Modeling Effects of Surface Properties and Probe Density for Nanoscale Biosensor Design: A Case Study of DNA Hybridization near Surfaces

Electric-field assisted silicon nanowire field effect transistor for the ultra-low concentration nucleic acid detection

Ultra-low concentration nucleic acid detection is crucial for disease diagnosis and prognosis. Silicon nanowire field-effect transistors (SiNW FETs) are promising due to their sensitivity, real-time capabilities, and compact design. A critical consideration for FETs is the reaction time required for nucleic acid diffusion to the detection surface, especially at low concentrations. This study utilizes polycrystalline silicon nanowire FETs (poly-SiNW FETs) as biosensors, employing a negative voltage on the liquid gate used for detection to create an electric field. This field accelerates nucleic acid diffusion towards the sensor surface to interact with immobilized probes. We adjusted the gate voltages and target solution injection flow rates to identify optimal parameters for nucleic acid detection and how the electrical field accelerates hybridization kinetics. We varied the probe immobilization times to show that higher ligand density accelerates the interaction with the immobilized probe. The study demonstrated stable diffusion of target DNA by combining FET with an electric field of -1 V and a slow injection flow rate, reducing the equilibrium time from 60 to 20 min. Additional improvement was achieved by enhancing probe immobilization density and applying an electric field, resulting in a faster probe-target hybridization reaction rate. These efforts significantly improved the signal to maintain superior performance and reduced the time required to reach equilibrium. This research pioneers using an external electric field to expedite detection time in field-effect transistors, demonstrating the potential for accelerated nucleic acid detection in nanowire FETs.

Molecular mechanics studies of factors affecting overall rate in cascade reactions: Multi-enzyme colocalization and environment

Millions of years of evolution have optimized many biosynthetic pathways by use of multi-step catalysis. In addition, multi-step metabolic pathways are commonly found in and on membrane-bound organelles in eukaryotic biochemistry. The fundamental mechanisms that facilitate these reaction processes provide strategies to bioengineer metabolic pathways in synthetic chemistry. Using Brownian dynamics simulations, here we modeled intermediate substrate transportation of colocalized yeast-ester biosynthesis enzymes on the membrane. The substrate acetate ion traveled from the pocket of aldehyde dehydrogenase to its target enzyme acetyl-CoA synthetase, then the substrate acetyl CoA diffused from Acs1 to the active site of the next enzyme, alcohol-O-acetyltransferase. Arranging two enzymes with the smallest inter-enzyme distance of 60 Å had the fastest average substrate association time as compared with anchoring enzymes with larger inter-enzyme distances. When the off-target side reactions were turned on, most substrates were lost, which suggests that native localization is necessary for efficient final product synthesis. We also evaluated the effects of intermolecular interactions, local substrate concentrations, and membrane environment to bring mechanistic insights into the colocalization pathways. The computation work demonstrates that creating spatially organized multi-enzymes on membranes can be an effective strategy to increase final product synthesis in bioengineering systems.

Biomolecular interactions on densely coated nanoparticles: a single-molecule perspective

DNA-modified gold nanoparticles (AuNPs) play a pivotal role in bio-nanotechnology, driving advancements in bio-sensing, bio-imaging, and drug delivery. Synthetic protocols have focused on maximizing the receptor density on particles by fine-tuning chemical conditions, particularly for DNA. Despite their significance, the understanding of hybridization kinetics on functionalized AuNPs is lacking, particularly how this kinetics depends on DNA density and to what extent it varies from particle-to-particle. This study explores the molecular mechanisms of DNA hybridization on densely coated AuNPs by employing a combination of single-molecule microscopy and coarse-grained molecular dynamics simulations providing a quantification of the molecular rate constants for single particles. Our findings demonstrate that DNA receptor density and the presence of spacer strands profoundly impact association kinetics, with short spacers enhancing association rates by up to ∼15-fold. In contrast, dissociation kinetics are largely unaffected by receptor density within the studied range. Single-particle analysis directly reveals variability in hybridization kinetics, which is analyzed in terms of intra- and inter-particle heterogeneity. A coarse-grained DNA model that quantifies hybridization kinetics on densely coated surfaces further corroborates our experimental results, additionally shedding light on how transient base pairing within the DNA coating influences kinetics. This integrated approach underscores the value of single-molecule studies and simulations for understanding DNA dynamics on densely coated nanoparticle surfaces, offering guidance for designing DNA-functionalized nanoparticles in sensor applications.

Highly specific and sensitive sandwich-type electrochemiluminescence biosensor for HPV16 DNA detection based on the base-stacking effect and bovine serum albumin carrier platform

The detection of specific DNA sequences and the identification of single nucleotide polymorphisms are important for disease diagnosis. Herein, by combining the high specificity of the base-stacking effect with the high reproducibility of bovine serum albumin (BSA) modified electrodes and the high loading performance of DNA nanoclews (DNA NCs), a novel sandwich-type electrochemiluminescence (ECL) biosensor is reported for the highly specific detection of HPV16 (chosen as the model target). The capture probes are loaded by BSA carrier platforms modified on the gold electrode surface to improve reproducibility. DNA NCs loaded with a large amount of Ru(phen)32+ worked as signal probes. The template probe is composed of the complementary strand of the target and two free nucleic acid anchors at the head and tail. In the presence of the target DNA, the template probes can form stacked base pairs with target, generating high base-stacking energy. This results in the shorter free anchors of template probes being able to bind to the capture and signal probes. This eventually forms a sandwich structure that allows Ru(phen)32+ to be near the electrode surface, producing an ECL signal. There is a linear relationship between the signal and the target concentration range from 10 fM to 100 pM, with a detection limit of 5.03 fM (S/N=3). Moreover, the base-stacking effect has single base recognition ability for base pairs, effectively avoiding false positive signals. The results of this strategy for clinical samples are consistent with classical methods.

On-chip multivariant COVID 19 photonic sensor based on silicon nitride double-microring resonators

Coronavirus disease 2019 (COVID-19) is a newly emerging human infectious disease that continues to develop new variants. A crucial step in the quest to reduce the infection is the development of rapid and reliable virus detectors. Here, we report a chip scale photonic sensing device consisting of a silicon-nitride double microring resonator (MRR) for detecting SARS-CoV-2 in clinical samples. The sensor is implemented by surface activation of one of the MRRs, acting as a probe, with DNA primers for SARS-CoV-2 RNA, whereas the other MRR is used as a reference. The performance of the sensor is determined by applying different amounts of SARS-CoV-2 complementary RNA. As will be shown in the paper, our device detects the RNA fragments at concentrations of 10 cp/μL and with sensitivity of 750 nm/RIU. As such, it shows a promise toward the implementation of label-free, small form factor, CMOS compatible biosensor for SARS-CoV-2, which is also environment, temperature, and pressure independent. Our approach can also be used for detecting other SARS-CoV-2 genes, as well as other viruses and pathogens.

GeomBD3: Brownian Dynamics Simulation Software for Biological and Engineered Systems

GeomBD3 is a robust Brownian dynamics simulation package designed to easily handle natural or engineered systems in diverse environments and arrangements. The software package described herein allows users to design, execute, and analyze BD simulations. The simulations use all-atom, rigid molecular models that diffuse according to overdamped Langevin dynamics and interact through electrostatic, Lennard-Jones, and ligand desolvation potentials. The program automatically calculates molecular association rates, surface residence times, and association statistics for any number of user-defined association criteria. Users can also extract molecular association pathways, diffusion coefficients, intermolecular interaction energies, intermolecular contact probability maps, and more using the provided supplementary analysis scripts. We detail the use of the package from start to finish and apply it to a protein-ligand system and a large nucleic acid biosensor. GeomBD3 provides a versatile tool for researchers from various disciplines that can aid in rational design of engineered systems or play an explanatory role as a complement to experiments. GeomBD version 3 is available on our website at http://chemcha-gpu0.ucr.edu/geombd3/ and KBbox at https://kbbox.h-its.org/toolbox/methods/molecular-simulation/geombd/.