i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites.

BACKGROUND: 5-Methylcytosine (5mC) plays a very important role in gene stability, transcription, and development. Therefore, accurate identification of the 5mC site is of key importance in genetic and pathological studies. However, traditional experimental methods for identifying 5mC sites are time-consuming and costly, so there is an urgent need to develop computational methods to automatically detect and identify these 5mC sites. RESULTS: Deep learning methods have shown great potential in the field of 5mC sites, so we developed a deep learning combinatorial model called i5mC-DCGA. The model innovatively uses the Convolutional Block Attention Module (CBAM) to improve the Dense Convolutional Network (DenseNet), which is improved to extract advanced local feature information. Subsequently, we combined a Bidirectional Gated Recurrent Unit (BiGRU) and a Self-Attention mechanism to extract global feature information. Our model can learn feature representations of abstract and complex from simple sequence coding, while having the ability to solve the sample imbalance problem in benchmark datasets. The experimental results show that the i5mC-DCGA model achieves 97.02%, 96.52%, 96.58% and 85.58% in sensitivity (Sn), specificity (Sp), accuracy (Acc) and matthews correlation coefficient (MCC), respectively. CONCLUSIONS: The i5mC-DCGA model outperforms other existing prediction tools in predicting 5mC sites, and it is currently the most representative promoter 5mC site prediction tool. The benchmark dataset and source code for the i5mC-DCGA model can be found in https://github.com/leirufeng/i5mC-DCGA .

Radical-Induced Photochromic Silver(I) Metal-Organic Frameworks: Alternative Topology, Dynamic Photoluminescence and Efficient Photothermal Conversion Modulated by Anionic Guests.

Photogenerated radicals are an indispensable member of the state-of-the-art photochromic material family, as they can effectively modulate the photoluminescence and photothermal conversion performance of radical-induced photochromic complexes. Herein, two novel radical-induced photochromic metal-organic frameworks (MOFs), [Ag(TEPE)](AC) ⋅ 7/4H2O ⋅ 5/4EtOH (1) and [Ag(TEPE)](NC) ⋅ 3H2O ⋅ EtOH (2), are reported. Distinctly different topological networks can be obtained by judiciously introducing alternative π-conjugated anionic guests, including a new topological structure (named as sfm) first reported in this work, describing as 4,4,4,4-c net. EPR data and UV-Vis spectra prove the radical-induced photochromic mechanism. Dynamic photochromism exhibits tunability in a wide CIE color space, with a linear segment from yellow to red for 1, while a curved coordinate line for 2, resulting in colorful emission from blue to orange. Moreover, photogenerated TEPE* radicals effectively activate the near-infrared (NIR) photothermal conversion effect of MOFs. Under 1 W cm-2 808 nm laser irradiation, the surface temperatures of photoproducts 1* and 2* can reach ~160 °C and ~120 °C, respectively, with competitive NIR photothermal conversion efficiencies η=51.8 % (1*) and 36.2 % (2*). This work develops a feasible electrostatic compensation strategy to accurately introduce photoactive anionic guests into MOFs to construct multifunctional radical-induced photothermal conversion materials with tunable photoluminescence behavior.

DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human mRNA Based on DenseNet and Bidirectional LSTM Methods.

Introduction: N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification that is essential for the regulation of immune functions in organisms. Currently, the identification of ac4C is primarily achieved using biological methods, which can be time-consuming and labor-intensive. In contrast, accurate identification of ac4C by computational methods has become a more effective method for classification and prediction. Aim: To the best of our knowledge, although there are several computational methods for ac4C locus prediction, the performance of the models they constructed is poor, and the network structure they used is relatively simple and suffers from the disadvantage of network degradation. This study aims to improve these limitations by proposing a predictive model based on integrated deep learning to better help identify ac4C sites. Methods: In this study, we propose a new integrated deep learning prediction framework, DLC-ac4C. First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding, nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second, one-dimensional convolutional layers and densely connected convolutional networks (DenseNet) are used to learn local features, and bi-directional long short-term memory networks (Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to determine the importance of sequence characteristics. Finally, a homomorphic integration strategy is used to limit the generalization error of the model, which further improves the performance of the model. Results: The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy (Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly better than the prediction accuracy of the existing methods. Conclusion: Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention mechanism to better capture hidden information features from a sequence perspective, and can identify ac4C sites more effectively.

iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences.

The promoter is a key DNA sequence whose primary function is to control the initiation time and the degree of expression of gene transcription. Accurate identification of promoters is essential for understanding gene expression studies. Traditional sequencing techniques for identifying promoters are costly and time-consuming. Therefore, the development of computational methods to identify promoters has become critical. Since deep learning methods show great potential in identifying promoters, this study proposes a new promoter prediction model, called iPro2L-DG. The iPro2L-DG predictor, based on an improved Densely Connected Convolutional Network (DenseNet) and a Global Attention Mechanism (GAM), is constructed to achieve the prediction of promoters. The promoter sequences are combined feature encoding using C2 encoding and nucleotide chemical property (NCP) encoding. An improved DenseNet extracts advanced feature information from the combined feature encoding. GAM evaluates the importance of advanced feature information in terms of channel and spatial dimensions, and finally uses a Full Connect Neural Network (FNN) to derive prediction probabilities. The experimental results showed that the accuracy of iPro2L-DG in the first layer (promoter identification) was 94.10% with Matthews correlation coefficient value of 0.8833. In the second layer (promoter strength prediction), the accuracy was 89.42% with Matthews correlation coefficient value of 0.7915. The iPro2L-DG predictor significantly outperforms other existing predictors in promoter identification and promoter strength prediction. Therefore, our proposed model iPro2L-DG is the most advanced promoter prediction tool. The source code of the iPro2L-DG model can be found in https://github.com/leirufeng/iPro2L-DG.

SNO-DCA: A model for predicting S-nitrosylation sites based on densely connected convolutional networks and attention mechanism.

Protein S-nitrosylation is a reversible oxidative reduction post-translational modification that is widely present in the biological community. S-nitrosylation can regulate protein function and is closely associated with a variety of diseases, thus identifying S-nitrosylation sites are crucial for revealing the function of proteins and related drug discovery. Traditional experimental methods are time-consuming and expensive; therefore, it is necessary to explore more efficient computational methods. Deep learning algorithms perform well in the field of bioinformatics sites prediction, and many studies show that they outperform existing machine learning algorithms. In this work, we proposed a deep learning algorithm-based predictor SNO-DCA for distinguishing between S-nitrosylated and non-S-nitrosylated sequences. First, one-hot encoding of protein sequences was performed. Second, the dense convolutional blocks were used to capture feature information, and an attention module was added to weigh different features to improve the prediction ability of the model. The 10-fold cross-validation and independent testing experimental results show that our SNO-DCA model outperforms existing S-nitrosylation sites prediction models under imbalanced data. In this paper, a web server prediction website: https://sno.cangmang.xyz/SNO-DCA/was established to provide an online prediction service for users. SNO-DCA can be available at https://github.com/peanono/SNO-DCA.

4mCPred-GSIMP: Predicting DNA N4-methylcytosine sites in the mouse genome with multi-Scale adaptive features extraction and fusion.

The epigenetic modification of DNA N4-methylcytosine (4mC) is vital for controlling DNA replication and expression. It is crucial to pinpoint 4mC's location to comprehend its role in physiological and pathological processes. However, accurate 4mC detection is difficult to achieve due to technical constraints. In this paper, we propose a deep learning-based approach 4mCPred-GSIMP for predicting 4mC sites in the mouse genome. The approach encodes DNA sequences using four feature encoding methods and combines multi-scale convolution and improved selective kernel convolution to adaptively extract and fuse features from different scales, thereby improving feature representation and optimization effect. In addition, we also use convolutional residual connections, global response normalization and pointwise convolution techniques to optimize the model. On the independent test dataset, 4mCPred-GSIMP shows high sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve, which are 0.7812, 0.9312, 0.8562, 0.7207 and 0.9233, respectively. Various experiments demonstrate that 4mCPred-GSIMP outperforms existing prediction tools.

Design of Novel 2-Phenylquinazolin-4-amines as Selective CYP1B1 Inhibitors for Overcoming Paclitaxel Resistance in A549 Cells.

Cytochrome P450 1B1 (CYP1B1) contributes to the metabolic inactivation of chemotherapeutics when overexpressed in tumor cells. Selective inhibition of CYP1B1 holds promise for reversing drug resistance. In our pursuit of potent CYP1B1 inhibitors, we designed and synthesized a series of 2-phenylquinazolin-4-amines. A substantial proportion of these newly developed inhibitors demonstrated inhibitory activity against CYP1B1, accompanied by improved water solubility. Remarkably, compound 14b exhibited exceptional inhibitory efficacy and selectivity toward CYP1B1. Molecular docking studies suggested that the expansion of the π-system through aromatization, the introduction of an amine group, and iodine atom augmented the binding affinity. Furthermore, inhibitors 14a, 14b, and 14e demonstrated the ability to significantly reduce the resistance in A549 cells to paclitaxel, while also inhibiting the migration and invasion of these cells. Finally, radioiodine labeling experiments shed light on the metabolic pathway of compound 5l in mice, highlighting the potential of 125I-5l as a radioactive probe for future research endeavors.

NIR-II photothermal conversion and imaging based on a cocrystal containing twisted components.

On account of the scarcity of molecules with a satisfactory second near-infrared (NIR-II) response, the design of high-performance organic NIR photothermal materials has been limited. Herein, we investigate a cocrystal incorporating tetrathiafulvalene (TTF) and tetrachloroperylene dianhydride (TCPDA) components. A stable radical was generated through charge transfer from TTF to TCPDA, which exhibits strong and wide-ranging NIR-II absorption. The metal-free TTF-TCPDA cocrystal in this research shows high photothermal conversion capability under 1064 nm laser irradiation and clear photothermal imaging. The remarkable conversion ability-which is a result of twisted components in the cocrystal-has been demonstrated by analyses of single crystal X-ray diffraction, photoluminescence and femtosecond transient absorption spectroscopy as well as theoretical calculations. We have discovered that space charge separation and the ordered lattice in the TTF-TCPDA cocrystal suppress the radiative decay, while simultaneously strong intermolecular charge transfer enhances the non-radiative decay. The twisted TCPDA component induces rapid charge recombination, while the distorted configuration in TTF-TCPDA favors an internal non-radiative pathway. This research has provided a comprehensive understanding of the photothermal conversion mechanism and opened a new way for the design of advanced organic NIR-II photothermal materials.

im5C-DSCGA: A Proposed Hybrid Framework Based on Improved DenseNet and Attention Mechanisms for Identifying 5-methylcytosine Sites in Human RNA.

BACKGROUND: 5-methylcytosine (m5C) is a key post-transcriptional modification that plays a critical role in RNA metabolism. Owing to the large increase in identified m5C modification sites in organisms, their epigenetic roles are becoming increasingly unknown. Therefore, it is crucial to precisely identify m5C modification sites to gain more insight into cellular processes and other mechanisms related to biological functions. Although researchers have proposed some traditional computational methods and machine learning algorithms, some limitations still remain. In this study, we propose a more powerful and reliable deep-learning model, im5C-DSCGA, to identify novel RNA m5C modification sites in humans. METHODS: Our proposed im5C-DSCGA model uses three feature encoding methods initially-one-hot, nucleotide chemical property (NCP), and nucleotide density (ND)-to extract the original features in RNA sequences and ensure splicing; next, the original features are fed into the improved densely connected convolutional network (DenseNet) and Convolutional Block Attention Module (CBAM) mechanisms to extract the advanced local features; then, the bidirectional gated recurrent unit (BGRU) method is used to capture the long-term dependencies from advanced local features and extract global features using Self-Attention; Finally, ensemble learning is used and full connectivity is used to classify and predict the m5C site. RESULTS: Unsurprisingly, the deep-learning-based im5C-DSCGA model performed well in terms of sensitivity (Sn), specificity (SP), accuracy (Acc), Matthew's correlation coefficient (MCC), and area under the curve (AUC), generating values of 81.0%, 90.8%, 85.9%, 72.1%, and 92.6%, respectively, in the independent test dataset following the use of three feature encoding methods. CONCLUSIONS: We critically evaluated the performance of im5C-DSCGA using five-fold cross-validation and independent testing and compared it to existing methods. The MCC metric reached 72.1% when using the independent test, which is 3.0% higher than the current state-of-the-art prediction method Deepm5C model. The results show that the im5C-DSCGA model achieves more accurate and stable performances and is an effective tool for predicting m5C modification sites. To the authors' knowledge, this is the first time that the improved DenseNet, BGRU, CBAM Attention mechanism, and Self-Attention mechanism have been combined to predict novel m5C sites in human RNA.