Prediction of Aptamer-Small-Molecule Interactions Using Metastable States from Multiple Independent Molecular Dynamics Simulations

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.

Computer aided aptamer selection for fabrication of electrochemical sensor to detect Aflatoxin B1

Aflatoxin B1 (AFB1) is a naturally occurring toxin produced by Aspergillus flavus and Aspergillus parasiticus. The AFB1 is classified as a potent carcinogen and poses significant health risks both to humans and animals. Early detection of the toxin in post-harvest agricultural products will save lives and promote healthy food production. In this study, stratified docking approach was utilized to screen and identify potential aptamers that can bind to AFB1. ssDNA sequences were acquired from the Mendeley dataset, secondary and tertiary structures were predicted through a series of bioinformatics pipelines. Further, the final DNA tertiary structures were minimized and SiteMap algorithm was used to probe and locate binding cavities. According to the final XP docking result, a 34 nt sequence (5'-ATCCTGTGAGGAATGCTCATGCATAGCAAGGGCT-3') aptamer with a docking score of -5.959 kcal/mol was considered for 200 ns MD Simulation. Finally, the screened DNA-aptamer was immobilized over the gold surface based on Au-S chemistry and utilized for the detection of AFB1. The fabricated DNA-aptamer electrode demonstrated a good analytical performance including wide linear range (1.0 to 1000 ng L-1), detection limit (1.0 ng L-1), high stability, and reproducibility.Communicated by Ramaswamy H. Sarma.

Structural basis for high-affinity recognition of aflatoxin B1 by a DNA aptamer

The 26-mer DNA aptamer (AF26) that specifically binds aflatoxin B1 (AFB1) with nM-level high affinity is rare among hundreds of aptamers for small molecules. Despite its predicted stem-loop structure, the molecular basis of its high-affinity recognition of AFB1 remains unknown. Here, we present the first high-resolution nuclear magnetic resonance structure of AFB1-AF26 aptamer complex in solution. AFB1 binds to the 16-residue loop region of the aptamer, inducing it to fold into a compact structure through the assembly of two bulges and one hairpin structure. AFB1 is tightly enclosed within a cavity formed by the bulges and hairpin, held in a place between the G·C base pair, G·G·C triple and multiple T bases, mainly through strong π-π stacking, hydrophobic and donor atom-π interactions, respectively. We further revealed the mechanism of the aptamer in recognizing AFB1 and its analogue AFG1 with only one-atom difference and introduced a single base mutation at the binding site of the aptamer to increase the discrimination between AFB1 and AFG1 based on the structural insights. This research provides an important structural basis for understanding high-affinity recognition of the aptamer, and for further aptamer engineering, modification and applications.

Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.