Differentiation of protein types extracted from tilapia byproducts by FTIR spectroscopy combined with chemometric analysis and their antioxidant protein hydrolysates

StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning

BACKGROUND

Antioxidant proteins are involved in several biological processes and can protect DNA and cells from the damage of free radicals. These proteins regulate the body's oxidative stress and perform a significant role in many antioxidant-based drugs. The current invitro-based medications are costly, time-consuming, and unable to efficiently screen and identify the targeted motif of antioxidant proteins.

METHODS

In this model, we proposed an accurate prediction method to discriminate antioxidant proteins namely StackedEnC-AOP. The training sequences are formulation encoded via incorporating a discrete wavelet transform (DWT) into the evolutionary matrix to decompose the PSSM-based images via two levels of DWT to form a Pseudo position-specific scoring matrix (PsePSSM-DWT) based embedded vector. Additionally, the Evolutionary difference formula and composite physiochemical properties methods are also employed to collect the structural and sequential descriptors. Then the combined vector of sequential features, evolutionary descriptors, and physiochemical properties is produced to cover the flaws of individual encoding schemes. To reduce the computational cost of the combined features vector, the optimal features are chosen using Minimum redundancy and maximum relevance (mRMR). The optimal feature vector is trained using a stacking-based ensemble meta-model.

RESULTS

Our developed StackedEnC-AOP method reported a prediction accuracy of 98.40% and an AUC of 0.99 via training sequences. To evaluate model validation, the StackedEnC-AOP training model using an independent set achieved an accuracy of 96.92% and an AUC of 0.98.

CONCLUSION

Our proposed StackedEnC-AOP strategy performed significantly better than current computational models with a ~ 5% and ~ 3% improved accuracy via training and independent sets, respectively. The efficacy and consistency of our proposed StackedEnC-AOP make it a valuable tool for data scientists and can execute a key role in research academia and drug design.

Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR

Pigmented rice contains beneficial phenolic antioxidants but analysing them across germplasm collections is laborious and time-consuming. Here we utilised rapid surface Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms (ML) to predict and classify polyphenolic antioxidants. Total phenolics, flavonoids, anthocyanins, and proanthocyanidins were quantified biochemically from 270 diverse global coloured rice collection and attenuated total reflectance (ATR) FTIR spectra were obtained by scanning whole grain surfaces at 800-4000 cm-1. Five ML classification models were optimised using the biochemical and spectral data which performed predictions with 93.5%-100% accuracy. Random Forest and Support Vector Machine models identified key FTIR peaks linked to flavonols, flavones and anthocyanins as important model predictors. This research successfully established direct and non-destructive surface chemistry spectroscopy of the aleurone layer of pigmented rice integrated with ML models as a viable high-throughput platform to accelerate the analysis and profiling of nutritionally valuable coloured rice varieties.

Rapid quantitative analysis of soybean protein isolates secondary structure by two-dimensional correlation infrared spectroscopy through pH perturbation

The infrared spectroscopy (IR) signal of protein is prone to being covered by impurity signals, and the accuracy of the secondary structure content calculated using spectral data is poor. To tackle this challenge, a rapid high-precision quantitative model for protein secondary structure was proposed. Firstly, a two-dimensional correlation calculation was performed based on 60 groups of soybean protein isolates (SPI) infrared spectroscopy data, resulting in a two-dimensional correlation infrared spectroscopy (2DCOS-IR). Subsequently, the optimal characteristic bands of the four secondary structures were extracted from the 2DCOS-IR. Ultimately, partial least squares (PLS), long short-term memory (LSTM), and bidirectional long short-term memory (BILSTM) algorithms were used to model the extracted characteristic bands and predict the content of SPI secondary structure. The findings suggested that BILSTM combined with 2DCOS-IR model (2DCOS-BILSTM) exhibited superior predictive performance. The prediction sets for α-helix, β-sheet, β-turn, and random coil were designated as 0.9257, 0.9077, 0.9476, and 0.8443, respectively, and their corresponding RMSEP values were 0.26, 0.48, 0.20, and 0.15. This strategy enhances the precision of IR and facilitates the rapid identification of secondary structure components within SPI, which is vital for the advancement of protein industrial production.

Integrated extraction, structural characterization, and activity assessment of squid pen protein hydrolysates and β-chitin with different protease hydrolysis

Squid pen (SP) is a valuable source of protein and β-chitin. However, current research has primarily focused on extracting β-chitin from SP. This study innovatively extracted both SP protein hydrolysates (SPPHs) and SP β-chitin (SPC) simultaneously using protease hydrolysis. The effects of different proteases on their structural characteristics and bioactivity were evaluated. The results showed that SP alcalase β-chitin (SPAC) had the highest degree of deproteinization (DP, 98.19 %) and SP alcalase hydrolysates (SPAH) had a degree of hydrolysis (DH) of 24.47 %. The analysis of amino acid composition suggested that aromatic amino acids accounted for 17.44 % in SPAH. Structural characterization revealed that SP flavourzyme hydrolysates (SPFH) had the sparsest structure. SPC exhibited an excellent crystallinity index (CI, over 60 %) and degree of acetylation (DA, over 70 %). During simulated gastrointestinal digestion (SGD), the hydroxyl radical scavenging activity, ABTS radical scavenging activity, Fe2+ chelating activity, and reducing power of the SPPHs remained stable or increased significantly. Additionally, SPFC exhibited substantial inhibitory effects on Staphylococcus aureus and Escherichia coli (S. aureus and E. coli), with inhibition circle diameters measuring 2.4 cm and 2.1 cm. These findings supported the potential use of SPPHs as natural antioxidant alternatives and suggested that SPC could serve as a potential antibacterial supplement.