Pyrene-based covalent organic framework for selective enrichment of hydrophobic peptides with simultaneous proteins exclusion

Effective extraction and determination of 24 quinolones in water and egg samples using a novel magnetic covalent organic framework combined with UPLC-MS/MS

The excessive use of quinolones (QNs) has seriously threatened human health. In this study, a novel functionalized magnetic covalent organic framework Fe3O4@SiO2@Ah-COF was fabricated with biphenyl-3,3',5,5'-tetracarbaldehyde and hydrazine hydrate (85%) as monomers and was used as a magnetic solid-phase extraction (MSPE) absorbent for the determination of 24 QNs in water and egg samples through ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The extraction parameters of MSPE were optimized, including pH, adsorbent dosage, adsorption time, and eluent type. An effective and rapid detection method was then established, which showed good linearity (R2 ≥ 0.9990), low limits of detection (0.003-0.036 μg L-1) and low limits of quantitation (0.008-0.110 μg L-1) for QNs. The good recoveries of 24 QNs in water and egg samples were in the range of 70.3-106.1% and 70.4-119.7%, respectively, with relative standard deviations lower than 10% (n = 5). As a result, Fe3O4@SiO2@Ah-COF is a promising magnetic adsorbent, and the established method was successfully applied for the determination of 24 QNs in water and egg samples.

Size-controllable synthesis of large-size spherical 3D covalent organic frameworks as efficient on-line solid-phase extraction sorbents coupled to HPLC

BACKGROUND

Covalent organic frameworks (COFs) have found promising applications in separation fields due to their large surface area and high adsorption capacity, but the exiting COFs can not be directly used as the packing materials of on-line solid-phase extraction (SPE) coupled to HPLC and HPLC because their nano/submicron size or irregular shapes might cause ultrahigh column back pressure and low column efficiency. To synthesize the large-size spherical COFs larger than 3 μm as sorbents might be able to address these problems, however it is still a great challenge till now.

RESULTS

In this work, two large-size spherical 3D COFs (COF-320 and COF-300) were size-controllably synthesized within 10-90 μm via a two-step strategy. These two spherical COFs showed large surface area, fine crystallinity, good chemical/mechanical stability, and good reproducibility. As an application case, when used as the on-line SPE sorbents coupled to HPLC, the large-size spherical COF-320 displayed high binding capacity for bisphenol F (Qmax of 452.49 mg/g), low column back pressure (6-8 psi at flow rate of 1 mL/min), and good reusability (at least 30 cycles). The developed on-line-SPE-HPLC-UV method presented good analytical performance with enrichment factor of 667 folds, linear range of 1.0-400 ng/mL, limit of detection (LOD, S/N = 3) of 0.3 ng/mL, limit of quantification (LOQ, S/N = 10) of 1.0 ng/mL, and recoveries of 100.3-103.2 % (RSDs of 2.0-3.5 %) and 95.2-97.0 % (RSDs of 4.3-5.6 %) for tap water and lake water samples, respectively.

SIGNIFICANCE

This is the first case to synthesize the large-size spherical COFs within 10-90 μm, and this work made it possible to directly use COFs as the filling materials of on-line SPE coupled to HPLC and HPLC. The developed analytical method can be potentially applied to the rapid and sensitive detection of trace bisphenol F in environmental water samples.

Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction?

Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) have been regarded as a relatively safe supplementary diet-therapy strategy for individuals with hypertension, and short-chain peptides may have more relevant antihypertensive benefits due to their direct intestinal absorption. Our previous explorations have confirmed that endogenous goat milk short-chain peptides are also an essential source of ANGICon-EIPs. Nonetheless, there are limited explorations on endogenous ANGICon-EIPs owing to the limitations of the extraction and enrichment of endogenous peptides, currently. This review outlined ameliorated pre-treatment strategies, data acquisition methods, and tools for the prediction of peptide structure and function, aiming to provide creative ideas for discovering novel ANGICon-EIPs. Currently, deep learning-based peptide structure and function prediction algorithms have achieved significant advancements. The convolutional neural network (CNN) and peptide sequence-based multi-label deep learning approach for determining the multi-functionalities of bioactive peptides (MLBP) can predict multiple peptide functions with absolute true value and accuracy of 0.699 and 0.708, respectively. Utilizing peptide sequence input, torsion angles, and inter-residue distance to train neural networks, APPTEST predicted the average backbone root mean square deviation (RMSD) value of peptide (5-40 aa) structures as low as 1.96 Å. Overall, with the exploration of more neural network architectures, deep learning could be considered a critical research tool to reduce the cost and improve the efficiency of identifying novel endogenous ANGICon-EIPs.