Shape complementarity processes for ultrashort-burst sensitive M13-PEG-WS2-powered MCF-7 cancer cell sensors.

Biomarkers have the potential to be utilized in disease diagnosis, prediction and monitoring. The cancer cell type is a leading candidate for next-generation biomarkers. Although traditional digital biomolecular sensor (DBS) technology has shown to be effective in assessing cell-based interactions, low cell-population detection of cancer cell types is extremely challenging. Here, we controlled the electrical signature of a two-dimensional (2D) nanomaterial, tungsten disulfide (WS2), by utilizing a combination of the Phage-integrated Polymer and the Nanosheet (PPN), viz., the integration of the M13-conjugated polyethylene glycol (PEG) and the WS2, through shape-complementarity phenomena, and developed a sensor system, i.e., the Phage-based DBS (P-DBS), for the specific, rapid, sensitive detection of clinically-relevant MCF-7 cells. The P-DBS attains a detection limit of 12 cells per µL, as well as a contrast of 1.25 between the MCF-10A sample signal and the MCF-7 sample signal. A reading length of 200 µs was further achieved, along with a relative cell viability of ∼100% for both MCF-7 and MCF-10A cells and with the PNN. Atomistic simulations reveal the structural origin of the shape complementarity-facilitated decrease in the output impedance of the P-DBS. The combination of previously unreported exotic sensing materials and digital sensor design represents an approach to unlocking the ultra-sensitive detection of cancer cell types and provides a promising avenue for early cancer diagnosis, staging and monitoring.

Nucleation of Biomolecular Condensates from Finite-Sized Simulations.

The nucleation of protein condensates is a concentration-driven process of assembly. When modeled in the canonical ensemble, condensation is affected by finite-size effects. Here, we present a general and efficient route for obtaining ensemble properties of protein condensates in the macroscopic limit from finite-sized nucleation simulations. The approach is based on a theoretical description of droplet nucleation in the canonical ensemble and enables estimation of thermodynamic and kinetic parameters, such as the macroscopic equilibrium density of the dilute protein phase, the surface tension of the condensates, and nucleation free energy barriers. We apply the method to coarse-grained simulations of NDDX4 and FUS-LC, two phase-separating disordered proteins with different physicochemical characteristics. Our results show that NDDX4 condensate droplets, characterized by lower surface tension, higher solubility, and faster monomer exchange dynamics compared to those of FUS-LC, form with negligible nucleation barriers. In contrast, FUS-LC condensates form via an activated process over a wide range of concentrations.

Droplet Microfluidics for the Label-Free Extraction of Complete Phase Diagrams and Kinetics of Liquid-Liquid Phase Separation in Finite Volumes.

Liquid-liquid phase separation of polymer and protein solutions is central in many areas of biology and material sciences. Here, an experimental and theoretical framework is provided to investigate the thermodynamics and kinetics of liquid-liquid phase separation in volumes comparable to cells. The strategy leverages droplet microfluidics to accurately measure the volume of the dense phase generated by liquid-liquid phase separation of solutions confined in micro-sized compartments. It is shown that the measurement of the volume fraction of the dense phase at different temperatures allows the evaluation of the binodal lines that determine the coexistence region of the two phases in the temperature-concentration phase diagram. By applying a thermodynamic model of phase separation in finite volumes, it is further shown that the platform can predict and validate kinetic barriers associated with the formation of a dense droplet in a parent dilute phase, therefore connecting thermodynamics and kinetics of liquid-liquid phase separation.

Ultrasensitive Detection of MCF-7 Cells with a Carbon Nanotube-Based Optoelectronic-Pulse Sensor Framework.

Biosensors are of vital significance for healthcare by supporting the management of infectious diseases for preventing pandemics and the diagnosis of life-threatening conditions such as cancer. However, the advancement of the field can be limited by low sensing accuracy. Here, we altered the bioelectrical signatures of the cells using carbon nanotubes (CNTs) via structural loosening effects. Using an alternating current (AC) pulse under light irradiation, we developed a photo-assisted AC pulse sensor based on CNTs to differentiate between healthy breast epithelial cells (MCF-10A) and luminal breast cancer cells (MCF-7) within a heterogeneous cell population. We observed a previously undemonstrated increase in current contrast for MCF-7 cells with CNTs compared to MCF-10A cells with CNTs under light exposure. Moreover, we obtained a detection limit of ∼1.5 × 103 cells below a baseline of ∼1 × 104 cells for existing electrical-based sensors for an adherent, heterogeneous cell population. All-atom molecular dynamics (MD) simulations reveal that interactions between the embedded CNT and cancer cell membranes result in a less rigid lipid bilayer structure, which can facilitate CNT translocation for enhancing current. This as-yet unconsidered cancer cell-specific method based on the unique optoelectrical properties of CNTs represents a strategy for unlocking the detection of a small population of cancer cells and provides a promising route for the early diagnosis, monitoring, and staging of cancer.

Ultralong recovery time in nanosecond electroporation systems enabled by orientational-disordering processes.

The growing importance of applications based on molecular medicine and genetic engineering is driving the need to develop high-performance electroporation technologies. The electroporation phenomenon involves disruption of the cell for increasing membrane permeability. Although there is a multitude of research focused on exploring new electroporation techniques, the engineering of programming schemes suitable for these electroporation methods remains a challenge. Nanosecond stimulations could be promising candidates for these techniques owing to their ability to generate a wide range of biological responses. Here we control the membrane permeabilization of cancer cells using different numbers of electric-field pulses through orientational disordering effects. We then report our exploration of a few-volt nanosecond alternating-current (AC) stimulation method with an increased number of pulses for developing electroporation systems. A recovery time of ∼720 min was achieved, which is above the average of ∼76 min for existing electroporation methods using medium cell populations, as well as a previously unreported increased conductance with an increase in the number of pulses using weak bias amplitudes. All-atom molecular dynamics (MD) simulations reveal the orientation-disordering-facilitated increase in the degree of permeabilization. These findings highlight the potential of few-volt nanosecond AC-stimulation with an increased number of pulse strategies for the development of next-generation low-power electroporation systems.

Modeling the Structure and Interactions of Intrinsically Disordered Peptides with Multiple Replica, Metadynamics-Based Sampling Methods and Force-Field Combinations.

Intrinsically disordered proteins play a key role in many biological processes, including the formation of biomolecular condensates within cells. A detailed characterization of their configurational ensemble and structure-function paradigm is crucial for understanding their biological activity and for exploiting them as building blocks in material sciences. In this work, we incorporate bias-exchange metadynamics and parallel-tempering well-tempered metadynamics with CHARMM36m and CHARMM22* to explore the structural and thermodynamic characteristics of a short archetypal disordered sequence derived from a DEAD-box protein. The conformational landscapes emerging from our simulations are largely congruent across methods and force fields. Nevertheless, differences in fine details emerge from varying combinations of force-fields and sampling methods. For this protein, our analysis identifies features that help to explain the low propensity of this sequence to undergo self-association in vitro, which are common to all force-field/sampling method combinations. Overall, our work demonstrates the importance of using multiple force-field and sampling method combinations for accurate structural and thermodynamic information in the study of disordered proteins.

Ultrasensitive two-dimensional material-based MCF-7 cancer cell sensor driven by perturbation processes.

Changes in lipid composition and structure during cell development can be markers for cell apoptosis or various diseases such as cancer. Although traditional fluorescence techniques utilising molecular probes have been studied, these methods are limited in studying these micro-changes as they require complex probe preparation and cannot be reused, making cell monitoring and detection challenging. Here, we developed a direct current (DC) resistance sensor based on two-dimensional (2D) molybdenum disulfide (MoS2) nanosheets to enable cancer cell-specific detection dependent on micro-changes in the cancer cell membrane. Atomistic molecular dynamics (MD) simulations were used to study the interaction between 2D MoS2 and cancer lipid bilayer systems, and revealed that previously unconsidered perturbations in the lipid bilayer can cause an increase in resistance. Under an applied DC sweep, we observed an increase in resistance when cancer cells were incubated with the nanosheets. Furthermore, a correlation was observed between the resistance and breast cancer epithelial cell (MCF-7) population, illustrating a cell population-dependent sensitivity of our method. Our method has a detection limit of ∼3 × 103 cells, below a baseline of ∼1 × 104 cells for the current state-of-the-art electrical-based biosensors using an adherent monolayer with homogenous cells. This combination of a unique 2D material and electrical resistance framework represents a promising approach for the early detection of cancerous cells and to reduce the risk of post-surgery cancer recurrence.

Simulating selective binding of a biological template to a nanoscale architecture: a core concept of a clamp-based binding-pocket-favored N-terminal-domain assembly.

The biological template and its mutants have vital significance in next generation remediation, electrochemical, photovoltaic, catalytic, sensing and digital memory devices. However, a microscopic model describing the biotemplating process is generally lacking on account of modelling complexity, which has prevented widespread commercial use of biotemplates. Here, we demonstrate M13-biotemplating kinetics in atomic resolution by leveraging large-scale molecular dynamics (MD) simulations. The model reveals the assembly of gold nanoparticles on two experimentally-based M13 phage types using full M13-capsid structural models and with polarizable gold nanoparticles in explicit solvent. Both mechanistic and structural insights into the selective binding affinity of the M13 phage to gold nanoparticles are obtained based on a previously unconsidered clamp-based binding-pocket-favored N-terminal-domain assembly and also on surface-peptide flexibility. These results provide a deeper level of understanding of protein sequence-based affinity and open the route for genetically engineering a wide range of 3D electrodes for high-density low-cost device integration.

Computational methodology for solubility prediction: Application to sparingly soluble organic/inorganic materials.

The solubility of a crystalline material can be estimated from the absolute free energy of the solid and the excess solvation free energy. In the earlier work, we presented a general-purpose molecular-dynamics-based methodology enabling solubility predictions of crystalline compounds, yielding accurate estimates of the aqueous solubilities of naphthalene at various pressures and temperatures. In the present work, we investigate a number of prototypical complex materials, including phenanthrene, calcite, and aragonite over a range of temperatures and pressures. Our results provide stronger evidence for the power of the methodology for universal solubility predictions.

Computational methodology for solubility prediction: Application to the sparingly soluble solutes.

The solubility of a crystalline substance in the solution can be estimated from its absolute solid free energy and excess solvation free energy. Here, we present a numerical method, which enables convenient solubility estimation of general molecular crystals at arbitrary thermodynamic conditions where solid and solution can coexist. The methodology is based on standard alchemical free energy methods, such as thermodynamic integration and free energy perturbation, and consists of two parts: (1) systematic extension of the Einstein crystal method to calculate the absolute solid free energies of molecular crystals at arbitrary temperatures and pressures and (2) a flexible cavity method that can yield accurate estimates of the excess solvation free energies. As an illustration, via classical Molecular Dynamic simulations, we show that our approach can predict the solubility of OPLS-AA-based (Optimized Potentials for Liquid Simulations All Atomic) naphthalene in SPC (Simple Point Charge) water in good agreement with experimental data at various temperatures and pressures. Because the procedure is simple and general and only makes use of readily available open-source software, the methodology should provide a powerful tool for universal solubility prediction.

The crucial effect of early-stage gelation on the mechanical properties of cement hydrates.

Gelation and densification of calcium-silicate-hydrate take place during cement hydration. Both processes are crucial for the development of cement strength, and for the long-term evolution of concrete structures. However, the physicochemical environment evolves during cement formation, making it difficult to disentangle what factors are crucial for the mechanical properties. Here we use Monte Carlo and Molecular Dynamics simulations to study a coarse-grained model of cement formation, and investigate the equilibrium and arrested states. We can correlate the various structures with the time evolution of the interactions between the nano-hydrates during the preparation of cement. The novel emerging picture is that the changes of the physicochemical environment, which dictate the evolution of the effective interactions, specifically favour the early gel formation and its continuous densification. Our observations help us understand how cement attains its unique strength and may help in the rational design of the properties of cement and related materials.