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The extracellular secreted protein of Bifidobacterium longum (B. longum) plays an important role in maintaining the homeostasis of the human intestinal microenvironment. However, the mechanism(s) of interaction remain unclear. Lysozyme is a kind of antibacterial peptide. In this study, the amino acid sequence of a lysozyme-like protein of B. longum based on whole-genome data of an isolate from human gut feces was found. We further predicted functional domains from the amino acid sequence, purified the protein, and verified its bioactivity. The growth of some bacteria were significantly delayed by the 020402_LYZ M1 protein. In addition, the gut microbiota was analyzed via high-throughput sequencing of 16S rRNA genes and an in vitro fermentation model, and the fluctuations in the gut microbiota under the treatment of 020402_LYZ M1 protein were characterized. The 020402_LYZ M1 protein affected the composition of human gut microbiota significantly, implying that the protein is able to communicate with intestinal microbes as a regulatory factor. [ABSTRACT FROM AUTHOR]

Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a "bio-word" segmentation system and a word representation model used for learning the distributed representation for each "bio-word". The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of "bio language processing technology," which could cause a technological revolution and could be applied to improve the quality of predictions in other problems. [ABSTRACT FROM AUTHOR]