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Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community. [ABSTRACT FROM AUTHOR]

This article discusses the importance of proteins and RNAs in biological pathways and cell function. It highlights the recent advancements in understanding protein and RNA structure and function through computational and experimental techniques. The article also explores the potential applications of this research in developing therapeutic strategies for diseases such as cancer and COVID-19. It focuses on innovative experimental and computational methods used to model proteins and RNAs, including cryo-EM mapping, cross-linking techniques, and molecular dynamics simulations. Additionally, the article discusses the development of AI computational methodologies for complex structure prediction and drug repositioning. The authors hope that these methods will provide valuable insights and advancements in protein and RNA research. [Extracted from the article]

RNA–protein complexes regulate a variety of biological functions. Thus, it is essential to explore and visualize RNA–protein structural interaction features, especially pocket interactions. In this work, we develop an easy-to-use bioinformatics resource: RPpocket. This database provides RNA–protein complex interactions based on sequence, secondary structure, and pocket topology analysis. We extracted 793 pockets from 74 non-redundant RNA–protein structures. Then, we calculated the binding- and non-binding pocket topological properties and analyzed the binding mechanism of the RNA–protein complex. The results showed that the binding pockets were more extended than the non-binding pockets. We also found that long-range forces were the main interaction for RNA–protein recognition, while short-range forces strengthened and optimized the binding. RPpocket could facilitate RNA–protein engineering for biological or medical applications. [ABSTRACT FROM AUTHOR]

We study ground states of a relativistic Fermi system involved with the pseudo-differential operator - c 2 Δ + c 4 m 2 - c 2 m in the L 2 -subcritical case, where m > 0 denotes the rest mass of fermions, and c ≥ 1 represents the speed of light. By employing Green's function and the variational principle of many-fermion systems, we prove the existence of ground states for the system. The asymptotic behavior of ground states for the system is also analyzed in the non-relativistic limit where c → ∞ . [ABSTRACT FROM AUTHOR]

Proteins and RNAs are primary biomolecules that are involved in most biological processes. 28521030 16 Zeng C., Jian Y., Vosoughi S., Zeng C., Zhao Y. Evaluating native-like structures of RNA-protein complexes through the deep learning method. 34446608 14 Huang Y., Liu S., Guo D., Li L., Xiao Y. A novel protocol for three-dimensional structure prediction of RNA-protein complexes. Huang et al. proposed 3dRPC to predict RNA-protein complexes by combining the RPDOCK and DECK-RP functions [[14]]. [Extracted from the article]

• LCFO/xNC hybrids are prepared using CNNS as carbon and nitrogen source. • The high dispersion of perovskite on the NC substrate exposes more active sites. • 2LCFO/NC has high concentration of oxygen vacancies to promote the adsorption. • The enhancement Co-O covalency is beneficial to the electrocatalytic process. • The reduction of RDS free energy change accelerate the reaction process. Perovskite oxides have been widely studied as one of the excellent bifunctional oxygen electrocatalysts (ORR: Oxygen Reduction Reaction, OER: Oxygen Evolution Reaction) due to its flexible crystal structure and low cost. Herein, 2LaCo 0.7 Fe 0.3 O 3 /N-doped carbon is prepared by sol-gel method using g-C 3 N 4 nanosheets as carbon source and nitrogen source. The LaCo 0.7 Fe 0.3 O 3 nanoparticles have excellent dispersibility on the N-doped carbon substrate and the composite material has higher oxygen vacancy concentration, showing enhanced bifunctional electrocatalytic activity (△ E =E j=10,OER -E 1/2,ORR =1.03 V) compared with the original LaCo 0.7 Fe 0.3 O 3 (△ E = 1.12 V). Moreover, 2LaCo 0.7 Fe 0.3 O 3 /N-doped carbon-based Zn-air battery presents excellent cycling stability (over 24 h) and high peak power density of 116 mW cm−2. Density functional theory (DFT) calculations revealed the mechanism of improvement electrocatalytic activity of 2LaCo 0.7 Fe 0.3 O 3 /N-doped carbon, which comes from the regulated electronic structure, including enhanced conductivity and the covalence of Co-O bond, reducing the free energy change of rate-determining step. This study provides a novel strategy for the design and prepare perovskite-type bifunctional electrocatalysts. Multi-factor optimization in 2LaCo 0.7 Fe 0.3 O 3 /N-doped carbon, including high oxygen vacancy concentration, improved conductivity and the covalence Co-O bond, and reducing the free energy change of rate-determining step. [Display omitted] [ABSTRACT FROM AUTHOR]

RNA–protein complexes play a crucial role in cellular functions, providing insights into cellular mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming and resource-intensive, and it rarely yields high-resolution data. Many computational approaches have been developed to predict RNA–protein complex structures in recent years. Despite these advances, achieving accurate and high-resolution predictions remains a formidable challenge, primarily due to the limitations inherent in current RNA–protein scoring functions. These scoring functions are critical tools for evaluating and interpreting RNA–protein interactions. This review comprehensively explores the latest advancements in scoring functions for RNA–protein docking, delving into the fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom knowledge-based, and machine-learning-based methods. We critically evaluate the strengths and limitations of existing scoring functions, providing a detailed performance assessment. Considering the significant progress demonstrated by machine learning techniques, we discuss emerging trends and propose future research directions to enhance the accuracy and efficiency of scoring functions in RNA–protein complex prediction. We aim to inspire the development of more sophisticated and reliable computational tools in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA's conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications. [ABSTRACT FROM AUTHOR]

This study evaluated the bacterial-reducing effects of different high voltage electrostatic field (HVEF) parameters (voltage 20,25,30,35 kV, time 5,10,15,20 min) on chilled pork, and studied the effects of HVEF bacterial-reducing treatment on the microbial number and the quality of pork during ice-temperature (−1 °C) storage. The results showed that HVEF of 35 kV-15 min was the best parameter, which made a 0.51 log CFU/g bacterial reduction. HVEF treatment reduced the number of Pseudomonas spp. by 0.50 log CFU/g, but no significant decrease was found in Brochothrix thermosphacta and lactobacilli. The abundance of Pseudomonas fragi (most abundant species in pork) significantly decreased after HVEF treatment, which may be the reason for the significant decrease of TVB-N and TBARS in HVEF group at the end of storage. During −1 °C storage, HVEF group exhibited better quality characteristics on shear force and cooking loss, which could effectively extend the shelf-life of chilled pork. High voltage electrostatic field (HVEF) is an emerging non-thermal inactivation technology. However, it is necessary to optimize the processing parameters based on the characteristics of the food. This study optimized the optimal processing parameters of HVEF for chilled pork and validated its contribution to maintaining the quality of pork during ice-temperature storage. These findings are of great significance for food processing, food preservation and the development of new refrigeration equipment. • HVEF significantly reduced the total colony count (0.51 log CFU/g) in pork. • HVEF had the most significant reduction in the number of Pseudomonas spp.. • HVEF changed the microbial community structure of pork. • HVEF could maintain pork quality during ice-temperature storage (−1 °C). • The shelf-life of pork at −1 °C was 15 days after HVEF treatment. [ABSTRACT FROM AUTHOR]

Background: The kinase pocket structural information is important for drug discovery targeting cancer or other diseases. Although some kinase sequence, structure or drug databases have been developed, the databases cannot be directly used in the kinase drug study. Therefore, a comprehensive database of human kinase protein pockets is urgently needed to be developed. Results: Here, we have developed HKPocket, a comprehensive Human Kinase Pocket database. This database provides sequence, structure, hydrophilic-hydrophobic, critical interactions, and druggability information including 1717 pockets from 255 kinases. We further divided these pockets into 91 pocket clusters using structural and position features in each kinase group. The pocket structural information would be useful for preliminary drug screening. Then, the potential drugs can be further selected and optimized by analyzing the sequence conservation, critical interactions, and hydrophobicity of identified drug pockets. HKPocket also provides online visualization and pse files of all identified pockets. Conclusion: The HKPocket database would be helpful for drug screening and optimization. Besides, drugs targeting the non-catalytic pockets would cause fewer side effects. HKPocket is available at http://zhaoserver.com.cn/HKPocket/HKPocket.html. [ABSTRACT FROM AUTHOR]