Quantitative prediction of the thermal motion and intrinsic disorder of protein cofactors in crystalline state: A case study on halide anions

Schiff Bases: A Versatile Fluorescence Probe in Sensing Cations

Metal cations such as Zn²⁺, Al³⁺, Hg²⁺, Cd²⁺, Sn²⁺, Fe²⁺, Fe³⁺ and Cu²⁺ play important roles in biology, medicine, and the environment. However, when these are not maintained in proper concentration, they can be lethal to life. Therefore, selective sensing of metal cations is of great importance in understanding various metabolic processes, disease diagnosis, checking the purity of environmental samples, and detecting toxic analytes. Schiff base probes have been largely used in designing fluorescent sensors for sensing metal ions because of their easy processing, availability, fast response time, and low detection limit. Herein, an in-depth report on metal ions recognition by some Schiff base fluorescent sensors, their sensing mechanism, their practical applicability in cell imaging, building logic gates, and analysis of real-life samples has been presented. The metal ions having biological, industrial, and environmental significance are targeted. The compiled information is expected to prove beneficial in designing and synthesis of the related Schiff base fluorescent sensors.

A new azo-azomethine sensor for detection of CN- and AcO- anions: Highly selective chemosensor for naked eye detection of sodium diclofenac

New azo-azomethine sensor, HL, has been synthesized and characterized using standard spectroscopic methods. HL was found to sense CN^- and AcO^- in DMSO and semi-aqueous media over other anions such as F-, Cl-, Br-, I-, H₂PO₄- and HSO₄-. The anions recognition ability of HL was also evaluated using UV-Vis absorption and ¹H NMR spectroscopy. Importantly, HL can detect CN- and AcO- in DMSO even at 0.7 ppm and 1.3 ppm, respectively. The binding stoichiometry between HL and the mentioned anions was found to be 1:1 with binding constants of 8.81 × 10³ M^-1 and 3.64 × 10⁴ M^-1 for CN^- and AcO^-, respectively. Successfully, HL was used for the detection of sodium diclofenac over the other opiate drugs such as codeine phosphate, noscapine hydrochloride, papaverine hydrochloride, and morphine sulfate. The designed chemosensor has also shown highly promising results for the qualitative and quantitative detection of sodium diclofenac in oral pills.

Design and synthesis of a new organic receptor and evaluation of colorimetric anion sensing ability in organo-aqueous medium

A new organic receptor has been designed and synthesized by the combination of aromatic dialdehyde with nitro-substituted aminophenol resulting in a Schiff base compound. The receptor exhibited a colorimetric response for F(-) and AcO(-) ion with a distinct color change from pale yellow to red and pink respectively in dry DMSO solvent and yellow to pale greenish yellow in DMSO:H2O (9:1, v/v). UV-Vis titration studies displayed a significant shift in absorption maxima in comparison with the free receptor. The shift could be attributed to the hydrogen bonding interactions between the active anions and the hydroxyl functionality aided by the electron withdrawing nitro substituent on the receptor. (1)H NMR titration and density functionality studies have been performed to understand the nature of interaction of receptor and anions. The lower detection limit of 1.12ppm was obtained in organic media for F(-) ion confirming the real time application of the receptor.

Predicting the Flexibility Profile of Ribosomal RNAs

Flexibility in biomolecules is an important determinant of biological functionality, which can be measured quantitatively by atomic Debye-Waller factor or B-factor. Although numerous works have been addressed on theoretical and computational studies of the B-factor profiles of proteins, the methods used for predicting B-factor values of nucleic acids, especially the complicated ribosomal RNAs (rRNAs), which are very functionally similar to proteins in providing matrix structures and in catalyzing biochemical reactions, still remain unexploited. In this article, we present a quantitative structure-flexibility relationship (QSFR) study with the aim at the quantitative prediction of rRNA B-factor based on primary sequences (sequence-based) and advanced structures (structure-based) by using both linear and nonlinear machine learning approaches, including partial least squares regression (PLS), least squares support vector machine (LSSVM), and Gaussian process (GP). By rigorously examining the performance and reliability of constructed statistical models and by comparing our models in detail to those developed previously for protein B-factors, we demonstrate that (i) rRNA B-factors could be predicted at a similar level of accuracy with that of protein, (ii) a structure-based approach performed much better as compared to sequence-based methods in modeling of rRNA B-factors, and (iii) rRNA flexibility is primarily governed by the local features of nonbonding potential landscapes, such as electrostatic and van der Waals forces.