Tcf1 and Lef1 transcription factors establish CD8(+) T cell identity through intrinsic HDAC activity.

The CD4(+) and CD8(+) T cell dichotomy is essential for effective cellular immunity. How individual T cell identity is established remains poorly understood. Here we show that the high-mobility group (HMG) transcription factors Tcf1 and Lef1 are essential for repressing CD4(+) lineage-associated genes including Cd4, Foxp3 and Rorc in CD8(+) T cells. Tcf1- and Lef1-deficient CD8(+) T cells exhibit histone hyperacetylation, which can be ascribed to intrinsic histone deacetylase (HDAC) activity in Tcf1 and Lef1. Mutation of five conserved amino acids in the Tcf1 HDAC domain diminishes HDAC activity and the ability to suppress CD4(+) lineage genes in CD8(+) T cells. These findings reveal that sequence-specific transcription factors can utilize intrinsic HDAC activity to guard cell identity by repressing lineage-inappropriate genes.

Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

RNet: a network strategy to predict RNA binding preferences.

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.

Prediction of allosteric druggable pockets of cyclin-dependent kinases.

Cyclin-dependent kinase (Cdk) proteins play crucial roles in the cell cycle progression and are thus attractive drug targets for therapy against such aberrant cell cycle processes as cancer. Since most of the available Cdk inhibitors target the highly conserved catalytic ATP pocket and their lack of specificity often lead to side effects, it is imperative to identify and characterize less conserved non-catalytic pockets capable of interfering with the kinase activity allosterically. However, a systematic analysis of these allosteric druggable pockets is still in its infancy. Here, we summarize the existing Cdk pockets and their selectivity. Then, we outline a network-based pocket prediction approach (NetPocket) and illustrate its utility for systematically identifying the allosteric druggable pockets with case studies. Finally, we discuss potential future directions and their challenges.

Discovery of a selective TRF2 inhibitor FKB04 induced telomere shortening and senescence in liver cancer cells.

Telomere repeat binding factor 2 (TRF2), a critical element of the shelterin complex, plays a vital role in the maintenance of genome integrity. TRF2 overexpression is found in a wide range of malignant cancers, whereas its down-regulation could cause cell death. Despite its potential role, the selectively small-molecule inhibitors of TRF2 and its therapeutic effects on liver cancer remain largely unknown. Our clinical data combined with bioinformatic analysis demonstrated that TRF2 is overexpressed in liver cancer and that high expression is associated with poor prognosis. Flavokavain B derivative FKB04 potently inhibited TRF2 expression in liver cancer cells while having limited effects on the other five shelterin subunits. Moreover, FKB04 treatment induced telomere shortening and increased the amounts of telomere-free ends, leading to the destruction of T-loop structure. Consequently, FKB04 promoted liver cancer cell senescence without modulating apoptosis levels. In corroboration with these findings, FKB04 inhibited tumor cell growth by promoting telomeric TRF2 deficiency-induced telomere shortening in a mouse xenograft tumor model, with no obvious side effects. These results demonstrate that TRF2 is a potential therapeutic target for liver cancer and suggest that FKB04 may be a selective small-molecule inhibitor of TRF2, showing promise in the treatment of liver cancer.

Evaluation of DNA-protein complex structures using the deep learning method.

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

Dynamic geometry design of cyclic peptide architectures for RNA structure.

Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility. Thus, the current approach usually needs to screen thousands of candidate inhibitors for binding. Here, we propose a dynamic geometry design approach to enrich the hits with only a tiny pool of designed geometrically compatible scaffold candidates. First, our method uses graph-based tree decomposition to explore the complementarity rigid binding cyclic peptide and design the amino acid side chain length and charge to fit the RNA pocket. Then, we perform an energy-based dynamical network algorithm to optimize the inhibitor-RNA hydrogen bonds. Dynamic geometry-guided design yields successful inhibitors with low micromolar binding affinity scaffolds and experimentally competes with the natural RNA chaperone. The results indicate that the dynamic geometry method yields higher efficiency and accuracy than traditional methods. The strategy could be further optimized to design the length and chirality by adopting nonstandard amino acids and facilitating RNA engineering for biological or medical applications.

RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study.

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.

Enhanced CO Adsorption by Modulating the Electron Density Distribution of Graphite-Copper Porphyrin Sorbents with Light.

The photo-responsive adsorption has emerged as a vibrant area, but its current methodology is limited by the well-defined photochromic units and their molecular deformation driven by photo-stimuli. Herein, a methodology of nondeforming photo-responsiveness is successfully exploited. With the exploiting agent of Cu-TCPP framework assembled on the graphite and strongly interacted with it, the sorbent generates two kinds of adsorption sites, over which the electron density distribution of the graphite layer can be modulated at the c-axis direction, which can further evolve due to photo-stimulated excited states. The excited states are stable enough to meet the timescale of microscopic adsorption equilibrium. Independent of the ultra-low specific surface area of the sorbent (20 m2 g-1 ), the CO adsorption capability can be improved from 0.50 mmol g-1 at the ground state to 1.24 mmol g-1 (0 °C, 1 bar) with the visible light radiation, rather than the photothermal desorption.

DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model.

Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's R = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.

cyy-287, a novel pyrimidine-2,4-diamine derivative, inhibits tumor growth of EGFR-driven non-small cell lung cancer via the ERK pathway.

In recent decades, EGFR-targeted tyrosine kinase inhibitors (TKIs) have been proven to be an effective therapy for EGFR-mutant non-small cell lung cancer (NSCLC). However, resistance to EGFR-TKIs limits their clinical application. In the present study, we investigate the antitumor effect and underlying mechanism of a novel pyrimidine-2,4-diamine derivative, cyy-287, in NSCLC. We find that cyy-287 has a high affinity for lung tissue and inhibits the proliferation of NSCLC cells. Interestingly, the significant suppression of migration and induction of apoptosis by cyy-287 are only observed in EGFR-driven but not in EGFR-wild-type (wt) cells. According to the RNA sequencing and KEGG enrichment analysis results, cyy-287 markedly inhibits the MAPK pathway in EGFR-driven PC9 cells, and western blot analysis results further indicate that cyy-287 selectively blocks the ERK pathway in EGFR-driven cells. Meanwhile, apoptosis induced by cyy-287 could be partially reversed by ERK pathway inhibition. Further experiment indicates that cyy-287 inhibits the EGFR pathway in both EGFR-driven and EGFR-overexpressing cells. Interestingly, it only induces apoptosis in EGFR-driven cells, not in EGFR-overexpressing cells. The growth of EGFR-driven cells is suppressed by cyy-287 in vivo, with fewer side effects. Our results suggest that cyy-287 may be a potential therapeutic drug with promising antitumor effects against NSCLC.

RPpocket: An RNA-Protein Intuitive Database with RNA Pocket Topology Resources.

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.

Health Assessment for Mountainous Rivers Based on Dominant Functions in the Huaijiu River, Beijing, China.

Dominant functions usually vary greatly in different reaches of mountainous rivers and are influenced by different adjacent land uses. Assessing river health based on dominant functions is of great practical value to river management. To reveal the health status of different reaches in Beijing's northern mountainous rivers, 60 investigated plots (river length 38.1 km) were surveyed in 2016 in the Huaijiu River, which is a typical mountainous river in northern Beijing, and a hierarchy-comprehensive analysis method was employed. Based on the degree of human influences, the Huaijiu River could be classified into six types, including natural reaches, near-natural reaches, artificial bank plant reaches, artificial bank ornamental plant reaches, artificial bank sparse plant dry-stone reaches and artificial bank masonry reaches. The river health assessment index system was established based on flood control, landscape, hydrology and water quality, and ecological functions. The analytic hierarchy process (AHP) was used to determine the weights of the function layer and indicator layer. The assessment results showed that healthy, subhealthy, slightly damaged, damaged and severely damaged plots accounted for 20.0%, 26.7%, 26.7%, 15.0% and 11.6% of the total plots, respectively. In summary, all plots in natural reaches, artificial bank plant reaches and artificial bank ornamental plant reaches were either healthy, subhealthy or slightly damaged. Plots in artificial bank masonry reaches were either subhealthy, slightly damaged, damaged or severely damaged, accounting for 9.1%, 27.3%, 27.3% and 36.4% of the total plots, respectively. The study proposed a method to assess mountainous river health based on dominant functions, which is a multiobjective approach and is not based solely on natural river functions. The assessment method is appropriate for the socioeconomic development and management of river basins.

The TAR binding dynamics and its implication in Tat degradation mechanism.

Human immunodeficiency virus (HIV) is a retrovirus that progressively attacks the human immune system. It is known that the HIV viral protein Tat recruits the host elongation factor, positive transcription elongation factor b (P-TEFb), onto the nascent HIV viral transactivation response element (TAR) RNA to overcome the elongation pause for active transcription of the entire viral genome. Interestingly, there exists an amplifying feedback loop between Tat and TAR-a reduction in Tat increases the elongation pause, resulting in more TAR RNA fragments instead of the entire viral genome transcript, and the TAR fragments as a scaffold for PRC2 complex in turn promote Tat ubiquitination and degradation. In this study, the structural ensembles and binding dynamics of various interfaces in the Tat/TAR/P-TEFb complex are probed by all-atom accelerated sampling molecular dynamics simulations. The results show that a protein-binding inhibitor F07#13 targeting the Tat/P-TEFb interface initiates the above feedback loop and shuts down the active transcription. Another RNA binding inhibitor, JB181, targeting the Tat/TAR interface, can prevent TAR from pulling down the Tat from P-TEFb protein and further reducing Tat degradation. The detailed mechanism of the complex dynamics helps elucidate how Tat and TAR coordinate the regulation between HIV genome transcription versus possible HIV latency.

RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources.

BACKGROUND: RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation. RESULTS: In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design. CONCLUSION: RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html .

CDK9 inhibition improves diabetic nephropathy by reducing inflammation in the kidneys.

Diabetic nephropathy (DN) is a chronic inflammatory renal disease induced by hyperglycemia. Recent studies have implicated cyclin-dependent kinase 9 (CDK9) in inflammatory responses and renal fibrosis. In this study, we explored a potential role of CDK9 in DN by using cultured mouse mesangial cell line SV40 MES-13 and streptozotocin-induced type 1 mouse model of diabetes. We inhibited CDK9 in mice and in cultured cells by a highly selective CDK9 inhibitor, LDC000067 (LDC), and evaluated inflammatory and fibrogenic outcome by mRNA and protein analyses. Our studies show that treatment of diabetic mice with LDC significantly inhibits the levels of inflammatory cytokines and fibrogenic genes in kidney specimens. These reductions were associated with improved renal function. We also found that LDC treatment suppressed MAPK-AP1 activation. We then confirmed the involvement of CDK9 in cultured SV40 MES-13 cells and showed that deficiency in CDK9 prevents glucose-induced inflammatory and fibrogenic proteins. This protection was also afforded by suppression of MAPK-AP1. Taken together, our results how that hyperglycemia activates CDK9-MAPK-AP1 axis in kidneys to induce inflammation and fibrosis, leading to renal dysfunction. Our findings also suggest that CDK9 may serve as a potential therapeutic target for DN.

A computational study of Tat-CDK9-Cyclin binding dynamics and its implication in transcription-dependent HIV latency.

HIV is a virus that attacks the T cells. HIV may either actively replicate or become latent within host cells for years. Since HIV uses its own protein Tat to hijack the host CDK9-Cyclin complex for transcription, Tat is implicated in transcription-dependent HIV latency. To quantify the impact of Tat binding, we propose a computational framework to probe the dynamics of the CDK9-Cyclin interface and the ATP pocket reorganization upon binding by different Tat mutants. Specifically, we focus on mutations at three Tat residues P10, W11, and N12 that are known to interact directly with CDK9 based on the crystal structure of the Tat-CDK9-Cyclin complex. Our molecular dynamics simulations show that the CDK9-Cyclin interface becomes slightly weaker for P10S and W11R mutants but tighter for the K12N mutant. Furthermore, the side chain orientation of residue K48 in the ATP pocket of CDK9 is similar to the inactive state in P10S and W11R simulations, but similar to the active state in K12N simulations. These are consistent with some existing but puzzling observations of latency for these mutants. This framework may hence help gain a better understanding of the role of Tat in the transcription-dependent HIV latency establishment.

HKPocket: human kinase pocket database for drug design.

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.

DIRECT: RNA contact predictions by integrating structural patterns.

BACKGROUND: It is widely believed that tertiary nucleotide-nucleotide interactions are essential in determining RNA structure and function. Currently, direct coupling analysis (DCA) infers nucleotide contacts in a sequence from its homologous sequence alignment across different species. DCA and similar approaches that use sequence information alone typically yield a low accuracy, especially when the available homologous sequences are limited. Therefore, new methods for RNA structural contact inference are desirable because even a single correctly predicted tertiary contact can potentially make the difference between a correct and incorrectly predicted structure. Here we present a new method DIRECT (Direct Information REweighted by Contact Templates) that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural features in contact inference. RESULTS: Benchmark tests demonstrate that DIRECT achieves better overall performance than DCA approaches. Compared to mfDCA and plmDCA, DIRECT produces a substantial increase of 41 and 18%, respectively, in accuracy on average for contact prediction. DIRECT improves predictions for long-range contacts and captures more tertiary structural features. CONCLUSIONS: We developed a hybrid approach that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural templates in contact inference. Our results demonstrate that DIRECT is able to improve the RNA contact prediction.