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Sequence variation in related proteins is an important characteristic that modulates activity and selectivity. An example of a protein family with a large degree of sequence variation is that of bacterial sortases, which are cysteine transpeptidases on the surface of gram-positive bacteria. Class A sortases are responsible for attachment of diverse proteins to the cell wall to facilitate environmental adaption and interaction. These enzymes are also used in protein engineering applications for sortase-mediated ligations (SML) or sortagging of protein targets. We previously investigated SrtA from Streptococcus pneumoniae, identifying a number of putative β7-β8 loop-mediated interactions that affected in vitro enzyme function. We identified residues that contributed to the ability of S. pneumoniae SrtA to recognize several amino acids at the P1 0 position of the substrate motif, underlined in LPXTG, in contrast to the strict P1 0 Gly recognition of SrtA from Staphylococcus aureus. However, motivated by the lack of a structural model for the active, monomeric form of S. pneumoniae SrtA, here, we expanded our studies to other Streptococcus SrtA proteins. We solved the first monomeric structure of S. agalactiae SrtA which includes the C-terminus, and three others of β7-β8 loop chimeras from S. pyogenes and S. agalactiae SrtA. These structures and accompanying biochemical data support our previously identified β7-β8 loop-mediated interactions and provide additional insight into their role in Class A sortase substrate selectivity. A greater understanding of individual SrtA sequence and structural determinants of target selectivity may also facilitate the design or discovery of improved sortagging tools.

Identification of the molecular networks that facilitated the evolution of multicellular animals from their unicellular ancestors is a fundamental problem in evolutionary cellular biology. Choanoflagellates are recognized as the closest extant nonmetazoan ancestors to animals. These unicellular eukaryotes can adopt a multicellular-like "rosette" state. Therefore, they are compelling models for the study of early multicellularity. Comparative studies revealed

In the context of big data, the exploration of the application effect of machine learning in intelligent encryption for real-time image text digital information aims to improve the privacy information security of people. Aiming at the problem of digital information leakage of real-time image text, the convolutional neural network is introduced and improved by adding a preprocessing module to form AlexNet, to encrypt the digital information of real-time image text. Besides, to take into account both the security effect and the real-time performance of the system, the image text is encrypted by the chaotic sequence generated by a one-dimensional chaotic system called Logistic-Sine and a multi-dimensional chaotic system named Lorenz. In this way, a real-time image text encryption model is constructed by combining the chaotic function and AlexNet. Finally, a simulation experiment is performed to analyze the performance of this model. The comparative analysis indicates that the recognition accuracy of feature extraction of image text by the intelligent encryption model reaches 94.37%, which is at least 3.05% higher than that of other neural network models by scholars in related fields. In the security analysis of image text encryption, the information entropy of pixel values at (0, 0) of the proposed model is close to the ideal value 8. Meanwhile, the value of the number of pixels change rate is generally more than 99.50%, and the value of the unified average changing intensity is generally more than 33.50%. This demonstrates that the model has good security in resisting attacks. Therefore, the constructed model can provide good security guarantee under the premise of ensuring the recognition accuracy, which can provide experimental basis for improving the security performance of real-time image text data in the future.

The evolution of signaling pathways is complex and well-studied. In particular, the emergence of animal multicellularity had a major impact on protein-protein interactions and signaling systems in eukaryotic cells. However, choanoflagellates, our closest non-metazoan ancestor, contain a number of closely related signaling and trafficking proteins and domains. In addition, because choanoflagellates can adopt a rosette-/multicellular-like state, a lot can be gained by comparing proteins involved in choanoflagellate and human signaling pathways. Here, we look at how selectivity determinants evolved in the PDZ domain. There are over 250 PDZ domains in the human proteome, which are involved in critical protein-protein interactions that result in large multimolecular complexes, e.g., in the postsynaptic density of neuronal synapses. Binding of C-terminal sequences by PDZ domains is often transient and recognition typically involves 6residues or less, with only 2 residues specifying the binding motif. We solved high resolution crystal structures of Monosiga brevicollis PDZ domains homologous to human Dlg1 PDZ2, Dlg1 PDZ3, GIPC, and SHANK1 PDZ domains to investigate if the non-motif preferences are conserved, despite hundreds of millions of years of evolution. We also calculated binding affinities for GIPC, SHANK1, and SNX27 PDZ domains from M. brevicollis. Overall, we found that peptide selectivity is conserved between these two disparate organisms, with one exception, mbDLG-3. In addition, we identify 178 PDZ domains in the M. brevicollis proteome, including 11 new sequences, which we verified using Rosetta and homology modeling. Overall, our results provide novel insight into signaling pathways in the choanoflagellate organism.