Silicon heterojunction back contact solar cells by laser patterning.

Back contact silicon solar cells, valued for their aesthetic appeal by removing grid lines on the sunny side, find applications in buildings, vehicles and aircrafts, enabling self-power generation without compromising appearance1-3. Patterning techniques arrange contacts on the shaded side of the silicon wafer, offering benefits for light incidence as well. However, the patterning process complicates production and causes power loss. Here we employ lasers to streamline back contact solar cell fabrication and enhance power conversion efficiency. Our approach produces the first silicon solar cell to exceed 27% efficiency. Hydrogenated amorphous silicon layers are deposited on the wafer for surface passivation and collection of light-generated carriers. A dense passivating contact, diverging from conventional technology practice, is developed. Pulsed picosecond lasers at different wavelengths are used to create back contact patterns. The developed approach is a streamlined process for producing high-performance back contact silicon solar cells, with a total effective processing time of about one-third that of emerging mainstream technology. To meet terawatt demand, we develop rare indium-less cells at 26.5% efficiency and precious silver-free cells at 26.2% efficiency. The integration of solar solutions in buildings and transportation is poised to expand with these technological advancements.

A comprehensive review of bioinformatics tools for chromatin loop calling.

Precisely calling chromatin loops has profound implications for further analysis of gene regulation and disease mechanisms. Technological advances in chromatin conformation capture (3C) assays make it possible to identify chromatin loops in the genome. However, a variety of experimental protocols have resulted in different levels of biases, which require distinct methods to call true loops from the background. Although many bioinformatics tools have been developed to address this problem, there is still a lack of special introduction to loop-calling algorithms. This review provides an overview of the loop-calling tools for various 3C-based techniques. We first discuss the background biases produced by different experimental techniques and the denoising algorithms. Then, the completeness and priority of each tool are categorized and summarized according to the data source of application. The summary of these works can help researchers select the most appropriate method to call loops and further perform downstream analysis. In addition, this survey is also useful for bioinformatics scientists aiming to develop new loop-calling algorithms.

Attention is all you need: utilizing attention in AI-enabled drug discovery.

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

Identification of target genes co-regulated by four key histone modifications of five key regions in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality. Studies have shown that histone modification plays an important regulatory role in the occurrence and development of HCC. However, the specific regulatory effects of histone modifications on gene expression in HCC are still unclear. This study focuses on HepG2 cell lines and hepatocyte cell lines. First, the distribution of histone modification signals in the two cell lines was calculated and analyzed. Then, using the random forest algorithm, we analyzed the effects of different histone modifications and their modified regions on gene expression in the two cell lines, four key histone modifications (H3K36me3, H3K4me3, H3K79me2, and H3K9ac) and five key regions that co-regulate gene expression were obtained. Subsequently, target genes regulated by key histone modifications in key regions were screened. Combined with clinical data, Cox regression analysis and Kaplan-Meier survival analysis were performed on the target genes, and four key target genes (CBX2, CEBPZOS, LDHA, and UMPS) related to prognosis were identified. Finally, through immune infiltration analysis and drug sensitivity analysis of key target genes, the potential role of key target genes in HCC was confirmed. Our results provide a theoretical basis for exploring the occurrence of HCC and propose potential biomarkers associated with histone modifications, which may be potential drug targets for the clinical treatment of HCC.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.

BACKGROUND: The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS: In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS: This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

Identification of RNA­dependent liquid­liquid phase separation proteins using an artificial intelligence strategy.

RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn.

Phosphoenolpyruvate carboxykinase-2 (PCK2) is a therapeutic target in triple-negative breast cancer.

PURPOSE: Metabolic rewiring in malignant transformation is often accompanied by altered expression of metabolic isozymes. Phosphoenolpyruvate carboxykinase-2 (PCK2) catalyzes the rate-limiting step of gluconeogenesis and is the dominant isoform in many cancers including triple-negative breast cancer (TNBC). Our goal was to identify small molecule inhibitors of PCK2 enzyme activity. METHODS: We assessed the impact of PCK2 down regulation with shRNA on TNBC cell growth in vitro and used AtomNet® deep convolutional neural network software to identify potential small molecule inhibitors of PCK2-based structure. We iteratively tested candidate compounds in an in vitro PCK-2 enzyme assay. The impact of the top hit on metabolic flux and cell viability was also assessed. RESULTS: PCK2 downregulation decreased growth of BT-549 and MDA-MB-231 cells and reduced metabolic flux through pyruvate carboxylase. The first AtomNet® in silico structural screen of 7 million compounds yielded 86 structures that were tested in PCK2 enzyme assay in vitro. The top hit (IC50 = 2.4 µM) was used to refine a second round of in silico screen that yielded 82 candidates to be tested in vitro, which resulted in 45 molecules with inhibition > 20%. In the second in vitro screen we also included 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate, previously suggested to be PCK2 inhibitor based on structure, which emerged as the top hit. The specificity of this compound was tested in PCK1 and PCK2 enzymatic assays and showed IC50 of 500 nM and 3.5-27 nM for PCK1 and PCK2, respectively. CONCLUSION: 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate is a high affinity PCK2 enzyme inhibitor that also has significant growth inhibitory activity in breast cell lines in vitro and represents a potential therapeutic lead compound.

[Ru(dpp)3]Cl2-Embedded Oxygen Nano Polymeric Sensors: A Promising Tool for Monitoring Intracellular and Intratumoral Oxygen Gradients with High Quantum Yield and Long Lifetime.

Unraveling the intricacies between oxygen dynamics and cellular processes in the tumor microenvironment (TME) hinges upon precise monitoring of intracellular and intratumoral oxygen levels, which holds paramount significance. The majority of these reported oxygen nanoprobes suffer compromised lifetime and quantum yield when exposed to the robust ROS activities prevalent in TME, limiting their prolonged in vitro usability. Herein, the ruthenium-embedded oxygen nano polymeric sensor (Ru-ONPS) is proposed for precise oxygen gradient monitoring within the cellular environment and TME. Ru-ONPS (≈64±7 nm) incorporates [Ru(dpp)3]Cl2 dye into F-127 and crosslinks it with urea and paraformaldehyde, ensuring a prolonged lifetime (5.4 µs), high quantum yield (66.65 ± 2.43% in N2 and 49.80 ± 3.14% in O2), superior photostability (>30 min), and excellent stability in diverse environmental conditions. Based on the Stern-Volmer plot, the Ru-ONPS shows complete linearity for a wide dynamic range (0-23 mg L-1), with a detection limit of 10 µg mL-1. Confocal imaging reveals Ru-ONPS cellular uptake and intratumoral distribution. After 72 h, HCT-8 cells show 5.20±1.03% oxygen levels, while NIH3T3 cells have 7.07±1.90%. Co-culture spheroids display declining oxygen levels of 17.90±0.88%, 10.90±0.88%, and 5.10±1.18%, at 48, 120, and 216 h, respectively. Ru-ONPS advances cellular oxygen measurement and facilitates hypoxia-dependent metastatic research and therapeutic target identification.

Bright Structural-Phase-Pure CsPbI3 Core-PbSO4 Shell Nanoplatelets With Ultra-Narrow Emission Bandwidth of 77 meV at 630 nm.

Achieving a narrow emission bandwidth is long pursued for display applications. Among all primary colors, obtaining pure red emission with high visual perception is the most challenging. In this work, CsPbI3 halide perovskite nanoplatelets (NPLs) with rigorously controlled 2D  [PbI6]4- octahedron layer number (n) are demonstrated. A perovskite core-PbSO4 shell structure is designed to prevent aggregation and fusion between NPLs, enabling consistent thickness and quantum confinement strength for each NPL. Consequently, exact n = 4 CsPbI3 NPLs are demonstrated, exhibiting emission peaks around 630 nm, with very narrow spectral bandwidths of <24 nm and high absolute photoluminescence quantum yields up to 85%. The emission of n = 4 NPLs falls exactly within the pure-red region, closely aligning with the International Telecommunication Union Recommendation BT.2020  standard. Measurements suggest predominant stability and color homogeneity compared to traditional red-emitting CsPbIxBr3- x nanocrystals. Finally, proof-of-concept pure-red emissive light-emitting diodes (LEDs) are demonstrated by integrating n = 4 CsPbI3 NPLs films with a blue LED chip, showing an excellent external quantum efficiency of 18.3% and high brightness exceeding 3 × 106 nits. Stringent requirements for future display technologies, are satisfied based on the high color purity, stability, and brightness of CsPbI3 NPLs.

In situ and General Multidentate Ligand Passivation Achieves Efficient and Ultra-Stable CsPbX3 Perovskite Quantum Dots for White Light-Emitting Diodes.

Inorganic CsPbX3 perovskite quantum dots (PeQDs) show great potential in white light-emitting diodes (WLEDs) due to excellent optoelectronic properties, but their practical application is hampered by low photoluminescence quantum yield (PLQY) and especially poor stability. Herein,  we developed an in-situ and general multidentate ligand passivation strategy that allows for CsPbX3 PeQDs not only near-unit PLQY, but significantly improved stability against storage, heat, and polar solvent. The enhanced optical property arises from high effectiveness of the multidentate ligand, diethylenetriaminepentaacetic acid (DTPA) with five carboxyl groups, in passivating uncoordinated Pb2+ defects and suppressing nonradiative recombination. First-principles calculations reveal that the excellent stability is attributed to tridentate binding mode of DTPA that remarkably boosts the adsorption capacity to PeQD core. Finally, combining the green and red PeQDs with blue chip,  we demonstrated highly luminous WLEDs with distinctly enhanced operation stability, a wide color gamut of 121.3% of national television system committee, standard white light of (0.33,0.33) in CIE 1931, and tunable color temperatures from warm to cold white light readily by emitters' ratio. This study provides an operando yet general approach to achieve efficient and stable PeQDs for WLEDs and accelerates their progress to commercialization.

Single Atom Ag Bonding Between PF3T Nanocluster and TiO2 Leads the Ultra-Stable Visible-Light-Driven Photocatalytic H2 Production.

Atomic Ag cluster bonding is employed to reinforce the interface between PF3T nano-cluster and TiO2 nanoparticle. With an optimized Ag loading (Ag/TiO2 = 0.5 wt%), the Ag atoms will uniformly disperse on TiO2 thus generating a high density of intermediate states in the band gap to form the electron channel between the terthiophene group of PF3T and the TiO2 in the hybrid composite (denoted as T@Ag05-P). The former expands the photon absorption band width and the latter facilitates the core-hole splitting by injecting the photon excited electron (from the excitons in PF3T) into the conduction band (CB) of TiO2. These characteristics enable the high efficiency of H2 production to 16 580 µmol h-1 g-1 and photocatalysis stability without degradation under visible light exposure for 96 h. Compared to that of hybrid material without Ag bonding (TiO2@PF3T), the H2 production yield and stability are improved by 4.1 and 18.2-fold which shows the best performance among existing materials in similar component combination and interfacial reinforcement. The unique bonding method offers a new prospect to accelerate the development of photocatalytic hydrogen production technologies.

N-linked glycoproteins and host proteases are involved in swine acute diarrhea syndrome coronavirus entry.

IMPORTANCE: Gaining insight into the cell-entry mechanisms of swine acute diarrhea syndrome coronavirus (SADS-CoV) is critical for investigating potential cross-species infections. Here, we demonstrated that pretreatment of host cells with tunicamycin decreased SADS-CoV attachment efficiency, indicating that N-linked glycosylation of host cells was involved in SADS-CoV entry. Common N-linked sugars Neu5Gc and Neu5Ac did not interact with the SADS-CoV S1 protein, suggesting that these molecules were not involved in SADS-CoV entry. Additionally, various host proteases participated in SADS-CoV entry into diverse cells with different efficiencies. Our findings suggested that SADS-CoV may exploit multiple pathways to enter cells, providing insights into intervention strategies targeting the cell entry of this virus.

Characterization of a mouse-adapted strain of bat severe acute respiratory syndrome-related coronavirus.

Bats carry genetically diverse severe acute respiratory syndrome-related coronaviruses (SARSr-CoVs). Some of them utilize human angiotensin-converting enzyme 2 (hACE2) as a receptor and cannot efficiently replicate in wild-type mice. Our previous study demonstrated that the bat SARSr-CoV rRsSHC014S induces respiratory infection and lung damage in hACE2 transgenic mice but not wild-type mice. In this study, we generated a mouse-adapted strain of rRsSHC014S, which we named SMA1901, by serial passaging of wild-type virus in BALB/c mice. SMA1901 showed increased infectivity in mouse lungs and induced interstitial lung pneumonia in both young and aged mice after intranasal inoculation. Genome sequencing revealed mutations in not only the spike protein but the whole genome, which may be responsible for the enhanced pathogenicity of SMA1901 in wild-type BALB/c mice. SMA1901 induced age-related mortality similar to that observed in SARS and COVID-19. Drug testing using antibodies and antiviral molecules indicated that this mouse-adapted virus strain can be used to test prophylactic and therapeutic drug candidates against SARSr-CoVs. IMPORTANCE The genetic diversity of SARSr-CoVs in wildlife and their potential risk of cross-species infection highlights the importance of developing a powerful animal model to evaluate the antibodies and antiviral drugs. We acquired the mouse-adapted strain of a bat-origin coronavirus named SMA1901 by natural serial passaging of rRsSHC014S in BALB/c mice. The SMA1901 infection caused interstitial pneumonia and inflammatory immune responses in both young and aged BALB/c mice after intranasal inoculation. Our model exhibited age-related mortality similar to SARS and COVID-19. Therefore, our model will be of high value for investigating the pathogenesis of bat SARSr-CoVs and could serve as a prospective test platform for prophylactic and therapeutic candidates.

A SARS-CoV-2-Related Virus from Malayan Pangolin Causes Lung Infection without Severe Disease in Human ACE2-Transgenic Mice.

Coronavirus disease 2019 (COVID-19), which is caused by the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the most severe emerging infectious disease in the current century. The discovery of SARS-CoV-2-related coronaviruses (SARSr-CoV-2) in bats and pangolins in South Asian countries indicates that SARS-CoV-2 likely originated from wildlife. To date, two SARSr-CoV-2 strains have been isolated from pangolins seized in Guangxi and Guangdong by the customs agency of China, respectively. However, it remains unclear whether these viruses cause disease in animal models and whether they pose a transmission risk to humans. In this study, we investigated the biological features of a SARSr-CoV-2 strain isolated from a smuggled Malayan pangolin (Manis javanica) captured by the Guangxi customs agency, termed MpCoV-GX, in terms of receptor usage, cell tropism, and pathogenicity in wild-type BALB/c mice, human angiotensin-converting enzyme 2 (ACE2)-transgenic mice, and human ACE2 knock-in mice. We found that MpCoV-GX can utilize ACE2 from humans, pangolins, civets, bats, pigs, and mice for cell entry and infect cell lines derived from humans, monkeys, bats, minks, and pigs. The virus could infect three mouse models but showed limited pathogenicity, with mild peribronchial and perivascular inflammatory cell infiltration observed in lungs. Our results suggest that this SARSr-CoV-2 virus from pangolins has the potential for interspecies infection, but its pathogenicity is mild in mice. Future surveillance among these wildlife hosts of SARSr-CoV-2 is needed to monitor variants that may have higher pathogenicity and higher spillover risk. IMPORTANCE SARS-CoV-2, which likely spilled over from wildlife, is the third highly pathogenic human coronavirus. Being highly transmissible, it is perpetuating a pandemic and continuously posing a severe threat to global public health. Several SARS-CoV-2-related coronaviruses (SARSr-CoV-2) in bats and pangolins have been identified since the SARS-CoV-2 outbreak. It is therefore important to assess their potential of crossing species barriers for better understanding of their risk of future emergence. In this work, we investigated the biological features and pathogenicity of a SARSr-CoV-2 strain isolated from a smuggled Malayan pangolin, named MpCoV-GX. We found that MpCoV-GX can utilize ACE2 from 7 species for cell entry and infect cell lines derived from a variety of mammalian species. MpCoV-GX can infect mice expressing human ACE2 without causing severe disease. These findings suggest the potential of cross-species transmission of MpCoV-GX, and highlight the need of further surveillance of SARSr-CoV-2 in pangolins and other potential animal hosts.

A deep learning model to identify gene expression level using cobinding transcription factor signals.

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confirmed that some TFs have capability to interact with methylated DNA fragments to further regulate gene expression. Although biochemical experiments could recognize TFs binding to methylated DNA sequences, these wet experimental methods are time-consuming and expensive. Machine learning methods provide a good choice for quickly identifying these TFs without experimental materials. Thus, this study aims to design a robust predictor to detect methylated DNA-bound TFs. We firstly proposed using tripeptide word vector feature to formulate protein samples. Subsequently, based on recurrent neural network with long short-term memory, a two-step computational model was designed. The first step predictor was utilized to discriminate transcription factors from non-transcription factors. Once proteins were predicted as TFs, the second step predictor was employed to judge whether the TFs can bind to methylated DNA. Through the independent dataset test, the accuracies of the first step and the second step are 86.63% and 73.59%, respectively. In addition, the statistical analysis of the distribution of tripeptides in training samples showed that the position and number of some tripeptides in the sequence could affect the binding of TFs to methylated DNA. Finally, on the basis of our model, a free web server was established based on the proposed model, which can be available at https://bioinfor.nefu.edu.cn/TFPM/.

PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization.

Many studies have proved that small nucleolar RNAs (snoRNAs) play critical roles in the development of various human complex diseases. Discovering the associations between snoRNAs and diseases is an important step toward understanding the pathogenesis and characteristics of diseases. However, uncovering associations via traditional experimental approaches is costly and time-consuming. This study proposed a bounded nuclear norm regularization-based method, called PSnoD, to predict snoRNA-disease associations. Benchmark experiments showed that compared with the state-of-the-art methods, PSnoD achieved a superior performance in the 5-fold stratified shuffle split. PSnoD produced a robust performance with an area under receiver-operating characteristic of 0.90 and an area under precision-recall of 0.55, highlighting the effectiveness of our proposed method. In addition, the computational efficiency of PSnoD was also demonstrated by comparison with other matrix completion techniques. More importantly, the case study further elucidated the ability of PSnoD to screen potential snoRNA-disease associations. The code of PSnoD has been uploaded to https://github.com/linDing-groups/PSnoD. Based on PSnoD, we established a web server that is freely accessed via http://psnod.lin-group.cn/.

iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism.

The location of microRNAs (miRNAs) in cells determines their function in regulation activity. Studies have shown that miRNAs are stable in the extracellular environment that mediates cell-to-cell communication and are located in the intracellular region that responds to cellular stress and environmental stimuli. Though in situ detection techniques of miRNAs have made great contributions to the study of the localization and distribution of miRNAs, miRNA subcellular localization and their role are still in progress. Recently, some machine learning-based algorithms have been designed for miRNA subcellular location prediction, but their performance is still far from satisfactory. Here, we present a new data partitioning strategy that categorizes functionally similar locations for the precise and instructive prediction of miRNA subcellular location in Homo sapiens. To characterize the localization signals, we adopted one-hot encoding with post padding to represent the whole miRNA sequences, and proposed a deep bidirectional long short-term memory with the multi-head self-attention algorithm to model. The algorithm showed high selectivity in distinguishing extracellular miRNAs from intracellular miRNAs. Moreover, a series of motif analyses were performed to explore the mechanism of miRNA subcellular localization. To improve the convenience of the model, a user-friendly web server named iLoc-miRNA was established (http://iLoc-miRNA.lin-group.cn/).

iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice.

Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS.