S-Nitrosylation of Septin2 Exacerbates Aortic Aneurysm and Dissection by Coupling the TIAM1-RAC1 Axis in Macrophages.

BACKGROUND: S-Nitrosylation (SNO), a prototypic redox-based posttranslational modification, is involved in cardiovascular disease. Aortic aneurysm and dissection are high-risk cardiovascular diseases without an effective cure. The aim of this study was to determine the role of SNO of Septin2 in macrophages in aortic aneurysm and dissection. METHODS: Biotin-switch assay combined with liquid chromatography-tandem mass spectrometry was performed to identify the S-nitrosylated proteins in aortic tissue from both patients undergoing surgery for aortic dissection and Apoe-/- mice infused with angiotensin II. Angiotensin II-induced aortic aneurysm model and ß-aminopropionitrile-induced aortic aneurysm and dissection model were used to determine the role of SNO of Septin2 (SNO-Septin2) in aortic aneurysm and dissection development. RNA-sequencing analysis was performed to recapitulate possible changes in the transcriptome profile of SNO-Septin2 in macrophages in aortic aneurysm and dissection. Liquid chromatography-tandem mass spectrometry and coimmunoprecipitation were used to uncover the TIAM1-RAC1 (Ras-related C3 botulinum toxin substrate 1) axis as the downstream target of SNO-Septin2. Both R-Ketorolac and NSC23766 treatments were used to inhibit the TIAM1-RAC1 axis. RESULTS: Septin2 was identified S-nitrosylated at cysteine 111 (Cys111) in both aortic tissue from patients undergoing surgery for aortic dissection and Apoe-/- mice infused with Angiotensin II. SNO-Septin2 was demonstrated driving the development of aortic aneurysm and dissection. By RNA-sequencing, SNO-Septin2 in macrophages was demonstrated to exacerbate vascular inflammation and extracellular matrix degradation in aortic aneurysm. Next, TIAM1 (T lymphoma invasion and metastasis-inducing protein 1) was identified as a SNO-Septin2 target protein. Mechanistically, compared with unmodified Septin2, SNO-Septin2 reduced its interaction with TIAM1 and activated the TIAM1-RAC1 axis and consequent nuclear factor-κB signaling pathway, resulting in stronger inflammation and extracellular matrix degradation mediated by macrophages. Consistently, both R-Ketorolac and NSC23766 treatments protected against aortic aneurysm and dissection by inhibiting the TIAM1-RAC1 axis. CONCLUSIONS: SNO-Septin2 drives aortic aneurysm and dissection through coupling the TIAM1-RAC1 axis in macrophages and activating the nuclear factor-κB signaling pathway-dependent inflammation and extracellular matrix degradation. Pharmacological blockade of RAC1 by R-Ketorolac or NSC23766 may therefore represent a potential treatment against aortic aneurysm and dissection.

DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks.

MicroRNAs (miRNAs) are human post-transcriptional regulators in humans, which are involved in regulating various physiological processes by regulating the gene expression. The subcellular localization of miRNAs plays a crucial role in the discovery of their biological functions. Although several computational methods based on miRNA functional similarity networks have been presented to identify the subcellular localization of miRNAs, it remains difficult for these approaches to effectively extract well-referenced miRNA functional representations due to insufficient miRNA-disease association representation and disease semantic representation. Currently, there has been a significant amount of research on miRNA-disease associations, making it possible to address the issue of insufficient miRNA functional representation. In this work, a novel model is established, named DAmiRLocGNet, based on graph convolutional network (GCN) and autoencoder (AE) for identifying the subcellular localizations of miRNA. The DAmiRLocGNet constructs the features based on miRNA sequence information, miRNA-disease association information and disease semantic information. GCN is utilized to gather the information of neighboring nodes and capture the implicit information of network structures from miRNA-disease association information and disease semantic information. AE is employed to capture sequence semantics from sequence similarity networks. The evaluation demonstrates that the performance of DAmiRLocGNet is superior to other competing computational approaches, benefiting from implicit features captured by using GCNs. The DAmiRLocGNet has the potential to be applied to the identification of subcellular localization of other non-coding RNAs. Moreover, it can facilitate further investigation into the functional mechanisms underlying miRNA localization. The source code and datasets are accessed at http://bliulab.net/DAmiRLocGNet.

iDRPro-SC: identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers.

Nucleic acid-binding proteins are proteins that interact with DNA and RNA to regulate gene expression and transcriptional control. The pathogenesis of many human diseases is related to abnormal gene expression. Therefore, recognizing nucleic acid-binding proteins accurately and efficiently has important implications for disease research. To address this question, some scientists have proposed the method of using sequence information to identify nucleic acid-binding proteins. However, different types of nucleic acid-binding proteins have different subfunctions, and these methods ignore their internal differences, so the performance of the predictor can be further improved. In this study, we proposed a new method, called iDRPro-SC, to predict the type of nucleic acid-binding proteins based on the sequence information. iDRPro-SC considers the internal differences of nucleic acid-binding proteins and combines their subfunctions to build a complete dataset. Additionally, we used an ensemble learning to characterize and predict nucleic acid-binding proteins. The results of the test dataset showed that iDRPro-SC achieved the best prediction performance and was superior to the other existing nucleic acid-binding protein prediction methods. We have established a web server that can be accessed online: http://bliulab.net/iDRPro-SC.

PreHom-PCLM: protein remote homology detection by combing motifs and protein cubic language model.

Protein remote homology detection is essential for structure prediction, function prediction, disease mechanism understanding, etc. The remote homology relationship depends on multiple protein properties, such as structural information and local sequence patterns. Previous studies have shown the challenges for predicting remote homology relationship by protein features at sequence level (e.g. position-specific score matrix). Protein motifs have been used in structure and function analysis due to their unique sequence patterns and implied structural information. Therefore, designing a usable architecture to fuse multiple protein properties based on motifs is urgently needed to improve protein remote homology detection performance. To make full use of the characteristics of motifs, we employed the language model called the protein cubic language model (PCLM). It combines multiple properties by constructing a motif-based neural network. Based on the PCLM, we proposed a predictor called PreHom-PCLM by extracting and fusing multiple motif features for protein remote homology detection. PreHom-PCLM outperforms the other state-of-the-art methods on the test set and independent test set. Experimental results further prove the effectiveness of multiple features fused by PreHom-PCLM for remote homology detection. Furthermore, the protein features derived from the PreHom-PCLM show strong discriminative power for proteins from different structural classes in the high-dimensional space. Availability and Implementation: http://bliulab.net/PreHom-PCLM.

PDB-BRE: A ligand-protein interaction binding residue extractor based on Protein Data Bank.

Proteins typically exert their biological functions by interacting with other biomolecules or ligands. The study of ligand-protein interactions is crucial in elucidating the biological mechanisms of proteins. Most existing studies have focused on analyzing ligand-protein interactions, and they ignore the additional situational of inserted and modified residues. Besides, the resources often support only a single ligand type and cannot obtain satisfied results in analyzing novel complexes. Therefore, it is important to develop a general analytical tool to extract the binding residues of ligand-protein interactions in complexes fully. In this study, we propose a ligand-protein interaction binding residue extractor (PDB-BRE), which can be used to automatically extract interacting ligand or protein-binding residues from complex three-dimensional (3D) structures based on the RCSB Protein Data Bank (RCSB PDB). PDB-BRE offers a notable advantage in its comprehensive support for analyzing six distinct types of ligands, including proteins, peptides, DNA, RNA, mixed DNA and RNA entities, and non-polymeric entities. Moreover, it takes into account the consideration of inserted and modified residues within complexes. Compared to other state-of-the-art methods, PDB-BRE is more suitable for massively parallel batch analysis, and can be directly applied for downstream tasks, such as predicting binding residues of novel complexes. PDB-BRE is freely available at http://bliulab.net/PDB-BRE.

NRF2 mutation enhances the immune escape of hepatocellular carcinoma by reducing STING activation.

The nuclear factor erythroid 2-related factor 2 (NRF2) is a transcription factor usually hyperactivated in hepatocellular carcinoma (HCC). In addition, about 14 % of HCC patients carry mutation in NRF2 or Kelch-like ECH-associated protein 1 (Keap1), a NRF2 inhibitor, both of which lead to constitutive activation of NRF2. It has been widely reported that NRF2 plays important roles in the proliferation, differentiation and metastasis of tumor cells. But as an important gene involved in antioxidation and anti-inflammation, little studies have focused on its role in tumor immune escape. Here we found that NRF2 gain-of-function mutation leads to reduced expression of STING and decreased infiltration of peripheral immune cells through which way it helps the tumor cells to evade from immune surveillance. This phenomenon can be reversed by STING overexpression. Our study also revealed that NRF2 mutation greatly reduced the effect of STING activating based immunotherapy. It is important to simultaneously inhibit the activity of NRF2 when using STING agonist for the treatment of HCC patients carrying NRF2 mutation.

iDRNA-ITF: identifying DNA- and RNA-binding residues in proteins based on induction and transfer framework.

Protein-DNA and protein-RNA interactions are involved in many biological activities. In the post-genome era, accurate identification of DNA- and RNA-binding residues in protein sequences is of great significance for studying protein functions and promoting new drug design and development. Therefore, some sequence-based computational methods have been proposed for identifying DNA- and RNA-binding residues. However, they failed to fully utilize the functional properties of residues, leading to limited prediction performance. In this paper, a sequence-based method iDRNA-ITF was proposed to incorporate the functional properties in residue representation by using an induction and transfer framework. The properties of nucleic acid-binding residues were induced by the nucleic acid-binding residue feature extraction network, and then transferred into the feature integration modules of the DNA-binding residue prediction network and the RNA-binding residue prediction network for the final prediction. Experimental results on four test sets demonstrate that iDRNA-ITF achieves the state-of-the-art performance, outperforming the other existing sequence-based methods. The webserver of iDRNA-ITF is freely available at http://bliulab.net/iDRNA-ITF.

PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework.

MOTIVATION: Therapeutic peptides play an important role in immune regulation. Recently various therapeutic peptides have been used in the field of medical research, and have great potential in the design of therapeutic schedules. Therefore, it is essential to utilize the computational methods to predict the therapeutic peptides. However, the therapeutic peptides cannot be accurately predicted by the existing predictors. Furthermore, chaotic datasets are also an important obstacle of the development of this important field. Therefore, it is still challenging to develop a multi-classification model for identification of therapeutic peptides and their types. RESULTS: In this work, we constructed a general therapeutic peptide dataset. An ensemble-learning method named PreTP-2L was developed for predicting various therapeutic peptide types. PreTP-2L consists of two layers. The first layer predicts whether a peptide sequence belongs to therapeutic peptide, and the second layer predicts if a therapeutic peptide belongs to a particular species. AVAILABILITY AND IMPLEMENTATION: A user-friendly webserver PreTP-2L can be accessed at http://bliulab.net/PreTP-2L.

sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure.

MOTIVATION: Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. RESULTS: In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. AVAILABILITY AND IMPLEMENTATION: A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning risk stratification for high-risk infant follow-up of term and late preterm infants.

BACKGROUND: Term and late preterm infants are not routinely referred to high-risk infant follow-up programs at neonatal intensive care unit (NICU) discharge. We aimed to identify NICU factors associated with abnormal developmental screening and develop a risk-stratification model using machine learning for high-risk infant follow-up enrollment. METHODS: We performed a retrospective cohort study identifying abnormal developmental screening prior to 6 years of age in infants born ≥34 weeks gestation admitted to a level IV NICU. Five machine learning models using NICU predictors were developed by classification and regression tree (CART), random forest, gradient boosting TreeNet, multivariate adaptive regression splines (MARS), and regularized logistic regression analysis. Performance metrics included sensitivity, specificity, accuracy, precision, and area under the receiver operating curve (AUC). RESULTS: Within this cohort, 87% (1183/1355) received developmental screening, and 47% had abnormal results. Common NICU predictors across all models were oral (PO) feeding, follow-up appointments, and medications prescribed at NICU discharge. Each model resulted in an AUC > 0.7, specificity >70%, and sensitivity >60%. CONCLUSION: Stratification of developmental risk in term and late preterm infants is possible utilizing machine learning. Applying machine learning algorithms allows for targeted expansion of high-risk infant follow-up criteria. IMPACT: This study addresses the gap in knowledge of developmental outcomes of infants ≥34 weeks gestation requiring neonatal intensive care. Machine learning methodology can be used to stratify early childhood developmental risk for these term and late preterm infants. Applying the classification and regression tree (CART) algorithm described in the study allows for targeted expansion of high-risk infant follow-up enrollment to include those term and late preterm infants who may benefit most.

MicrobeTCM: A comprehensive platform for the interactions of microbiota and traditional Chinese medicine.

Thanks to the advancements in bioinformatics, drugs, and other interventions that modulate microbes to treat diseases have been emerging continuously. In recent years, an increasing number of databases related to traditional Chinese medicine (TCM) or gut microbes have been established. However, a database combining the two has not yet been developed. To accelerate TCM research and address the traditional medicine and micro ecological system connection between short board, we have developed the most comprehensive micro-ecological database of TCM. This initiative includes the standardization of the following advantages: (1) A repeatable process achieved through the standardization of a retrieval strategy to identify literature. This involved identifying 419 experiment articles from PubMed and six authoritative databases; (2) High-quality data integration achieved through double-entry extraction of literature, mitigating uncertainties associated with natural language extraction; (3) Implementation of a similar strategy aiding in the prediction of mechanisms of action. Leveraging drug similarity, target entity similarity, and known drug-target entity association, our platform enables the prediction of the effects of a new herb or acupoint formulas using the existing data. In total, MicrobeTCM includes 171 diseases, 725 microbes, 1468 herb-formulas, 1032 herbs, 15780 chemical compositions, 35 acupoint-formulas, and 77 acupoints. For further exploration, please visit https://www.microbetcm.com.

Surface mobility gradient and emergent facilitation in glassy films.

Confining glassy polymers into films can substantially modify their local and film-averaged properties. We present a lattice model of film geometry with void-mediated facilitation behaviors but free from any elasticity effect. We analyze the spatially varying viscosity to delineate the transport properties of glassy films. The film mobility measurements reported by Yang et al., Science, 2010, 328, 1676 are successfully reproduced. The flow exhibits a crossover from a simple viscous flow to a surface-dominated regime as the temperature decreases. The propagation of a highly mobile front induced by the free surface is visualized in real space. Our approach provides a microscopic treatment of the observed glassy phenomena.

Correction: Surface mobility gradient and emergent facilitation in glassy films.

Correction for 'Surface mobility gradient and emergent facilitation in glassy films' by Qiang Zhai et al., Soft Matter, 2024, https://doi.org/10.1039/D4SM00221K.

Shell thickness-induced thermal dependence: highly sensitive core-shell CdSe/ZnS/POSS-based temperature probes.

Fluorescence nanothermometry based on quantum dots is a current research hotspot for novel non-contact temperature monitoring, and is of vital significance for the modulation and design of the sensing properties of sensors. Herein, a design strategy to modulate the temperature-sensing characteristics of quantum dots based on the thickness of a shell is proposed. In this study, CdSe/ZnS quantum dot/POSS-based temperature probe films with varying fluorescence characteristics were developed, and the influence of the ZnS shell on temperature sensing was examined by varying the thickness of the ZnS shell. The temperature dependency, linearity, range of applications, and reversibility of quantum dot thin film probes were all considerably regulated by the ZnS shell, according to research on quantum dot/POSS-based films coated with various shell thicknesses. The CdSe/ZnS temperature probe with 4 monolayers (MLs) stood out among the rest due to its strong thermal stability (at least 5 cycles), large usable temperature range (20-80 °C), and excellent temperature sensitivity (R2 > 0.994). The results demonstrated that the temperature sensing performance of quantum dots was the consequence of the combined effect of multiple temperature response properties induced by the thickness of the shell, and the shell control of quantum dots to optimize the temperature sensing performance was an essential approach for the design of temperature probes. This work demonstrates the great potential of the shell in tuning the temperature sensing performance of quantum dots and provides a viable approach for the design of quantum dot temperature probes.

Outcomes of a dexamethasone-prednisone combination treatment course for status asthmaticus.

OBJECTIVES: Dexamethasone has become the standard of care for pediatric patients with status asthmaticus in the emergency department (ED) setting. Inpatient providers often must decide between continuing the second dose of dexamethasone or transitioning to prednisone. The effectiveness of receiving dexamethasone followed by prednisone (combination therapy) compared to only prednisone or dexamethasone remains unclear. This study compares patient characteristics and ED reutilization/hospital readmission outcomes of dexamethasone, prednisone, and combination therapy for inpatient asthma management. METHODS: A retrospective study was conducted at our tertiary children's hospital of children aged 2 to 18 years hospitalized between March 2016 and December 2018 with a primary discharge diagnosis of asthma, reactive airway disease, or bronchospasm. The differences between steroid groups were compared using Fisher's exact or Chi-square tests for categorical variables, and a Kruskal-Wallis test for continuous variables. A multivariable logistic regression was performed to analyze ED reutilization and hospital readmission rates. RESULTS: 1697 subjects met inclusion criteria. 115 (6.8%) patients received dexamethasone, 597 (35.2%) received prednisone, and 985 (58.0%) received combination therapy. Patients prescribed combination therapy had a lower exacerbation severity than patients prescribed prednisone, but higher severity than patients prescribed dexamethasone (p < .001, p = .001, respectively). Dexamethasone and combination therapy were not associated with increased 30-day ED reutilization/hospital readmissions compared to prednisone (p > .05). CONCLUSIONS: In our study, most patients hospitalized for status asthmaticus received combination therapy. Despite the differences in severity between steroid groups, outcomes of combination therapy and dexamethasone monotherapy, as measured by frequency of ED reutilizations/hospital readmissions, are comparable to prednisone monotherapy.

Effect of voltage gradients on EK-PRB remediation: Experimental and molecular dynamics simulations.

Electrokinetic-Permeable Reaction Barrier (EK-PRB) coupled remediation technology can effectively treat heavy metal-contaminated soil near coal mines. This study was conducted on cadmium (Cd), a widely present element in the soil of the mining area. To investigate the impact of the voltage gradient on the remediation effect of EK-PRB, the changes in current, power consumption, pH, and Cd concentration content during the macroscopic experiment were analyzed. A three-dimensional visualized kaolinite-heavy metal-water simulation system was constructed and combined with the Molecular Dynamics (MD) simulations to elucidate the migration mechanism and binding active sites of Cd on the kaolinite (001) crystalline surface at the microscopic scale. The results showed that the voltage gradient positively correlates with the current, power consumption, and Cd concentration during EK-PRB remediation, and the average removal efficiency increases non-linearly with increasing voltage gradient. Considering power consumption, average removal efficiency, and cost-effectiveness, the voltage range is between 1.5 and 3.0 V/cm, with 2.5 V/cm being the optimal value. The results of MD simulations and experiments correspond to each other. Cd2+ formed a highly stable adsorption structure in contrast to the Al-O sheet on the kaolinite (001) crystalline surface. The mean square displacement (MSD) curve of Cd2+ under the electric field exhibits anisotropy, the total diffusion coefficient DTotal increases and the Cd2+ migration rate accelerates. The electric field influences the microstructure of Cd2+ complexes. With the enhancement of the voltage gradient, the complexation between Cd2+ and water molecules is enhanced, and the interaction between Cd2+ and Cl- in solution is weakened.

Prediction of short- and long-term outcomes using pre-operative ventricular size in infants with post-hemorrhagic ventricular dilation.

PURPOSE: Post-hemorrhagic ventricular dilation (PHVD) leads to developmental delays in premature infants, yet the optimal timing of neurosurgical interventions is unknown. Neuroimaging modalities have emerged to delineate injury and follow the progression of PHVD. Fronto-temporal horn ratio (FTHR) is used as a marker of ventricular dilation and can be a standardized tool to direct the timing of neurosurgical intervention. Our study determined a pre-operative FTHR measurement threshold to predict short- and long-term outcomes. METHODS: This is a retrospective cohort study of premature infants with severe intraventricular hemorrhage (IVH) who developed PHVD requiring neurosurgical intervention and were treated in a level IV NICU between 2012 and 2019. Receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses were performed to evaluate the accuracy of pre-operative FTHR for predicting developmental delay. In-hospital outcomes and developmental assessments were analyzed. RESULTS: We reviewed 121 charts of infants with IVH and identified 43 infants with PHVD who required neurosurgical intervention. We found FTHR measurements were an excellent predictor of cognitive and motor delay with an AUC of 0.89 and 0.88, respectively. An average pre-operative FTHR of ≥ 0.67 was also associated with worse lung and feeding outcomes. There was excellent inter-observer reliability of individual components of FTHR measurements. CONCLUSIONS: Early intervention for PHVD is ideal but not always practical. Identification of ventricular size thresholds associated with better outcomes is needed to direct timing of neurosurgical intervention.

Effects of KLF11 on Vascular Smooth Muscle Cells and its Underlying Mechanisms in Intracranial Aneurysm.

Vascular smooth muscle cells (VSMCs) affect the phenotypic changes in intracranial aneurysm (IA). They exhibit enhanced dissociation and migration and play a key role in IA pathogenesis. KLF transcription factor 11 (KLF11), a member of the KLF family, significantly affects the cancer cell proliferation, differentiation, and apoptosis. However, its expression, biological functions, and latent action mechanisms in IA remain unclear. This study aimed to analyze the effects of KLF11 on H2O2-induced human brain VSMCs (HBVSMCs) in IA. We determined the mRNA levels of KLF11 in 15 paired arterial wall tissues of patients with IA and healthy volunteers. HBVSMCs were stimulated with H2O2 for 6 h to establish an IA model in vitro. Cell viability, apoptosis, and inflammatory cytokine (interleukin [IL-1ß, tumor necrosis factor-α, and IL-6) levels were assessed using the 3-(4,5-dimethylthiazol-2-yl)-2-5-diphenyltetrazolium bromide, flow cytometry, and enzyme-linked immunosorbent assays, respectively. KLF11 expression was determined via quantitative reverse transcription-polymerase chain reaction, western blotting, and immunofluorescence analyses. Furthermore, p-p38, p38, cleaved-caspase 3, and caspase 3 levels were determined via western blotting. KLF11 levels were downregulated in the arterial wall tissues of patients with IA than in those of the control group. KLF11 upregulation by KLF11-plasmid promoted the cell viability, reduced apoptosis, decreased cleaved-caspase 3 expression, and inhibited the secretion of inflammatory factors in H2O2-induced HBVSMCs. KLF11-plasmid remarkably reduced p-p38 expression and p-p38/p-38 ratio; however, these effects were reversed by P79350 treatment. Overall, KLF11 upregulation improved the HBVSMC functions and exerted protective effects against IA, suggesting its potential for IA treatment.

TPpred-LE: therapeutic peptide function prediction based on label embedding.

BACKGROUND: Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data. RESULTS: In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data. CONCLUSIONS: In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data.

Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm.

BACKGROUND: Intrinsically disordered regions (IDRs) are widely distributed in proteins and related to many important biological functions. Accurately identifying IDRs is of great significance for protein structure and function analysis. Because the long disordered regions (LDRs) and short disordered regions (SDRs) share different characteristics, the existing predictors fail to achieve better and more stable performance on datasets with different ratios between LDRs and SDRs. There are two main reasons. First, the existing predictors construct network structures based on their own experiences such as convolutional neural network (CNN) which is used to extract the feature of neighboring residues in protein, and long short-term memory (LSTM) is used to extract the long-distance dependencies feature of protein residues. But these networks cannot capture the hidden feature associated with the length-dependent between residues. Second, many algorithms based on deep learning have been proposed but the complementarity of the existing predictors is not fully explored and used. RESULTS: In this study, the neural architecture search (NAS) algorithm was employed to automatically construct the network structures so as to capture the hidden features in protein sequences. In order to stably predict both the LDRs and SDRs, the model constructed by NAS was combined with length-dependent models for capturing the unique features of SDRs or LDRs and general models for capturing the common features between LDRs and SDRs. A new predictor called IDP-Fusion was proposed. CONCLUSIONS: Experimental results showed that IDP-Fusion can achieve more stable performance than the other existing predictors on independent test sets with different ratios between SDRs and LDRs.