MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

FHND004 inhibits malignant proliferation of multiple myeloma by targeting PDZ-binding kinase in MAPK pathway.

Inhibitors of Epidermal growth factor receptor tyrosine kinase (EGFR-TKIs) are producing impressive benefits to responsive types of cancers but challenged with drug resistances. FHND drugs are newly modified small molecule inhibitors based on the third-generation EGFR-TKI AZD9291 (Osimertinib) that are mainly for targeting the mutant-selective EGFR, particularly for the non-small cell lung cancer (NSCLC). Successful applications of EGFR-TKIs to other cancers are less certain, thus the present pre-clinical study aims to explore the anticancer effect and downstream targets of FHND in multiple myeloma (MM), which is an incurable hematological malignancy and reported to be insensitive to first/second generation EGFR-TKIs (Gefitinib/Afatinib). Cell-based assays revealed that FHND004 and FHND008 significantly inhibited MM cell proliferation and promoted apoptosis. The RNA-seq identified the involvement of the MAPK signaling pathway. The protein chip screened PDZ-binding kinase (PBK) as a potential drug target. The interaction between PBK and FHND004 was verified by molecular docking and microscale thermophoresis (MST) assay with site mutation (N124/D125). Moreover, the public clinical datasets showed high expression of PBK was associated with poor clinical outcomes. PBK overexpression evidently promoted the proliferation of two MM cell lines, whereas the FHND004 treatment significantly inhibited survival of 5TMM3VT cell-derived model mice and growth of patient-derived xenograft (PDX) tumors. The mechanistic study showed that FHND004 downregulated PBK expression, thus mediating ERK1/2 phosphorylation in the MAPK pathway. Our study not only demonstrates PBK as a promising novel target of FHND004 to inhibit MM cell proliferation, but also expands the EGFR kinase-independent direction for developing anti-myeloma therapy.

Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory.

DNA-protein binding is crucial for the normal development and function of organisms. The significance of accurately identifying DNA-protein binding sites lies in its role in disease prevention and the development of innovative approaches to disease treatment. In the present study, we introduce a precise and robust identifier for DNA-protein binding residues. In the context of protein representation, we combine the evolutionary information of the protein, represented by its position-specific scoring matrix, with the spatial information of the protein's secondary structure, enriching the overall informational content. This approach initially employs a combination of Bi-directional Long Short-Term Memory and Transformer encoder to jointly extract the interdependencies among residues within the protein sequence. Subsequently, convolutional operations are applied to the resulting feature matrix to capture local features of the residues. Experimental results on the benchmark dataset demonstrate that our method exhibits a higher level of competitiveness when compared to contemporary classifiers. Specifically, our method achieved an MCC of 0.349, SP of 96.50%, SN of 44.03% and ACC of 94.59% on the PDNA-41 dataset.

AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism.

The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA.

GTAMP-DTA: Graph transformer combined with attention mechanism for drug-target binding affinity prediction.

Drug target affinity prediction (DTA) is critical to the success of drug development. While numerous machine learning methods have been developed for this task, there remains a necessity to further enhance the accuracy and reliability of predictions. Considerable bias in drug target binding prediction may result due to missing structural information or missing information. In addition, current methods focus only on simulating individual non-covalent interactions between drugs and proteins, thereby neglecting the intricate interplay among different drugs and their interactions with proteins. GTAMP-DTA combines special Attention mechanisms, assigning each atom or amino acid an attention vector. Interactions between drug forms and protein forms were considered to capture information about their interactions. And fusion transformer was used to learn protein characterization from raw amino acid sequences, which were then merged with molecular map features extracted from SMILES. A self-supervised pre-trained embedding that uses pre-trained transformers to encode drug and protein attributes is introduced in order to address the lack of labeled data. Experimental results demonstrate that our model outperforms state-of-the-art methods on both the Davis and KIBA datasets. Additionally, the model's performance undergoes evaluation using three distinct pooling layers (max-pooling, mean-pooling, sum-pooling) along with variations of the attention mechanism. GTAMP-DTA shows significant performance improvements compared to other methods.

Identification of membrane protein types via deep residual hypergraph neural network.

A membrane protein's functions are significantly associated with its type, so it is crucial to identify the types of membrane proteins. Conventional computational methods for identifying the species of membrane proteins tend to ignore two issues: High-order correlation among membrane proteins and the scenarios of multi-modal representations of membrane proteins, which leads to information loss. To tackle those two issues, we proposed a deep residual hypergraph neural network (DRHGNN), which enhances the hypergraph neural network (HGNN) with initial residual and identity mapping in this paper. We carried out extensive experiments on four benchmark datasets of membrane proteins. In the meantime, we compared the DRHGNN with recently developed advanced methods. Experimental results showed the better performance of DRHGNN on the membrane protein classification task on four datasets. Experiments also showed that DRHGNN can handle the over-smoothing issue with the increase of the number of model layers compared with HGNN. The code is available at https://github.com/yunfighting/Identification-of-Membrane-Protein-Types-via-deep-residual-hypergraph-neural-network.

DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features.

Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of protein phosphorylation sites. However, most existing methods rely on simple protein sequence features that provide limited contextual information. To overcome this limitation, we propose DeepMPSF, a phosphorylation site prediction model based on multiple protein sequence features. There are two types of features: sequence semantic features, which comprise protein residue type information and relative position information within protein sequence, and protein background biophysical features, which include global semantic information containing more comprehensive protein background information obtained from pretrained models. To extract these features, DeepMPSF employs two separate subnetworks: the S71SFE module and the BBFE module, which automatically extract high-level semantic features. Our model incorporates a learning strategy for handling imbalanced datasets through ensemble learning during training and prediction. DeepMPSF is trained and evaluated on a well-established dataset of human proteins. Comparing the analysis with other benchmark methods reveals that DeepMPSF outperforms in predicting both S/T residues and Y residues. In particular, DeepMPSF showed excellent generalization performance in cross-species blind test performance, with an average improvement of 5.63%/5.72%, 22.28%/25.94%, 20.11%/17.49%, and 26.40%/28.33% for Mus musculus/Rattus norvegicus test sets in area under curves (AUCs) of ROC curve, AUC of the PR curve, F1-score, and MCC metrics, respectively. Furthermore, it also shows excellent performance in the latest updated case of natural proteins with functional phosphorylation sites. Through an ablation study and visual analysis, we uncover that the design of different feature modules significantly contributes to the accurate classification of DeepMPSF, which provides valuable insights for predicting phosphorylation sites and offers effective support for future downstream research.

TrGPCR:GPCR-ligand Binding Affinity Predicting based on Dynamic Deep Transfer Learning.

Predicting G protein-coupled receptor (GPCR)-ligand binding affinity plays a crucial role in drug development. However, determining GPCR-ligand binding affinities is time-consuming and resource-intensive. Although many studies used data-driven methods to predict binding affinity, most of these methods required protein 3D structure, which was often unknown. Moreover, part of these studies only considered the sequence characteristics of the protein, ignoring the secondary structure of the protein. The number of known GPCR for affinity prediction is only a few thousand, which is insufficient for deep learning training. Therefore, this study aimed to propose a deep transfer learning method called TrGPCR, which used dynamic transfer learning to solve the problem of insufficient GPCR data. We used the Binding Database(BindingDB) as the source domain and the GLASS(GPCR-Ligand Association) database as the target domain. We also introduced protein secondary structures, called pockets, as features to predict binding affinities. Compared with DeepDTA, our model improved by 5.2% on RMSE(root mean square error) and 4.5% on MAE(mean squared error).

Inhibition of VCP modulates NF-κB signaling pathway to suppress multiple myeloma cell proliferation and osteoclast differentiation.

Multiple myeloma (MM) is the second most common hematological malignancy, in which the dysfunction of the ubiquitin-proteasome pathway is associated with the pathogenesis. The valosin containing protein (VCP)/p97, a member of the AAA+ ATPase family, possesses multiple functions to regulate the protein quality control including ubiquitin-proteasome system and molecular chaperone. VCP is involved in the occurrence and development of various tumors while still elusive in MM. VCP inhibitors have gradually shown great potential for cancer treatment. This study aims to identify if VCP is a therapeutic target in MM and confirm the effect of a novel inhibitor of VCP (VCP20) on MM. We found that VCP was elevated in MM patients and correlated with shorter survival in clinical TT2 cohort. Silencing VCP using siRNA resulted in decreased MM cell proliferation via NF-κB signaling pathway. VCP20 evidently inhibited MM cell proliferation and osteoclast differentiation. Moreover, exosomes containing VCP derived from MM cells partially alleviated the inhibitory effect of VCP20 on cell proliferation and osteoclast differentiation. Mechanism study revealed that VCP20 inactivated the NF-κB signaling pathway by inhibiting ubiquitination degradation of IκBα. Furthermore, VCP20 suppressed MM cell proliferation, prolonged the survival of MM model mice and improved bone destruction in vivo. Collectively, our findings suggest that VCP is a novel target in MM progression. Targeting VCP with VCP20 suppresses malignancy progression of MM via inhibition of NF-κB signaling pathway.

Are there reliable multiparametric MRI criteria for differential diagnosis between intracranial meningiomas and solitary intracranial dural metastases?

Intracranial meningiomas are the most common tumors of the central nervous system (CNS). Meningiomas account for up to 36% of all brain tumors. The incidence of metastatic brain lesions has not been determined. Up to 30% of adult patients with cancer of one localization or another suffer from a secondary tumor lesion of the brain. The vast majority of meningiomas have meningeal localization; >90% are solitary. The incidence of intracranial dural metastases (IDM) is 8-9% of cases, while in 10% of cases, the brain is the only localization, and in 50% of cases the metastases are solitary. Typically, the task of distinguishing between meningioma and dural metastasis does not involve difficulties. Periodically, there is a situation when the differential diagnosis between these tumors is ambiguous, since meningiomas and solitary IDM may have similar characteristics, in particular, a cavity-less solid structure, limited diffusion of water molecules, the presence of extensive peritumoral edema, and an identical contrast pattern. The present study included 100 patients with newly diagnosed tumors of the CNS, who subsequently underwent examination and neurosurgical treatment at the Federal Center for Neurosurgery with histological verification between May 2019 and October 2022. Depending on the histological conclusion, two study groups of patients were distinguished: The first group consisted of patients diagnosed with intracranial meningiomas (n=50) and the second group of patients were diagnosed with IDM (n=50). The study was performed using a magnetic resonance imaging (MRI) General Electric Discovery W750 3T before and after contrast enhancement. The diagnostic value of this study was estimated using Receiver Operating Characteristic curve and area under the curve analysis. Based on the results of the study, it was found that the use of multiparametric MRI (mpMRI) in the differential diagnosis of intracranial meningiomas and IDM was limited by the similarity of the values of the measured diffusion coefficient. The assumption, previously put forward in the literature, regarding the presence of a statistically significant difference in the apparent diffusion coefficient values, which make it possible to differentiate tumors, was not confirmed. When analyzing perfusion data, IDM showed higher cerebral blood flow (CBF) values compared with intracranial meningiomas (P≤0.001). A threshold value of the CBF index was revealed, which was 217.9 ml/100 g/min, above which it is possible to predict IDM with a sensitivity and specificity of 80.0 and 86.0%, respectively. Diffusion-weighted images are not reliable criteria for differentiating intracranial meningiomas from IDM and should not influence the diagnosis suggested by imaging. The technique for assessing the perfusion of a meningeal lesion makes it possible to predict metastases with a sensitivity and specificity close to 80-90% and deserves attention when making a diagnosis. In the future, in order to reduce the number of false negative and false positive results, mpMRI would require additional criteria to be included in the protocol. Since IDM differs from intracranial meningiomas in the severity of neoangiogenesis and, accordingly, in greater vascular permeability, the technique for assessing vascular permeability (wash-in parameter with dynamic contrast enhancement) may serve as a refining criterion for distinguishing between dural lesions.

Identification of DNA-binding protein based multiple kernel model.

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.

DNA protein binding recognition based on lifelong learning.

In recent years, research in the field of bioinformatics has focused on predicting the raw sequences of proteins, and some scholars consider DNA-binding protein prediction as a classification task. Many statistical and machine learning-based methods have been widely used in DNA-binding proteins research. The aforementioned methods are indeed more efficient than those based on manual classification, but there is still room for improvement in terms of prediction accuracy and speed. In this study, researchers used Average Blocks, Discrete Cosine Transform, Discrete Wavelet Transform, Global encoding, Normalized Moreau-Broto Autocorrelation and Pseudo position-specific scoring matrix to extract evolutionary features. A dynamic deep network based on lifelong learning architecture was then proposed in order to fuse six features and thus allow for more efficient classification of DNA-binding proteins. The multi-feature fusion allows for a more accurate description of the desired protein information than single features. This model offers a fresh perspective on the dichotomous classification problem in bioinformatics and broadens the application field of lifelong learning. The researchers ran trials on three datasets and contrasted them with other classification techniques to show the model's effectiveness in this study. The findings demonstrated that the model used in this research was superior to other approaches in terms of single-sample specificity (81.0%, 83.0%) and single-sample sensitivity (82.4%, 90.7%), and achieves high accuracy on the benchmark dataset (88.4%, 80.0%, and 76.6%).

Extracting biomedical relation from cross-sentence text using syntactic dependency graph attention network.

In biomedical literature, cross-sentence texts can usually express rich knowledge, and extracting the interaction relation between entities from cross-sentence texts is of great significance to biomedical research. However, compared with single sentence, cross-sentence text has a longer sequence length, so the research on cross-sentence text information extraction should focus more on learning the context dependency structural information. Nowadays, it is still a challenge to handle global dependencies and structural information of long sequences effectively, and graph-oriented modeling methods have received more and more attention recently. In this paper, we propose a new graph attention network guided by syntactic dependency relationship (SR-GAT) for extracting biomedical relation from the cross-sentence text. It allows each node to pay attention to other nodes in its neighborhood, regardless of the sequence length. The attention weight between nodes is given by a syntactic relation graph probability network (SR-GPR), which encodes the syntactic dependency between nodes and guides the graph attention mechanism to learn information about the dependency structure. The learned feature representation retains information about the node-to-node syntactic dependency, and can further discover global dependencies effectively. The experimental results demonstrate on a publicly available biomedical dataset that, our method achieves state-of-the-art performance while requiring significantly less computational resources. Specifically, in the "drug-mutation" relation extraction task, our method achieves an advanced accuracy of 93.78% for binary classification and 92.14% for multi-classification. In the "drug-gene-mutation" relation extraction task, our method achieves an advanced accuracy of 93.22% for binary classification and 92.28% for multi-classification. Across all relation extraction tasks, our method improves accuracy by an average of 0.49% compared to the existing best model. Furthermore, our method achieved an accuracy of 69.5% in text classification, surpassing most existing models, demonstrating its robustness in generalization across different domains without additional fine-tuning.

Characteristics of antibiotic resistance mechanisms and genes of Klebsiella pneumoniae.

Klebsiella pneumoniae is an important multidrug-resistant (MDR) pathogen that can cause a range of infections in hospitalized patients. With the growing use of antibiotics, MDR K. pneumoniae is more prevalent, posing additional difficulties and obstacles in clinical therapy. To provide a valuable reference to deeply understand K. pneumoniae, and also to provide the theoretical basis for clinical prevention of such bacteria infections, the antibiotic resistance and mechanism of K. pneumoniae are discussed in this article. We conducted a literature review on antibiotic resistance of K. pneumoniae. We ran a thorough literature search of PubMed, Web of Science, and Scopus, among other databases. We also thoroughly searched the literature listed in the papers. We searched all antibiotic resistance mechanisms and genes of seven important antibiotics used to treat K. pneumoniae infections. Antibiotics such as ß-lactams, aminoglycosides, and quinolones are used in the treatment of K. pneumoniae infection. With both chromosomal and plasmid-encoded ARGs, this pathogen has diverse resistance genes. Carbapenem resistance genes, enlarged-spectrum ß-lactamase genes, and AmpC genes are the most often ß-lactamase resistance genes. K. pneumoniae is a major contributor to antibiotic resistance worldwide. Understanding K. pneumoniae antibiotic resistance mechanisms and molecular characteristics will be important for the design of targeted prevention and novel control strategies against this pathogen.

The deep origin of ground fissures in the Kenya Rift Valley.

Intense volcanic and geothermal activities characterize the Great Rift Valley of East Africa. Ground fissure disasters of the Great Rift Valley have garnered increasing attention in recent years. Through field investigations, trenching, geophysical exploration, gas sampling and analysis, we determined the distribution and origin of 22 ground fissures within the Kedong Basin of the Central Kenya Rift. These ground fissures caused varying degrees of damage to roads, culverts, railways, and communities. Trenching and geophysical exploration have shown that ground fissures in sediments are connected to rock fractures with gas escaping. The gases expelled from the rock fractures contained methane and SO2, which were absent in the normal atmosphere, and 3He/4He ratios in gases measured further indicated that the volatiles were derived from the mantle, suggesting that these rock fractures extended deep into the underlying bedrock. Spatial correlations with rock fractures demonstrate the deep origin of these ground fissures, which are associated with active rifting, plate separation, and volcanism. The ground fissures are formed due to movement on the deeper rock fractures, and then the gas escapes through the fissures. Determining the unusual origin of these ground fissures can not only guide infrastructure development and urban planning but also contribute to the safety of local communities.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks.

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides. Here, we propose a deep graph convolutional network (GCN)-based method called GraphPPepIS to predict the interaction sites of protein-peptide complexes using protein and peptide structural information. We also propose a companion method, SeqPPepIS, for assisting with the lack of structural information and the flexibility of peptides. SepPPepIS replaces the peptide structural features in GraphPPepIS by learning features from peptide sequences. We performed a comprehensive evaluation of the benchmark data sets, and the results show that our two methods outperform state-of-the-art methods on the accurate interaction sites of both protein and peptide sides. We show that our methods can help improve protein-peptide docking. For docking data sets, our methods maintain robust performance in identifying binding sites, thereby enhancing the prediction of peptide binding poses. Finally, we visualized the analysis of protein and peptide graph embedding to demonstrate the learning ability of graph convolution in predicting interaction sites, which was mainly obtained through the shared parameters of a protein graph and peptide graph.

Integrative Analysis Reveals the Expression Pattern of SOX9 in Satellite Glial Cells after Sciatic Nerve Injury.

BACKGROUND: Several complex cellular and gene regulatory processes are involved in peripheral nerve repair. This study uses bioinformatics to analyze the differentially expressed genes (DEGs) in the satellite glial cells of mice following sciatic nerve injury. METHODS: R software screens differentially expressed genes, and the WebGestalt functional enrichment analysis tool conducts Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway analysis. The Search Tool for the Retrieval of Interacting Genes/Proteins constructs protein interaction networks, and the cytoHubba plug-in in the Cytoscape software predicts core genes. Subsequently, the sciatic nerve injury model of mice was established and the dorsal root ganglion satellite glial cells were isolated and cultured. Satellite glial cells-related markers were verified by immunofluorescence staining. Real-time polymerase chain reaction assay and Western blotting assay were used to detect the mRNA and protein expression of Sox9 in satellite glial cells. RESULTS: A total of 991 DEGs were screened, of which 383 were upregulated, and 508 were downregulated. The GO analysis revealed the processes of biosynthesis, negative regulation of cell development, PDZ domain binding, and other biological processes were enriched in DEGs. According to the KEGG pathway analysis, DEGs are primarily involved in steroid biosynthesis, hedgehog signaling pathway, terpenoid backbone biosynthesis, American lateral skeleton, and melanoma pathways. According to various cytoHubba algorithms, the common core genes in the protein-protein interaction network are Atf3, Mmp2, and Sox9. Among these, Sox9 was reported to be involved in the central nervous system and the generation and development of astrocytes and could mediate the transformation between neurogenic and glial cells. The experimental results showed that satellite glial cell marker GS were co-labeled with Sox9; stem cell characteristic markers Nestin and p75NTR were labeled satellite glial cells. The mRNA and protein expression of Sox9 in satellite glial cells were increased after sciatic nerve injury. CONCLUSIONS: In this study, bioinformatics was used to analyze the DEGs of satellite glial cells after sciatic nerve injury, and transcription factors related to satellite glial cells were screened, among which Sox9 may be associated with the fate of satellite glial cells.

Protein-DNA Binding Residues Prediction Using a Deep Learning Model With Hierarchical Feature Extraction.

Biologically important effects occur when proteins bind to other substances, of which binding to DNA is a crucial one. Therefore, accurate identification of protein-DNA binding residues is important for further understanding of the protein-DNA interaction mechanism. Although wet-lab methods can accurately obtain the location of bound residues, it requires significant human, financial and time costs. There is thus an urgent need to develop efficient computational-based methods. Most current state-of-the-art methods are two-step approaches: the first step uses a sliding window technique to extract residue features; the second step uses each residue as an input to the model for prediction. This has a negative impact on the efficiency of prediction and ease of use. In this study, we propose a sequence-to-sequence (seq2seq) model that can input the entire protein sequence of variable length and use two modules, Transformer Encoder Block and Feature Extracting Block, for hierarchical feature extraction, where Transformer Encoder Block is used to extract global features, and then Feature Extracting Block is used to extract local features to further improve the recognition capability of the model. The comparison results on two benchmark datasets, namely PDNA-543 and PDNA-41, prove the effectiveness of our method in identifying protein-DNA binding residues.

MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins.

DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of DBPs is a crucial issue. And it is greatly helpful to understand the mechanism of protein-DNA interactions. In traditional experimental methods, it is significant time-consuming and labor-consuming to identify DBPs. In recent years, many researchers have proposed lots of different DBP identification methods based on machine learning algorithm to overcome shortcomings mentioned above. However, most existing methods cannot get satisfactory results. In this paper, we focus on developing a new predictor of DBPs, called Multi-View Hypergraph Restricted Kernel Machines (MV-H-RKM). In this method, we extract five features from the three views of the proteins. To fuse these features, we couple them by means of the shared hidden vector. Besides, we employ the hypergraph regularization to enforce the structure consistency between original features and the hidden vector. Experimental results show that the accuracy of MV-H-RKM is 84.09% and 85.48% on PDB1075 and PDB186 data set respectively, and demonstrate that our proposed method performs better than other state-of-the-art approaches. The code is publicly available at https://github.com/ShixuanGG/MV-H-RKM.

Effects of ischemic postconditioning and long non-coding RNAs in ischemic stroke.

Stroke is a main cause of disability and death among adults in China, and acute ischemic stroke accounts for 80% of cases. The key to ischemic stroke treatment is to recanalize the blocked blood vessels. However, more than 90% of patients cannot receive effective treatment within an appropriate time, and delayed recanalization of blood vessels causes reperfusion injury. Recent research has revealed that ischemic postconditioning has a neuroprotective effect on the brain, but the mechanism has not been fully clarified. Long non-coding RNAs (lncRNAs) have previously been associated with ischemic reperfusion injury in ischemic stroke. LncRNAs regulate important cellular and molecular events through a variety of mechanisms, but a comprehensive analysis of potential lncRNAs involved in the brain protection produced by ischemic postconditioning has not been conducted. In this review, we summarize the common mechanisms of cerebral injury in ischemic stroke and the effect of ischemic postconditioning, and we describe the potential mechanisms of some lncRNAs associated with ischemic stroke.