Feature Selection Assists BLSTM for the Ultrasensitive Detection of Bioflavonoids in Different Biological Matrices Based on the 3D Fluorescence Spectra of Gold Nanoclusters

Deep convolutional neural network-based 3D fluorescence sensor array for sugar identification in serum based on the oxidase-mimicking property of CuO nanoparticles

Developing sensor arrays capturing comprehensive fluorescence (FL) spectra from a single probe is crucial for understanding sugar structures with very high similarity in biofluids. Therefore, the analysis of highly similar sugar' structures in biofluids based on the entire FL of a single nanozyme probe needs more concern, which makes the development of novel alternative approaches highly wanted for biomedical and other applications. Herein, a well-designed deep learning model with intrinsic information of 3D FL of CuO nanoparticles (NPs)' oxidase-like activity was developed to classify and predict the concentration of a group of sugars with very similar chemical structures in different media. The findings presented that the overall accuracy of the developed model in classifying the nine selected sugars was (99-100 %), which prompted us to transfer the developed model to predict the concentration of the selected sugars at a concentration range of (1-100 μM). The transferred model also gave excellent results (R2 = 97-100 %). Therefore, the model was extended to other more complex applications, namely the identification of mixtures of sugars in serum and the detection of polysaccharides in different media such as serum and lake water. Notably, LOD for fructose was determined at 4.23 nM, marking a 120-fold decrease compared to previous studies. Our developed model was also compared with other deep learning-based models, and the results have demonstrated remarkable progress. Moreover, the identification of other possible coexisting interference substances in lake water samples was considered. This work marks a significant advancement, opening avenues for the widespread application of sensor arrays integrating nanozymes and deep learning techniques in biomedical and other diverse fields.

Photoluminescent Characterization of Metal Nanoclusters: Basic Parameters, Methods, and Applications

Metal nanoclusters (MNCs) can be synthesized with atomically precise structures and molecule formulae due to the rapid development of nanocluster science in recent decades. The ultrasmall size range (normally < 2 nm) endows MNCs with plenty of molecular-like properties, among which photoluminescent properties have aroused extensive attention. Tracing the research and development processes of luminescent nanoclusters, various photoluminescent analysis and characterization methods play a significant role in elucidating luminescent mechanism and analyzing luminescent properties. In this review, it is aimed to systematically summarize the normally used photoluminescent characterizations in MNCs including basic parameters and methods, such as excitation/emission wavelength, quantum yield, and lifetime. For each key parameter, first its definition and meaning is introduced and then the relevant characterization methods including measuring principles and the revelation of luminescent properties from the collected data are discussed. Then, it is discussed in details how to explore the luminescent mechanism of MNCs and construct NC-based applications based on the measured data. By means of these characterization strategies, the luminescent properties of MNCs and NC-based designs can be explained quantitatively and qualitatively. Hence, this review is expected to provide clear guidance for researchers to characterize luminescent MNCs and better understand the luminescent mechanism from the measured results.

An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion

BACKGROUND

As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs.

RESULTS

A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models.

SIGNIFICANCE

The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.

Characterization of the interactions between Fulvic acid and Trypsin with Spectroscopic and Molecular Docking technology

The interaction mechanism between trypsin and fulvic acid was analyzed by multispectral method and molecular docking simulation. The fluorescence spectra showed that fulvic acid induced static quenching of trypsin. The validity of this conclusion was further substantiated through the computation of the binding constants. The thermodynamic parameters show that the reaction is mainly controlled by van der Waals force and hydrogen bond force, and the reaction is spontaneous. In addition, based on the obtained binding distance, there may be a non-radiative energy transfer between the two. The ultraviolet spectrum showed that fulvic acid could shift the absorption peak of trypsin, indicating that fulvic acid had an effect on the secondary structure of trypsin. According to the synchronous fluorescence spectrum results, fulvic acid primarily interacts with tryptophan residues in trypsin and induces alterations in their microenvironment. Three-dimensional fluorescence spectrum and circular dichroism further proves this conclusion. The molecular docking simulation reveals that the interaction between the two groups primarily arises from hydrogen bonding and van der Waals forces. The findings suggest that FA has the ability to induce conformational changes in trypsin's secondary structure.