Fourier spotting: a novel setup for single-color reflectometry

Single-color reflectrometry is a sensitive and robust detection method in optical biosensor applications, for example for bioanalysis. It is based on the interference of reflected monochromatic radiation and is label free. We present a novel setup for single-color reflectometry based on the patented technology of Berner et al. from 2016. Tilting areas of micro-mirrors allow us to encode the optical reflection signal of an analyte and reference channel into a particular carrier frequency with the amplitude being proportional to the local reflection. Therefore, a single photodiode is sufficient to collect the signals from both channels simultaneously. A 180∘ phase shift in the tilt frequency of two calibrated micro-mirror areas leads to a superposition of the analyte and reference signal which enables an efficient reduction of the baseline offset and potential baseline offset drift. A performance test reveals that we are able to detect changes of the refractive index n down to Δn < 0.01 of saline solutions as regents. A further test validates the detection of heterogeneous binding interaction. This test compromises immobilized testosterone-bovine serum albumin on a three-dimensional layer of biopolymer as ligand and monoclonal anti-testosterone antibodies as analyte. Antibody/antigen binding induces a local growth of the biolayer and change in the refractive index, which is measured via the local change of the reflection. Reproducible measurements enable for the analysis of the binding kinetics by determining the affinity constant KA = 1.59 × 10− 7 M− 1. In summary, this work shows that the concept of differential Fourier spotting as novel setup for single-color reflectometry is suitable for reliable bioanalysis.

Molecular Dynamics Free Energy Simulation study to Investigate Binding Pattern of Isoliquiritigenin as PPAR𝛾 Agonist

Phytochemicals are rich source of bioactive constituents and can be used as another alternative to currently used drugs for diseases like Diabetes mellitus. The potential of Isoliquiritigenin (a constituent of Pterocarpus marsupium) as PPAR𝛾 agonist was evaluated by in silico technique. Autodock results showed that Tyr327, and Tyr473 of the PPARγ forms H-bonds with Isoliquiritigenin (binding energy of -7.46 kcal/mol) and Troglitazone (known drug) showed H bond with Tyr327, Ser289, with binding energy of -11.01 kcal/mol. Isoliquiritigenin, binding energy in Extra precision (XP) was -6.74 kcal/mol while Troglitazone docking, gave binding energy in XP mode as -9.59 kcal/mol. The best Induced fit docking (IFD) score of the optimised PPARγ- Isoliquiritigenin complexes was -9.39 Kcal/mol. The important residues in IFD forming H bond were Cys 285, Arg 288, Tyr 327 and Leu 340. The post docking MM/GBSA free energy for PPARγ with Isoliquiritigenin and Troglitazone was -49.29 and -71.48 Kcal/mol respectively. Binding interaction in MD simulation and Principal Component Analysis studies revealed stable binding throughout 100 ns simulation. Post Simulation MM/PBSA free energy was calculated. The results indicated that compound possessed a negative binding free energy with -114.37KJ/mol. It was observed that van der Waals, electrostatic interactions and non-polar solvation energy negatively contributed to the total interaction energy while only polar solvation energy positively contributed to total free binding energy. The Isoliquiritigenin fulfils the criteria of drug-likeness property. Thus, study presents a systematic analysis on molecular mechanism of action of Isoliquiritigenin as PPARγ agonist in controlling Diabetes mellitus.

Synthesis, Molecular Docking and DFT Studies of Biologically Active N-((3-(4-Nitrophenyl)-1-phenyl-1H-pyrazol-4-yl)methylene)aniline Derivatives

Some biologically active pyrazole clubbed imino molecules have been designed and synthesized from 1-phenyl-3-nitro phenyl-1H- pyrazol-4-carboxaldehyde and substituted aromatic amines via acid catalyzed condensation reaction. All the synthesized molecules were characterized by IR, 1H NMR, 13C NMR and mass spectral techniques. The in vitro antibactericidal property of the synthesized compounds was screened and compared with the results of theoretical molecular docking. Optimization of molecular geometry, DNA binding interaction and FMO analysis were also investigated by computational studies using Gaussian 16 package at B3LYP/6-31G(d,p) level. All the synthesized compounds exhibited moderate to good biological activities both experimentally and theoretically.

Synthesis, DFT Calculations, DNA Binding and Molecular Docking Studies on Biologically Active N-((3-(4-Methoxyphenyl)-1-phenyl-1H-pyrazol-4-yl)methylene)naphthyl Derivatives

The biologically active pyrazole clubbed imino naphthyl derivatives have been designed and synthesized from 1-phenyl-3-methoxy phenyl-1H-pyrazol-4-carboxaldehyde and substituted naphthyl amines via acid catalyzed condensation reaction. All the synthesized compounds were well characterized by different spectroscopic and mass spectral techniques. The in vitro antibacterial, antifungal and antituberculosis studies were carried out. The molecular docking study was also done with the software Arguslab 4.0.1. The studied compounds showed moderate to good biological activities both experimentally and theoretically. Geometry optimization, DNA binding interaction and FMO analysis were also investigated with the help of Gaussian 16 package at B3LYP/6-31G(d,p) level.

DeepMHCII: A Novel Binding Core-Aware Deep Interaction Model for Accurate MHC II-peptide Binding Affinity Prediction

Computationally predicting MHC-peptide binding affinity is an important problem in immunological bioinformatics. Recent cutting-edge deep learning-based methods for this problem are unable to achieve satisfactory performance for MHC class II molecules. This is because such methods generate the input by simply concatenating the two given sequences: (the estimated binding core of) a peptide and (the pseudo sequence of) an MHC class II molecule, ignoring the biological knowledge behind the interactions of the two molecules. We thus propose a binding core-aware deep learning-based model, DeepMHCII, with binding interaction convolution layer (BICL), which allows integrating all potential binding cores (in a given peptide) and the MHC pseudo (binding) sequence, through modeling the interaction with multiple convolutional kernels. Extensive empirical experiments with four large-scale datasets demonstrate that DeepMHCII significantly outperformed four state-of-the-art methods under numerous settings, such as five-fold cross-validation, leave one molecule out, validation with independent testing sets, and binding core prediction. All these results with visualization of the predicted binding cores indicate the effectiveness and importance of properly modeling biological facts in deep learning for high performance and knowledge discovery. DeepMHCII is publicly available at https://weilab.sjtu.edu.cn/DeepMHCII/.

A Sensitive Capacitive Biosensor for Protein a Detection Using Human IgG Immobilized on an Electrode Using Layer-by-Layer Applied Gold Nanoparticles

A capacitive biosensor for the detection of protein A was developed. Gold electrodes were fabricated by thermal evaporation and patterned by photoresist photolithography. A layer-by-layer (LbL) assembly of thiourea (TU) and HAuCl4 and chemical reduction was utilized to prepare a probe with a different number of layers of TU and gold nanoparticles (AuNPs). The LbL-modified electrodes were used for the immobilization of human IgG. The binding interaction between human IgG and protein A was detected as a decrease in capacitance signal, and that change was used to investigate the correlation between the height of the LbL probe and the sensitivity of the capacitive measurement. The results showed that the initial increase in length of the LbL probe can enhance the amount of immobilized human IgG, leading to a more sensitive assay. However, with thicker LbL layers, a reduction of the sensitivity of the measurement was registered. The performance of the developed system under optimum set-up showed a linearity in response from 1 × 10−16 to 1 × 10−13 M, with the limit detection of 9.1 × 10−17 M, which could be interesting for the detection of trace amounts of protein A from affinity isolation of therapeutic monoclonal antibodies.