaches, such as aptamer-protein interaction (API) classifiers that use sequence patterns to determine the binding affinity between RNA aptamers and proteins. Some of these methods generate candidate RNA aptamer sequences that bind to a target protein, but they are limited to producing candidates of a specific size. In this study, we present a machine learning approach for selecting candidate sequences of various sizes that have a high binding affinity for a specific sequence of a target protein. We applied the Monte Carlo tree search (MCTS) algorithm for generating the candidate sequences using a score function based on an API classifier. The tree structure that we designed with MCTS enables nucleotide sequence sampling, and the obtained sequences are potential aptamer candidates. We performed a quality assessment using the scores of docking simulations. Our validation datasets revealed that our model showed similar or better docking scores in ZDOCK docking simulations than the known aptamers. We expect that our method, which is size-independent and easy to use, can provide insights into searching for an appropriate aptamer sequence for a target protein during the simulation step of SELEX.

What effect does the aptamer complementary element have on duplexed aptamers' performance - unraveling thermodynamic studies?

Duplexed aptamers (DAs) are widespread aptasensor formats that simultaneously recognize and signal the concentration of target molecules. They are composed of an aptamer and aptamer complementary element (ACE) which consists of a short oligonucleotide that partially inhibits the aptamer sequence. Although the design principles to engineer DAs are straightforward, the tailored development of DAs for a particular target is currently based on trial and error due to limited knowledge of how the ACE sequence affects the final performance of DA biosensors. Therefore, we have established a thermodynamic model describing the influence of the ACE on the performance of DAs applied in equilibrium assays and demonstrated that this relationship can be described by the binding strength between the aptamer and ACE. To validate our theoretical findings, the model was applied to the 29-mer anti-thrombin aptamer as a case study, and an experimental relation between the aptamer-ACE binding strength and performance of DAs was established. The obtained results indicated that our proposed model could accurately describe the effect of the ACE sequence on the performance of the established DAs for thrombin detection, applied for equilibrium assays. Furthermore, to characterize the binding strength between the aptamer and ACEs evaluated in this work, a set of fitting equations was derived which enables thermodynamic characterization of DNA-based interactions through thermal denaturation experiments, thereby overcoming the limitations of current predictive software and chemical denaturation experiments. Altogether, this work encourages the development, characterization, and use of DAs in the field of biosensing.

How can AptaNet be employed as a deep learning approach for aptamer-protein interaction prediction?

Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet-a new deep neural network-to predict the aptamer-protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .

What is identified and characterized through the investigation of thiamine pyrophosphate (TPP) riboswitches within Elaeis guineensis?

The oil palm (Elaeis guineensis) is an important crop in Malaysia but its productivity is hampered by various biotic and abiotic stresses. Recent studies suggest the importance of signalling molecules in plants in coping against stresses, which includes thiamine (vitamin B1). Thiamine is an essential microelement that is synthesized de novo by plants and microorganisms. The active form of thiamine, thiamine pyrophosphate (TPP), plays a prominent role in metabolic activities particularly as an enzymatic cofactor. Recently, thiamine biosynthesis pathways in oil palm have been characterised but the search of novel regulatory element known as riboswitch is yet to be done. Previous studies showed that thiamine biosynthesis pathway is regulated by an RNA element known as riboswitch. Riboswitch binds a small molecule, resulting in a change in production of the proteins encoded by the mRNA. TPP binds specifically to TPP riboswitch to regulate thiamine biosynthesis through a variety of mechanisms found in archaea, bacteria and eukaryotes. This study was carried out to hunt for TPP riboswitch in oil palm thiamine biosynthesis gene. Riboswitch detection software like RiboSW, RibEx, Riboswitch Scanner and Denison Riboswitch Detector were utilised in order to locate putative TPP riboswitch in oil palm ThiC gene sequence that encodes for the first enzyme in the pyrimidine branch of the pathway. The analysis revealed a 192 bp putative TPP riboswitch located at the 3' untranslated region (UTR) of the mRNA. Further comparative gene analysis showed that the 92-nucleotide aptamer region, where the metabolite binds was conserved inter-species. The secondary structure analysis was also carried out using Mfold Web server and it showed a stem-loop structure manifested with stems (P1-P5) with minimum free energy of -12.26 kcal/mol. Besides that, the interaction of riboswitch and its ligand was determined using isothermal titration calorimetry (ITC) and it yielded an exothermic reaction with 1:1 stoichiometry interaction with binding affinities of 0.178 nM, at 30 degrees C. To further evaluate the ability of riboswitch to control the pathway, exogenous thiamine was applied to four months old of oil palm seedlings and sampling of spear leaves tissue was carried out at days 0, 1, 2 and 3 post-treatment for expression analysis of ThiC gene fragment via quantitative polymerase chain reaction (qPCR). Results showed an approximately 5-fold decrease in ThiC gene expression upon application of exogenous thiamine. Quantification of thiamine and its derivatives was carried out via HPLC and the results showed that it was correlated to the down regulation of ThiC gene expression. The application of exogenous thiamine to oil palm affected ThiC gene expression, which supported the prediction of the presence of TPP riboswitch in the gene. Overall, this study provides the first evidence on the presence, binding and the functionality of TPP riboswitch in oil palm. This study is hoped to pave a way for better understanding on the regulation of thiamine biosynthesis pathway in oil palm, which can later be exploited for various purposes especially in manipulation of thiamine biosynthesis pathways in combating stresses in oil palm.

What capabilities does PPAI offer as a web server for predicting protein-aptamer interactions?

BACKGROUND: The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies. RESULTS: In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions. CONCLUSION: These results indicate that PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.

How can single-strand DNA-like oligonucleotide aptamers be selected against Proprotein Convertase Subtilisin/Kexin 9 using CE-SELEX?

BACKGROUND: Proprotein convertase subtilisin/kexin 9 (PCSK9) serves a key regulatory function in the metabolism of low-density lipoprotein (LDL)-cholesterol (LDL-C) through interaction with the LDL receptor (LDLR) followed by its destruction that results in the elevation of the plasma levels of LDL-C. The aims of the present study were to separate and select a number of single-stranded DNA (ssDNA) aptamers against PCSK9 from a library pool (n > 10(12)) followed by their characterization. METHODS: The aptamers obtained from the DNA-PCSK9 complexes which presented the highest affinity against PCSK9 were separated and selected using capillary electrophoresis evolution of ligands by exponential enrichment (CE-SELEX). The selected aptamers were amplified and cloned into a T/A vector. The plasmids from the positive clones were extracted and sequenced. The Mfold web server was used to predict the secondary structure of the aptamers. RESULTS: Following three rounds of CE-SELEX, the identified anti-PCSK9 ssDNA aptamers, namely aptamer 1 (AP-1) and aptamer 2 (AP-2), presented half maximal inhibitory concentrations of 325 and 327 nM, lowest dissociation constants of 294 and 323 nM, and most negative Gibbs free energy values of - 9.17 and - 8.28 kcal/mol, respectively. CONCLUSION: The results indicated that the selected aptamers (AP-1 and AP-2) induced potent inhibitory effects against PCSK9. Further in vivo studies demand to find out AP-1 and AP-2 aptamers as suitable candidates, instead of antibodies, for using in therapeutic purposes in patients with hypercholesterolemia and cardiovascular disease.

How does adipose-specific aptamer adipo-8 recognize and interact with APMAP to ameliorate fat deposition both in vitro and in vivo?

AIMS: To identify the target of an adipose specific aptamer adipo-8, predict the potential interaction between adipo-8 and its target, and investigate lipid-lowering effect of adipo-8 in vitro and in vivo. MAIN METHODS: Distinct membranous protein of 3T3-L1 adipocyte pulled-down by adipo-8 was mass-spectrometry analyzed as target candidate(s), and affinity of adipo-8 to target protein-silent adipocyte was detected to validate it. Interaction between adipo-8 and target was predicted by bioinformatic analysis, further confirmed by aptamer truncation and competitive binding assay. To investigate lipid-lowering effect of adipo-8 and mechanism behind, 250 nmol/L adipo-8 or library was incubated with 3T3-L1 adipocyte or target-protein-silent adipocyte for 24 h, and 0.01 mug/g/day adipo-8 or library was administrated to high-fat-fed male mice for 21 days. KEY FINDINGS: APMAP (Adipocyte Plasma Membrane Associated Protein) was identified as adipo-8 target, and adipo-8 affinity to adipocytes was in proportional to APMAP expression. Docking model between the stem-loop structure of adipo-8 and APMAP were predicted that adipo-8 was likely to interact with APMAP at its amino-acid 275-411 sequence. Moreover, adipo-8 could ameliorate fat deposition through interaction with APMAP in vitro, and administration of adipo-8 in high-fat-diet fed mice resulted in body weight loss and blood triglyceride decrease without liver or renal dysfunction. SIGNIFICANCE: Adipo-8 could recognize APMAP specifically and interact with its targets to ameliorate fat deposition in vitro and in vivo. Aptamer adipo-8 has potential to act as an effective and safe targeted drug for obesity and obesity related diseases.

How can high-affinity RNA aptamers targeting epithelial cellular adhesion molecule dimers be designed and validated through molecular simulations?

Nucleic acid aptamers hold great promise for therapeutic applications due to their favorable intrinsic properties, as well as high-throughput experimental selection techniques. Despite the utility of the systematic evolution of ligands by the exponential enrichment (SELEX) method for aptamer determination, complementary in silico aptamer design is highly sought after to facilitate virtual screening and increased understanding of important nucleic acid-protein interactions. Here, with a combined experimental and theoretical approach, we have developed two optimal epithelial cellular adhesion molecule (EpCAM) aptamers. Our structure-based in silico method first predicts their binding modes and then optimizes them for EpCAM with molecular dynamics simulations, docking, and free energy calculations. Our isothermal titration calorimetry experiments further confirm that the EpCAM aptamers indeed exhibit enhanced affinity over a previously patented nanomolar aptamer, EP23. Moreover, our study suggests that EP23 and the de novo designed aptamers primarily bind to EpCAM dimers (and not monomers, as hypothesized in previous published works), suggesting a paradigm for developing EpCAM-targeted therapies.

In what ways do transcriptional and translational S-box riboswitches differ regarding their ligand-binding properties?

There are a number of riboswitches that utilize the same ligand-binding domain to regulate transcription or translation. S-box (SAM-I) riboswitches, including the riboswitch present in the Bacillus subtilis metI gene, which encodes cystathionine gamma-synthase, regulate the expression of genes involved in methionine metabolism in response to SAM, primarily at the level of transcriptional attenuation. A rarer class of S-box riboswitches is predicted to regulate translation initiation. Here we identified and characterized a translational S-box riboswitch in the metI gene from Desulfurispirillum indicum The regulatory mechanisms of riboswitches are influenced by the kinetics of ligand interaction. The half-life of the translational D. indicum metI RNA-SAM complex is significantly shorter than that of the transcriptional B. subtilis metI RNA. This finding suggests that, unlike the transcriptional RNA, the translational metI riboswitch can make multiple reversible regulatory decisions. Comparison of both RNAs revealed that the second internal loop of helix P3 in the transcriptional RNA usually contains an A residue, whereas the translational RNA contains a C residue that is conserved in other S-box RNAs that are predicted to regulate translation. Mutational analysis indicated that the presence of an A or C residue correlates with RNA-SAM complex stability. Biochemical analyses indicate that the internal loop sequence critically determines the stability of the RNA-SAM complex by influencing the flexibility of residues involved in SAM binding and thereby affects the molecular mechanism of riboswitch function.

How can thrombin binding aptamer's G-quadruplex be stabilized using pyrene-modified nucleotides?

Guanine-rich regions of the human genome can adopt non-canonical secondary structures. Their role in regulating gene expression has turned them into promising targets for therapeutic intervention. Ligands based on polyaromatic moieties are especially suitable for targeting G-quadruplexes utilizing their size complementarity to interact with the large exposed surface area of four guanine bases. A predictable way of (de)stabilizing specific G-quadruplex structures through efficient base stacking of polyaromatic functional groups could become a valuable tool in our therapeutic arsenal. We have investigated the effect of pyrene-modified uridine nucleotides incorporated at several positions of the thrombin binding aptamer (TBA) as a model system. Characterization using spectroscopic and biophysical methods provided important insights into modes of interaction between pyrene groups and the G-quadruplex core as well as (de)stabilization by enthalpic and entropic contributions. NMR data demonstrated that incorporation of pyrene group into G-rich oligonucleotide such as TBA may result in significant changes in 3D structure such as formation of novel dimeric topology. Site specific structural changes induced by stacking of the pyrene moiety on nearby nucleobases corelate with distinct thrombin binding affinities and increased resistance against nuclease degradation.

How to predict RNA-protein interactions using feature extraction?

Feature extraction is one of the most important preprocessing steps in predicting the interactions between RNAs and proteins by applying machine learning approaches. Despite many efforts in this area, still, no suitable structural feature extraction tool has been designed. Therefore, an online toolbox, named RPINBASE which can be applied to different scopes of biological applications, is introduced in this paper. This toolbox employs efficient nested queries that enhance the speed of the requests and produces desired features in the form of positive and negative samples. To show the capabilities of the proposed toolbox, the developed toolbox was investigated in the aptamer design problem, and the obtained results are discussed. RPINBASE is an online toolbox and is accessible at http://rpinbase.com.

How can protein-binding RNA motifs be discovered using a generative model of RNA sequences?

Recent advances in high-throughput experimental technologies have generated a huge amount of data on interactions between proteins and nucleic acids. Motivated by the big experimental data, several computational methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new nucleic acid sequences that bind to a target protein because they are classifiers rather than generators. In this paper we propose a generative model for constructing protein-binding RNA sequences and motifs using a long short-term memory (LSTM) neural network. Testing the model for several target proteins showed that RNA sequences generated by the model have high binding affinity and specificity for their target proteins and that the protein-binding motifs derived from the generated RNA sequences are comparable to the motifs from experimentally validated protein-binding RNA sequences. The results are promising and we believe this approach will help design more efficient in vitro or in vivo experiments by suggesting potential RNA aptamers for a target protein.

What's the approach for constructing nucleic acid sequences that bind to a protein using a generative model?

BACKGROUND: Interactions between protein and nucleic acid molecules are essential to a variety of cellular processes. A large amount of interaction data generated by high-throughput technologies have triggered the development of several computational methods either to predict binding sites in a sequence or to determine whether a pair of sequences interacts or not. Most of these methods treat the problem of the interaction of nucleic acids with proteins as a classification problem rather than a generation problem. RESULTS: We developed a generative model for constructing single-stranded nucleic acids binding to a target protein using a long short-term memory (LSTM) neural network. Experimental results of the generative model are promising in the sense that DNA and RNA sequences generated by the model for several target proteins show high specificity and that motifs present in the generated sequences are similar to known protein-binding motifs. CONCLUSIONS: Although these are preliminary results of our ongoing research, our approach can be used to generate nucleic acid sequences binding to a target protein. In particular, it will help design efficient in vitro experiments by constructing an initial pool of potential aptamers that bind to a target protein with high affinity and specificity.

Which method constructs potential RNA aptamers for a protein target using a constructive prediction approach?

Aptamers are short single-stranded nucleic acids that bind to target molecules with high affinity and selectivity. Aptamers are generally identified in vitro by performing SELEX (systematic evolution of ligands by exponential enrichment). Complementing the SELEX process, several computational methods have been proposed in the search for aptamers. However, many of these methods cannot be applied for finding new aptamers, either because they are classifiers for determining whether an RNA and protein interact with each other, or because they are limited to a specific target only. Hence, we developed a new random forest (RF) model for finding potential RNA aptamers for a protein target. From an extensive analysis of protein-RNA complexes including RNA aptamers-protein complexes, we identified key features of interacting RNA and protein molecules, and structural constraints on RNA aptamers. The potential RNA aptamers predicted by our method reveal similar secondary and protein-binding structures as the actual RNA aptamers. The RF model showed a reliable performance in both cross validations and independent testing. The key features of interacting RNA and protein molecules and the structural constraints identified in our study were effective in finding potential aptamers for a protein target. Although preliminary, our results are promising, and we believe this approach will be useful in reducing time and money spent on in vitro experiments by substantially limiting the size of the initial pool of nucleic acid sequences.

In how many ways does the Spherical-Shape Assumption for Protein-Aptamer Complexes aid in predicting their Electrophoretic Mobility?

DNA aptamers are single-strand DNA (ssDNA) capable of selectively and tightly binding a target molecule. Capillary electrophoresis-based selection of aptamers for protein targets requires the knowledge of electrophoretic mobilities of protein-aptamer complexes, while measuring these mobilities requires having the aptamers. Here, we report on breaking this vicious circle. We introduce a mathematical model that allows prediction of protein-aptamer complex mobility, while requiring only three easy-to-determine input parameters: the number N of nucleotides in the aptamer, electrophoretic mobility of N-nucleotide-long ssDNA, and a sum molecular weight of the protein-aptamer complex. The model was derived upon simplifying assumptions of a spherical shape of the protein-aptamer complex. According to this model, the protein-aptamer complex mobility is a linear function of a combination of the three input parameters with empirically determined line's intercept and slope. The intercept and slope were determined using experimental data for seven complexes. The model was then cross-validated with the leave-one-out approach revealing only 2% residual standard deviations for both the slope and the intercept. Such a precise determination of these constants allowed accurate mobility prediction for the excluded complexes with only a 3% maximum deviation from the experimentally determined mobilities. The model was tested by applying it to three protein-aptamer complexes that were not a part of the training/cross-validation set; deviations of the predicted mobilities from the experimentally determined ones were within 5% of the latter. To complete this study, the model was fine-tuned using the 10 complexes. Our results strongly suggest the validity of the spherical-shape assumption for the protein-aptamer complexes when considering complex mobility. The developed model will make it possible to rationally design capillary electrophoresis-based selection of DNA aptamers for protein targets.

What structural insights into aptamer response from MD simulations are gained through studying the impact of positioning 5-Furyl-2'-Deoxyuridine nucleosides on thrombin DNA aptamer-protein complexes?

Aptamers are functional nucleic acids that bind to a range of targets (small molecules, proteins or cells) with a high affinity and specificity. Chemically-modified aptamers are of interest because the incorporation of novel nucleobase components can enhance aptamer binding to target proteins, while fluorescent base analogues permit the design of functional aptasensors that signal target binding. However, since optimally modified nucleoside designs have yet to be identified, information about how to fine tune aptamer stability and target binding affinity is required. The present work uses molecular dynamics (MD) simulations to investigate modifications to the prototypical thrombin-binding aptamer (TBA), which is a 15-mer DNA sequence that folds into a G-quadruplex structure connected by two TT loops and one TGT loop. Specifically, we modeled a previously synthesized thymine (T) analog, namely 5-furyl-2'-deoxyuridine (5FurU), into each of the six aptamer locations occupied by a thymine base in the TT or TGT loops of unbound and thrombin bound TBA. This modification and aptamer combination were chosen as a proof-of-principle because previous experimental studies have shown that TBA displays emissive sensitivity to target binding based on the local environment polarity at different 5FurU modification sites. Our simulations reveal that the chemically-modified base imparts noticeable structural changes to the aptamer without affecting the global conformation. Depending on the modification site, 5FurU performance is altered due to changes in the local environment, including the modification site structural dynamics, degree of solvent exposure, stacking with neighboring bases, and interactions with thrombin. Most importantly, these changes directly correlate with the experimentally-observed differences in the stability, binding affinity and emissive response of the modified aptamers. Therefore, the computational protocols implemented in the present work can be used in subsequent studies in a predictive way to aid the fine tuning of aptamer target recognition for use as biosensors (aptasensors) and/or therapeutics.

How does IMP3 recognize clustered RNA elements through combinatorial recognition?

How multidomain RNA-binding proteins recognize their specific target sequences, based on a combinatorial code, represents a fundamental unsolved question and has not been studied systematically so far. Here we focus on a prototypical multidomain RNA-binding protein, IMP3 (also called IGF2BP3), which contains six RNA-binding domains (RBDs): four KH and two RRM domains. We establish an integrative systematic strategy, combining single-domain-resolved SELEX-seq, motif-spacing analyses, in vivo iCLIP, functional validation assays, and structural biology. This approach identifies the RNA-binding specificity and RNP topology of IMP3, involving all six RBDs and a cluster of up to five distinct and appropriately spaced CA-rich and GGC-core RNA elements, covering a >100 nucleotide-long target RNA region. Our generally applicable approach explains both specificity and flexibility of IMP3-RNA recognition, allows the prediction of IMP3 targets, and provides a paradigm for the function of multivalent interactions with multidomain RNA-binding proteins in gene regulation.

Which features are extracted for predicting aptamer-protein interacting pairs using sparse autoencoder feature extraction and ensemble classifier?

Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore, it is vital to construct a high prediction performance model for identifying aptamer-target interacting pairs. In this study, a novel ensemble method is presented to predict aptamer-protein interacting pairs by integrating sequence characteristics derived from aptamers and the target proteins. The features extracted for aptamers were the compositions of amino acids and pseudo K-tuple nucleotides. In addition, a sparse autoencoder was used to characterize features for the target protein sequences. To remove redundant features, gradient boosting decision tree (GBDT) and incremental feature selection (IFS) methods were used to obtain the optimum combination of sequence characters. Based on 616 selected features, an ensemble of three sub- support vector machine (SVM) classifiers was used to construct our prediction model. Evaluated on an independent dataset, our predictor obtained an accuracy of 75.7%, Matthew's Correlation Coefficient of 0.478, and Youden's Index of 0.538, which were superior to the values reached using other existing predictors. The results show that our model can be used to distinguishing novel aptamer-protein interacting pairs and revealing the interrelation between aptamers and proteins.

What does ToGo-WF predict regarding RNA tertiary structures and RNA-RNA/protein interactions using KNIME workflow?

Recent progress in molecular biology has revealed that many non-coding RNAs regulate gene expression or catalyze biochemical reactions in tumors, viruses and several other diseases. The tertiary structure of RNA molecules and RNA-RNA/protein interaction sites are of increasing importance as potential targets for new medicines that treat a broad array of human diseases. Current RNA drugs are split into two groups: antisense RNA molecules and aptamers. In this report, we present a novel workflow to predict RNA tertiary structures and RNA-RNA/protein interactions using the KNIME environment, which enabled us to assemble a combination of RNA-related analytical tools and databases. In this study, three analytical workflows for comprehensive structural analysis of RNA are introduced: (1) prediction of the tertiary structure of RNA; (2) prediction of the structure of RNA-RNA complexes and analysis of their interactions; and (3) prediction of the structure of RNA-protein complexes and analysis of their interactions. In an RNA-protein case study, we modeled the tertiary structure of pegaptanib, an aptamer drug, and performed docking calculations of the pegaptanib-vascular endothelial growth factor complex using a fragment of the interaction site of the aptamer. We also present molecular dynamics simulations of the RNA-protein complex to evaluate the affinity of the complex by mutating bases at the interaction interface. The results provide valuable information for designing novel features of aptamer-protein complexes.

In what way does the discovery of a new conformer assess the quality of in silico produced biomolecules?

The computational procedures for predicting the 3D structure of aptamers interacting with different biological molecules have gained increasing attention in recent years. The information acquired through these methods represents a crucial input for research, especially when relevant crystallographic data are not available. A number of software programs able to perform macromolecular docking are currently accessible, leading to the prediction of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Nevertheless, the scoring protocols employed for ranking the candidate structures do not always produce satisfactory results, making difficult the identification of structures that are most likely to occur in nature. In this paper, we propose a novel procedure to improve the predictive performances of computational scoring protocols, using a maximum likelihood estimate based on topological and electrical properties of interacting biomolecules. The reliability of the new computational approach, enabling the ranking of aptamer-protein configurations produced by an open source docking program, has been assessed by its successful application to a set of antiangiopoietin aptamers, for which experimental data highlighting the sequence-dependent affinity toward the target protein are available. The procedure led to the identification of two main types of aptamer conformers involved in angiopoietin binding. Interestingly, one of these reproduces the arrangement of angiopoietin with its natural target, tyrosine kinase, while the other one is completely unexpected. The possible scenarios related to these results have been discussed. The methodology here described can be used to refine the outcomes of different computational procedures and can be applied to a wide range of biological molecules, thus representing a new tool for guiding the design of bioinspired sensors with enhanced selectivity.

What's the technology used for target detection in label-free optical biosensors based on simulation-assisted catalyzed hairpin assembly?

The development of efficient and convenient strategy without involving enzyme or complex nanomaterial for the micro molecules detection has profound meaning in the diagnosis of diseases. Herein, taking the advantages of the strong affinity of aptamer and catalyzed hairpin assembly, we develop a new non-label optical amplified strategy for thrombin detection in this work. To support both biological inquiry and technological innovation, thermodynamic models are introduced to predict the minimum energy secondary structure of interacting nucleic acid strands and calculate the partition function and equilibrium concentration for complexes in our system. Then, the thermodynamics properties of interacting DNA strands and the reactions of toehold strand displacement-driven assembly have been simulated, validating the feasibility of the theory and optimizing the follow-up lab tests. Following that, our strategy for thrombin detection is proved to be feasible and effective in biological experiment. Taken together, such a biosensor has a good potential in bioactive molecules detection and disease diagnosis for future biological research.

What are the RNA aptamers against MLL?

Mixed lineage leukemia proteins (MLL) are the key histone lysine methyltransferases that regulate expression of diverse genes. Aberrant activation of MLL promotes leukemia as well as solid tumors in humans, highlighting the urgent need for the development of an MLL inhibitor. We screened and isolated MLL1-binding ssRNAs using SELEX (Systemic Evolution of Ligands by Exponential enrichment) technology. When sequences in sub-libraries were obtained using next-generation sequencing (NGS), the most enriched aptamers-APT1 and APT2-represented about 30% and 26% of sub-library populations, respectively. Motif analysis of the top 50 sequences provided a highly conserved sequence: 5΄-A[A/C][C/G][G/U][U/A]ACAGAGGG[U/A]GG[A/C] GAGUGGGU-3΄. APT1, APT2, and APT5 embracing this motif generated secondary structures with similar topological characteristics. We found that APT1 and APT2 have a good binding activity and the analysis using mutated aptamer variants showed that the site information in the central region was critical for binding. In vitro enzyme activity assay showed that APT1 and APT2 had MLL1 inhibitory activity. Three-dimensional structure prediction of APT1-MLL1 complex indicates multiple weak interactions formed between MLL1 SET domain and APT1. Our study confirmed that NGS-assisted SELEX is an efficient tool for aptamer screening and that aptamers could be useful in diagnosis and treatment of MLL1-mediated diseases.

How can detect exosomes using aptamers in a self-standard ratiometric FRET platform?

Exosomes, as novel noninvasive biomarkers for disease prediction and diagnosis, have shown fascinating prospects in monitoring cancer-linked public health issues. Herein, a unique Cy3 labeled CD63 aptamer (Cy3-CD63 aptamer)/Ti(3)C(2) MXenes nanocomplex was constructed as a self-standard ratiometric fluorescence resonance energy transfer (FRET) nanoprobe for quantitative detection of exosomes. The Cy3-CD63 aptamer can be selectively adsorbed onto the Ti(3)C(2) MXene nanosheets by hydrogen bond and metal chelate interaction between the aptamer and MXenes, and the fluorescence signal from Cy3-CD63 aptamer was quenched quickly owing to the FRET between the Cy3 and MXenes. The fluorescence of Cy3 greatly recovered after the addition of the exosomes which can specifically combine with the aptamer and release from the surface of Ti(3)C(2) MXenes due to the high affinity between the aptamer and CD63 protein on exosome surface. Meanwhile, the self-fluorescence signal of MXenes in the whole process showed little change, which can be used as a standard reference. Based on the self-standard turn-on FRET biosensing platform the detection limit of exosome was determined as 1.4 x 10(3) particles mL(-1), which was over 1000x lower than that of conventional ELISA method. This fluorescence sensor can also be used for the identification of multiple biomarkers on the exosome surface and different kinds of exosomes, combining with the fluorescent confocal scanning microscope image. The proposed strategy not only provides a universal nanoplatform for exosomes, but also can be extensively expanded to multiple biomarkers detection, which may promise the prospect of MXenes as robust candidates in biological fields.

What role do DNA aptamers against HspX play in accurate diagnosis of tuberculous meningitis?

Tuberculous meningitis (TBM) is the most severe manifestation of tuberculosis and its diagnosis remains a challenge even today due to the lack of an adequate test. HspX antigen of Mycobacterium tuberculosis was previously established as a reliable diagnostic biomarker for TBM in an ELISA test format using anti-HspX polyclonal antibodies. Towards overcoming the limitations of batch-to-batch variation and challenges of scalability in antibody generation, we utilized Systematic Evolution of Ligands by EXponential enrichment (SELEX) to develop high affinity DNA aptamers against HspX as an alternative diagnostic reagent. Post-SELEX optimization of the best-performing aptamer candidate, H63, established its derivative H63 SL-2 M6 to be superior to its parent. Aptamer H63 SL-2 M6 displayed a specific and high affinity interaction with HspX (K(d) approximately 9.0 x 10(-8) M). In an Aptamer Linked Immobilized Sorbent Assay (ALISA), H63 SL-2 M6 significantly differentiated between cerebrospinal fluid specimens from TBM and non-TBM subjects (n = 87, ***p < 0.0001) with approximately 100% sensitivity and approximately 91% specificity. Notably, ALISA exhibited comparable performance with previously reported antibody-based ELISA and qPCR. Altogether, our findings establish the utility of HspX aptamer for the reliable diagnosis of TBM and pave the way for developing an aptamer-based point-of-care test for TBM.

How can small molecules be detected using aptamer based lateral flow assays by employing aptamer-C-reactive protein cross-recognition for ampicillin detection?

Aptamer-based lateral flow assays (LFAs) are an emerging field of aptamer applications due to numerous potential applications. When compared to antibodies, potential advantages like cost effectiveness or lower batch to batch variations are evident. The development of LFAs for small molecules, however, is still challenging due to several reasons, primarily linked to target size and accessible interaction sites. In small molecule analysis, however, aptamers in many cases are preferable since immunogenicity is not required and they may exhibit even higher target selectivity. We report the first cross-recognition of a small molecule (ampicillin) and a protein (C-reactive protein), predicted by in-silico analysis, then experimentally confirmed - using two different aptamers. These features can be exploited for developing an aptamer-based LFA for label-free ampicillin detection, functioning also for analysis in milk extract. Most importantly, the principal setup denotes a novel, transferable and versatile general approach for detection of small molecules using competitive LFAs, unlikely to be generally realized by aptamer-DNA-binding otherwise.

In what capacity does an aptamer-conjugated DNA nano-ring function as a carrier for drug molecules?

Due to its predictable self-assembly and structural stability, structural DNA nanotechnology is considered one of the main interdisciplinary subjects encompassing conventional nanotechnology and biotechnology. Here we have fabricated the mucin aptamer (MUC1)-conjugated DNA nano-ring intercalated with doxorubicin (DNR(A)-DOX) as potential therapeutics for breast cancer. DNR(A)-DOX exhibited significantly higher cytotoxicity to the MCF-7 breast cancer cells than the controls, including DOX alone and the aptamer deficient DNA nano-ring (DNR) with doxorubicin. Interactions between DOX and DNR(A) were studied using spectrophotometric measurements. Dose-dependent cytotoxicity was performed to prove that both DNR and DNR(A) were non-toxic to the cells. The drug release profile showed a controlled release of DOX at normal physiological pH 7.4, with approximately 61% released, but when exposed to lysosomal of pH 5.5, the corresponding 95% was released within 48 h. Owing to the presence of the aptamer, DNR(A)-DOX was effectively taken up by the cancer cells, as confirmed by confocal microscopy, implying that it has potential for use in targeted drug delivery.

What unique thermal stability properties do unnatural Hydrophobic Ds Bases possess in Double-Stranded DNAs?

Genetic alphabet expansion technology, the introduction of unnatural bases or base pairs into replicable DNA, has rapidly advanced as a new synthetic biology area. A hydrophobic unnatural base pair between 7-(2-thienyl)imidazo[4,5-b]pyridine (Ds) and 2-nitro-4-propynylpyrrole (Px) exhibited high fidelity as a third base pair in PCR. SELEX methods using the Ds-Px pair enabled high-affinity DNA aptamer generation, and introducing a few Ds bases into DNA aptamers extremely augmented their affinities and selectivities to target proteins. Here, to further scrutinize the functions of this highly hydrophobic Ds base, the thermal stabilities of double-stranded DNAs (dsDNA) containing a noncognate Ds-Ds or G-Ds pair were examined. The thermal stability of the Ds-Ds self-pair was as high as that of the natural G-C pair, and apart from the generally higher stability of the G-C pair than that of the A-T pair, most of the 5'-pyrimidine-Ds-purine-3' sequences, such as CDsA and TDsA, exhibited higher stability than the 5'-purine-Ds-pyrimidine-3' sequences, such as GDsC and ADsC, in dsDNAs. This trait enabled the GC-content-independent control of the thermal stability of the designed dsDNA fragments. The melting temperatures of dsDNA fragments containing the Ds-Ds pair can be predicted from the nearest-neighbor parameters including the Ds base. In addition, the noncognate G-Ds pair can efficiently distinguish its neighboring cognate natural base pairs from noncognate pairs. We demonstrated that real-time PCR using primers containing Ds accurately detected a single-nucleotide mismatch in target DNAs. These unique properties of the Ds base that affect the stabilities of the neighboring base pairs could impart new functions to DNA molecules and technologies.

How does targeting nucleolin contribute to improved survival in diffuse large B-cell lymphoma?

Anthracyclines have been a cornerstone in the cure of diffuse large B-cell lymphoma (DLBCL) and other hematological cancers. The ability of anthracyclines to eliminate DLBCL depends on the presence of topoisomerase-II-alpha (TopIIA), a DNA repair enzyme complex. We identified nucleolin as a novel binding partner of TopIIA. Abrogation of nucleolin sensitized DLBCL cells to TopIIA targeting agents (doxorubicin/etoposide). Silencing nucleolin and challenging DLBCL cells with doxorubicin enhanced the phosphorylation of H2AX (gammaH2AX-marker of DNA damage) and allowed DNA fragmentation. Reconstitution of nucleolin expression in nucleolin-knockdown DLBCL cells prevented TopIIA targeting agent-induced apoptosis. Nucleolin binding to TopIIA was mapped to RNA-binding domain 3 of nucleolin, and this interaction was essential for blocking DNA damage and apoptosis. Nucleolin silencing decreased TopIIA decatenation activity, but enhanced formation of TopIIA-DNA cleavable complexes in the presence of etoposide. Moreover, combining nucleolin inhibitors: aptamer AS1411 or nucant N6L with doxorubicin reduced DLBCL cell survival. These findings are of clinical importance because low nucleolin levels versus high nucleolin levels in DLBCL predicted 90-month estimated survival of 70% versus 12% (P<0.0001) of patients treated with R-CHOP-based therapy.

What interactions occur between aptabodies and aptatope in aptablotting assays?

We demonstrate an aptablotting assay method that involves direct and indirect aptabody recognition. Nanoscale single-stranded DNA aptamers against GST and DIG-tags are utilized as aptabodies (GST-2 and DIG-1, respectively), and the GST-2 aptabody binding site, or aptatope, as predicted by a MOE-docking simulation of the protein-aptamer complex, shows the interaction of the GST-2 aptabody at the catalytically active region. The aptabody-aptatope interaction was evaluated by an in vitro enzyme inhibitory analysis. The binding capacity of the GST-2 aptabody was assessed by dot-blot, EMSA and SDS-PAGE/electroblot analyses, and the results showed that the aptabodies interact with both the native mono-/dimeric form and the denatured GST form on a membrane. The use of aptabodies can overcome the obstacles of current immunoblot assays, and these molecules are easily assessable via ELISA systems. Moreover, the hybridization of aptabodies and antibodies (hybrid-aptablotting) may have considerable impacts on the design of bioassay platforms.

Which five riboswitches and one ribozyme have their 3D structures predicted in RNA-Puzzles Round III?

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5'-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

How do functionally-interdependent shape-switching nanoparticles exhibit controllable properties?

We introduce a new concept that utilizes cognate nucleic acid nanoparticles which are fully complementary and functionally-interdependent to each other. In the described approach, the physical interaction between sets of designed nanoparticles initiates a rapid isothermal shape change which triggers the activation of multiple functionalities and biological pathways including transcription, energy transfer, functional aptamers and RNA interference. The individual nanoparticles are not active and have controllable kinetics of re-association and fine-tunable chemical and thermodynamic stabilities. Computational algorithms were developed to accurately predict melting temperatures of nanoparticles of various compositions and trace the process of their re-association in silico. Additionally, tunable immunostimulatory properties of described nanoparticles suggest that the particles that do not induce pro-inflammatory cytokines and high levels of interferons can be used as scaffolds to carry therapeutic oligonucleotides, while particles with strong interferon and mild pro-inflammatory cytokine induction may qualify as vaccine adjuvants. The presented concept provides a simple, cost-effective and straightforward model for the development of combinatorial regulation of biological processes in nucleic acid nanotechnology.

How to design RNA origami structures?

RNA nanostructures can be used as scaffolds to organize, combine, and control molecular functionalities, with great potential for applications in nanomedicine and synthetic biology. The single-stranded RNA origami method allows RNA nanostructures to be folded as they are transcribed by the RNA polymerase. RNA origami structures provide a stable framework that can be decorated with functional RNA elements such as riboswitches, ribozymes, interaction sites, and aptamers for binding small molecules or protein targets. The rich library of RNA structural and functional elements combined with the possibility to attach proteins through aptamer-based binding creates virtually limitless possibilities for constructing advanced RNA-based nanodevices.In this chapter we provide a detailed protocol for the single-stranded RNA origami design method using a simple 2-helix tall structure as an example. The first step involves 3D modeling of a double-crossover between two RNA double helices, followed by decoration with tertiary motifs. The second step deals with the construction of a 2D blueprint describing the secondary structure and sequence constraints that serves as the input for computer programs. In the third step, computer programs are used to design RNA sequences that are compatible with the structure, and the resulting outputs are evaluated and converted into DNA sequences to order.

How to use In Silico Aptamer Docking Studies for studying TIM3 Aptamers Binding.

Complementing Systematic Evolution of Ligands by EXponential Enrichment (SELEX) technologies with in silico prediction of aptamer binders has attracted a lot of interest in the recent years. We propose a workflow involving 2D structure prediction, 3D RNA modeling using Rosetta and docking to the target protein with 3dRPC for: (i) prediction of the binding mode of our two previously reported potent (Kd < 50 nmol/l) murine TIM3 aptamers, and (ii) the prioritization of TIM3 aptamers that were enriched in the SELEX workflow. The procedure was first validated in five different study cases. As a novelty, cluster analysis of the docked poses was carried out and shown to be useful in reproducing the binding mode or at least in identifying the binding site and the experimental aptamer-protein interactions. For TIM3, our therapeutic target of interest, a plausible binding site and binding mode was identified that might explain the lack of cross-reactivity in murine over human TIM-3. Concerning the prioritization of the aptamers, the inclusion of the cluster analysis as an additional criterion following a rank-by-rank approach is discussed and compared with the performance of the docking scoring function alone for two validation cases and for the prospective assessment of the novel aptamers as TIM3 binders.

What are examples of aptamers binidng to human heart type fatty acid binding protein?

BACKGROUND: Aptamer-protein interaction studies have been mainly confined to dissociation constant (K(d)) determination. A combinatorial approach involving limited proteolysis mass spectroscopy, molecular docking and CD studies is reported here to elucidate the specific interactions involved. METHODS: To generate aptamers specific for human FABP3, SELEX was performed incorporating counter SELEX cycles against control FABPs and GST tag, followed by their characterization by EMSA, CD and SVD analysis. Based on computationally obtained aptamer-protein complex models, the interacting aptamer, and protein residues were predicted and supported by limited proteolysis experiments. RESULTS: Two aptamers N13 and N53 specific for human fatty acid binding protein (FABP3) were isolated with corresponding K(d) of 0.0743+/-0.0142muM and 0.3337+/-0.1485muM for FABP3 interactions. Both aptamers possess stable B-DNA structures at salt concentration of 100mM and pH range (6-9). The N13 aptamer led interaction involved 3 salt bridges and 2 hydrogen bonds, whereas N53 had 2 salt bridges with 8 hydrogen and 7 hydrophobic interactions. CONCLUSIONS: The aptamers generated are the first to be reported against human FABP3. The higher interaction footprint of N53 incited synergistic conformational changes in both N53 and FABP3 during interaction, leading to a decline in binding affinity in comparison to N13 which corroborated to the calculated K(d) values. GENERAL SIGNIFICANCE: This combinatorial method may be used to retrieve the possible specific binding modes and interaction patterns involved in large aptamer-protein complexes. Thus the method can be exploited to identify the optimum aptamer length for in-depth structure-function studies and its tailored applications.

How do quadruplex-flanking stem structures affect the stability and metal ion preferences in RNA mimics of GFP?

The spinach family of RNA aptamers are RNA mimics of green fluorescent protein (GFP) that have previously been designed to address the challenges of imaging RNA inside living cells. However, relatively low levels of free intracellular magnesium limited the practical use of these aptamers. Recent cell-based selections identified the broccoli RNA aptamer, which requires less magnesium for fluorescence, but the basis for magnesium preference remained unclear. Here, we find that the broccoli RNA structure is very similar to that of baby spinach, a truncated version of the spinach aptamer. Differences in stability and metal ion preferences between these two aptamers, and among broccoli mutants, are primarily associated with the sequence and structure of predicted quadruplex-flanking stem structures. Mutation of purine-purine pairs in broccoli at the terminal stem-quadruplex transition caused reversion of broccoli to a higher magnesium dependence. Unique duplex-to-quadruplex transitions in GFP-mimic RNAs likely explain their sensitivity to magnesium for stability and fluorescence. Thus, optimizations designed to improve aptamers should take into consideration the role of metal ions in stabilizing the transitions and interactions between independently folding RNA structural motifs.

What are the findings on the interaction between chromatin remodeling protein hINO80 and DNA?

The presence of a highly conserved DNA binding domain in INO80 subfamily predicted that INO80 directly interacts with DNA and we demonstrated its DNA binding activity in vitro. Here we report the consensus motif recognized by the DBINO domain identified by SELEX method and demonstrate the specific interaction of INO80 with the consensus motif. We show that INO80 significantly down regulates the reporter gene expression through its binding motif, and the repression is dependent on the presence of INO80 but not YY1 in the cell. The interaction is lost if specific residues within the consensus motif are altered. We identify a large number of potential target sites of INO80 in the human genome through in silico analysis that can grouped into three classes; sites that contain the recognition sequence for INO80 and YY1, only YY1 and only INO80. We demonstrate the binding of INO80 to a representative set of sites in HEK cells and the correlated repressive histone modifications around the binding motif. In the light of the role of INO80 in homeotic gene regulation in Drosophila as an Enhancer of trithorax and polycomb protein (ETP) that can modify the effect of both repressive complexes like polycomb as well as the activating complex like trithorax, it remains to be seen if INO80 can act as a recruiter of chromatin modifying complexes.

Can aptamer-protein interacting pairs be predicted using an ensemble classifier and protein sequence attributes?

BACKGROUND: Aptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies. RESULTS: In this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden's Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden's Index of 0.451. CONCLUSIONS: These results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.

How can computational methods be used for analyzing isolated ssDNA aptamers against angiotensin II?

Aptamers are oligonucleotides with highly structured molecules that can bind to their targets through specific 3-D conformation. Commonly, not all the nucleotides such as primer binding fixed region and some other sequences are vital for aptamers folding and interaction. Elimination of unnecessary regions needs trustworthy prediction tools to reduce experimental efforts and errors. Here we introduced a manipulated in-silico approach to predict the 3-D structure of aptamers and their target interactions. To design an approach for computational analysis of isolated ssDNA aptamers (FLC112, FLC125 and their truncated core region including CRC112 and CRC125), their secondary and tertiary structures were modeled by Mfold and RNA composer respectively. Output PDB files were modified from RNA to DNA in the discovery studio visualizer software. Using ZDOCK server, the aptamer-target interactions were predicted. Finally, the interaction scores were compared with the experimental results. In-silico interaction scores and the experimental outcomes were in the same descending arrangement of FLC112>CRC125>CRC112>FLC125 with similar intensity. The consistent results of innovative in-silico method with experimental outputs, affirmed that the present method may be a reliable approach. Also, it showed that the exact in-silico predictions can be utilized as a credible reference to find aptameric fragments binding potency.

Does type I interferon have contrasting effects on Coxiella burnetii replication depending on tissue type?

Coxiella burnetii is an intracellular pathogen and the cause of Q fever. Gamma interferon (IFN-gamma) is critical for host protection from infection, but a role for type I IFN in C. burnetii infection has not been determined. Type I IFN supports host protection from a related pathogen, Legionella pneumophila, and we hypothesized that it would be similarly protective in C. burnetii infection. In contrast to our prediction, IFN-alpha receptor-deficient (IFNAR(-/-)) mice were protected from C. burnetii-induced infection. Therefore, the role of type I IFN in C. burnetii infection was distinct from that in L. pneumophila Mice treated with a double-stranded-RNA mimetic were protected from C. burnetii-induced weight loss through an IFNAR-independent pathway. We next treated mice with recombinant IFN-alpha (rIFN-alpha). When rIFN-alpha was injected by the intraperitoneal route during infection, disease-induced weight loss was exacerbated. Mice that received rIFN-alpha by this route had dampened interleukin 1beta (IL-1beta) expression in bronchoalveolar lavage fluids. However, when rIFN-alpha was delivered to the lung, bacterial replication was decreased in all tissues. Thus, the presence of type I IFN in the lung protected from infection, but when delivered to the periphery, type I IFN enhanced disease, potentially by dampening inflammatory cytokines. To better characterize the capacity for type I IFN induction by C. burnetii, we assessed expression of IFN-beta transcripts by human macrophages following stimulation with lipopolysaccharide (LPS) from C. burnetii Understanding innate responses in C. burnetii infection will support the discovery of novel therapies that may be alternative or complementary to the current antibiotic treatment.

Which novel secondary structures have been identified within hepatitis C virus genome that contribute to genome packaging?

The specific packaging of the hepatitis C virus (HCV) genome is hypothesised to be driven by Core-RNA interactions. To identify the regions of the viral genome involved in this process, we used SELEX (systematic evolution of ligands by exponential enrichment) to identify RNA aptamers which bind specifically to Core in vitro. Comparison of these aptamers to multiple HCV genomes revealed the presence of a conserved terminal loop motif within short RNA stem-loop structures. We postulated that interactions of these motifs, as well as sub-motifs which were present in HCV genomes at statistically significant levels, with the Core protein may drive virion assembly. We mutated 8 of these predicted motifs within the HCV infectious molecular clone JFH-1, thereby producing a range of mutant viruses predicted to possess altered RNA secondary structures. RNA replication and viral titre were unaltered in viruses possessing only one mutated structure. However, infectivity titres were decreased in viruses possessing a higher number of mutated regions. This work thus identified multiple novel RNA motifs which appear to contribute to genome packaging. We suggest that these structures act as cooperative packaging signals to drive specific RNA encapsidation during HCV assembly.

How can maximum relevance minimum redundancy and nearest neighbor algorithm be applied for aptamer-compound interaction analysis?

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.

How can computational docking be used for aptamer design targeting estrogen receptor alpha using estrogen response elements?

Aptamers, the chemical-antibody substitute to conventional antibodies, are primarily discovered through SELEX technology involving multi-round selections and enrichment. Circumventing conventional methodology, here we report an in silico selection of aptamers to estrogen receptor alpha (ERalpha) using RNA analogs of human estrogen response elements (EREs). The inverted repeat nature of ERE and the ability to form stable hairpins were used as criteria to obtain aptamer-alike sequences. Near-native RNA analogs of selected single stranded EREs were modelled and their likelihood to emerge as ERalpha aptamer was examined using AutoDock Vina, HADDOCK and PatchDock docking. These in silico predictions were validated by measuring the thermodynamic parameters of ERalpha -RNA interactions using isothermal titration calorimetry. Based on the in silico and in vitro results, we selected a candidate RNA (ERaptR4; 5'-GGGGUCAAGGUGACCCC-3') having a binding constant (Ka) of 1.02 +/- 0.1 x 10(8) M(-1) as an ERalpha-aptamer. Target-specificity of the selected ERaptR4 aptamer was confirmed through cytochemistry and solid-phase immunoassays. Furthermore, stability analyses identified ERaptR4 resistant to serum and RNase A degradation in presence of ERalpha. Taken together, an efficient ERalpha-RNA aptamer is identified using a non-SELEX procedure of aptamer selection. The high-affinity and specificity can be utilized in detection of ERalpha in breast cancer and related diseases.

What unique RNA sequence does the nucleolar PUF RNA-binding protein bind to?

PUF proteins are a conserved group of sequence specific RNA-binding proteins that bind to RNA in a modular fashion. The RNA-binding domain of PUF proteins typically consists of eight clustered Puf repeats. Plant genomes code for large families of PUF proteins that show significant variability in their predicted Puf repeat number, organization, and amino acid sequence. Here we sought to determine whether the observed variability in the RNA-binding domains of four plant PUFs results in a preference for nonclassical PUF RNA target sequences. We report the identification of a novel RNA binding sequence for a nucleolar Arabidopsis PUF protein that contains an atypical RNA-binding domain. The Arabidopsis PUM23 (APUM23) binding sequence was 10 nucleotides in length, contained a centrally located UUGA core element, and had a preferred cytosine at nucleotide position 8. These RNA sequence characteristics differ from those of other PUF proteins, because all natural PUFs studied to date bind to RNAs that contain a conserved UGU sequence at their 5' end and lack specificity for cytosine. Gel mobility shift assays validated the identity of the APUM23 binding sequence and supported the location of 3 of the 10 predicted Puf repeats in APUM23, including the cytosine-binding repeat. The preferred 10-nucleotide sequence bound by APUM23 is present within the 18S rRNA sequence, supporting the known role of APUM23 in 18S rRNA maturation. This work also reveals that APUM23, an ortholog of yeast Nop9, could provide an advanced structural backbone for Puf repeat engineering and target-specific regulation of cellular RNAs.

How can computational prediction be combined with experimental validation for novel RNA aptamers targeting Rift Valley fever virus nucleocapsid protein?

Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications.

How can gene expression in Plasmodium falciparum be regulated using an inducible protein-RNA interaction system?

The available tools for conditional gene expression in Plasmodium falciparum are limited. Here, to enable reliable control of target gene expression, we build a system to efficiently modulate translation. We overcame several problems associated with other approaches for regulating gene expression in P. falciparum. Specifically, our system functions predictably across several native and engineered promoter contexts, and affords control over reporter and native parasite proteins irrespective of their subcellular compartmentalization. Induction and repression of gene expression are rapid, homogeneous and stable over prolonged periods. To demonstrate practical application of our system, we used it to reveal direct links between antimalarial drugs and their native parasite molecular target. This is an important outcome given the rapid spread of resistance, and intensified efforts to efficiently discover and optimize new antimalarial drugs. Overall, the studies presented highlight the utility of our system for broadly controlling gene expression and performing functional genetics in P. falciparum.

How can proteomic analysis be conducted on Entamoeba histolytica for studying pre-mRNA splicing complexes in vivo?

The genome of the human intestinal parasite Entamoeba histolytica contains nearly 3000 introns and bioinformatic predictions indicate that major and minor spliceosomes occur in Entamoeba. However, except for the U2-, U4-, U5- and U6 snRNAs, no other splicing factor has been cloned and characterized. Here, we HA-tagged cloned the snRNP component U1A and assessed its expression and nuclear localization. Because the snRNP-free U1A form interacts with polyadenylate-binding protein, HA-U1A immunoprecipitates could identify early and late splicing complexes. Avoiding Entamoeba's endonucleases and ensuring the precipitation of RNA-binding proteins, parasite cultures were UV cross-linked prior to nuclear fraction immunoprecipitations with HA antibodies, and precipitates were subjected to tandem mass spectrometry (MS/MS) analyses. To discriminate their nuclear roles (chromatin-, co-transcriptional-, splicing-related), MS/MS analyses were carried out with proteins eluted with MS2-GST-sepharose from nuclear extracts of an MS2 aptamer-tagged Rabx13 intron amoeba transformant. Thus, we probed thirty-six Entamoeba proteins corresponding to 32 cognate splicing-specific factors, including 13 DExH/D helicases required for all stages of splicing, and 12 different splicing-related helicases were identified also. Furthermore 50 additional proteins, possibly involved in co-transcriptional processes were identified, revealing the complexity of co-transcriptional splicing in Entamoeba. Some of these later factors were not previously found in splicing complex analyses. BIOLOGICAL SIGNIFICANCE: Numerous facts about the splicing of the nearly 3000 introns of the Entamoeba genome have not been unraveled, particularly the splicing factors and their activities. Considering that many of such introns are located in metabolic genes, the knowledge of the splicing cues has the potential to be used to attack or control the parasite. We have found numerous new splicing-related factors which could have therapeutic benefit. We also detected all the DExH/A RNA helicases involved in splicing and splicing proofreading control. Still, Entamoeba is very inefficient in splicing fidelity, thus we may have found a possible model system to study these processes.

What insights can be gained from novel quantitative structure activity relationship approaches for anti-influenza aptamer design?

This study describes the development of aptamers as a therapy against influenza virus infection. Aptamers are oligonucleotides (like ssDNA or RNA) that are capable of binding to a variety of molecular targets with high affinity and specificity. We have studied the ssDNA aptamer BV02, which was designed to inhibit influenza infection by targeting the hemagglutinin viral protein, a protein that facilitates the first stage of the virus' infection. While testing other aptamers and during lead optimization, we realized that the dominant characteristics that determine the aptamer's binding to the influenza virus may not necessarily be sequence-specific, as with other known aptamers, but rather depend on general 2D structural motifs. We adopted QSAR (quantitative structure activity relationship) tool and developed computational algorithm that correlate six calculated structural and physicochemical properties to the aptamers' binding affinity to the virus. The QSAR study provided us with a predictive tool of the binding potential of an aptamer to the influenza virus. The correlation between the calculated and actual binding was R2 = 0.702 for the training set, and R2 = 0.66 for the independent test set. Moreover, in the test set the model's sensitivity was 89%, and the specificity was 87%, in selecting aptamers with enhanced viral binding. The most important properties that positively correlated with the aptamer's binding were the aptamer length, 2D-loops and repeating sequences of C nucleotides. Based on the structure-activity study, we have managed to produce aptamers having viral affinity that was more than 20 times higher than that of the original BV02 aptamer. Further testing of influenza infection in cell culture and animal models yielded aptamers with 10 to 15 times greater anti-viral activity than the BV02 aptamer. Our insights concerning the mechanism of action and the structural and physicochemical properties that govern the interaction with the influenza virus are discussed.

How can unfolding and conformational variations be studied for thrombin-binding DNA aptamers through synthesis, circular dichroism, and molecular dynamics simulations?

Thrombin-binding DNA aptamer (TBA), with a consensus 15-base sequence: d(GGTTGGTGTGGTTGG), can fold into an antiparallel unimolecular G-quadruplex structure that is necessary for its interaction with thrombin. For the first time, using steered molecular dynamics (SMD) simulations, we have successfully simulated the unfolding process of native TBA G-quadruplex. The unfolding pathway proposed is in agreement with previously reported experimental NMR data. Moreover, the critical intermediate structure in the unfolding pathway, predicted by the NMR results, was identified. The structural characteristics of several TBA oligonucleotides modified with locked nucleoside (LNA) or 2'-O-methyl-nucleoside (MNA) at different positions and number were also investigated by CD spectroscopy. An oligonucleotide substituted with either LNA or MNA at position 2 folds into a native-like G-quadruplex, while doubly substituted derivatives of TBA where LNA or MNA is incorporated at positions 11 and 14 are no longer able to form a G-quadruplex. Starting from the same initial intermediate structure, we successfully overcame sampling limitations, and simulated the large conformational variations in structures of native TBA and modified TBAs by classic MD. Analysis of the models showed that inversion of the glycosyl orientation at position 14 contributes significantly to the disruption of G-quadruplex formation in both of the di-substituted modified TBA systems. Our calculations provide a simple and reliable theoretical model that can be used to investigate and predict the effects of the modifications of an oligonucleotide on the resultant G-quadruplex structure. In addition, the computational protocol described can also be applied for other G-quadruplex systems.

Can high-throughput quantitation of protein-RNA UV-crosslinking efficiencies ibe used to predicting RNA binding proteins?

UV-crosslinking has proven to be an invaluable tool for the identification of RNA-protein interactomes. The paucity of methods for distinguishing background from bona fide RNA-protein interactions however makes attribution of RNA binding function on UV-crosslinking alone challenging. To address this need, we previously reported an RNA binding protein (RBP) confidence scoring metric, (RCS), incorporating both signal-to-noise (S:N) and protein abundance determinations to distinguish high and low confidence candidate RBPs. Although RCS has utility, we sought a direct metric for quantification and comparative evaluation of protein-RNA interactions. Here we propose the use of protein-specific UV-crosslinking efficiency (%CL), representing the molar fraction of a protein that is crosslinked to RNA, for functional evaluation of candidate RBPs. Application to the HeLa RNA interactome yielded %CL values for 1,097 proteins. Remarkably, %CL values span over five orders of magnitude. For the HeLa RNA interactome, %CL values comprise a range from high efficiency, high specificity interactions, e.g., the Elav protein HuR and the Pumilio homolog Pum2, with %CL values of 45.9 and 24.2, respectively, to very low efficiency and specificity interactions e.g., the metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and alpha-enolase, with %CL values of 0.0016, 0.006, and 0.008, respectively. We further extend the utility of %CL through prediction of protein domains and classes with known RNA-binding functions, thus establishing it as a useful metric for RNA interactome analysis. We anticipate that this approach will benefit efforts to establish functional RNA interactomes and support development of more predictive computational approaches for RNA binding protein identification.

Can chromatography and mass spectrometry (SEC-seq) be used to study aptamer-protein interaction?

New methods for the global identification of RNA-protein interactions have led to greater recognition of the abundance and importance of RNA-binding proteins (RBPs) in bacteria. Here, we expand this tool kit by developing SEC-seq, a method based on a similar concept as the established Grad-seq approach. In Grad-seq, cellular RNA and protein complexes of a bacterium of interest are separated in a glycerol gradient, followed by high-throughput RNA-sequencing and mass spectrometry analyses of individual gradient fractions. New RNA-protein complexes are predicted based on the similarity of their elution profiles. In SEC-seq, we have replaced the glycerol gradient with separation by size exclusion chromatography, which shortens operation times and offers greater potential for automation. Applying SEC-seq to Escherichia coli, we find that the method provides a higher resolution than Grad-seq in the lower molecular weight range up to ~500 kDa. This is illustrated by the ability of SEC-seq to resolve two distinct, but similarly sized complexes of the global translational repressor CsrA with either of its antagonistic small RNAs, CsrB and CsrC. We also characterized changes in the SEC-seq profiles of the small RNA MicA upon deletion of its RNA chaperones Hfq and ProQ and investigated the redistribution of these two proteins upon RNase treatment. Overall, we demonstrate that SEC-seq is a tractable and reproducible method for the global profiling of bacterial RNA-protein complexes that offers the potential to discover yet-unrecognized associations between bacterial RNAs and proteins.

Aremachine learning classifiers effective for predicting protein-binding nucleotides in RNA sequences?

RNA-protein interactions play vital roles in driving the cellular machineries. Despite significant involvement in several biological processes, the underlying molecular mechanism of RNA-protein interactions is still elusive. This may be due to the experimental difficulties in solving co-crystallized RNA-protein complexes. Inherent flexibility of RNA molecules to adopt different conformations makes them functionally diverse. Their interactions with protein have implications in RNA disease biology. Thus, study of binding interfaces can provide a mechanistic insight of the molecular functioning and aberrations caused due to altered interactions. Moreover, high-throughput sequencing technologies have generated huge sequence data compared to available structural data of RNA-protein complexes. In such a scenario, efficient computational algorithms are required for identification of protein-binding interfaces of RNA in the absence of known structures. We have investigated several machine learning classifiers and various features derived from nucleotide sequences to identify protein-binding nucleotides in RNA. We achieve best performance with nucleotide-triplet and nucleotide-quartet feature-based random forest models. An overall accuracy of 84.8%, sensitivity of 83.2%, specificity of 86.1%, MCC of 0.70 and AUC of 0.93 is achieved. We have further implemented the developed models in a user-friendly webserver "Nucpred", which is freely accessible at "http://www.csb.iitkgp.ac.in/applications/Nucpred/index".

Can deep learning datasets be used to predict RNA-protein interactions?

Circular RNAs (circRNAs), as a rising star in the RNA world, play important roles in various biological processes. Understanding the interactions between circRNAs and RNA binding proteins (RBPs) can help reveal the functions of circRNAs. For the past decade, the emergence of high-throughput experimental data, like CLIP-Seq, has made the computational identification of RNA-protein interactions (RPIs) possible based on machine learning methods. However, as the underlying mechanisms of RPIs have not been fully understood yet and the information sources of circRNAs are limited, the computational tools for predicting circRNA-RBP interactions have been very few. In this study, we propose a deep learning method to identify circRNA-RBP interactions, called DeCban, which is featured by hybrid double embeddings for representing RNA sequences and a cross-branch attention neural network for classification. To capture more information from RNA sequences, the double embeddings include pre-trained embedding vectors for both RNA segments and their converted amino acids. Meanwhile, the cross-branch attention network aims to address the learning of very long sequences by integrating features of different scales and focusing on important information. The experimental results on 37 benchmark datasets show that both double embeddings and the cross-branch attention model contribute to the improvement of performance. DeCban outperforms the mainstream deep learning-based methods on not only prediction accuracy but also computational efficiency. The data sets and source code of this study are freely available at: https://github.com/AaronYll/DECban.

Can the FOREST tool be used to predict RNA-protein interactions?

Biochemical assays and computational analyses have discovered RNA structures throughout various transcripts. However, the roles of these structures are mostly unknown. Here we develop folded RNA element profiling with structure library (FOREST), a multiplexed affinity assay system to identify functional interactions from transcriptome-wide RNA structure datasets. We generate an RNA structure library by extracting validated or predicted RNA motifs from gene-annotated RNA regions. The RNA structure library with an affinity enrichment assay allows for the comprehensive identification of target-binding RNA sequences and structures in a high-throughput manner. As a proof-of-concept, FOREST discovers multiple RNA-protein interaction networks with quantitative scores, including translational regulatory elements that function in living cells. Moreover, FOREST reveals different binding landscapes of RNA G-quadruplex (rG4) structures-binding proteins and discovers rG4 structures in the terminal loops of precursor microRNAs. Overall, FOREST serves as a versatile platform to investigate RNA structure-function relationships on a large scale.

How can RPI-SE be used for predicting ncRNA-protein interactions using sequence information?

BACKGROUND: The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions. RESULTS: In this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k-mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods. CONCLUSIONS: The results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It's anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.

What role do distributed representations of RNA and protein sequences play in predicting lncRNA-protein interactions?

The long noncoding RNAs (lncRNAs) are ubiquitous in organisms and play crucial role in a variety of biological processes and complex diseases. Emerging evidences suggest that lncRNAs interact with corresponding proteins to perform their regulatory functions. Therefore, identifying interacting lncRNA-protein pairs is the first step in understanding the function and mechanism of lncRNA. Since it is time-consuming and expensive to determine lncRNA-protein interactions by high-throughput experiments, more robust and accurate computational methods need to be developed. In this study, we developed a new sequence distributed representation learning based method for potential lncRNA-Protein Interactions Prediction, named LPI-Pred, which is inspired by the similarity between natural language and biological sequences. More specifically, lncRNA and protein sequences were divided into k-mer segmentation, which can be regard as "word" in natural language processing. Then, we trained out the RNA2vec and Pro2vec model using word2vec and human genome-wide lncRNA and protein sequences to mine distribution representation of RNA and protein. Then, the dimension of complex features is reduced by using feature selection based on Gini information impurity measure. Finally, these discriminative features are used to train a Random Forest classifier to predict lncRNA-protein interactions. Five-fold cross-validation was adopted to evaluate the performance of LPI-Pred on three benchmark datasets, including RPI369, RPI488 and RPI2241. The results demonstrate that LPI-Pred can be a useful tool to provide reliable guidance for biological research.

How does mRNA structure determine modification by pseudouridine synthase 1?

Pseudouridine (Psi) is a post-transcriptional RNA modification that alters RNA-RNA and RNA-protein interactions that affect gene expression. Messenger RNA pseudouridylation was recently discovered as a widespread and conserved phenomenon, but the mechanisms responsible for selective, regulated pseudouridylation of specific sequences within mRNAs were unknown. Here, we have revealed mRNA targets for five pseudouridine synthases and probed the determinants of mRNA target recognition by the predominant mRNA pseudouridylating enzyme, Pus1, by developing high-throughput kinetic analysis of pseudouridylation in vitro. Combining computational prediction and rational mutational analysis revealed an RNA structural motif that is both necessary and sufficient for mRNA pseudouridylation. Applying this structural context information predicted hundreds of additional mRNA targets that were pseudouridylated in vivo. These results demonstrate a structure-dependent mode of mRNA target recognition by a conserved pseudouridine synthase and implicate modulation of RNA structure as the probable mechanism to regulate mRNA pseudouridylation.

What rules have been discovered for target RNA binding and cleavage by AGO2 through high-throughput analysis?

Argonaute proteins loaded with microRNAs (miRNAs) or small interfering RNAs (siRNAs) form the RNA-induced silencing complex (RISC), which represses target RNA expression. Predicting the biological targets, specificity, and efficiency of both miRNAs and siRNAs has been hamstrung by an incomplete understanding of the sequence determinants of RISC binding and cleavage. We applied high-throughput methods to measure the association kinetics, equilibrium binding energies, and single-turnover cleavage rates of mouse AGO2 RISC. We find that RISC readily tolerates insertions of up to 7 nt in its target opposite the central region of the guide. Our data uncover specific guide:target mismatches that enhance the rate of target cleavage, suggesting novel siRNA design strategies. Using these data, we derive quantitative models for RISC binding and target cleavage and show that our in vitro measurements and models predict knockdown in an engineered cellular system.

What is the accuracy of machine learning approaches in predicting m6A modification sites? (WHISTLE)

N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA-protein interaction. We report here a prediction framework WHISTLE for transcriptome-wide m6A RNA-methylation site prediction. When tested on six independent datasets, our approach, which integrated 35 additional genomic features besides the conventional sequence features, achieved a major improvement in the accuracy of m6A site prediction (average AUC: 0.948 and 0.880 under the full transcript or mature messenger RNA models, respectively) compared to the state-of-the-art computational approaches MethyRNA (AUC: 0.790 and 0.732) and SRAMP (AUC: 0.761 and 0.706). It also out-performed the existing epitranscriptome databases MeT-DB (AUC: 0.798 and 0.744) and RMBase (AUC: 0.786 and 0.736), which were built upon hundreds of epitranscriptome high-throughput sequencing samples. To probe the putative biological processes impacted by changes in an individual m6A site, a network-based approach was implemented according to the 'guilt-by-association' principle by integrating RNA methylation profiles, gene expression profiles and protein-protein interaction data. Finally, the WHISTLE web server was built to facilitate the query of our high-accuracy map of the human m6A epitranscriptome, and the server is freely available at: www.xjtlu.edu.cn/biologicalsciences/whistle and http://whistle-epitranscriptome.com.

What are the results of blind tests for RNA-protein binding affinity prediction?

Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-DeltaDeltaG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-DeltaDeltaG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.

How can high speed ELM learning be combined with deep convolutional neural network feature encoding for predicting protein-RNA interactions?

Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable information for the initial identification of RNA-protein interactions (RPIs), but with the increasing complexity of RPIs networks, this method gradually falls into expensive and time-consuming situations. Therefore, there is an urgent need for high speed and reliable methods to predict RNA-protein interactions. In this study, we propose a computational method for predicting the RNA-protein interactions using sequence information. The deep learning convolution neural network (CNN) algorithm is utilized to mine the hidden high-level discriminative features from the RNA and protein sequences and feed it into the extreme learning machine (ELM) classifier. The experimental results with 5-fold cross-validation indicate that the proposed method achieves superior performance on benchmark datasets (RPI1807, RPI2241, and RPI369) with the accuracy of 98.83, 90.83, and 85.63 percent, respectively. We further evaluate the performance of the proposed model by comparing it with the state-of-the-art SVM classifier and other existing methods on the same benchmark data set. In addition, we predicted the independent NPInter v2.0 data set using the model trained on RPI369. The experimental results show that our model can serve as a useful tool for predicting RNA-protein interactions.

Can aptamer-mediated silencing of FoxP3 be used to inhibit Treg cells and potentiates antitumor response.

The inhibition of immunosuppressive mechanisms may switch the balance between tolerance and surveillance, leading to an increase in antitumor activity. Regulatory T cells play an important role in the control of immunosuppression, exhibiting the unique property of inhibiting T cell proliferation. These cells migrate to tumor sites or may be generated at the tumor site itself from the conversion of lymphocytes exposed to tumor microenvironment signaling. Because of the high similarity between regulatory T cells and other lymphocytes, the available approaches to inhibit this population are nonspecific and may antagonize antitumor response. In this work we explore a new strategy for inhibition of regulatory T cells based on the use of a chimeric aptamer targeting a marker of immune activation harboring a small antisense RNA molecule for transcriptional gene silencing of Fox p 3, which is essential for the control of the immunosuppressive phenotype. The silencing of Fox p 3 inhibits the immunosuppressive phenotype of regulatory T cells and potentiates the effect of the GVAX antitumor vaccine in immunocompetent animals challenged with syngeneic tumors. This novel approach highlights an alternative method to antagonize regulatory T cell function to augment antitumor immune responses.

Does PDGFR aptamer targeted therapy potentiates immune checkpoint blockade in triple-negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is a uniquely aggressive cancer with high rates of relapse due to resistance to chemotherapy. TNBC expresses higher levels of programmed cell death-ligand 1 (PD-L1) compared to other breast cancers, providing the rationale for the recently approved immunotherapy with anti-PD-L1 monoclonal antibodies (mAbs). A huge effort is dedicated to identify actionable biomarkers allowing for combination therapies with immune-checkpoint blockade. Platelet-derived growth factor receptor beta (PDGFRbeta) is highly expressed in invasive TNBC, both on tumor cells and tumor microenvironment. We recently proved that tumor growth and lung metastases are impaired in mouse models of human TNBC by a high efficacious PDGFRbeta aptamer. Hence, we aimed at investigating the effectiveness of a novel combination treatment with the PDGFRbeta aptamer and anti-PD-L1 mAbs in TNBC. METHODS: The targeting ability of the anti-human PDGFRbeta aptamer toward the murine receptor was verified by streptavidin-biotin assays and confocal microscopy, and its inhibitory function by transwell migration assays. The anti-proliferative effects of the PDGFRbeta aptamer/anti-PD-L1 mAbs combination was assessed in human MDA-MB-231 and murine 4 T1 TNBC cells, both grown as monolayer or co-cultured with lymphocytes. Tumor cell lysis and cytokines secretion by lymphocytes were analyzed by LDH quantification and ELISA, respectively. Orthotopic 4 T1 xenografts in syngeneic mice were used for dissecting the effect of aptamer/mAb combination on tumor growth, metastasis and lymphocytes infiltration. Ex vivo analyses through immunohistochemistry, RT-qPCR and immunoblotting were performed. RESULTS: We show that the PDGFRbeta aptamer potentiates the anti-proliferative activity of anti-PD-L1 mAbs on both human and murine TNBC cells, according to its human/mouse cross-reactivity. Further, by binding to activated human and mouse lymphocytes, the aptamer enhances the anti-PD-L1 mAb-induced cytotoxicity of lymphocytes against tumor cells. Importantly, the aptamer heightens the antibody efficacy in inhibiting tumor growth and lung metastases in mice. It acts on both tumor cells, inhibiting Akt and ERK1/2 signaling pathways, and immune populations, increasing intratumoral CD8 + T cells and reducing FOXP3 + Treg cells. CONCLUSION: Co-treatment of PDGFRbeta aptamer with anti-PD-L1 mAbs is a viable strategy, thus providing for the first time an evidence of the efficacy of PDGFRbeta/PD-L1 co-targeting combination therapy in TNBC.

How to use RNA aptamers to target dendritic cells to modulate allergic responses.

BACKGROUND: Sublingual immunotherapy (SLIT) was introduced to deliver allergens in an effective and non-invasive route, which can be considered as an alternative for allergen-specific subcutaneous immunotherapy (SCIT). On the other hand, the use of gold nanoparticles (AuNPs) in allergen delivery has beneficial effects on sublingual immunotherapy. In addition, the molecular targeting agents like aptamers (Apt), have been widely applied for targeted drug delivery. Therefore, the current study aimed to evaluate the effects of dendritic cells (DCs)-specific Aptamer-modified AuNPs coated with ovalbumin (OVA) on the improvement of the SLIT outcome in the mouse model of allergy. MATERIAL AND METHODS: AuNPs with approximately 15 nm diameter were prepared by citrate reduction of HAuCl4. Afterward, Apt-modified AuNP complex was prepared and OVA was then loaded onto this complex. Following sensitization of Balb/c mice to OVA, SLIT was performed with Apt-AuNPs containing 5 microg OVA twice a week for a 2-month period. Allergen-specific IgE in serum, as well as cytokines secretion of spleen cells, were analyzed using ELISA. Also, nasopharyngeal lavage Fluid (NALF) was collected for total and eosinophil counts. Moreover, the lungs were removed for histopathological examination. RESULTS: SLIT with Apt-modified AuNPs complex containing 5 mug OVA, decreased the IgE levels compared to the other groups. Also, IL-4 production has significantly decreased in spleen cells, while TGF-beta and IFN-gamma have significantly increased. The assessment of NALF in the group treated by this complex showed a decrease in total cell as well as in eosinophil count. Also, the examination of lung tissues revealed that, in the group treated by this complex, inflammation and perivascular infiltration were lesser than the other groups, which were observed in only one vessel of tissue. CONCLUSION: It was shown that, Sublingual immunotherapy with DC specific Apt-modified AuNPs containing 5 mug OVA can improve the Th1 and Treg immunomodulatory responses.

Can targeting Foxp3 by a CD28 2'-Fluro oligonucleotide aptamer conjugated to P60-peptide enhance active cancer immunotherapy?

The specific inhibition of Treg function has long been a major technical challenge in cancer immunotherapy. So far no single cell-surface marker has been identified that could be used to distinguish Treg cells from other lymphocytes. The only available specific marker mostly expressed in Treg is Foxp3, which is an intracellular transcription factor. A targeting molecule able to penetrate the membrane and inhibit Foxp3 within the cell is needed. P60-peptide is able to do that, but due to lack of target specificity, the doses are extremely high. In this study we have shown as a proof of concept that P60 Foxp3 inhibitor peptide can be conjugated with a CD28 targeting aptamer to deliver the peptide to CD28-expressing cells. The AptCD28-P60 construct is a clinically feasible reagent that improves the efficacy of the unconjugated P60 peptide very significantly. This approach was used to inhibit Treg function in a vaccination context, and it has shown a significant improvement in the induced immune response, entailing a lower tumor load in an antigen-specific cancer vaccine protocol.

Can RNA aptamers be used to target TIM3 as an immunotherapy strategy?

TIM3 belongs to a family of receptors that are involved in T-cell exhaustion and Treg functions. The development of new therapeutic agents to block this type of receptors is opening a new avenue in cancer immunotherapy. There are currently several clinical trials ongoing to combine different immune-checkpoint blockades to improve the outcome of cancer patients. Among these combinations we should underline PD1:PDL1 axis and TIM3 blockade, which have shown very promising results in preclinical settings. Most of these types of therapeutic agents are protein cell-derived products, which, although broadly used in clinical settings, are still subject to important limitations. In this work we identify by HT-SELEX TIM3 non-antigenic oligonucleotide aptamers (TIM3Apt) that bind with high affinity and specificity to the extracellular motives of TIM3 on the cell surface. The TIM3Apt1 in its monomeric form displays a potent antagonist capacity on TIM3-expressing lymphocytes, determining the increase of IFN-gamma secretion. In colon carcinoma tumor-bearing mice, the combinatorial treatment of TIM3Apt1 and PDL1-antibody blockade is synergistic with a remarkable antitumor effect. Immunotherapeutic aptamers could represent an attractive alternative to monoclonal antibodies, as they exhibit important advantages; namely, lower antigenicity, being chemically synthesized agents with a lower price of manufacture, providing higher malleability, and antidote availability.

Do aptamers inhibit SARS-CoV-2 replication by targeting its nucleocapsid protein?

The nucleocapsid protein of SARS-CoV-2 plays significant roles in viral assembly, immune evasion, and viral stability. Due to its immunogenicity, high expression levels during COVID-19, and conservation across viral strains, it represents an attractive target for antiviral treatment. In this study, we identified and characterized a single-stranded DNA aptamer, N-Apt17, which effectively disrupts the liquid-liquid phase separation (LLPS) mediated by the N protein. To enhance the aptamer's stability, a circular bivalent form, cb-N-Apt17, was designed and evaluated. Our findings demonstrated that cb-N-Apt17 exhibited improved stability, enhanced binding affinity, and superior inhibition of N protein LLPS; thus, it has the potential inhibition ability on viral replication. These results provide valuable evidence supporting the potential of cb-N-Apt17 as a promising candidate for the development of antiviral therapies against COVID-19.IMPORTANCEVariants of SARS-CoV-2 pose a significant challenge to currently available COVID-19 vaccines and therapies due to the rapid epitope changes observed in the viral spike protein. However, the nucleocapsid (N) protein of SARS-CoV-2, a highly conserved structural protein, offers promising potential as a target for inhibiting viral replication. The N protein forms complexes with genomic RNA, interacts with other viral structural proteins during virion assembly, and plays a critical role in evading host innate immunity by impairing interferon production during viral infection. In this investigation, we discovered a single-stranded DNA aptamer, designated as N-Apt17, exhibiting remarkable affinity and specificity for the N protein. Notably, N-Apt17 disrupts the liquid-liquid phase separation (LLPS) of the N protein. To enhance the stability and molecular recognition capabilities of N-Apt17, we designed a circular bivalent DNA aptamer termed cb-N-Apt17. In both in vivo and in vitro experiments, cb-N-Apt17 exhibited increased stability, enhanced binding affinity, and superior LLPS disrupting ability. Thus, our study provides essential proof-of-principle evidence supporting the further development of cb-N-Apt17 as a therapeutic candidate for COVID-19.

Can aptamer-based functional moieties modulate immunostimulation by fibrous nucleic acid nanoparticles?

Fibrous nanomaterials containing silica, titanium oxide, and carbon nanotubes are notoriously known for their undesirable inflammatory responses and associated toxicities that have been extensively studied in the environmental and occupational toxicology fields. Biopersistance and inflammation of "hard" nanofibers prevent their broader biomedical applications. To utilize the structural benefits of fibrous nanomaterials for functionalization with moieties of therapeutic significance while preventing undesirable immune responses, researchers employ natural biopolymers horizontal line RNA and DNA horizontal line to design "soft" and biodegradable nanomaterials with controlled immunorecognition. Nucleic acid nanofibers have been shown to be safe and efficacious in applications that do not require their delivery into the cells such as the regulation of blood coagulation. Previous studies demonstrated that unlike traditional therapeutic nucleic acids (e.g., CpG DNA oligonucleotides) nucleic acid nanoparticles (NANPs), when used without a carrier, are not internalized by the immune cells and, as such, do not induce undesirable cytokine responses. In contrast, intracellular delivery of NANPs results in cytokine responses that are dependent on the physicochemical properties of these nanomaterials. However, the structure-activity relationship of innate immune responses to intracellularly delivered fibrous NANPs is poorly understood. Herein, we employ the intracellular delivery of model RNA/DNA nanofibers functionalized with G-quadruplex-based DNA aptamers to investigate how their structural properties influence cytokine responses. We demonstrate that nanofibers' scaffolds delivered to the immune cells using lipofectamine induce interferon response via the cGAS-STING signaling pathway activation and that DNA aptamers incorporation shields the fibers from recognition by cGAS and results in a lower interferon response. This structure-activity relationship study expands the current knowledge base to inform future practical applications of intracellularly delivered NANPs as vaccine adjuvants and immunotherapies.

How can YTHDF2 be used as a therapeutic target for HCC by suppressing immune evasion and angiogenesis through ETV5/PD-L1/VEGFA axis?

N6-methyladenosine (m(6) A) modification orchestrates cancer formation and progression by affecting the tumor microenvironment (TME). For hepatocellular carcinoma (HCC), immune evasion and angiogenesis are characteristic features of its TME. The role of YTH N6-methyladenosine RNA binding protein 2 (YTHDF2), as an m(6) A reader, in regulating HCC TME are not fully understood. Herein, it is discovered that trimethylated histone H3 lysine 4 and H3 lysine 27 acetylation modification in the promoter region of YTHDF2 enhanced its expression in HCC, and upregulated YTHDF2 in HCC predicted a worse prognosis. Animal experiments demonstrated that Ythdf2 depletion inhibited spontaneous HCC formation, while its overexpression promoted xenografted HCC progression. Mechanistically, YTHDF2 recognized the m(6) A modification in the 5'-untranslational region of ETS variant transcription factor 5 (ETV5) mRNA and recruited eukaryotic translation initiation factor 3 subunit B to facilitate its translation. Elevated ETV5 expression induced the transcription of programmed death ligand-1 and vascular endothelial growth factor A, thereby promoting HCC immune evasion and angiogenesis. Targeting YTHDF2 via small interference RNA-containing aptamer/liposomes successfully both inhibited HCC immune evasion and angiogenesis. Together, this findings reveal the potential application of YTHDF2 in HCC prognosis and targeted treatment.

How can multivalent aptamer-based DNA nanostructures be used for regulating multiheteroreceptor-mediated tumor recognition?

Precise mapping and regulation of cell surface receptors hold immense significance in disease treatment, such as cancer, infection, and neurodisorders, but also face enormous challenges. In this study, we designed a series of adjustable multivalent aptamer-based DNA nanostructures to precisely control their interaction with receptors in tumor cells. By profiling surface receptors on 12 cell lines using 10 different aptamers, we generated a heatmap that accurately distinguished between various tumor types based on multiple markers. We then incorporated these aptamers onto DNA origami structures to regulate receptor recognition, with patch-like structures demonstrating a tendency to be trapped on the cell surface and with tube-like structures showing a preference for internalization. Through precise control of aptamer species, valence, and geometric patterns, we found that multiheteroreceptor-mediated recognition not only favored the specific binding of nanostructures to tumor cells but also greatly enhanced intracellular uptake by promoting clathrin-dependent endocytosis. Specifically, we achieved over 5-fold uptake in different tumor cells versus normal cells using tube-like structures modified with different diheteroaptamer pairs, facilitating targeted drug delivery. Moreover, patch-like structures with triheteroaptamers guided specific interactions between macrophages and tumor cells, leading to effective immune clearance. This programmable multivalent system allows for the precise regulation of cell recognition using multiple parameters, demonstrating great potential for personalized tumor treatment.

How does a FRET-based ultrasensitive fluorescent aptasensor detect 6'-sialyllactose?

As a kind of human milk oligosaccharide, 6'-sialyllactose (6'-SL) plays an important role in promoting infant brain development and improving infant immunity. The content of 6'-SL in infant formula milk powder is thus one of the important nutritional indexes. Since the lacking of efficient and rapid detection methods for 6'-SL, it is of great significance to develop specific recognition elements and establish fast and sensitive detection methods for 6'-SL. Herein, using 6'-SL specific aptamer as the recognition element, catalytic hairpin assembly as the signal amplification technology and quantum dots as the signal label, a fluorescence biosensor based on fluorescence resonance energy transfer (FRET) was constructed for ultra-sensitive detection of 6'-SL. The detection limit of this FRET-based fluorescent biosensor is 0.3 nM, and it has some outstanding characteristics such as high signal-to-noise ratio, low time-consuming, simplicity and high efficiency in the actual sample detection. Therefore, it has broad application prospect in 6'-SL detection.

How can multiple ssDNA be used in fluorescent aptasensors for sensitive detection of acetamiprid in vegetables using magnetic Fe(3)O(4)/C-assisted separation?

Acetamiprid (ACE) is a highly effective broad-spectrum insecticide, and its widespread use is potentially harmful to human health and environmental safety. In this study, magnetic Fe(3)O(4)/carbon (Fe(3)O(4)/C), a derivative of metal-organic framework MIL-101 (Fe), was synthesized by a two-step calcination method. And a fluorescent sensing strategy was developed for the efficient and sensitive detection of ACE using Fe(3)O(4)/C and multiple complementary single-stranded DNA (ssDNA). By using aptamer with multiple complementary ssDNA, the immunity of interference of the aptasensor was improved, and the aptasensor showed high selectivity and sensitivity. When ACE was present, the aptamer (Apt) combined with ACE. The complementary strand of Apt (Cs1) combined with two short complementary strands of Cs1, fluorophore 6-carboxyfluorescein-labeled complementary strand (Cs2-FAM) and the other strand Cs3. The three strands formed a double-stranded structure, and fluorescence would not be quenched by Fe(3)O(4)/C. In the absence of ACE, Cs2-FAM would be in a single-chain state and would be adsorbed by Fe(3)O(4)/C, and the fluorescence of FAM would be quenched by Fe(3)O(4)/C via photoelectron transfer. This aptasensor sensitively detected ACE over a linear concentration range of 10-1000 nM with a limit of detection of 3.41 nM. The recoveries of ACE spiked in cabbage and celery samples ranged from 89.49% to 110.76% with high accuracy.

How can mammalian gene expression be controlled by modulating polyA signal cleavage at 5' UTR?

The ability to control gene expression in mammalian cells is crucial for safe and efficacious gene therapies and for elucidating gene functions. Current gene regulation systems have limitations such as harmful immune responses or low efficiency. We describe the pA regulator, an RNA-based switch that controls mammalian gene expression through modulation of a synthetic polyA signal (PAS) cleavage introduced into the 5' UTR of a transgene. The cleavage is modulated by a 'dual-mechanism'-(1) aptamer clamping to inhibit PAS cleavage and (2) drug-induced alternative splicing that removes the PAS, both activated by drug binding. This RNA-based methodology circumvents the immune responses observed in other systems and achieves a 900-fold induction with an EC(50) of 0.5 microg ml(-)(1) tetracycline (Tc), which is well within the FDA-approved dose range. The pA regulator effectively controls the luciferase transgene in live mice and the endogenous CD133 gene in human cells, in a dose-dependent and reversible manner with long-term stability.

How can ultra-sensitive detection of tumor necrosis factor alpha be achieved using silver-coated gold core shell and magnetically separated recognition of SERS aptamer sensors?

A highly sensitive tumor necrosis factor alpha (TNF-alpha) detection method based on a surface-enhanced Raman scattering (SERS) magnetic patch sensor is reported. Magnetic beads (MNPs) and core shells were used as the capture matrix and signaling probe, respectively. For this purpose, antibodies were immobilized on the surface of magnetic beads, and then Au@4-MBN@Ag core-shell structures coupled with aptamers and TNF-alpha antigen were added sequentially to form a sandwich immune complex. Quantitative analysis was performed by monitoring changes in the characteristic SERS signal intensity of the Raman reporter molecule 4-MBN. The results showed that the limit of detection (LOD) of the proposed method was 4.37 x 10(-15) mg.mL(-1) with good linearity (R(2) = 0.9918) over the concentration range 10(-12) to 10(-5) mg.mL(-1). Excellent assay accuracy was also demonstrated, with recoveries in the range 102% to 114%. Since all reactions occur in solution and are separated by magnetic adsorption of magnetic beads, this SERS-based immunoassay technique solves the kinetic problems of limited diffusion and difficult separation on solid substrates. The method is therefore expected to be a good clinical tool for the diagnosis of the inflammatory biomarker THF-alpha and in vivo inflammation screening.

How do plant mRNAs move into a fungal pathogen via extracellular vesicles in order to reduce infection?

Cross-kingdom small RNA trafficking between hosts and microbes modulates gene expression in the interacting partners during infection. However, whether other RNAs are also transferred is unclear. Here, we discover that host plant Arabidopsis thaliana delivers mRNAs via extracellular vesicles (EVs) into the fungal pathogen Botrytis cinerea. A fluorescent RNA aptamer reporter Broccoli system reveals host mRNAs in EVs and recipient fungal cells. Using translating ribosome affinity purification profiling and polysome analysis, we observe that delivered host mRNAs are translated in fungal cells. Ectopic expression of two transferred host mRNAs in B. cinerea shows that their proteins are detrimental to infection. Arabidopsis knockout mutants of the genes corresponding to these transferred mRNAs are more susceptible. Thus, plants have a strategy to reduce infection by transporting mRNAs into fungal cells. mRNAs transferred from plants to pathogenic fungi are translated to compromise infection, providing knowledge that helps combat crop diseases.

Are there RNA Aptamers to Inhibit the Action of Effector Proteins of Plant Pathogens?

In previous work, we experimentally demonstrated the possibility of using RNA aptamers to inhibit endogenous protein expression and their function within plant cells In the current work, we show that our proposed method is suitable for inhibiting the functions of exogenous, foreign proteins delivered into the plant via various mechanisms, including pathogen proteins. Stringent experimentation produced robust RNA aptamers that are able to bind to the recombinant HopU1 effector protein of P. syringae bacteria. This research uses genetic engineering methods to constitutively express/transcribe HopU1 RNA aptamers in transgenic A. thaliana. Our findings support the hypothesis that HopU1 aptamers can actively interfere with the function of the HopU1 protein and thereby increase resistance to phytopathogens of the genus P. syringae pv. tomato DC 3000.

Do human plasma proteins interfere with in vivo activity of aptamers?

Aptamers are single-stranded DNA or RNA molecules capable of recognizing targets via specific three-dimensional structures. Taking advantage of this unique targeting function, aptamers have been extensively applied to bioanalysis and disease theranostics. However, the targeting functionality of aptamers in the physiological milieu is greatly impeded compared with their in vitro applications. To investigate the physiological factors that adversely affect the in vivo targeting ability of aptamers, we herein systematically studied the interactions between human plasma proteins and aptamers and the specific effects of plasma proteins on aptamer targeting. Microscale thermophoresis and flow cytometry analysis showed that plasma interacted with aptamers, restricting their affinity toward targeted tumor cells. Further pull-down assay and proteomic identification verified that the interactions between aptamers and plasma proteins were mainly involved in complement activation and immune response as well as showed structure-selective and sequence-specific features. Particularly, the fibronectin 1 (FN1) protein showed dramatically specific interactions with nucleolin (NCL) targeting aptamer AS1411. The competitive binding between FN1 and NCL almost deprived the AS1411 aptamer's targeting ability in vivo. In order to maintain the targeting function in the physiological milieu, a series of optimizations were performed via the chemical modifications of AS1411 aptamer, and 3'-terminal pegylation was demonstrated to be resistant to the interaction with FN1, leading to improved tumor-targeting effects. This work emphasizes the physiological environment influences on aptamers targeting functionality and suggests that rational design and modification of aptamers to minimize the nonspecific interaction with plasma proteins might be effective to maintain aptamer functionality in future clinical uses.

Can aptamers be used to detect aflatoxin B1?

Aflatoxin B1 (AFB1) is one of the most toxic mycotoxins, which is frequently detected in agricultural products. Herein, a novel functional DNA -linked immunosorbent assay (DLISA) with dual-modality based on hybrid chain reaction (HCR) has been successfully developed for ultrasensitive detection of AFB1. The strategy relies on AFB1 immune-bridged occurrence of HCR and the salt-induced aggregation of gold nanoparticles (AuNPs). An aptamer-initiator stand (Apt-Ini stand) is designed for the AFB1 recognition and the activation of HCR, which can recognize the matched hairpins and cause the crossing-opening of H1 and H2, producing a long double-stranded DNA polymer. The addition of SYBR Green I achieves the fluorescent signal output. Remaining less DNA hairpins were added and stuck on the surface of AuNPs, which were insufficient to protect the AuNPs, resulting in the salt-induced aggregation with the color change from red to blue. The dual-modality provides limits of detections of 1.333 x 10(-14) g/mL and 2.471 x 10(-15) g/mL, respectively. This DLISA with dual-modality provides not only a colorimetry that can meet the needs of on-the-spot preliminary inspection, but also a fluorescence assay that can acquire the precise results.

Can aptamers be used to measure IL6?

A major societal challenge is the development of the necessary tools for early diagnosis of diseases such as cancer and sepsis. Consequently, there is a concerted push to develop low-cost and non-invasive methods of analysis with high sensitivity and selectivity. A notable trend is the development of highly sensitive methods that are not only amenable for point-of-care (POC) testing, but also for wearable devices allowing continuous monitoring of biomarkers. In this context, a non-invasive test for the detection of a promising biomarker, the protein Interleukin-6 (IL-6), could represent a significant advance in the clinical management of cancer, in monitoring the chemotherapy response, or for prompt diagnosis of sepsis. This work reports a capacitive electrochemical impedance spectroscopy sensing platform tailored towards POC detection and treatment monitoring in human serum. The specific recognition of IL-6 was achieved employing gold surfaces modified with an anti-IL6 nanobody (anti-IL-6 VHH) or a specific IL-6 aptamer. In the first system, the anti-IL-6 VHH was covalently attached to the gold surface using a binary self-assembled-monolayer (SAM) of 6-mercapto-1-hexanol (MCH) and 11-mercaptoundecanoic acid. In the second system, the aptamer was chemisorbed onto the surface in a mixed SAM layer with MCH. The analytical performance for each label-free sensor was evaluated in buffer and 10% human serum samples and then compared. The results of this work were generated using a low-cost, thin film eight-channel gold sensor array produced on a flexible substrate providing useful information on the future design of POC and wearable impedance biomarker detection platforms.

Do multivalent aptamer drug conjugates enhance targeting HER2-positive gastric cancer?

Antibody drug conjugates (ADCs) have shown promise to be the mainstream chemotherapeutics for advanced HER2-positive cancers, yet the issues of poor drug delivery efficiency, limited chemotherapeutic effects, severe immune responses, and drug resistance remain to be addressed before the clinical applications of ADCs. The DNA aptamer-guided drug conjugates (ApDCs) are receiving growing attention for specific tumors due to their excellent tumor affinity and low cost. Therefore, developing a multivalent ApDC nanomedicine by combining anti-HER2 aptamer (HApt), tetrahedral framework nucleic acid (tFNA), and deruxtecan (Dxd) together to form HApt-tFNA@Dxd might help to address these concerns. In this study, the HER2-targeted DNA aptamer modified DNA tetrahedron (HApt-tFNA) was employed as a system for drug delivery, and the adoption of tFNA could effectively enlarge the drug-loading rate compared to aptamer-guided ApDCs previously reported. Compared with free Dxd and tFNA@Dxd, HApt-tFNA@Dxd showed better structural stability, excellent targeted cytotoxicity to HER2-positive gastric cancer, and increased tissue aggregation ability in tumors. These features and superiorities make HApt-tFNA@Dxd a promising chemotherapeutic medicine for HER2-positive tumors. Our work developed a new targeting nanomedicine by combining DNA nanomaterials and chemotherapeutic agents, which represents a critical advance toward developing novel DNA-based nanomaterials and promoting their potential applications for HER2-positive cancer therapy.

Do Mn(2+) /Ir(3+) -doped Prussian Blue Nanoparticles be used for tumor microenvironment modulation and image-guided synergistic therapy?

The development of smart theranostic nanoplatforms has gained great interest in effective cancer treatment against the complex tumor microenvironment (TME), including weak acidity, hypoxia, and glutathione (GSH) overexpression. Herein, a TME-responsive nanoplatform named PMIC(Apt) /ICG, based on PB:Mn&Ir@CaCO(3) (Aptamer) /ICG, is designed for the competent synergistic photothermal therapy and photodynamic therapy (PDT) under the guidance of photothermal and magnetic resonance imaging. The nanoplatform's aptamer modification targeting the transferrin receptor and the epithelial cell adhesion molecule on breast cancer cells, and the acid degradable CaCO(3) shell allow for effective tumor accumulation and TME-responsive payload release in situ. The nanoplatform also exhibits excellent PDT properties due to its ability to generate O(2) and consume antioxidant GSH in tumors. Additionally, the synergistic therapy is achieved by a single wavelength of near-infrared laser. RNA sequencing is performed to identify differentially expressed genes, which show that the expressions of proliferation and migration-associated genes are inhibited, while the apoptosis and immune response gene expressions are upregulated after the synergistic treatments. This multifunctional nanoplatform that responds to the TME to realize the on-demand payload release and enhance PDT induced by TME modulation holds great promise for clinical applications in tumor therapy.

Do Inhalable DNA tetrahedron nanoplatforms boost anti-tumor immune responses?

Despite advancements in the treatment of pulmonary cancer, the existence of mucosal barriers in lung still hampered the penetration and diffusion of therapeutic agents and greatly limited the therapeutic benefits. In this work, we reported a novel inhalable pH-responsive tetrahedral DNA nanomachines with simultaneous delivery of immunomodulatory CpG oligonucleotide and PD-L1-targeting antagonistic DNA aptamer (CP@TDN) for efficient treatment of pulmonary metastatic cancer. By precisely controlling the ratios of CpG and PD-L1 aptamer, the obtained CP@TDN could specifically release PD-L1 aptamer to block PD-1/PD-L1 immune checkpoint axis in acidic tumor microenvironment, followed by endocytosis by antigen-presenting cells to generate anti-tumor immune activation and secretion of anti-tumor cytokines. Moreover, inhalation delivery of CP@TDN showed highly-efficient lung deposition with greatly enhanced intratumoral accumulation, ascribing to the DNA tetrahedron-mediated penetration of pulmonary mucosa. Resultantly, CP@TDN could significantly inhibit the growth of metastatic orthotopic lung tumors via the induction of robust antitumor responses. Therefore, our work presents an attractive approach by virtue of biocompatible DNA tetrahedron as the inhalation delivery system for effective treatment of metastatic lung cancer.

Can aptamer-based aptasensor be used to detect aflatoxin b1?

Realizing the simultaneous speedy detection of multiple mycotoxins in contaminated food and feed is of great practical importance in the domain of food manufacturing and security. Herein, a fluorescent aptamer sensor based on self-assembled DNA double-crossover was developed and used for effective simultaneous quantitative detection of aflatoxins M(1) and B(1) by fluorescence resonance energy transfer (FRET). Fluorescent dye-modified aflatoxin M(1) and B(1) aptamers are selected as recognition elements and signal probes, and DNA double crosses are consistently locked by the aflatoxin aptamers, which results in a "turn-off" of the fluorescent signal. In the presence of AFM(1) and AFB(1), the aptamer sequences are more inclined to form Apt-AFM(1) and Apt-AFB(1) complexes, and the fluorescent probes are released from the DNA double-crossing platform, leading to an enhanced fluorescent signal (Cy3: 568 nm; Cy5: 660 nm). Under the optimal conditions, the signal response of the constructed fluorescent aptamer sensor showed good linearity with the logarithm of AFM(1) and AFB(1) concentrations, with detection limits of 6.24 pg/mL and 9.0 pg/mL, and a wide linear range of 0.01-200 ng/mL and 0.01-150 ng/mL, respectively. In addition, the effect of potential interfering substances in real samples was analyzed, and the aptasensor presented a good interference immunity. Moreover, by modifying and designing aptamer probes, the sensor can be applied to high-throughput simultaneous screening of other analytes, providing a new approach for the development of fluorescent aptamer sensors.

What are Aptamer-functionalized DNA circuits that favor T cell-cancer cell interactions?

Nongenetic strategies that enable control over the cell-cell interaction network would be highly desired, particularly in T cell-based cancer immunotherapy. In this work, we developed an aptamer-functionalized DNA circuit to modulate the interaction between T cells and cancer cells. This DNA circuit was composed of recognition-then-triggering and aggregation-then-activation modules. Upon recognizing target cancer cells, the triggering strand was released to induce aggregation of immune receptors on the T cell surface, leading to an enhancement of T cell activity for effective cancer eradication. Our results demonstrated the feasibility of this DNA circuit for promoting target cancer cell-directed stimulation of T cells, which, consequently, enhanced their killing effect on cancer cells. This DNA circuit, as a modular strategy to modulate intercellular interactions, could lead to a new paradigm for the development of nongenetic T cell-based immunotherapy.

Can urinary CXCL9 be evaluated as a biomarker using aptamer technology for acute interstitial nephritis diagnosis?

BackgroundAcute tubulointerstitial nephritis (AIN) is one of the few causes of acute kidney injury with diagnosis-specific treatment options. However, due to the need to obtain a kidney biopsy for histological confirmation, AIN diagnosis can be delayed, missed, or incorrectly assumed. Here, we identify and validate urinary CXCL9, an IFN-gamma-induced chemokine involved in lymphocyte chemotaxis, as a diagnostic biomarker for AIN.MethodsIn a prospectively enrolled cohort with pathologist-adjudicated histological diagnoses, termed the discovery cohort, we tested the association of 180 immune proteins measured by an aptamer-based assay with AIN and validated the top protein, CXCL9, using sandwich immunoassay. We externally validated these findings in 2 cohorts with biopsy-confirmed diagnoses, termed the validation cohorts, and examined mRNA expression differences in kidney tissue from patients with AIN and individuals in the control group.ResultsIn aptamer-based assay, urinary CXCL9 was 7.6-fold higher in patients with AIN than in individuals in the control group (P = 1.23 x 10-5). Urinary CXCL9 measured by sandwich immunoassay was associated with AIN in the discovery cohort (n = 204; 15% AIN) independently of currently available clinical tests for AIN (adjusted odds ratio for highest versus lowest quartile: 6.0 [1.8-20]). Similar findings were noted in external validation cohorts, where CXCL9 had an AUC of 0.94 (0.86-1.00) for AIN diagnosis. CXCL9 mRNA expression was 3.9-fold higher in kidney tissue from patients with AIN (n = 19) compared with individuals in the control group (n = 52; P = 5.8 x 10-6).ConclusionWe identified CXCL9 as a diagnostic biomarker for AIN using aptamer-based urine proteomics, confirmed this association using sandwich immunoassays in discovery and external validation cohorts, and observed higher expression of this protein in kidney biopsies from patients with AIN.FundingThis study was supported by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) awards K23DK117065 (DGM), K08DK113281 (KM), R01DK128087 (DGM), R01DK126815 (DGM and LGC), R01DK126477 (KNC), UH3DK114866 (CRP, DGM, and FPW), R01DK130839 (MES), and P30DK079310 (the Yale O'Brien Center). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.