Transmembrane determinants of voltage-gating differences between BK (Slo1) and Slo3 channels.

Voltage-gated potassium channels are critical in modulating cellular excitability, with Slo (slowpoke) channels forming a unique family characterized by their large conductance and dual regulation by electrical signals and intracellular messengers. Despite their structural and evolutionary similarities, Slo1 and Slo3 channels exhibit significant differences in their voltage-gating properties. This study investigates the molecular determinants that differentiate the voltage-gating properties of human Slo1 and mouse Slo3 channels. Utilizing Slo1/Slo3 chimeras, we pinpointed the selectivity filter region as a key factor in the Slo3 channel's reduced conductance at negative voltages. The S6 transmembrane (TM) segment was identified as pivotal for the Slo3 channel's biphasic deactivation kinetics at these voltages. Additionally, the S4 and S6 TM segments were found to be responsible for the gradual slope in the Slo3 channel's conductance-voltage relationship, while multiple TM regions appear to be involved in the Slo3 channel's requirement of strong depolarization for activation. Mutations in the Slo1's S5 and S6 TM segments revealed three residues (I233, L302, and M304) that likely play a crucial role in the allosteric coupling between the voltage sensors and the pore gate.

3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction.

MOTIVATION: Drug-drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the combination medication or adverse reactions so as to avoid the side effects. Although many artificial intelligence methods predict and mine potential DDI, they ignore the 3D structure information of drug molecules and do not fully consider the contribution of molecular substructure in DDI. RESULTS: We proposed a new deep learning architecture, 3DGT-DDI, a model composed of a 3D graph neural network and pre-trained text attention mechanism. We used 3D molecular graph structure and position information to enhance the prediction ability of the model for DDI, which enabled us to deeply explore the effect of drug substructure on DDI relationship. The results showed that 3DGT-DDI outperforms other state-of-the-art baselines. It achieved an 84.48% macro F1 score in the DDIExtraction 2013 shared task dataset. Also, our 3D graph model proves its performance and explainability through weight visualization on the DrugBank dataset. 3DGT-DDI can help us better understand and identify potential DDI, thereby helping to avoid the side effects of drug mixing. AVAILABILITY: The source code and data are available at https://github.com/hehh77/3DGT-DDI.

FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction.

The prediction of drug-target affinity (DTA) plays an increasingly important role in drug discovery. Nowadays, lots of prediction methods focus on feature encoding of drugs and proteins, but ignore the importance of feature aggregation. However, the increasingly complex encoder networks lead to the loss of implicit information and excessive model size. To this end, we propose a deep-learning-based approach namely FusionDTA. For the loss of implicit information, a novel muti-head linear attention mechanism was utilized to replace the rough pooling method. This allows FusionDTA aggregates global information based on attention weights, instead of selecting the largest one as max-pooling does. To solve the redundancy issue of parameters, we applied knowledge distillation in FusionDTA by transfering learnable information from teacher model to student. Results show that FusionDTA performs better than existing models for the test domain on all evaluation metrics. We obtained concordance index (CI) index of 0.913 and 0.906 in Davis and KIBA dataset respectively, compared with 0.893 and 0.891 of previous state-of-art model. Under the cold-start constrain, our model proved to be more robust and more effective with unseen inputs than baseline methods. In addition, the knowledge distillation did save half of the parameters of the model, with only 0.006 reduction in CI index. Even FusionDTA with half the parameters could easily exceed the baseline on all metrics. In general, our model has superior performance and improves the effect of drug-target interaction (DTI) prediction. The visualization of DTI can effectively help predict the binding region of proteins during structure-based drug design.

NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug-target binding affinity prediction.

MOTIVATION: Large-scale prediction of drug-target affinity (DTA) plays an important role in drug discovery. In recent years, machine learning algorithms have made great progress in DTA prediction by utilizing sequence or structural information of both drugs and proteins. However, sequence-based algorithms ignore the structural information of molecules and proteins, while graph-based algorithms are insufficient in feature extraction and information interaction. RESULTS: In this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for interpretable DTA prediction. It can adaptively acquire feature representations of drugs and proteins and allow information to interact at the graph level, effectively combining the advantages of both sequence-based and graph-based approaches. Experimental results have shown that NHGNN-DTA achieved new state-of-the-art performance. It achieved the mean squared error (MSE) of 0.196 on the Davis dataset (below 0.2 for the first time) and 0.124 on the KIBA dataset (3% improvement). Meanwhile, in the case of cold start scenario, NHGNN-DTA proved to be more robust and more effective with unseen inputs than baseline methods. Furthermore, the multi-head self-attention mechanism endows the model with interpretability, providing new exploratory insights for drug discovery. The case study on Omicron variants of SARS-CoV-2 illustrates the efficient utilization of drug repurposing in COVID-19. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/hehh77/NHGNN-DTA.

MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma.

OBJECTIVES: It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC. METHODS: A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model. RESULTS: This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000). CONCLUSION: The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC. CLINICAL RELEVANCE STATEMENT: This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making. KEY POINTS: • It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.

The leucine-rich repeat domains of BK channel auxiliary γ subunits regulate their expression, trafficking, and channel-modulation functions.

As high-conductance calcium- and voltage-dependent potassium channels, BK channels consist of pore-forming, voltage-, and Ca2+-sensing α and auxiliary subunits. The leucine-rich repeat (LRR) domain-containing auxiliary γ subunits potently modulate the voltage dependence of BK channel activation. Despite their dominant size in whole protein masses, the function of the LRR domain in BK channel γ subunits is unknown. We here investigated the function of these LRR domains in BK channel modulation by the auxiliary γ1-3 (LRRC26, LRRC52, and LRRC55) subunits. Using cell surface protein immunoprecipitation, we validated the predicted extracellular localization of the LRR domains. We then refined the structural models of mature proteins on the membrane via molecular dynamic simulations. By replacement of the LRR domain with extracellular regions or domains of non-LRR proteins, we found that the LRR domain is nonessential for the maximal channel-gating modulatory effect but is necessary for the all-or-none phenomenon of BK channel modulation by the γ1 subunit. Mutational and enzymatic blockade of N-glycosylation in the γ1-3 subunits resulted in a reduction or loss of BK channel modulation by γ subunits. Finally, by analyzing their expression in whole cells and on the plasma membrane, we found that blockade of N-glycosylation drastically reduced total expression of the γ2 subunit and the cell surface expression of the γ1 and γ3 subunits. We conclude that the LRR domains play key roles in the regulation of the expression, cell surface trafficking, and channel-modulation functions of the BK channel γ subunits.

Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

With the rapid development of proteomics and the rapid increase of target molecules for drug action, computer-aided drug design (CADD) has become a basic task in drug discovery. One of the key challenges in CADD is molecular representation. High-quality molecular expression with chemical intuition helps to promote many boundary problems of drug discovery. At present, molecular representation still faces several urgent problems, such as the polysemy of substructures and unsmooth information flow between atomic groups. In this research, we propose a deep contextualized Bi-LSTM architecture, Mol2Context-vec, which can integrate different levels of internal states to bring dynamic representations of molecular substructures. And the obtained molecular context representation can capture the interactions between any atomic groups, especially a pair of atomic groups that are topologically distant. Experiments show that Mol2Context-vec achieves state-of-the-art performance on multiple benchmark datasets. In addition, the visual interpretation of Mol2Context-vec is very close to the structural properties of chemical molecules as understood by humans. These advantages indicate that Mol2Context-vec can be used as a reliable and effective tool for molecular expression. Availability: The source code is available for download in https://github.com/lol88/Mol2Context-vec.

Predicting Drug-Target Interactions with Deep-Embedding Learning of Graphs and Sequences.

Computational approaches for predicting drug-target interactions (DTIs) play an important role in drug discovery since conventional screening experiments are time-consuming and expensive. In this study, we proposed end-to-end representation learning of a graph neural network with an attention mechanism and an attentive bidirectional long short-term memory (BiLSTM) to predict DTIs. For efficient training, we introduced a bidirectional encoder representations from transformers (BERT) pretrained method to extract substructure features from protein sequences and a local breadth-first search (BFS) to learn subgraph information from molecular graphs. Integrating both models, we developed a DTI prediction system. As a result, the proposed method achieved high performances with increases of 2.4% and 9.4% for AUC and recall, respectively, on unbalanced datasets compared with other methods. Extensive experiments showed that our model can relatively screen potential drugs for specific protein. Furthermore, visualizing the attention weights provides biological insight.

A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potential inhibitors from Traditional Chinese Medicine Database. The potent top 10 compounds were selected as candidates by Dock Score. In order to further screen the candidates, we used numbers of machine learning regression models containing support vector machines, bagging, random forest and other regression algorithms, as well as deep neural network models to predict the activity of the candidates. In addition, as a traditional method, 2D QSAR (multiple linear regression) and 3D QSAR methods are also applied. The AI methods got a better performance than the traditional 2D QSAR method. Moreover, we also built a framework composed of deep neural networks and transformer to predict the binding affinity of candidates and DPP4. Artificial intelligence methods and QSAR models illustrated the compound, 2007_4105, was a potent inhibitor. The 2007_4105 compound was finally validated by molecular dynamics simulations. Combining all the models and algorithms constructed and the results, Hypecoum leptocarpum might be a potential and effective medicine herb for the treatment of DM.

Comprehensive Transcriptome Reveals an Opposite Regulatory Effect of Plant Growth Retardants in Controlling Seedling Overgrowth between Roots and Shoots.

Seedling overgrowth always develops in undernourished plants due to biotic or abiotic stresses, which significantly decrease the yield of crops and vegetables. It is known that the plant growth retardants paclobutrazol (PBZ) and chlormequat chloride (CCC) are the most commonly used chemicals in controlling seedling height in plants by regulating the gibberellin (GA) biosynthesis pathway. However, the exact molecular regulation mechanism remains largely unknown. This study performed a comprehensive transcriptome profile to identify significantly differentially expressed genes after adding CCC and PBZ to the water culture seedling raising system for the first time. According to the obviously restrained shoots and roots, the GA biosynthesis genes were significantly decreased, as well as the endogenous GA content being reduced. Intriguingly, the GA signaling pathway genes were affected in opposite ways, increasing in roots but decreasing in shoots, especially regarding the phytochrome interacting factor SlPIF1 and the downstream genes expansins (SlEXPs), which promote cell wall remodeling. Further study found that the most down-regulated genes SlEXPA5 and SlEXPA15 were expressed specifically in shoot tissue, performing the function of repressing elongation, while the up-regulated genes SlEXPB2 and SlEXPB8 were proven to be root-specific expressed genes, which may promote horizontal elongation in roots. This research reported the comprehensive transcriptome profiling of plant growth retardants in controlling seedling overgrowth and restraining GA biosynthesis through the regulation of the GA signaling-related genes SlPIF1 and SlEXPs, with an opposite expression pattern between roots and shoots.

In vivo phosphoproteome characterization reveals key starch granule-binding phosphoproteins involved in wheat water-deficit response.

BACKGROUND: Drought stress during grain development causes significant yield loss in cereal production. The phosphorylated modification of starch granule-binding proteins (SGBPs) is an important mechanism regulating wheat starch biosynthesis. In this study, we performed the first proteomics and phosphoproteomics analyses of SGBPs in elite Chinese bread wheat (Triticum aestivum L.) cultivar Jingdong 17 under well-watered and water-stress conditions. RESULTS: Water stress treatment caused significant reductions in spike grain numbers and weight, total starch and amylopectin content, and grain yield. Two-dimensional gel electrophoresis revealed that the quantity of SGBPs was reduced significantly by water-deficit treatment. Phosphoproteome characterization of SGBPs under water-deficit treatment demonstrated a reduced level of phosphorylation of main starch synthesis enzymes, particularly for granule-bound starch synthase (GBSS I), starch synthase II-a (SS II-a), and starch synthase III (SS III). Specifically, the Ser34 site of the GBSSI protein, the Tyr358 site of SS II-a, and the Ser837 site of SS III-a exhibited significant less phosphorylation under water-deficit treatment than well-watered treatment. Furthermore, the expression levels of several key genes related with starch biosynthesis detected by qRT-PCR were decreased significantly at 15 days post-anthesis under water-deficit treatment. Immunolocalization showed a clear movement of GBSS I from the periphery to the interior of starch granules during grain development, under both water-deficit and well-watered conditions. CONCLUSIONS: Our results demonstrated that the reduction in gene expression or transcription level, protein expression and phosphorylation levels of starch biosynthesis related enzymes under water-deficit conditions is responsible for the significant decrease in total starch content and grain yield.

Transcriptome analysis of mud crab (Scylla paramamosain) gills in response to Mud crab reovirus (MCRV).

Mud crab (Scylla paramamosain) is an economically important marine cultured species in China's coastal area. Mud crab reovirus (MCRV) is the most important pathogen of mud crab, resulting in large economic losses in crab farming. In this paper, next-generation sequencing technology and bioinformatics analysis are used to study transcriptome differences between MCRV-infected mud crab and normal control. A total of 104.3 million clean reads were obtained, including 52.7 million and 51.6 million clean reads from MCRV-infected (CA) and controlled (HA) mud crabs respectively. 81,901, 70,059 and 67,279 unigenes were gained respectively from HA reads, CA reads and HA&CA reads. A total of 32,547 unigenes from HA&CA reads called All-Unigenes were matched to at least one database among Nr, Nt, Swiss-prot, COG, GO and KEGG databases. Among these, 13,039, 20,260 and 11,866 unigenes belonged to the 3, 258 and 25 categories of GO, KEGG pathway, and COG databases, respectively. Solexa/Illumina's DGE platform was also used, and about 13,856 differentially expressed genes (DEGs), including 4444 significantly upregulated and 9412 downregulated DEGs were detected in diseased crabs compared with the control. KEGG pathway analysis revealed that DEGs were obviously enriched in the pathways related to different diseases or infections. This transcriptome analysis provided valuable information on gene functions associated with the response to MCRV in mud crab, as well as detail information for identifying novel genes in the absence of the mud crab genome database.

[Laparoscopic percutaneous extraperitoneal closure does not affect vas deferens orientation or testicular volume and perfusion].

OBJECTIVE: To investigate the influence of single-port laparoscopic percutaneous extraperitoneal closure (LPEC) on the orientation of the vas deferens and the volume and perfusion of the testis in pediatric patients undergoing inguinal hernia repair. METHODS: A total of 92 consecutively enrolled boys diagnosed with unilateral inguinal hernia underwent single-port LPEC between June 2013 and June 2014. The orientation of the vas deferens and the testicular volume and perfusion of the patients were ultrasonographically assessed preoperatively and at 1 and 6 months after surgery. RESULTS: All the surgical procedures were performed successfully without conversion or serious perioperative complications. Ultrasonography showed no angulation or distortion of the vas deferens on the surgical side during a six-month follow-up period. Similarly, no obvious changes were observed in the testicular volume or perfusion. CONCLUSIONS: Single-port LPEC is safe and effective in the treatment of pediatric inguinal hernia and does not affect the orientation of the vas deferens or testicular volume and perfusion.

An integrative proteome analysis of different seedling organs in tolerant and sensitive wheat cultivars under drought stress and recovery.

Roots, leaves, and intermediate sections between roots and leaves (ISRL) of wheat seedlings show different physiological functions at the protein level. We performed the first integrative proteomic analysis of different tissues of the drought-tolerant wheat cultivar Hanxuan 10 (HX-10) and drought-sensitive cultivar Chinese Spring (CS) during a simulated drought and recovery. Differentially expressed proteins (DEPs) in the roots (122), ISRLs (146), and leaves (163) showed significant changes in expression in response to drought stress and recovery. Numerous DEPs associated with cell defense and detoxifications were significantly regulated in roots and ISRLs, while in leaves, DEPs related to photosynthesis showed significant changes in expression. A significantly larger number of DEPs related to stress defense were upregulated in HX-10 than in CS. Expression of six HSPs potentially related to drought tolerance was significantly upregulated under drought conditions, and these proteins were involved in a complex protein-protein interaction network. Further phosphorylation analysis showed that the phosphorylation levels of HSP60, HSP90, and HOP were upregulated in HX-10 under drought stress. We present an overview of metabolic pathways in wheat seedlings based on abscisic acid signaling and important protein expression patterns.

N-linked glycoproteome profiling of seedling leaf in Brachypodium distachyon L.

Brachypodium distachyon L., a model plant for cereal crops, has become important as an alternative and potential biofuel grass. In plants, N-glycosylation is one of the most common and important protein modifications, playing important roles in signal recognition, increase in protein activity, stability of protein structure, and formation of tissues and organs. In this study, we performed the first glycoproteome analysis in the seedling leaves of B. distachyon. Using lectin affinity chromatography enrichment and mass-spectrometry-based analysis, we identified 47 glycosylation sites representing 46 N-linked glycoproteins. Motif-X analysis showed that two conserved motifs, N-X-T/S (X is any amino acid, except Pro), were significantly enriched. Further functional analysis suggested that some of these identified glycoproteins are involved in signal transduction, protein trafficking, and quality control and the modification and remodeling of cell-wall components such as receptor-like kinases, protein disulfide isomerase, and polygalacturonase. Moreover, transmembrane helices and signal peptide prediction showed that most of these glycoproteins could participate in typical protein secretory pathways in eukaryotes. The results provide a general overview of protein N-glycosylation modifications during the early growth of seedling leaves in B. distachyon and supplement the glycoproteome databases of plants.

iTRAQ-based quantitative proteome and phosphoprotein characterization reveals the central metabolism changes involved in wheat grain development.

BACKGROUND: Wheat (Triticum aestivum L.) is an economically important grain crop. Two-dimensional gel-based approaches are limited by the low identification rate of proteins and lack of accurate protein quantitation. The recently developed isobaric tag for relative and absolute quantitation (iTRAQ) method allows sensitive and accurate protein quantification. Here, we performed the first iTRAQ-based quantitative proteome and phosphorylated proteins analyses during wheat grain development. RESULTS: The proteome profiles and phosphoprotein characterization of the metabolic proteins during grain development of the elite Chinese bread wheat cultivar Yanyou 361 were studied using the iTRAQ-based quantitative proteome approach, TiO2 microcolumns, and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Among 1,146 non-redundant proteins identified, 421 showed at least 2-fold differences in abundance, and they were identified as differentially expressed proteins (DEPs), including 256 upregulated and 165 downregulated proteins. Of the 421 DEPs, six protein expression patterns were identified, most of which were up, down, and up-down expression patterns. The 421 DEPs were classified into nine functional categories mainly involved in different metabolic processes and located in the membrane and cytoplasm. Hierarchical clustering analysis indicated that the DEPs involved in starch biosynthesis, storage proteins, and defense/stress-related proteins significantly accumulated at the late grain development stages, while those related to protein synthesis/assembly/degradation and photosynthesis showed an opposite expression model during grain development. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis of 12 representative genes encoding different metabolic proteins showed certain transcriptional and translational expression differences during grain development. Phosphorylated proteins analyses demonstrated that 23 DEPs such as AGPase, sucrose synthase, Hsp90, and serpins were phosphorylated in the developing grains and were mainly involved in starch biosynthesis and stress/defense. CONCLUSIONS: Our results revealed a complex quantitative proteome and phosphorylation profile during wheat grain development. Numerous DEPs are involved in grain starch and protein syntheses as well as adverse defense, which set an important basis for wheat yield and quality. Particularly, some key DEPs involved in starch biosynthesis and stress/defense were phosphorylated, suggesting their roles in wheat grain development.

Molecular cloning, phylogenetic analysis, and expression profiling of endoplasmic reticulum molecular chaperone BiP genes from bread wheat (Triticum aestivum L.).

BACKGROUND: The endoplasmic reticulum chaperone binding protein (BiP) is an important functional protein, which is involved in protein synthesis, folding assembly, and secretion. In order to study the role of BiP in the process of wheat seed development, we cloned three BiP homologous cDNA sequences in bread wheat (Triticum aestivum), completed by rapid amplification of cDNA ends (RACE), and examined the expression of wheat BiP in wheat tissues, particularly the relationship between BiP expression and the subunit types of HMW-GS using near-isogenic lines (NILs) of HMW-GS silencing, and under abiotic stress. RESULTS: Sequence analysis demonstrated that all BiPs contained three highly conserved domains present in plants, animals, and microorganisms, indicating their evolutionary conservation among different biological species. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) revealed that TaBiP (Triticum aestivum BiP) expression was not organ-specific, but was predominantly localized to seed endosperm. Furthermore, immunolocalization confirmed that TaBiP was primarily located within the protein bodies (PBs) in wheat endosperm. Three TaBiP genes exhibited significantly down-regulated expression following high molecular weight-glutenin subunit (HMW-GS) silencing. Drought stress induced significantly up-regulated expression of TaBiPs in wheat roots, leaves, and developing grains. CONCLUSIONS: The high conservation of BiP sequences suggests that BiP plays the same role, or has common mechanisms, in the folding and assembly of nascent polypeptides and protein synthesis across species. The expression of TaBiPs in different wheat tissue and under abiotic stress indicated that TaBiP is most abundant in tissues with high secretory activity and with high proportions of cells undergoing division, and that the expression level of BiP is associated with the subunit types of HMW-GS and synthesis. The expression of TaBiPs is developmentally regulated during seed development and early seedling growth, and under various abiotic stresses.

Dynamic development of starch granules and the regulation of starch biosynthesis in Brachypodium distachyon: comparison with common wheat and Aegilops peregrina.

BACKGROUND: Thorough understanding of seed starch biosynthesis and accumulation mechanisms is of great importance for agriculture and crop improvement strategies. We conducted the first comprehensive study of the dynamic development of starch granules and the regulation of starch biosynthesis in Brachypodium distachyon and compared the findings with those reported for common wheat (Chinese Spring, CS) and Aegilops peregrina. RESULTS: Only B-granules were identified in Brachypodium Bd21, and the shape variation and development of starch granules were similar in the B-granules of CS and Bd21. Phylogenetic analysis showed that most of the Bd21 starch synthesis-related genes were more similar to those in wheat than in rice. Early expression of key genes in Bd21 starch biosynthesis mediate starch synthesis in the pericarp; intermediate-stage expression increases the number and size of starch granules. In contrast, these enzymes in CS and Ae. peregrina were mostly expressed at intermediate stages, driving production of new B-granules and increasing the granule size, respectively. Immunogold labeling showed that granule-bound starch synthase (GBSSI; related to amylose synthesis) was mainly present in starch granules: at lower levels in the B-granules of Bd21 than in CS. Furthermore, GBSSI was phosphorylated at threonine 183 and tyrosine 185 in the starch synthase catalytic domain in CS and Ae. peregrina, but neither site was phosphorylated in Bd21, suggesting GBSSI phosphorylation could improve amylose biosynthesis. CONCLUSIONS: Bd21 contains only B-granules, and the expression of key genes in the three studied genera is consistent with the dynamic development of starch granules. GBSSI is present in greater amounts in the B-granules of CS than in Bd21; two phosphorylation sites (Thr183 and Tyr185) were found in Triticum and Aegilops; these sites were not phosphorylated in Bd21. GBSSI phosphorylation may reflect its importance in amylose synthesis.

MFTrans: A multi-feature transformer network for protein secondary structure prediction.

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning-based multi-feature fusion network aimed at enhancing the precision and efficiency of Protein Secondary Structure Prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi-view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi-feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

PSSP-MFFNet: A Multifeature Fusion Network for Protein Secondary Structure Prediction.

Protein secondary structure prediction (PSSP) is a fundamental task in modern bioinformatics research and is particularly important for uncovering the functional mechanisms of proteins. To improve the accuracy of PSSP, various general and essential features generated from amino acid sequences are often used for predicting the secondary structure. In this paper, we propose PSSP-MFFNet, a deep learning-based multi-feature fusion network for PSSP, which incorporates a multi-view deep learning architecture with the multiple sequence alignment (MSA) Transformer to efficiently capture global and local features of protein sequences. In practice, PSSP-MFFNet adopts a multi-feature fusion strategy, integrating different features generated from protein sequences, including MSA, sequence information, evolutionary information, and hidden state information. Moreover, we employ the MSA Transformer to interleave row and column attention across the input MSA. A hybrid network architecture of convolutional neural networks and long short-term memory networks is applied to extract high-level features after feature fusion. Furthermore, we introduce a transformer encoder to enhance the extracted high-level features. Comparative experimental results on independent tests demonstrate that PSSP-MFFNet has excellent generalization ability, outperforming other state-of-the-art PSSP models by an average of 1% on public benchmarks, including CASP12, CASP13, CASP14, TEST2018, and CB513. Our method can contribute to a better understanding of the biological functions of proteins, which has significant implications for drug discovery, disease diagnosis, and protein engineering.