Intracellular protein-lipid interactions drive presynaptic assembly prior to neurexin recruitment.

Neurexin cell-adhesion molecules regulate synapse development and function by recruiting synaptic components. Here, we uncover a mechanism for presynaptic assembly that precedes neurexin recruitment, mediated by interactions between cytosolic proteins and membrane phospholipids. Developmental imaging in C.

Rationalize the Functional Roles of Protein-Protein Interactions in Targeted Protein Degradation by Kinetic Monte Carlo Simulations.

Targeted protein degradation is a promising therapeutic strategy to tackle disease-causing proteins that lack binding pockets for traditional small-molecule inhibitors. Its first step is to trigger the proximity between a ubiquitin ligase complex and a target protein through a heterobifunctional molecule, such as proteolysis targeting chimeras (PROTACs), leading to the formation of a ternary complex. The properties of protein-protein interactions play an important regulatory role during this process, which can be reflected by binding cooperativity.

SuperWater: Predicting Water Molecule Positions on Protein Structures by Generative AI.

Water molecules play a significant role in maintaining protein structural stability and facilitating molecular interactions. Accurate prediction of water molecule positions around protein structures is essential for understanding their biological roles and has significant implications for protein engineering and drug discovery. Here, we introduce SuperWater, a novel generative AI framework that integrates a score-based diffusion model with equivariant graph neural networks to predict water molecule placements around proteins with high accuracy.

Rationalize the Functional Roles of Protein-Protein Interactions in Targeted Protein Degradation by Kinetic Monte-Carlo Simulations.

Targeted protein degradation is a promising therapeutic strategy to tackle disease-causing proteins that lack binding pockets for traditional small-molecule inhibitors. Its first step is to trigger the proximity between a ubiquitin ligase complex and a target protein through a heterobifunctional molecule, such as proteolysis targeting chimeras (PROTACs), leading to the formation of a ternary complex. The properties of protein-protein interactions play an important regulatory role during this process, which can be reflected by binding cooperativity.

Machine learning and single-cell analysis identify the mitophagy-associated gene TOMM22 as a potential diagnostic biomarker for intervertebral disc degeneration.

Background: Mitophagy selectively eliminates potentially cytotoxic and damaged mitochondria and effectively prevents excessive cytotoxicity from damaged mitochondria, thereby attenuating inflammatory and oxidative responses. However, the potential role of mitophagy in intervertebral disc degeneration remains to be elucidated.

Methods: The GSVA method, two machine learning methods (SVM-RFE algorithm and random forest), the CIBERSORT and MCPcounter methods, as well as the consensus clustering method and the WGCNA algorithm were used to analyze the involvement of mitophagy in intervertebral disc degeneration, the diagnostic value of mitophagy-associated genes in intervertebral disc degeneration, and the infiltration of immune cells, and identify the gene modules that were closely related to mitophagy.

Machine-learning-based structural analysis of interactions between antibodies and antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem.

Designing a photocatalytic and self-renewed g-CN nanosheet/poly-Schiff base composite coating towards long-term biofouling resistance.

Inhibiting the adhesion and growth of marine microorganisms through photocatalysis is a potentially efficient and environmentally friendly antifouling strategy. However, the undesired "shading effect" caused by resin coatings and microbial deposition reduces the utilization of the catalysts and leads to a failure in the antifouling active substance on the coating surface. Here, we successfully developed a composite coating (DPC-) combining g-CN nanosheet (g-C-NS) photocatalysts with degradable green poly-Schiff base resins, which integrates the dual functions of enhanced dynamic self-renewal and photocatalytic antibacterial activities towards long-term anti-biofouling.

Fine-tuning spatial-temporal dynamics and surface receptor expression support plasma cell-intrinsic longevity.

Durable serological memory following vaccination is critically dependent on the production and survival of long-lived plasma cells (LLPCs). Yet, the factors that control LLPC specification and survival remain poorly resolved. Using intravital two-photon imaging, we find that in contrast to most plasma cells (PCs) in the bone marrow (BM), LLPCs are uniquely sessile and organized into clusters that are dependent on APRIL, an important survival factor.

Ultrasmall copper nanoclusters as an efficient antibacterial agent for primary peritonitis therapy.

The urgent need to develop biocompatible, non-resistant antibacterial agents to effectively combat Gram-negative bacterial infections, particularly for the treatment of peritonitis, presents a significant challenge. In this study, we introduce our water-soluble Cu nanoclusters (NCs) as a potent and versatile antibacterial agent tailored for addressing peritonitis. The as-synthesized atomically precise Cu NCs demonstrate exceptional broad-spectrum antibacterial performance, and especially outstanding bactericidal activity of 100% against Gram-negative ().

Encoding the space of protein-protein binding interfaces by artificial intelligence.

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence.

Removal of type IV pili by a small RNA virus.

The retractile type IV pilus (T4P) is important for virulence of the opportunistic human pathogen . The single-stranded RNA (ssRNA) phage PP7 binds to T4P and is brought to the cell surface through pilus retraction. Using fluorescence microscopy, we discovered that PP7 detaches T4P, which impairs cell motility and restricts the pathogen's virulence.

2D Nanomaterials Reinforced Organic Coatings for Marine Corrosion Protection: State of the Art, Challenges, and Future Prospectives.

2D nanomaterials, with extraordinary physical and chemical characteristics, have long been regarded as promising nanofillers in organic coatings for marine corrosion protection. The past decade has witnessed the high-speed progress of 2D nanomaterial-reinforced organic composite coatings, and plenty of breakthroughs have been achieved as yet. This review covers an in-depth and all-around outline of the up-to-date advances in 2D nanomaterial-modified organic coatings employed for the marine corrosion protection realm.

The preventive effects of Lactobacillus casei 03 on Escherichia coli-induced mastitis in vitro and in vivo.

Background: Lactobacillus casei possesses many kinds of bioactivities, such as anti-inflammation and anti-oxidant, and has been applied to treating multiple inflammatory diseases. However, its role in mastitis prevention has remained ambiguous.

Methods: This study aimed to examine the mechanisms underlying the preventive effects of L.

 (Primulaceae), a new species from Sichuan, China.

A new species, W.B.Ju, L.

Computational and experimental approaches to quantify protein binding interactions under confinement.

Crowded environments and confinement alter the interactions of adhesion proteins confined to membranes or narrow, crowded gaps at adhesive contacts. Experimental approaches and theoretical frameworks were developed to quantify protein binding constants in these environments. However, recent predictions and the complexity of some protein interactions proved challenging to address with prior experimental or theoretical approaches.

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens.

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem.

Computational simulations of bispecific T cell engagers by a multiscale model.

The use of bispecific antibodies as T cell engagers can bypass the normal T cell receptor-major histocompatibility class interaction, redirect the cytotoxic activity of T cells, and lead to highly efficient tumor cell killing. However, this immunotherapy also causes significant on-target off-tumor toxicologic effects, especially when it is used to treat solid tumors. To avoid these adverse events, it is necessary to understand the fundamental mechanisms involved in the physical process of T cell engagement.

Prediction of interactions between cell surface proteins by machine learning.

Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques.

Protein-lipid interactions drive presynaptic assembly upstream of cell adhesion molecules.

Textbook models of synaptogenesis position cell adhesion molecules such as neurexin as initiators of synapse assembly. Here we discover a mechanism for presynaptic assembly that occurs prior to neurexin recruitment, while supporting a role for neurexin in synapse maintenance. We find that the cytosolic active zone scaffold SYD-1 interacts with membrane phospholipids to promote active zone protein clustering at the plasma membrane, and subsequently recruits neurexin to stabilize those clusters.

A computational study for understanding the impact of p120-catenin on the cis-dimerization of cadherin.

A prototype of cross-membrane signal transduction is that extracellular binding of cell surface receptors to their ligands induces intracellular signalling cascades. However, much less is known about the process in the opposite direction, called inside-out signalling. Recent studies show that it plays a more important role in regulating the functions of many cell surface receptors than we used to think.

Prediction of Interactions between Cell Surface Proteins by Machine Learning.

Cells detect changes of external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and thus challenging to detect using traditional experimental techniques.

Molecular Characteristics of Bovine Viral Diarrhea Virus Strains Isolated from Persistently Infected Cattle.

In this study, we reported the isolation, identification, and molecular characteristics of nine BVDV strains that were isolated from the serum of persistently infected cattle. The new strains were designated as BVDV TJ2101, TJ2102, TJ2103, TJ2104, TJ2105, TJ2106, TJ2107, TJ2108 and TJ2109. The TJ2102 and TJ2104 strains were found to be cytopathic BVDV, and the other strains were non-cytopathic BVDV.

Association of PFN1 Gene Polymorphisms with Bone Mineral Density, Bone Turnover Markers, and Osteoporotic Fractures in Chinese Population.

Recent studies have discovered an association between the PFN1 gene and Paget's disease. However, it is currently unknown whether the PFN1 gene is related to osteoporosis. This study was performed to investigate the association of Single-Nucleotide Polymorphisms (SNPs) in the PFN1 gene with Bone Mineral Density (BMD) as well as bone turnover markers and osteoporotic fractures in Chinese subjects.