Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval.

The release of AlphaFold2 has sparked a rapid expansion in protein model databases. Efficient protein structure retrieval is crucial for the analysis of structure models, while measuring the similarity between structures is the key challenge in structural retrieval. Although existing structure alignment algorithms can address this challenge, they are often time-consuming. Currently, the state-of-the-art approach involves converting protein structures into three-dimensional (3D) Zernike descriptors and assessing similarity using Euclidean distance. However, the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based, thus limiting their application in studying custom datasets. To overcome this limitation, we developed FP-Zernike, a user-friendly toolkit for computing different types of Zernike descriptors based on feature points. Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets. FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets. In addition, we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank (PDB) dataset to facilitate the local deployment of this tool for interested readers. Our demonstration contained 590,685 structures, and at this scale, our system required only 4-9 s to complete a retrieval. The experiments confirmed that it achieved the state-of-the-art accuracy level. FP-Zernike is an open-source toolkit, with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1, as well as through a webserver at http://www.structbioinfo.cn/.

Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features.

Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is a feature-based cryo-EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in terms of both alignment accuracy and speed.

Screening and validation of an alkaline-tolerant biomimetic affinity chromatography A5-87 resin for purification of discarded bovine serum Immunoglobulin G.

It is important to recycle the bovine blood discarded at slaughter and develop it into high value-added bovine serum products. Biomimetic affinity chromatography (BiAC) resins have been developed to specifically purify bovine serum immunoglobulin G (Bs-IgG). The BiAC strategy was used to screen the resins with the best purification effect on Bs-IgG. Four resins with specificity for Bs-IgG adsorption were selected from 90 BiAC resins. Finally, BiAC-A5-87 was selected and used to purify Bs-IgG based on the results of SDS-PAGE and BCA protein quantification analysis. The adsorption capacity and purity of BiAC-A5-87 were 32.79 ± 3.57 mg/mL and 85.9 ± 1.21 % for Bs-IgG, respectively. The total protein recovery rate of Bs-IgG purified by BiAC-A5-87 was 89.78±3.52 %. The resin of BiAC-A5-87 column was recycled in 40 breakthrough cycles, and its Bs-IgG adsorption efficiency decreased by less than 10 %. After soaking BiAC-A5-87 in 1.0 moL NaOH solution for 64 h, its adsorption capacity for Bs-IgG was almost the same as that before soaking. The development of waste bovine serum not only realizes the utilization of blood resources and produces high economic benefits but also reduces the pollution of the environment.

trRosettaRNA: automated prediction of RNA 3D structure with transformer network.

RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and 3D structure folding by energy minimization. Benchmark tests suggest that trRosettaRNA outperforms traditional automated methods. In the blind tests of the 15th Critical Assessment of Structure Prediction (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions for the natural RNAs are competitive with the top human predictions. trRosettaRNA also outperforms other deep learning-based methods in CASP15 when measured by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains challenging to predict accurate structures for synthetic RNAs with an automated approach. We hope this work could be a good start toward solving the hard problem of RNA structure prediction with deep learning.

Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction.

RNAs are fundamental in living cells and perform critical functions determined by their tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. We present a novel method, DRfold, to predict RNA tertiary structures by simultaneous learning of local frame rotations and geometric restraints from experimentally solved RNA structures, where the learned knowledge is converted into a hybrid energy potential to guide RNA structure assembly. The method significantly outperforms previous approaches by >73.3% in TM-score on a sequence-nonredundant dataset containing recently released structures. Detailed analyses showed that the major contribution to the improvements arise from the deep end-to-end learning supervised with the atom coordinates and the composite energy function integrating complementary information from geometry restraints and end-to-end learning models. The open-source DRfold program with fast training protocol allows large-scale application of high-resolution RNA structure modeling and can be further improved with future expansion of RNA structure databases.

7-Methoxyisoflavone ameliorates atopic dermatitis symptoms by regulating multiple signaling pathways and reducing chemokine production.

7-Met, a derivative of soybean isoflavone, is a natural flavonoid compound that has been reported to have multiple signaling pathways regulation effects. This study investigated the therapeutic effects of 7-Met on mice with atopic dermatitis induced by fluorescein isothiocyanate (FITC), or oxazolone (OXZ). 7-Met ameliorated FITC or OXZ-induced atopic dermatitis symptoms by decreasing ear thickness, spleen index, mast cell activation, neutrophil infiltration and serum IgE levels in female BALB/c mice. In FITC-induced atopic dermatitis mice, 7-Met reduced Th1 cytokines production and regulated Th1/Th2 balance by downregulating the secretion of thymic stromal lymphopoietin (TSLP) via inactivation of the NF-κB pathway. In OXZ-induced atopic dermatitis, 7-Met functioned through the reduction of Th17 cytokine production. Our study showed that 7-Methoxyisoflavone alleviated atopic dermatitis by regulating multiple signaling pathways and downregulating chemokine production.

Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement.

Macromolecules, such as RNAs, reside in crowded cell environments, which could strongly affect the folded structures and stability of RNAs. The emergence of RNA-driven phase separation in biology further stresses the potential functional roles of molecular crowding. In this work, we employed the coarse-grained model that was previously developed by us to predict 3D structures and stability of the mouse mammary tumor virus (MMTV) pseudoknot under different spatial confinements over a wide range of salt concentrations. The results show that spatial confinements can not only enhance the compactness and stability of MMTV pseudoknot structures but also weaken the dependence of the RNA structure compactness and stability on salt concentration. Based on our microscopic analyses, we found that the effect of spatial confinement on the salt-dependent RNA pseudoknot stability mainly comes through the spatial suppression of extended conformations, which are prevalent in the partially/fully unfolded states, especially at low ion concentrations. Furthermore, our comprehensive analyses revealed that the thermally unfolding pathway of the pseudoknot can be significantly modulated by spatial confinements, since the intermediate states with more extended conformations would loss favor when spatial confinements are introduced.

IL-21 Stimulates the expression and activation of cell cycle regulators and promotes cell proliferation in EBV-positive diffuse large B cell lymphoma.

The clinical features of EBV-positive diffuse large B cell lymphoma (DLBCL) indicate a poorer prognosis than EBV-negative DLBCL. Currently, there is no efficacious drug for EBV-positive DLBCL. The cytokine interleukin-21 (IL-21) has been reported to be pro-apoptotic in DLBCL cell lines and is being explored as a new therapeutic strategy for this type of lymphomas. However, our previous studies showed that IL-21 stimulation of EBV-positive DLBCL cell lines leads to increased proliferation. Here, analysis of a rare clinical sample of EBV-positive DLBCL, in combination with a NOD/SCID mouse xenograft model, confirmed the effect of IL-21 on the proliferation of EBV-positive DLBCL cells. Using RNA-sequencing, we identified the pattern of differentially-expressed genes following IL-21 treatment and verified the expression of key genes at the protein level using western blotting. We found that IL-21 upregulates expression of the host MYC and AP-1 (composed of related Jun and Fos family proteins) and STAT3 phosphorylation, as well as expression of the viral LMP-1 protein. These proteins are known to promote the G1/S phase transition to accelerate cell cycle progression. Furthermore, in NOD/SCID mouse xenograft model experiments, we found that IL-21 treatment increases glucose uptake and angiogenesis in EBV-positive DLBCL tumours. Although more samples are needed to validate these observations, our study reconfirms the adverse effects of IL-21 on EBV-positive DLBCL, which has implications for the drug development of DLBCL.

Visible-near-infrared-responsive g-C3N4Hx+ reduced decatungstate with excellent performance for photocatalytic removal of petroleum hydrocarbon.

The development of photocatalysts making full use of natural light sources is highly desired for the remediation of marine oil spill pollution, which is full of challenges. Herein, we demonstrate a well-defined visible-near-infrared-responsive g-C3N4Hx+ reduced decatungstate charge-transfer salt (RCD-CTS), which possess efficient light-absorption ability ranging from visible light to the near infrared region. The RCD-CTS photocatalyst exhibits excellent performance for photocatalytic removal of petroleum hydrocarbon. The structural characterization and theoretical calculation confirmed strong chemical interaction between components and partly reduction of decatungstate results in the plasmonic properties and the absorption of near infrared light. As a results, it is proposed that"hot electrons"transfer process generated by plasmon effect promotes the efficient separation of charge-carriers. Ultimately, this work sheds light on the discovery and application of visible-near-infrared-responsive optical materials that may be exploited further in artificial photosynthesis, solar energy conversion, and phototherapy.

What is the best reference state for building statistical potentials in RNA 3D structure evaluation?

Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walk-chain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.

Modeling Structure, Stability, and Flexibility of Double-Stranded RNAs in Salt Solutions.

Double-stranded (ds) RNAs play essential roles in many processes of cell metabolism. The knowledge of three-dimensional (3D) structure, stability, and flexibility of dsRNAs in salt solutions is important for understanding their biological functions. In this work, we further developed our previously proposed coarse-grained model to predict 3D structure, stability, and flexibility for dsRNAs in monovalent and divalent ion solutions through involving an implicit structure-based electrostatic potential. The model can make reliable predictions for 3D structures of extensive dsRNAs with/without bulge/internal loops from their sequences, and the involvement of the structure-based electrostatic potential and corresponding ion condition can improve the predictions for 3D structures of dsRNAs in ion solutions. Furthermore, the model can make good predictions for thermal stability for extensive dsRNAs over the wide range of monovalent/divalent ion concentrations, and our analyses show that the thermally unfolding pathway of dsRNA is generally dependent on its length as well as its sequence. In addition, the model was employed to examine the salt-dependent flexibility of a dsRNA helix, and the calculated salt-dependent persistence lengths are in good accordance with experiments.

Predicting 3D structure and stability of RNA pseudoknots in monovalent and divalent ion solutions.

RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions. The comparisons with available experimental data show that our model can successfully predict the 3D structures of RNA pseudoknots from their sequences, and can also make reliable predictions for the stability of RNA pseudoknots with different lengths and sequences over a wide range of monovalent/divalent ion concentrations. Furthermore, we made comprehensive analyses on the unfolding pathway for various RNA pseudoknots in ion solutions. Our analyses for extensive pseudokonts and the wide range of monovalent/divalent ion concentrations verify that the unfolding pathway of RNA pseudoknots is mainly dependent on the relative stability of unfolded intermediate states, and show that the unfolding pathway of RNA pseudoknots can be significantly modulated by their sequences and solution ion conditions.