Machine learning guided aptamer refinement and discovery

Nucleic acid-based wearable and implantable electrochemical sensors

The rapid advancements in nucleic acid-based electrochemical sensors for implantable and wearable applications have marked a significant leap forward in the domain of personal healthcare over the last decade. This technology promises to revolutionize personalized healthcare by facilitating the early diagnosis of diseases, monitoring of disease progression, and tailoring of individual treatment plans. This review navigates through the latest developments in this field, focusing on the strategies for nucleic acid sensing that enable real-time and continuous biomarker analysis directly in various biofluids, such as blood, interstitial fluid, sweat, and saliva. The review delves into various nucleic acid sensing strategies, emphasizing the innovative designs of biorecognition elements and signal transduction mechanisms that enable implantable and wearable applications. Special perspective is given to enhance nucleic acid-based sensor selectivity and sensitivity, which are crucial for the accurate detection of low-level biomarkers. The integration of such sensors into implantable and wearable platforms, including microneedle arrays and flexible electronic systems, actualizes their use in on-body devices for health monitoring. We also tackle the technical challenges encountered in the development of these sensors, such as ensuring long-term stability, managing the complexity of biofluid dynamics, and fulfilling the need for real-time, continuous, and reagentless detection. In conclusion, the review highlights the importance of these sensors in the future of medical engineering, offering insights into design considerations and future research directions to overcome existing limitations and fully realize the potential of nucleic acid-based electrochemical sensors for healthcare applications.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Functional Selection of Tau Oligomerization-Inhibiting Aptamers

Pathological hyperphosphorylation and aggregation of microtubule-associated Tau protein contribute to Alzheimer's Disease (AD) and other related tauopathies. Currently, no cure exists for Alzheimer's Disease. Aptamers offer significant potential as next-generation therapeutics in biotechnology and the treatment of neurological disorders. Traditional aptamer selection methods for Tau protein focus on binding affinity rather than interference with pathological Tau. In this study, we developed a new selection strategy to enrich DNA aptamers that bind to surviving monomeric Tau protein under conditions that would typically promote Tau aggregation. Employing this approach, we identified a set of aptamer candidates. Notably, BW1c demonstrates a high binding affinity (Kd=6.6 nM) to Tau protein and effectively inhibits arachidonic acid (AA)-induced Tau protein oligomerization and aggregation. Additionally, it inhibits GSK3β-mediated Tau hyperphosphorylation in cell-free systems and okadaic acid-mediated Tau hyperphosphorylation in cellular milieu. Lastly, retro-orbital injection of BW1c tau aptamer shows the ability to cross the blood brain barrier and gain access to neuronal cell body. Through further refinement and development, these Tau aptamers may pave the way for a first-in-class neurotherapeutic to mitigate tauopathy-associated neurodegenerative disorders.

Assembling the RNA therapeutics toolbox

From the approval of COVID-19 mRNA vaccines to the 2023 Nobel Prize awarded for nucleoside base modifications, RNA therapeutics have entered the spotlight and are transforming drug development. While the term "RNA therapeutics" has been used in various contexts, this review focuses on treatments that utilize RNA as a component or target RNA for therapeutic effects. We summarize the latest advances in RNA-targeting tools and RNA-based technologies, including but not limited to mRNA, antisense oligos, siRNAs, small molecules and RNA editors. We focus on the mechanisms of current FDA-approved therapeutics but also provide a discussion on the upcoming workforces. The clinical utility of RNA-based therapeutics is enabled not only by the advances in RNA technologies but in conjunction with the significant improvements in chemical modifications and delivery platforms, which are also briefly discussed in the review. We summarize the latest RNA therapeutics based on their mechanisms and therapeutic effects, which include expressing proteins for vaccination and protein replacement therapies, degrading deleterious RNA, modulating transcription and translation efficiency, targeting noncoding RNAs, binding and modulating protein activity and editing RNA sequences and modifications. This review emphasizes the concept of an RNA therapeutic toolbox, pinpointing the readers to all the tools available for their desired research and clinical goals. As the field advances, the catalog of RNA therapeutic tools continues to grow, further allowing researchers to combine appropriate RNA technologies with suitable chemical modifications and delivery platforms to develop therapeutics tailored to their specific clinical challenges.

Computational aptamer design for spike glycoprotein (S) (SARS CoV-2) detection with an electrochemical aptasensor

A new bioinformatic platform (APTERION) was used to design in a short time and with high specificity an aptamer for the detection of the spike protein, a structural protein of SARS-CoV-2 virus, responsible for the COVID-19 pandemic. The aptamer concentration on the carbon electrode surface was optimized using static contact angle and fluorescence method, while specificity was tested using differential pulse voltammetry (DPV) associated to carbon screen-printed electrodes. The data obtained demonstrated the good features of the aptamer which could be used to create a rapid method for the detection of SARS-CoV-2 virus. In fact, it is specific for spike also when tested against bovine serum albumin and lysozyme, competitor proteins if saliva is used as sample to test for the virus presence. Spectrofluorometric characterization allowed to measure the amount of aptamer present on the carbon electrode surface, while DPV measurements proved the affinity of the aptamer towards the spike protein and gave quantitative results. The acquired data allowed to conclude that the APTERION bioinformatic platform is a good method for aptamer design for rapidity and specificity. KEY POINTS: • Spike protein detection using an electrochemical biosensor • Aptamer characterization by contact angle and fluorescent measurements on electrode surface • Computational design of specific aptamers to speed up the aptameric sequence time.

Aptamer Technologies in Neuroscience, Neuro-Diagnostics and Neuro-Medicine Development

Aptamers developed using in vitro Systematic Evolution of Ligands by Exponential Enrichment (SELEX) technology are single-stranded nucleic acids 10-100 nucleotides in length. Their targets, often with specificity and high affinity, range from ions and small molecules to proteins and other biological molecules as well as larger systems, including cells, tissues, and animals. Aptamers often rival conventional antibodies with improved performance, due to aptamers' unique biophysical and biochemical properties, including small size, synthetic accessibility, facile modification, low production cost, and low immunogenicity. Therefore, there is sustained interest in engineering and adapting aptamers for many applications, including diagnostics and therapeutics. Recently, aptamers have shown promise as early diagnostic biomarkers and in precision medicine for neurodegenerative and neurological diseases. Here, we critically review neuro-targeting aptamers and their potential applications in neuroscience research, neuro-diagnostics, and neuro-medicine. We also discuss challenges that must be overcome, including delivery across the blood-brain barrier, increased affinity, and improved in vivo stability and in vivo pharmacokinetic properties.

Extracellular vesicles for developing targeted hearing loss therapy

Substantial efforts have been made for local administration of small molecules or biologics in treating hearing loss diseases caused by either trauma, genetic mutations, or drug ototoxicity. Recently, extracellular vesicles (EVs) naturally secreted from cells have drawn increasing attention on attenuating hearing impairment from both preclinical studies and clinical studies. Highly emerging field utilizing diverse bioengineering technologies for developing EVs as the bioderived therapeutic materials, along with artificial intelligence (AI)-based targeting toolkits, shed the light on the unique properties of EVs specific to inner ear delivery. This review will illuminate such exciting research field from fundamentals of hearing protective functions of EVs to biotechnology advancement and potential clinical translation of functionalized EVs. Specifically, the advancements in assessing targeting ligands using AI algorithms are systematically discussed. The overall translational potential of EVs is reviewed in the context of auditory sensing system for developing next generation gene therapy.

Clinical Translation of Aptamers for COVID-19

The COVID-19 etiologic agent, SARS-CoV-2, continues to be one of the leading causes of death on a global scale. Although efficient methods for diagnosis and treatment of COVID-19 have been developed, new methods of battling SARS-CoV-2 variants and long COVID are still urgently needed. A number of aptamers have demonstrated tremendous potential to be developed into diagnostic and therapeutic agents for COVID-19. The translation of the aptamers for clinical uses, however, has been extremely slow. Overcoming the difficulties faced by aptamers would advance this technology toward clinical use for COVID-19 and other serious disorders.

Development and validation of machine learning-based model for mortality prediction in patients with acute basilar artery occlusion receiving endovascular treatment: multicentric cohort analysis

BACKGROUND

Predicting mortality in stroke patients using information available before endovascular treatment (EVT) is an essential component for supporting clinical decision-making. Although the mortality rate of acute basilar artery occlusion (ABAO) after EVT has reached 40%, few studies have focused on predicting mortality in these individuals. Thus, we aimed to develop and validate a machine learning-based mortality prediction tool based on preoperative information for ABAO patients receiving EVT.

METHODS

The derivation cohort comprised patients from southern provinces of China in the BASILAR registry. The model (POSITIVE: Predicting mOrtality of baSilar artery occlusion patIents Treated wIth EVT) was trained and optimized using a fivefold cross-validation method in which hyperparameters were selected and fine-tuned. This model was retrospectively tested in patients from the northern provinces of China from the BASILAR registry. A prospective test of POSITIVE was performed on consecutive patients from two hospitals between January 2020 and June 2022.

RESULTS

Extreme gradient boosting was employed to construct the POSITIVE model, which achieved the best predictive performance among the eight machine learning algorithms and showed excellent discrimination (area under the curve (AUC) 0.83, 95% confidence interval (95% CI) 0.80 to 0.87) and calibration (Hosmer-Lemeshow test, P>0.05) in the development cohort. AUC yielded by the POSITIVE model for the retrospective test was 0.79 (95% CI 0.71 to 0.85), higher than that obtained by traditional models. Prospective comparisons showed that the POSITIVE model achieved the highest AUC (0.82, 95% CI 0.74 to 0.90) among all prediction models.

CONCLUSION

We developed a machine learning algorithm and retrospective and prospective testing with multicentric cohorts, which exhibited a solid predictive performance and may act as a convenient reference to guide decision-making for ABAO patients. The POSITIVE model is presented online for user-friendly access.

Computational generation and characterization of IsdA-binding aptamers with single-molecule FRET analysis

Staphylococcus aureus is a major foodborne bacterial pathogen. Early detection of S. aureus is crucial to prevent infections and ensure food quality. The iron-regulated surface determinant protein A (IsdA) of S. aureus is a unique surface protein necessary for sourcing vital iron from host cells for the survival and colonization of the bacteria. The function, structure, and location of the IsdA protein make it an important protein for biosensing applications relating to the pathogen. Here, we report an in-silico approach to develop and validate high-affinity binding aptamers for the IsdA protein detection using custom-designed in-silico tools and single-molecule Fluorescence Resonance Energy Transfer (smFRET) measurements. We utilized in-silico oligonucleotide screening methods and metadynamics-based methods to generate 10 aptamer candidates and characterized them based on the Dissociation Free Energy (DFE) of the IsdA-aptamer complexes. Three of the aptamer candidates were shortlisted for smFRET experimental analysis of binding properties. Limits of detection in the low picomolar range were observed for the aptamers, and the results correlated well with the DFE calculations, indicating the potential of the in-silico approach to support aptamer discovery. This study showcases a computational SELEX method in combination with single-molecule binding studies deciphering effective aptamers against S. aureus IsdA, protein. The established approach demonstrates the ability to expedite aptamer discovery that has the potential to cut costs and predict binding efficacy. The application can be extended to designing aptamers for various protein targets, enhancing molecular recognition, and facilitating the development of high-affinity aptamers for multiple uses.

Functionally Enhanced XNA Aptamers Discovered by Parallelized Library Screening

In vitro evolution strategies have been used for >30 years to generate nucleic acid aptamers against therapeutic targets of interest, including disease-associated proteins. However, this process requires many iterative cycles of selection and amplification, which severely restricts the number of target and library design combinations that can be explored in parallel. Here, we describe a single-round screening approach to aptamer discovery that relies on function-enhancing chemotypes to increase the distribution of high-affinity sequences in a random-sequence library. We demonstrate the success of de novo discovery by affinity selection of threomers against the receptor binding domain of the S1 protein from SARS-CoV-2. Detailed biochemical characterization of the enriched population identified threomers with binding affinity values that are comparable to aptamers produced by conventional SELEX. This work establishes a highly parallelizable path for querying diverse chemical repertoires and may offer a viable route for accelerating the discovery of therapeutic aptamers.

Aptamer-Protein Interactions: From Regulation to Biomolecular Detection

Serving as the basis of cell life, interactions between nucleic acids and proteins play essential roles in fundamental cellular processes. Aptamers are unique single-stranded oligonucleotides generated by in vitro evolution methods, possessing the ability to interact with proteins specifically. Altering the structure of aptamers will largely modulate their interactions with proteins and further affect related cellular behaviors. Recently, with the in-depth research of aptamer-protein interactions, the analytical assays based on their interactions have been widely developed and become a powerful tool for biomolecular detection. There are some insightful reviews on aptamers applied in protein detection, while few systematic discussions are from the perspective of regulating aptamer-protein interactions. Herein, we comprehensively introduce the methods for regulating aptamer-protein interactions and elaborate on the detection techniques for analyzing aptamer-protein interactions. Additionally, this review provides a broad summary of analytical assays based on the regulation of aptamer-protein interactions for detecting biomolecules. Finally, we present our perspectives regarding the opportunities and challenges of analytical assays for biological analysis, aiming to provide guidance for disease mechanism research and drug discovery.

Parameterizing the Binding Properties of XNA Aptamers Isolated from a Low Stringency Selection

Machine learning offers a guided approach to aptamer discovery, but more information is needed to develop algorithms that can intelligently identify high-performing aptamers to a broad array of targets. Critical to this effort is the need to experimentally parameterize the difference between low and high affinity binders to a given target. Although classical selection experiments help define the upper limit by converging on a small number of tight binding sequences, very little is known about the lower limit of binding that defines the boundary between binders and nonbinders. Here, we apply a quantitative approach to explore the diversity of aptamers isolated from two identical in vitro selections performed under low stringency conditions. Starting from a library of 1 trillion unique threose nucleic acid (TNA) sequences, 7 rounds of selection were performed to enrich binders to a known aptagenic target. High density sequencing of each round of selection followed by a detailed kinetic analysis of 136 TNA aptamers yielded a narrow range of equilibrium dissociation constants (KD = ∼ 1-15 nM) that were consistent between two experimental replicates. These findings offer insights into the lower limit of binding that may be expected for aptamers generated against aptagenic targets and could provide useful constraints for evaluating the results of experimental and computational approaches.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

In vitro selection of aptamers and their applications

The introduction of the in-vitro evolution method known as SELEX (Systematic Evolution of Ligands by Exponential enrichment) more than 30 years ago led to the conception of versatile synthetic receptors known as aptamers. Offering many benefits such as low cost, high stability and flexibility, aptamers have sparked innovation in molecular diagnostics, enabled advances in synthetic biology and have facilitated new therapeutic approaches. The SELEX method itself is inherently adaptable and offers near limitless possibilities in yielding functional nucleic acid ligands. This Primer serves to provide guidance on experimental design and highlight new growth areas for this impactful technology.

Highly Parallelized Screening of Functionally Enhanced XNA Aptamers in Uniform Hydrogel Particles

Xeno-nucleic acid (XNA) aptamers based on evolvable non-natural genetic polymers hold enormous potential as future diagnostic and therapeutic agents. However, time-consuming and costly procedures requiring the purification of individual XNA sequences produced by large-scale polymerase-mediated primer extension reactions pose a major bottleneck to the discovery of highly active XNA motifs for biomedical applications. Here, we describe a straightforward approach for rapidly surveying the binding properties of XNA aptamers identified by in vitro selection. Our strategy involves preparing XNA aptamer particles in which many copies of the same aptamer sequence are distributed throughout the gel matrix of a polyacrylamide-encapsulated magnetic particle. Aptamer particles are then screened by flow cytometry to assess target binding affinity and deduce structure-activity relationships. This generalizable and highly parallel assay dramatically accelerates the pace of secondary screening by allowing a single researcher to evaluate 48-96 sequences per day.

RNA aptamer-mediated gene therapy of prostate cancer: lessons from the past and future directions

INTRODUCTION

Prostate cancer (PCa) is one of the most prevalent cancers in the world, and the fifth cause of death from cancer in men. Among the non-surgical treatments for PCa, gene therapy strategies are in the early stages of development and recent clinical trials have provided new insights suggesting promising future.

AREAS COVERED

Recently, the creation of targeted gene delivery systems, based on specific PCa cell surface markers, has been viewed as a viable therapeutic approach. Prostate-specific membrane antigen (PSMA) is vastly expressed in nearly all prostate malignancies, and the intensity of expression increases with tumor aggressiveness, androgen independence, and metastasis. RNA aptamers are short and single-stranded oligonucleotides, which selectively bind to a specific ligand on the surface of the cells, which makes them fascinating small molecules for target delivery of therapeutics. PSMA-selective RNA aptamers represent great potential for developing targeted-gene delivery tools for PCa.

EXPERT OPINION

This review provides a thorough horizon for the researchers interested in developing targeted gene delivery systems for PCa via PSMA RNA aptamers. In addition, we provided general information about different prospects of RNA aptamers including discovery approaches, stability, safety, and pharmacokinetics.

A novel fluorescent biosensor based on affinity-enhanced aptamer-peptide conjugate for sensitive detection of lead(II) in aquatic products

Lead contamination is a major concern in food safety and, as such, many lead detection methods have been developed, especially aptamer-based biosensors. However, the sensitivity and environmental tolerance of these sensors require improvement. A combination of different types of recognition elements is an effective way to improve the detection sensitivity and environmental tolerance of biosensors. Here, we provide a novel recognition element, an aptamer-peptide conjugate (APC), to achieve enhanced affinity of Pb²⁺. The APC was synthesized from Pb²⁺ aptamers and peptides through clicking chemistry. The binding performance and environmental tolerance of APC with Pb²⁺ was studied through isothermal titration calorimetry (ITC); the binding constant (K_a) was 1.76*10⁶ M^-1, indicating that the APC's affinity was increased by 62.96% and 802.56% compared with the aptamers and peptides, respectively. Besides, APC demonstrated better anti-interference (K⁺) than aptamer and peptide. Through the molecular dynamics (MD) simulation, we found that more binding sites and stronger binding energy between APC with Pb²⁺are the reasons for higher affinity between APC with Pb²⁺. Finally, a carboxyfluorescein (FAM)-labeled APC fluorescent probe was synthesized and a fluorescent detection method for Pb²⁺ was established. The limit of detection of the FAM-APC probe was calculated to be 12.45 nM. This detection method was also applied to the swimming crab and showed great potential in real food matrix detection.

BinderSpace: A package for sequence space analyses for datasets of affinity-selected oligonucleotides and peptide-based molecules

Discovery of target-binding molecules, such as aptamers and peptides, is usually performed with the use of high-throughput experimental screening methods. These methods typically generate large datasets of sequences of target-binding molecules, which can be enriched with high affinity binders. However, the identification of the highest affinity binders from these large datasets often requires additional low-throughput experiments or other approaches. Bioinformatics-based analyses could be helpful to better understand these large datasets and identify the parts of the sequence space enriched with high affinity binders. BinderSpace is an open-source Python package that performs motif analysis, sequence space visualization, clustering analyses, and sequence extraction from clusters of interest. The motif analysis, resulting in text-based and visual output of motifs, can also provide heat maps of previously measured user-defined functional properties for all the motif-containing molecules. Users can also run principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) analyses on whole datasets and on motif-related subsets of the data. Functionally important sequences can also be highlighted in the resulting PCA and t-SNE maps. If points (sequences) in two-dimensional maps in PCA or t-SNE space form clusters, users can perform clustering analyses on their data, and extract sequences from clusters of interest. We demonstrate the use of BinderSpace on a dataset of oligonucleotides binding to single-wall carbon nanotubes in the presence and absence of a bioanalyte, and on a dataset of cyclic peptidomimetics binding to bovine carbonic anhydrase protein. BinderSpace is openly accessible to the public via the GitHub website: https://github.com/vukoviclab/BinderSpace.

Selection and truncation of aptamers as fluorescence sensing platforms for selective and sensitive detection of nitrofurazone

Nitrofurazone (NFZ) is an antibiotic banned in many countries, as its residue seriously harms the human body. Herein, anti-NFZ aptamers were selected and identified based on the magnetic bead SELEX technique using a ssDNA library with a full length of 90 nucleotides (nt). Five full sequence candidate aptamers (NFZ8, NFZ24, NFZ28, NFZ34, and NFZ70) were obtained by secondary structure analysis. We optimized the entire sequence to obtain a truncated aptamer, a 16 nt sequence (NFZ8-1:5'-GTTCTATTGAAAAAAC-3') that showed the highest affinity for NFZ (K_d = 76.11 nM). The binding site of NFZ and aptamer NFZ8-1 was found to be "GAA" by molecular docking. In addition, utilizing the most special truncated aptamer NFZ8-1 as the identification probe, a graphene oxide fluorescent aptasensor is an innovative for the detection of NFZ residue that showed a wide linear reach from 1.25 to 160 ng/mL and a low limit of detection of 1.13 ng/mL. In the actual water environment sample detection, the recovery rate ranged from 95.21 to 113.66%, and the coefficient of variation ranged from 3.53 to 11.24%. These results demonstrate that the NFZ-truncated aptamer applied to the aptasensor provides a novel methodology for recognizing NFZ residues.

Recent advances in electrochemical nanomaterial-based aptasensors for the detection of cancer biomarkers

New technologies have provided suitable tools for rapid diagnosis of cancer which can reduce treatment costs and even increase patients' survival rates. Recently, the development of electrochemical aptamer-based nanobiosensors has raised great hopes for early, sensitive, selective, and low-cost cancer diagnosis. Here, we reviewed the flagged recent research (2021-2023) developed as a series of biosensors equipped with nanomaterials and aptamer sequences (nanoaptasensors) to diagnose/prognosis of various types of cancers. Equipping these aptasensors with nanomaterials and using advanced biomolecular technologies have provided specified biosensing interfaces for more optimal and reliable detection of cancer biomarkers. The primary intention of this review was to present and categorize the latest innovations used in the design of these diagnostic tools, including the hottest surface modifications and assembly of sensing bioplatforms considering diagnostic mechanisms. The main classification is based on applying various nanomaterials and sub-classifications considered based on the type of analyte and other vital features. This review may help design subsequent electrochemical aptasensors. Likewise, the up-to-date status, remaining limitations, and possible paths for translating aptasensors to clinical cancer assay tools can be clarified.

Aptamer-programmable adeno-associated viral vectors as a novel platform for cell-specific gene transfer

Adeno-associated viruses (AAVs) are commonly used for in vivo gene therapy. Nevertheless, the wide tropism that characterizes these vectors limits specific targeting to a particular cell type or tissue. Here, we developed new chemically modified AAV vectors (Nε-AAVs) displaying a single site substitution on the capsid surface for post-production vector engineering through biorthogonal copper-free click chemistry. We were able to identify AAV vectors that would tolerate the unnatural amino acid substitution on the capsid without disrupting their packaging efficiency. We functionalized the Nε-AAVs through conjugation with DNA (AS1411) or RNA (E3) aptamers or with a folic acid moiety (FA). E3-, AS1411-, and FA-AAVs showed on average a 3- to 9-fold increase in transduction compared with their non-conjugated counterparts in different cancer cell lines. Using specific competitors, we established ligand-specific transduction. In vivo studies confirmed the selective uptake of FA-AAV and AS1411-AAV without off-target transduction in peripheral organs. Overall, the high versatility of these novel Nε-AAVs might pave the way to tailoring gene therapy vectors toward specific types of cells both for ex vivo and in vivo applications.

Flow-Cell-Based Technology for Massively Parallel Characterization of Base-Modified DNA Aptamers

Aptamers incorporating chemically modified bases can achieve superior affinity and specificity compared to natural aptamers, but their characterization remains a labor-intensive, low-throughput task. Here, we describe the "non-natural aptamer array" (N2A2) system, in which a minimally modified Illumina MiSeq instrument is used for the high-throughput generation and characterization of large libraries of base-modified DNA aptamer candidates based on both target binding and specificity. We first demonstrate the capability to screen multiple different base modifications to identify the optimal chemistry for high-affinity target binding. We next use N2A2 to generate aptamers that can maintain excellent specificity even in complex samples, with equally strong target affinity in both buffer and diluted human serum. For both aptamers, affinity was formally calculated with gold-standard binding assays. Given that N2A2 requires only minor mechanical modifications to the MiSeq, we believe that N2A2 offers a broadly accessible tool for generating high-quality affinity reagents for diverse applications.

Efforts toward the continuous monitoring of molecular markers of performance

OBJECTIVES

Technologies supporting the continuous, real-time measurement of blood oxygen saturation and plasma glucose levels have improved our ability to monitor performance status. Our ability to monitor other molecular markers of performance, however, including the hormones known to indicate overtraining and general health, has lagged. That is, although a number of other molecular markers of performance status have been identified, we have struggled to develop viable technologies supporting their real-time monitoring in the body. Here we review biosensor approaches that may support such measurements, as well as the molecules potentially of greatest interest to monitor.

DESIGN

Narrative literature review.

METHOD

Literature review.

RESULTS

Significant effort has been made to harness the specificity, affinity, and generalizability of biomolecular recognition in a platform technology supporting continuous in vivo molecular measurements. Most biosensor approaches, however, are either not generalizable to most targets, or fail when challenged in the complex environments found in vivo. Electrochemical aptamer-based sensors, in contrast, are the first technology to simultaneously achieve both of these critical attributes. In an effort to illustrate the potential of this platform technology, we both critically review the literature describing it and briefly survey some of the molecular performance markers we believe will prove advantageous to monitor using it.

CONCLUSIONS

Electrochemical aptamer-based sensors may be the first truly generalizable technology for monitoring specific molecules in situ in the body and how adaptation of the platform to subcutaneous microneedles will enable the real-time monitoring of performance markers via a wearable, minimally invasive device.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Machine Learning Directed Aptamer Search from Conserved Primary Sequences and Secondary Structures

Computer-aided prediction of aptamer sequences has been focused on primary sequence alignment and motif comparison. We observed that many aptamers have a conserved hairpin, yet the sequence of the hairpin can be highly variable. Taking such secondary structure information into consideration, a new algorithm combining conserved primary sequences and secondary structures is developed, which combines three scores based on sequence abundance, stability, and structure, respectively. This algorithm was used in the prediction of aptamers from the caffeine and theophylline selections. In the late rounds of the selections, when the libraries were converged, the predicted sequences matched well with the most abundant sequences. When the libraries were far from convergence and the sequences were deemed challenging for traditional analysis methods, this algorithm still predicted aptamer sequences that were experimentally verified by isothermal titration calorimetry. This algorithm paves a new way to look for patterns in aptamer selection libraries and mimics the sequence evolution process. It will help shorten the aptamer selection time and promote the biosensor and chemical biology applications of aptamers.

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, and so on. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.g., SELEX) and then optimize performance by introducing only minor modifications to the empirically found candidates. Sequences that possess the desired properties but differ drastically in chemical composition will add diversity to the search space and facilitate the discovery of useful nucleic acid aptamers. Systematic diversification protocols are needed. Here we propose to use an unsupervised machine learning model known as the Potts model to discover new, useful sequences with controllable sequence diversity. We start by training a Potts model using the maximum entropy principle on a small set of empirically identified sequences unified by a common feature. To generate new candidate sequences with a controllable degree of diversity, we take advantage of the model's spectral feature: an "energy" bandgap separating sequences that are similar to the training set from those that are distinct. By controlling the Potts energy range that is sampled, we generate sequences that are distinct from the training set yet still likely to have the encoded features. To demonstrate performance, we apply our approach to design diverse pools of sequences with specified secondary structure motifs in 30-mer RNA and DNA aptamers.

Wearable chemical sensors for biomarker discovery in the omics era

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.

De novo selected hACE2 mimics that integrate hotspot peptides with aptameric scaffolds for binding tolerance of SARS-CoV-2 variants

The frequent occurrence of viral variants is a critical problem in developing antiviral prophylaxis and therapy; along with stronger recognition of host cell receptors, the variants evade the immune system-based vaccines and neutralizing agents more easily. In this work, we focus on enhanced receptor binding of viral variants and demonstrate generation of receptor-mimicking synthetic reagents, capable of strongly interacting with viruses and their variants. The hotspot interaction of viruses with receptor-derived short peptides is maximized by aptamer-like scaffolds, the compact and stable architectures of which can be in vitro selected from a myriad of the hotspot peptide-coupled random nucleic acids. We successfully created the human angiotensin-converting enzyme 2 (hACE2) receptor-mimicking hybrid ligand that recruits the hACE2-derived receptor binding domain-interacting peptide to directly interact with a binding hotspot of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Experiencing affinity boosting by ~500% to Omicron, the de novo selected hACE2 mimic exhibited a great binding tolerance to all SARS-CoV-2 variants of concern.

Conformal prediction under feedback covariate shift for biomolecular design

An increasingly high-impact application of machine learning in scientific discovery is its use in the design of novel objects with desired properties, such as the design of proteins, small molecules, and materials. Although a variety of algorithms have been developed for this purpose, it remains unclear when practitioners can trust the predictions made by learned models for designed objects, since design algorithms induce a distinctive shift between the training and test data distributions. We propose a method that provides confidence sets for designed objects, which we show satisfy finite-sample guarantees of statistical validity, for any design algorithm involving any learned regression model. Our work enables more trustworthy use of machine learning for design.

Many applications of machine-learning methods involve an iterative protocol in which data are collected, a model is trained, and then outputs of that model are used to choose what data to consider next. For example, a data-driven approach for designing proteins is to train a regression model to predict the fitness of protein sequences and then use it to propose new sequences believed to exhibit greater fitness than observed in the training data. Since validating designed sequences in the wet laboratory is typically costly, it is important to quantify the uncertainty in the model’s predictions. This is challenging because of a characteristic type of distribution shift between the training and test data that arises in the design setting—one in which the training and test data are statistically dependent, as the latter is chosen based on the former. Consequently, the model’s error on the test data—that is, the designed sequences—has an unknown and possibly complex relationship with its error on the training data. We introduce a method to construct confidence sets for predictions in such settings, which account for the dependence between the training and test data. The confidence sets we construct have finite-sample guarantees that hold for any regression model, even when it is used to choose the test-time input distribution. As a motivating use case, we use real datasets to demonstrate how our method quantifies uncertainty for the predicted fitness of designed proteins and can therefore be used to select design algorithms that achieve acceptable tradeoffs between high predicted fitness and low predictive uncertainty.

Large-scale distributed linear algebra with tensor processing units

We have repurposed Google tensor processing units (TPUs), application-specific chips developed for machine learning, into large-scale dense linear algebra supercomputers. The TPUs' fast intercore interconnects (ICIs), physically two-dimensional network topology, and high-bandwidth memory (HBM) permit distributed matrix multiplication algorithms to rapidly become computationally bound. In this regime, the matrix-multiply units (MXUs) dominate the runtime, yielding impressive scaling, performance, and raw size: Operating in float32 precision, a full 2,048-core pod of third-generation TPUs can multiply two matrices with linear size [Formula: see text] in about 2 min. Via curated algorithms emphasizing large, single-core matrix multiplications, other tasks in dense linear algebra can similarly scale. As examples, we present 1) QR decomposition; 2) resolution of linear systems; and 3) the computation of matrix functions by polynomial iteration, demonstrated by the matrix polar factorization.

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This “smart” SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor’s response. This can be explained by considering the aptamers’ conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule.

Characterization and Identification of Aptamers against CD49c for the Detection, Capture, and Release of Cancer Cells

As a kind of recognition molecule, aptamers can be inserted into some regulatory sequences for the smart response of their targets. However, the molecular engineering might lead to the change of the binding affinity. Here, we present a stable aptamer ZAJ-2c and an environmentally sensitive aptamer ZAJ-2d optimized from an original cell-binding aptamer ZAJ-2, and the molecular target was further identified as CD49c on the cell membrane. ZAJ-2c was characterized with high binding ability independent of the presence of divalent cations at a temperature range from 4 to 37 °C, showing promise for measuring the expression of CD49c on cancer cells. Moreover, ZAJ-2d had a nanomolar binding affinity in the binding buffer at 4 °C, the same as ZAJ-2c, but lost the binding ability in a PBS buffer supplemented with 5 mM EDTA at 37 °C. This aptamer variant proved to selectively capture and release the CD49c positive cells by simply adjusting the temperatures and divalent cations. This set of aptamers might provide a toolbox for monitoring and operating of a wide range of cancer cells with CD49c expression on the surface, which will be helpful for the studying the heterogeneity of rare cells.

Applications of Smartphone-Based Aptasensor for Diverse Targets Detection

Aptamers are a particular class of functional recognition ligands with high specificity and affinity to their targets. As the candidate recognition layer of biosensors, aptamers can be used to sense biomolecules. Aptasensors, aptamer-based biosensors, have been demonstrated to be specific, sensitive, and cost-effective. Furthermore, smartphone-based devices have shown their advantages in binding to aptasensors for point-of-care testing (POCT), which offers an immediate or spontaneous responding time for biological testing. This review describes smartphone-based aptasensors to detect various targets such as metal ions, nucleic acids, proteins, and cells. Additionally, the focus is also on aptasensors-related technologies and configurations.

Optical Sensing Strategies for Probing Single-Cell Secretion

Measuring cell secretion events is crucial to understand the fundamental cell biology that underlies cell-cell communication, migration, proliferation, and differentiation. Although strategies targeting cell populations have provided significant information about live cell secretion, they yield ensemble profiles that obscure intrinsic cell-to-cell variations. Innovation in single-cell analysis has made breakthroughs allowing accurate sensing of a wide variety of secretions and their release dynamics with high spatiotemporal resolution. This perspective focuses on the power of single-cell protocols to revolutionize cell-secretion analysis by allowing real-time and real-space measurements on single live cell resolution. We begin by discussing recent progress on single-cell bioanalytical techniques, specifically optical sensing strategies such as fluorescence-, surface plasmon resonance-, and surface-enhanced Raman scattering-based strategies, capable of in situ real-time monitoring of single-cell released ions, metabolites, proteins, and vesicles. Single-cell sensing platforms which allow for high-throughput high-resolution analysis with enough accuracy are highlighted. Furthermore, we discuss remaining challenges that should be addressed to get a more comprehensive understanding of secretion biology. Finally, future opportunities and potential breakthroughs in secretome analysis that will arise as a result of further development of single-cell sensing approaches are discussed.

Nucleic Acid Aptamers Emerging as Modulators of G-Protein-Coupled Receptors: Challenge to Difficult Cell Surface Proteins

G-protein-coupled receptors (GPCRs), among various cell surface proteins, are essential targets in the fields of basic science and drug discovery. The discovery and development of modulators for the receptors have provided deep insights into the mechanism of action of receptors and have led to a new therapeutic option for human diseases. Although various modulators against GPCRs have been developed to date, the identification of new modulators for GPCRs remains a challenge due to several technical problems and limitations. To overcome this situation, a variety of strategies have been developed by several modalities, including nucleic acid aptamers, which are emerging as unique molecules isolated by a repetitive selection process against various types of targets from an enormous combinatorial library. This review summarized the achievements in the development of aptamers targeting GPCRs, and discussed their isolation methods and the diverse functional features of aptamers against GPCRs.

Electrochemical Aptasensors for Antibiotics Detection: Recent Achievements and Applications for Monitoring Food Safety

Antibiotics are often used in human and veterinary medicine for the treatment of bacterial diseases. However, extensive use of antibiotics in agriculture can result in the contamination of common food staples such as milk. Consumption of contaminated products can cause serious illness and a rise in antibiotic resistance. Conventional methods of antibiotics detection such are microbiological assays chromatographic and mass spectroscopy methods are sensitive; however, they require qualified personnel, expensive instruments, and sample pretreatment. Biosensor technology can overcome these drawbacks. This review is focused on the recent achievements in the electrochemical biosensors based on nucleic acid aptamers for antibiotic detection. A brief explanation of conventional methods of antibiotic detection is also provided. The methods of the aptamer selection are explained, together with the approach used for the improvement of aptamer affinity by post-SELEX modification and computer modeling. The substantial focus of this review is on the explanation of the principles of the electrochemical detection of antibiotics by aptasensors and on recent achievements in the development of electrochemical aptasensors. The current trends and problems in practical applications of aptasensors are also discussed.

A Review of Apta-POF-Sensors: The Successful Coupling between Aptamers and Plastic Optical Fibers for Biosensing Applications

Aptamers represent the next frontier as biorecognition elements in biosensors thanks to a smaller size and lower molecular weight with respect to antibodies, more structural flexibility with the possibility to be regenerated, reduced batch-to-batch variation, and a potentially lower cost. Their high specificity and small size are particularly interesting for their application in optical biosensors since the perturbation of the evanescent field are low. Apart from the conventional plasmonic optical sensors, platforms based on silica and plastic optical fibers represent an interesting class of devices for point-of-care testing (POCT) in different applications. The first example of the coupling between aptamers and silica optical fibers was reported by Pollet in 2009 for the detection of IgE molecules. Six years later, the first example was published using a plastic optical fiber (POF) for the detection of Vascular Endothelial Growth Factor (VEGF). The excellent flexibility, great numerical aperture, and the large diameter make POFs extremely promising to be coupled to aptamers for the development of a sensitive platform easily integrable in portable, small-size, and simple devices. Starting from silica fiber-based surface plasmon resonance devices, here, a focus on significant biological applications based on aptamers, combined with plasmonic-POF probes, is reported.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

Directed Evolution of Aptamer Discovery Technologies

Although antibodies are a powerful tool for molecular biology and clinical diagnostics, there are many emerging applications for which nucleic acid-based aptamers can be advantageous. However, generating high-quality aptamers with sufficient affinity and specificity for biomedical applications is a challenging feat for most research laboratories. In this Account, we describe four techniques developed in our laboratory to accelerate the discovery of high-quality aptamer reagents that can achieve robust binding even for challenging molecular targets. The first method is particle display, in which we convert solution-phase aptamers into aptamer particles that can be screened via fluorescence-activated cell sorting (FACS) to quantitatively isolate individual aptamer particles based on their affinity. This enables the efficient isolation of high-affinity aptamers in fewer selection rounds than conventional methods, thereby minimizing selection biases and reducing the emergence of artifacts in the final aptamer pool. We subsequently developed the multiparametric particle display (MPPD) method, which employs two-color FACS to isolate aptamer particles based on both affinity and specificity, yielding aptamers that exhibit excellent target binding even in complex matrixes such as serum. The third method is an alkyne-azide chemistry ("click chemistry")-based particle display (click-PD) that enables the generation and screening of "non-natural" aptamers with a wide range of base modifications. We have shown that these base-modified aptamers can achieve robust affinity and specificity for targets that have proven challenging or inaccessible with natural nucleotide-based aptamer libraries. Finally, we describe the non-natural aptamer array (N2A2) platform in which a modified benchtop sequencing instrument is used to characterize base-modified aptamers in high throughput, enabling the efficient identification of molecules with excellent affinity and specificity for their targets. This system first generates aptamer clusters on the flow-cell surface that incorporate alkyne-modified nucleobases and then performs a click reaction to couple those nucleobases to an azide-modified chemical moiety. This yields a sequence-defined array of tens of millions of base-modified sequences, which can then be characterized for affinity and specificity in a high-throughput fashion. Collectively, we believe that these advancements are helping to make aptamer technology more accessible, efficient, and robust, thereby enabling the use of these affinity reagents for a wider range of molecular recognition and detection-based applications.

Aptamers and New Bioreceptors for the Electrochemical Detection of Biomarkers Expressed in Hepatocellular Carcinoma

Hepatocellular carcinoma is a malignancy associated with high mortality and increasing incidence. Early detection of this disease could help increase survival and overall patient benefit. Non-invasive strategies for the diagnosis of this medical condition are of utmost importance. In this scope, the detection of hepatocellular carcinoma biomarkers could provide a useful diagnostic tool. Aptamers represent as short, single-stranded DNAs or RNAs that can specifically bind selected analytes, and also as pseudo-biorecognition elements that can be employed for electrode functionalization. Also, other types of DNA sequences can be used for the construction of DNA-based biosensors applied for the quantification of hepatocellular carcinoma biomarkers. Herein, we will be analyzing recent examples of aptasensors and DNA biosensors for the detection of hepatocellular carcinoma biomarkers like micro-RNAs, long non-coding RNAs, exosomes, circulating tumor cells and proteins. The literature data is discussed comparatively in a critical manner highlighting the advantages of using electrochemical biosensors in diagnosis, as well as the use of nanomaterials and biocomponents in the functionalization of electrodes for improved sensitivity and selectivity.

Glycans as Targets for Drug Delivery in Cancer

Simple Summary

Alterations in glycosylation are frequently observed in cancer cells. Different strategies have been proposed to increase drug delivery to the tumor site in order to improve the therapeutic efficacy of anti-cancer drugs and avoid collateral cytotoxicity. The exploitation of drug delivery approaches directed to cancer-associated glycans has the potential to pave the way for better and more efficient personalized treatment practices. Such strategies taking advantage of aberrant cell surface glycosylation patterns enhance the targeting efficiency and optimize the delivery of clinically used drugs to cancer cells, with major potential for the clinical applications.

Abstract

Innovative strategies have been proposed to increase drug delivery to the tumor site and avoid cytotoxicity, improving the therapeutic efficacy of well-established anti-cancer drugs. Alterations in normal glycosylation processes are frequently observed in cancer cells and the resulting cell surface aberrant glycans can be used as direct molecular targets for drug delivery. In the present review, we address the development of strategies, such as monoclonal antibodies, antibody–drug conjugates and nanoparticles that specific and selectively target cancer-associated glycans in tumor cells. The use of nanoparticles for drug delivery encompasses novel applications in cancer therapy, including vaccines encapsulated in synthetic nanoparticles and specific nanoparticles that target glycoproteins or glycan-binding proteins. Here, we highlight their potential to enhance targeting approaches and to optimize the delivery of clinically approved drugs to the tumor microenvironment, paving the way for improved personalized treatment approaches with major potential importance for the pharmaceutical and clinical sectors.

ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes

We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid-identifying barcodes on the chip. The end goal is that, over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide. Toward this, we demonstrate successful target identification with two sets of target-binder pairs: DNA-DNA and Peptide-Protein. For DNA-DNA binding, we show assembly and sequencing of DNA barcodes over six consecutive binding cycles. Intriguingly, our computational simulation predicts that a small set of semi-selective DNA binders offers significant coverage of the human proteome. Toward this end, we introduce a binder discovery pipeline that ultimately could merge with the chip assay into a technology called ProtSeq, for future high-throughput, single-molecule protein sequencing.

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Designed ProtSeq protein sequencing method compatible with widely used NGS technology

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Built Target-Switch SELEX to isolate aptamers specific to N-terminal amino acids (AAs)

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Showed binding, ligation, cleavage, and NGS of six DNA binders in ordered barcode chain

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Developed pipeline to deconvolve AAs from DNA barcodes to identify putative proteins

Evolution of Functionally Enhanced α-l-Threofuranosyl Nucleic Acid Aptamers

Synthetic genetic polymers (xeno-nucleic acids, XNAs) have the potential to transition aptamers from laboratory tools to therapeutic agents, but additional functionality is needed to compete with antibodies. Here, we describe the evolution of a biologically stable artificial genetic system composed of α-l-threofuranosyl nucleic acid (TNA) that facilitates the production of backbone- and base-modified aptamers termed "threomers" that function as high quality protein capture reagents. Threomers were discovered against two prototypical protein targets implicated in human diseases through a combination of in vitro selection and next-generation sequencing using uracil nucleotides that are uniformly equipped with aromatic side chains commonly found in the paratope of antibody-antigen crystal structures. Kinetic measurements reveal that the side chain modifications are critical for generating threomers with slow off-rate binding kinetics. These findings expand the chemical space of evolvable non-natural genetic systems to include functional groups that enhance protein target binding by mimicking the structural properties of traditional antibodies.

An aptamer highly specific to cellulose enables the analysis of the association of cellulose with matrix cell wall polymers in vitro and in muro

The current toolbox of cell wall-directed molecular probes has been pivotal for advancing basic and application-oriented plant carbohydrate research; however, it still exhibits limitations regarding target diversity and specificity. Scarcity of probes targeting intramolecular associations between cell wall polymers particularly hinders our understanding of the cell wall microstructure and affects the development of effective means for its efficient deconstruction for bioconversion. Here we report a detailed characterization of a cellulose-binding DNA aptamer CELAPT MINI using a combination of various in vitro biochemical, biophysical, and molecular biology techniques. Our results show evidence for its high specificity towards long non-substituted β-(1-4)-glucan chains in both crystalline and amorphous forms. Fluorescent conjugates of CELAPT MINI are applicable as in situ cellulose probes and are well suited for various microscopy techniques, including super-resolution imaging. Compatibility of fluorescent CELAPT MINI variants with immunodetection of cell wall matrix polymers enabled them simultaneously to resolve the fibrillar organization of complex cellulose-enriched pulp material and to quantify the level of cellulose masking by xyloglucan and xylan. Using enzymatically, chemically, or genetically modulated Brachypodium internode sections we showed the diversity in cell wall packing among various cell types and even cell wall microdomains. We showed that xylan is the most prominent, but not the only, cellulose-masking agent in Brachypodium internode tissues. These results collectively highlight the hitherto unexplored potential to expand the cell wall probing toolbox with highly specific and versatile in vitro generated polynucleotide probes.