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Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

Summary: Linoleic acid has been shown as a very important precursor for meat flavour and heating parameters may affect the reaction of linoleic acid with free amino acids (FAAs) or myofibrillar proteins (MPs) and the production of volatile organic compounds (VOCs). This study was aimed to identify the VOCs during heating of linoleic acid only or with FAAs and MPs for different times by gas chromatography‐ion mobility spectrometry (GC‐IMS). Reaction systems were set up to simulate meat, and the transformation pathway of VOCs was tracked by GC‐IMS. Finally, sixty‐two VOCs were identified, of which the content of aldehydes, which had the greatest impact on the aroma of meat, reached the peak (15.94%) when heated to 60 min and then decreased. Principal component analysis (PCA) showed that the effect of heating temperature on the flavour of the system was much greater than that of heating time, and heating at 90 °C for 60 min might be considered to obtain the best flavour. The results showed that FAAs played a critical role in the formation of VOCs that were derived from the oxidation and thermal degradation of linoleic acid, controlling the composition of FAAs may further optimise the flavour of processed meat products. [ABSTRACT FROM AUTHOR]

[Display omitted] • Chinese sausages with high (30%) fat content had high protein digestibility. • Fat promoted the natural fermentation process of Chinese sausages. • Fat promoted the binding of digestive enzymes to proteins. • Fermentation for 10–20 d provided the best digestive properties for Chinese sausages. Chinese sausage is a popular traditional Chinese meat product, but its high-fat content makes consumers hesitant. The purpose of this study is to compare the nutritional differences of Chinese sausages with different fermentation times (0, 10, 20, 30 d) and fat content (the initial content was 11.59% and 20.14%) during digestion. The comparison of digestion degree, protein structure, and peptide composition between different sausages were studied through in vitro simulated digestion. Chinese sausages with high-fat content had higher α-helix, β-turn, and random coil, making them easier to digest. The fermentation process made this phenomenon more pronounced. The high-fat sausage fermented for 10 d showed the highest release of primary amino acids (about 9.5%), which was about 3.5% higher than the low-fat sausage under the same conditions. The results of peptidomics confirmed the relevant conclusions. After gastric digestion, the types of peptides in the digestive fluid of high-fat sausages were generally more than those in low-fat sausages, while after intestinal digestion, the opposite results were observed. The type of peptide reached its peak after fermentation for 20 d. These findings are of obvious significance for selecting the appropriate fermentation time and fat content of Chinese sausages. [ABSTRACT FROM AUTHOR]