NAGase sensing in 3% milk: FET-based specific and label-free sensing in ultra-small samples of high ionic strength and high concentration of non-specific proteins.

Biosensing with biological field-effect transistors (bioFETs) is a promising technology toward specific, label-free, and multiplexed sensing in ultra-small samples. The current study employs the field-effect meta-nano-channel biosensor (MNC biosensor) for the detection of the enzyme N-acetyl-beta-D-glucosaminidase (NAGase), a biomarker for milk cow infections. The measurements are performed in a 0.5 µL drops of 3% commercial milk spiked with NAGase concentrations in the range of 30.3 aM-3.03 µM (Note that there is no background NAGase concentration in commercial milk). Specific and label-free sensing of NAGase is demonstrated with a limit-of-detection of 30.3 aM, a dynamic range of 11 orders of magnitude and with excellent linearity and sensitivity. Additional two important research outcomes are reported. First, the ionic strength of the examined milk is ∼120 mM which implies a bulk Debye screening length <1 nm. Conventionally, a 1 nm Debye length excludes the possibility of sensing with a recognition layer composed of surface bound anti-NAGase antibodies with a size of ∼10 nm. This apparent contradiction is removed considering the ample literature reporting antibody adsorption in a predominantly surface tilted configuration (side-on, flat-on, etc.). Secondly, milk contains a non-specific background protein concentration of 33 mg/ml, in addition to considerable amounts of micron-size heterogeneous fat structures. The reported sensing was performed without the customarily exercised surface blocking and without washing of the non-specific signal. This suggests that the role of non-specific adsorption to the BioFET sensing signal needs to be further evaluated. Control measurements are reported.

From sensing interactions to controlling the interactions: a novel approach to obtain biological transistors for specific and label-free immunosensing.

Antibody-antigen interactions are shaped by the solution pH level, ionic strength, and electric fields, if present. In biological field-effect transistors (BioFETs), the interactions take place at the sensing area in which the pH level, ionic strength and electric fields are determined by the Poisson-Boltzmann equation and the boundary conditions at the solid-solution interface and the potential applied at the solution electrode. The present study demonstrates how a BioFET solution electrode potential affects the sensing area double layer pH level, ionic strength, and electric fields and in this way shapes the biological interactions at the sensing area. We refer to this as 'active sensing'. To this end, we employed the meta-nano-channel (MNC) BioFET and demonstrate how the solution electrode can determine the antibody-antigen equilibrium constant and allows the control and tuning of the sensing performance in terms of the dynamic range and limit-of-detection. In the current work, we employed this method to demonstrate the specific and label-free sensing of Alpha-Fetoprotein (AFP) molecules from 0.5 µL drops of 1 : 100 diluted serum. AFP was measured during pregnancy as part of the prenatal screening program for fetal anomalies, chromosomal abnormalities, and abnormal placentation. We demonstrate AFP sensing with a limit-of-detection of 10.5 aM and a dynamic range of 6 orders of magnitude in concentration. Extensive control measurements are reported.

MolOptimizer: A Molecular Optimization Toolkit for Fragment-Based Drug Design.

MolOptimizer is a user-friendly computational toolkit designed to streamline the hit-to-lead optimization process in drug discovery. MolOptimizer extracts features and trains machine learning models using a user-provided, labeled, and small-molecule dataset to accurately predict the binding values of new small molecules that share similar scaffolds with the target in focus. Hosted on the Azure web-based server, MolOptimizer emerges as a vital resource, accelerating the discovery and development of novel drug candidates with improved binding properties.