Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

Develop Targeted Protein Drug Carriers through a High-Throughput Screening Platform and Rational Design.

Protein-based drugs offer advantages, such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design is developed. Using this approach to streamline polymer selection for targeted protein delivery, candidate polymers from commercially available options are identified and a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity is developed. A branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration is also developed. The high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics.

Porous microneedle patch with sustained delivery of extracellular vesicles mitigates severe spinal cord injury.

The transplantation of mesenchymal stem cells-derived secretome, particularly extracellular vesicles is a promising therapy to suppress spinal cord injury-triggered neuroinflammation. However, efficient delivery of extracellular vesicles to the injured spinal cord, with minimal damage, remains a challenge. Here we present a device for the delivery of extracellular vesicles to treat spinal cord injury. We show that the device incorporating mesenchymal stem cells and porous microneedles enables the delivery of extracellular vesicles. We demonstrate that topical application to the spinal cord lesion beneath the spinal dura, does not damage the lesion. We evaluate the efficacy of our device in a contusive spinal cord injury model and find that it reduces the cavity and scar tissue formation, promotes angiogenesis, and improves survival of nearby tissues and axons. Importantly, the sustained delivery of extracellular vesicles for at least 7 days results in significant functional recovery. Thus, our device provides an efficient and sustained extracellular vesicles delivery platform for spinal cord injury treatment.

Protective Effect of Levilactobacillus brevis Against Yersinia enterocolitica Infection in Mouse Model via Regulating MAPK and NF-κB Pathway.

Although the use of the probiotic bacterium Lactobacillus for the treatment and prevention of diseases caused by various pathogenic bacteria has received increasing attention in recent years, its mechanism remains incompletely understood. Levilactobacillus brevis 23017 is a select probiotic strain that can regulate the immunity of host animals and resist pathogen infections. In this study, we analyzed the effect of L. brevis 23017 on Yersinia enterocolitica intestinal infection in a BALB/c mouse model and discussed its underlying mechanism. We found that in the mouse model, L. brevis 23017 prevented the damage of villi in the small intestine and decelerated weight loss after Y. enterocolitica infection. Moreover, we focused on the mechanism of the protective effect of L. brevis 23017 from the perspective of the damage and repair of the intestinal mucosal barrier. We observed that L. brevis 23017 maintained a normal mucosal barrier by altering the expression of tight junction proteins. Notably, our results indicated that L. brevis 23017 effectively promoted the secretion of the intestine-specific secretory immunoglobulin A (SIgA) by B cells via regulating cytokines and oxidative damage levels. This mechanism may be the reason for its protective role in Y. enterocolitica infection. In addition, our results demonstrated that the mechanism of L. brevis 23017 was related to antibacterial colonization and inflammation regulation and closely related to antioxidative stress and SIgA promotion. The protective effect of L. brevis 23017 on mice was related to the signaling pathway protein p38 MAPK and the phosphorylation levels of NF-κB. Our study provided novel insight into the mechanism of Lactobacillus against pathogenic bacterial infections. Such insight is of great importance for the prevention, diagnosis, and treatment of related diseases.

Dual-functionalized gold nanoparticles probe based bio-barcode immuno-PCR for the detection of glyphosate.

Glyphosate (GLYP) was the most widely used broad-spectrum herbicide in the world. Herein, a gold nanoparticle (AuNP) probe dual-functionalized with anti-GLYP antibody and double-stranded oligonucleotides was synthesized. An AuNP-based bio-barcode immuno-PCR (AuNP-BB-iPCR) based on the probe was developed for sensitive detection of GLYP in food samples without high-cost and time-consuming experiments. GLYP detection was accomplished with a linear range from 61.1 pg g-1 to 31.3 ng g-1 and a detection limit of 4.5 pg g-1 which was 7 orders of magnitude lower than that of conventional ELISA (70 µg g-1) developed using the same antibody. The recoveries of GLYP from soybean, cole and maize samples were 99.8%, 102.6% and 103.7%, respectively, and all relative standard deviation values were below 12.9%. The assay time (including food samples preparation) of AuNP-BB-iPCR was 4 h. The proposed AuNP-BB-iPCR exhibits potential for sensitive detection of GLYP in foodstuffs and environment.