Prediction Of Protein-protein Interactions Research Articles

SWATH-MS based proteomics reveals the role of photosynthesis related proteins and secondary metabolic pathways in the colored leaves of sweet olive (Osmanthus fragrans)

Colored leaves, a notable horticultural trait, have high research and ornamental value. The evergreen sweet olive (Osmanthus fragrans), one of the top ten traditional flowers in China, has been cultivated for more than two thousand years. However, in recent years, an increasing number of O. fragrans cultivars with colored leaves have been cultivated for their ornamental value. To study the molecular mechanism underlying the observed changes in leaf color, we selected O. fragrans ‘Yinbi Shuanghui’ (Y), which has yellow-white leaves, and O. fragrans ‘Sijigui’ (S), which has green leaves, as materials. Pigment content measurement showed that the chlorophyll, carotenoid and anthocyanin contents in Y were lower than in S. According to the SWATH-MS sequencing results, a total of 3,959 proteins were quantitatively identified, 1,300 of which were differentially expressed proteins (DEPs), including 782 up-regulated and 518 down-regulated proteins in Y compared to S. Functional enrichment analysis of DEPs revealed that down-regulated expression of photosynthesis related proteins may lead to the inhibition of chlorophyll synthesis in Y, this may be the main cause of leaf color change. Moreover, a protein interaction prediction model also showed that proteins such as PetC, PsbO, PsbP, and PsbQ were key proteins in the interaction network, and the up-regulated proteins participating in the anthocyanin and carotenoid pathways may be related to the formation of yellow-white leaves. Taken together, our findings represent the first SWATH-MS-based proteomic report on colored leaf O. fragrans and reveal that chlorophyll synthesis and secondary metabolism pathways contribute to the changes in leaf color.

Genome-wide identification and expression analysis of two-component system genes in switchgrass (Panicum virgatum L.)

The two-component system (TCS) consists of histidine kinase (HK), histidine phosphate transfer protein (HP), and response regulatory factor (RR). It is one of the most crucial components of signal transduction in plants, playing a significant role in regulating plant growth, development, and responses to various abiotic stresses. Although TCS genes have been extensively identified in a variety of plants, the genome-wide recognition and examination of TCS in switchgrass remain unreported. Accordingly, this study identified a total of 87 TCS members in the genome of switchgrass, comprising 20 HK(L)s, 10 HPs, and 57 RRs. Detailed analyses were also conducted on their gene structures, conserved domains, and phylogenetic relationships. Moreover, this study analysed the gene expression profiles across diverse organs and investigated their response patterns to adverse environmental stresses. Results revealed that 87 TCS genes were distributed across 18 chromosomes, with uneven distribution. Expansion of these genes in switchgrass was achieved through both fragment and tandem duplication. PvTCS members are relatively conservative in the evolutionary process, but the gene structure varies significantly. Various cis-acting elements, varying in types and amounts, are present in the promoter region of PvTCSs, all related to plant growth, development, and abiotic stress, due to the TCS gene structure. Protein-protein interaction and microRNA prediction suggest complex interactions and transcriptional regulation among TCS members. Additionally, most TCS members are expressed in roots and stems, with some genes showing organ-specific expression at different stages of leaf and inflorescence development. Under conditions of abiotic stress such as drought, low temperature, high temperature, and salt stress, as well as exogenous abscisic acid (ABA), the expression of most TCS genes is either stimulated or inhibited. Our systematic analysis could offer insight into the characterization of the TCS genes, and further the growth of functional studies in switchgrass.

SVOH: Rigorous Selection Approach for Optimal Hyperparameter Values

The problem we address in this paper is a model selection problem. We consider the k-fold cross-validation (KCV) technique, applied to the Gaussian support vector machine (SVM) classification algorithm. In the cross-vali- dation process, the value of k for the number of subsets is generally chosen and set aprioristically (without any ex- periment). However, the value of k affects the choice of the best compromise between the estimation error and the ap- proximation error of the model. In this way, the k value of the number of subsets can severely influence the optimal values of the SVM classifier's hyperparameters and conse- quently affect the performance of the selected model and its ability to generalize. In this work, we propose a rigorous approach for finding the values of the hyperparameters of the Gaussian SVM known as SVOH (Selection of Optimal Hyperparam- eter Values) in a context of protein-protein interaction (PPI) prediction, where it is necessary to classify the pairs of pro- teins that interact together and those that do not interact together. The proposed approach considers the k value of the number of subsets as an influential parameter of the model and therefore performs learning to find an optimal value of k.

Ensemble learning model for Protein-Protein interaction prediction with multiple Machine learning techniques

Protein-Protein Interactions (PPIs) encompass the physical interactions or chemical combinations between proteins, which cells employ to carry out diverse biological functions. PPIs Prediction is of great significance for understanding and studying molecular mechanisms and disease mechanisms in organisms. Traditional machine learning algorithms for predicting PPIs face challenges related to imbalanced sample data, loss of long sequence features, and variations in datasets across different species. This paper takes advantage of the attention mechanism in processing sequence tasks and proposes the fusion of Bi-directional Long Short- Term Memory, an integrated learning model of convolutional neural network and multi-head attention mechanism, referred to as CNN BiLSTM Multi-head Att. The model first performs one-hot encoding on the protein sequence and obtains a low-dimensional word vector representation, and then calculates the encoding matrix through the embedding matrix; it uses the convolutional neural network (CNN) and the bidirectional long short-term memory network (Bi-LSTM) to extract the amino acids in the protein sequence. The feature matrix is obtained by collecting information on the time, site, and physical and chemical characteristics; the multi-head attention mechanism is used in weighted calculation and merger of the subspace features of each sequence, and classification is performed after global average pooling and the PPI prediction is output. Cross-validation experiments on different proportions of positive and negative protein data sets and proteins of different species show that CNN BiLSTM Multi-head Att can better predict imbalanced data sets and protein interactions of different species. Compared to the state-of-the-art model, our model improves accuracy by 0.025, precision by 0.046, F1-score by 0.052, and recall by 0.014. Exploring the scalability and adaptability of the proposed model for large-scale biological datasets, along with investigating its potential in real-time applications, could lead to significant advancements in the field of protein–protein interaction prediction.

Comprehensive Analysis of BrDUF506 Genes Across the Brassica rapa Genome Uncovers Potential Functions in Sexual Reproduction and Abiotic Stress Tolerance.

The Domain of Unknown Function 506 (DUF506) belongs to the PD-(D/E) XK nuclease superfamily and has been reported to play critical roles in growth and development as well as responses to abiotic stresses. However, the function of DUF506 genes in Brassica rapa (B. rapa) remains unclear. In this study, a total of 18 BrDUF506 genes were identified and randomly distributed across eight chromosomes, categorized into four subfamilies. Analyzing their promoter sequences has uncovered various stress-responsive elements, such as those for drought, methyl jasmonate (MeJA), and abscisic acid (ABA). Bra000098 and Bra017099 exhibit significantly enhanced expression in response to heat and drought stress. Protein interaction predictions indicate that Bra000098 homolog, At2g38820, is interacting with ERF012 and PUB48 and is involved in abiotic stress regulation. Furthermore, gene expression profiling has identified Bra026262 with a high expression level in flowers and significantly decreased in female sterile mutants. Protein interaction prediction further revealed that its homolog, At4g32480, interacts with MYB and AGL proteins, suggesting the potential roles in female gametophyte development. The current study enhances our understanding of the functional roles of BrDUF506s, providing significant insights that are valuable in investigating sexual reproduction and abiotic stress responses in B. rapa.

Genome-wide profiling of bZIP transcription factors in Camelina sativa: implications for development and stress response

BackgroundThe bZIP transcription factor family, characterized by a bZIP domain, plays vital roles in plant stress responses and development. While this family has been extensively studied in various plant species, its specific functions in Camelina sativa (False Flax) remain underexplored.Methods and resultsThis study identified 71 bZIP transcription factors in C. sativa, classified into nine distinct groups based on phylogenetic analysis. Subcellular localization predicted a nucleus-specific expression for these bZIPs. Analysis of GRAVY scores revealed a range from 0.469 to -1.256, indicating a spectrum from hydrophobic to hydrophilic properties. Motif analysis uncovered 10 distinct motifs, with one motif being universally present in all CsbZIPs. Conserved domain analysis highlighted several domains beyond the core bZIP domain. Protein-protein interaction predictions suggested a robust network involving CsbZIPs. Moreover, promoter analysis revealed over 60 types of cis-elements, including those responsive to stress. Expression studies through RNA-seq and Real-time RT-qPCR demonstrated high expression of CsbZIPs in roots, leaves, flowers, and stems. Specifically, CsbZIP01, CsbZIP02, CsbZIP44, and CsbZIP60 were consistently up-regulated under cold, salt, and drought stresses, whereas CsbZIP34 and CsbZIP35 were down-regulated.ConclusionThis study presents the first comprehensive genome-wide profiling of bZIP transcription factors in Camelina sativa, providing novel insights into their roles in plant development and stress response mechanisms. By identifying and characterizing the bZIP gene family in C. sativa, this research offers new opportunities for improving stress tolerance and crop resilience through targeted genetic approaches, addressing key challenges in agriculture under changing environmental conditions.

Cimicifuga heracleifolia kom. Attenuates ulcerative colitis through the PI3K/AKT/NF-κB signaling pathway

Ethnopharmacological relevanceCimicifuga heracleifolia Kom. (C. heracleifolia) has demonstrated efficacy in treating gastrointestinal disorders, including splenasthenic diarrhea. Ulcerative colitis (UC), a chronic inflammatory bowel disease, shares similarities with splenasthenic diarrhea. However, the pharmacological effects of C. heracleifolia on UC and the underlying mechanisms remain unexplored. Aim of the studyThe present study investigates the therapeutic potential and mechanisms of C. heracleifolia in UC. MethodsInitially, network pharmacology analysis, encompassing ingredient screening, target prediction, protein-protein interaction (PPI) network analysis, and enrichment analysis, was employed to predict the mechanisms of C. heracleifolia. The findings were further validated using transcriptomics and functional assays in a dextran sulfate sodium (DSS)-induced UC model. Additionally, bioactive compounds were identified through surface plasmon resonance (SPR) analysis, molecular docking, and cell-based assays. ResultsA total of 52 ingredients of C. heracleifolia were screened, and 32 key targets were identified within a PPI network comprising 285 potential therapeutic targets. Enrichment analysis indicated that the anti-UC effects of C. heracleifolia are mediated through immune response modulation and the inhibition of inflammatory signaling pathways. In vivo experiments showed that C. heracleifolia mitigated histological damage in the colon, reduced the expression of phosphorylated Akt1, nuclear factor-kappa B (NF-κB) p65, and inhibitor of Kappa B kinase α/β (IKKα/β), suppressed the content of interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), and enhanced the expression of tight junction proteins. Moreover, cimigenoside, caffeic acid, and methyl caffeate were identified as the bioactive constituents responsible for the UC treatment effects of C. heracleifolia. ConclusionsIn summary, this study is the first to demonstrate that C. heracleifolia exerts therapeutic effects on UC by enhancing the intestinal mucosal barrier and inhibiting the phosphatidylinositol 3-kinase (PI3K)/AKT/NF-κB signaling pathway. These findings offer valuable insights into the clinical application of C. heracleifolia for UC management.

Combining handcrafted and learned features using deep learning to improve protein-protein interaction prediction performance

ABSTRACT Understanding protein-protein interactions (PPIs) is essential because knowledge regarding PPIs helps in determining the biochemical functions of organisms. Advances made in machine learning techniques, for example, DeepFE-PPI, GcForest-PPI, and DeepPPI, have been applied to enhance PPI prediction performance. However, most methods use either handcrafted or learned features. Improving protein representation quality can significantly impact PPI model performance. This study aims to propose a deep neural network-based model, in which both handcrafted and learned features are fused to improve PPI prediction. In our method, the handcrafted features are computed by sequence descriptors, including Amino Acid Composition (AAC), Local Descriptors, Pseudo-AAC, Amphiphilic Pseudo-AAC, and Quasi-Sequence-Order. The learned features are generated by Word2vec after being trained on a large database of protein sequences. Standard evaluation methods and benchmark datasets are used to assess the proposed model. The results show that our model achieves 95.8% and 99.3% accuracy on the Saccharomyces cerevisiae and Human datasets, respectively. Additionally, the generalizability of our model is evaluated through independent tests, and our findings indicate that the proposed feature combination method correctly classifies experimentally validated interactions in eight datasets, including cross-species and PPI networks. The source code and data for this work are included at https://github.com/npxquynhdhsp/ComFeatPPI.git.

Predicting protein interactions of the kinase Lck critical to T cell modulation

Protein-protein interactions (PPIs) play pivotal roles in directing Tcell fate. One key player is the non-receptor tyrosine protein kinase Lck that helps to transduce Tcell activation signals. Lck is mediated by other proteins via interactions that are inadequately understood. Here, we use the deep learning method AF2Complex to predict PPIs involving Lck, by screening it against ∼1,000 proteins implicated in immune responses, followed by extensive structural modeling for selected interactions. Remarkably, we describe how Lck may be specifically targeted by a palmitoyltransferase using a phosphotyrosine motif. We uncover "hotspot" interactions between Lck and the tyrosine phosphatase CD45, leading to a significant conformational shift of Lck for activation. Lastly, we present intriguing interactions between the phosphotyrosine-binding domain of Lck and the cytoplasmic tail of the immune checkpoint LAG3 and propose a molecular mechanism for its inhibitory role. Together, this multifaceted study provides valuable insights into Tcell regulation and signaling.

Pair-EGRET: Enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.

Proteins are responsible for most biological functions, many of which require the interaction of more than one protein molecule. However, accurately predicting protein-protein interaction (PPI) sites (the interfacial residues of a protein that interact with other protein molecules) remains a challenge. The growing demand and cost associated with the reliable identification of PPI sites using conventional experimental methods call for computational tools for automated prediction and understanding of PPIs. We present Pair-EGRET, an edge-aggregated graph attention network that leverages the features extracted from pre-trained transformer-like models to accurately predict PPI sites. Pair-EGRET works on a k-nearest neighbor graph, representing the three-dimensional structure of a protein, and utilizes the cross-attention mechanism for accurate identification of interfacial residues of a pair of proteins. Through an extensive evaluation study using a diverse array of experimental data, evaluation metrics, and case studies on representative protein sequences, we demonstrate that Pair-EGRET can achieve remarkable performance in predicting PPI sites. Moreover, Pair-EGRET can provide interpretable insights from the learned cross-attention matrix. Pair-EGRET is freely available in open source form at the GitHub Repository https://github.com/1705004/Pair-EGRET. Supplementary data are available at Bioinformatics online.

Anti-symmetric framework for balanced learning of protein-protein interactions.

Protein-protein interactions (PPIs) are essential for the regulation and facilitation of virtually all biological processes. Computational tools, particularly those based on deep learning, are preferred for the efficient prediction of PPIs. Despite recent progress, two challenges remain unresolved: (i) the imbalanced nature of PPI characteristics is often ignored and (ii) there exists a high computational cost associated with capturing long-range dependencies within protein data, typically exhibiting quadratic complexity relative to the length of the protein sequence. Here, we propose an anti-symmetric graph learning model, BaPPI, for the balanced prediction of PPIs and extrapolation of the involved patterns in PPI network. In BaPPI, the contextualized information of protein data is efficiently handled by an attention-free mechanism formed by recurrent convolution operator. The anti-symmetric graph convolutional network is employed to model the uneven distribution within PPI networks, aiming to learn a more robust and balanced representation of the relationships between proteins. Ultimately, the model is updated using asymmetric loss. The experimental results on classical baseline datasets demonstrate that BaPPI outperforms four state-of-the-art PPI prediction methods. In terms of Micro-F1, BaPPI exceeds the second-best method by 6.5% on SHS27K and 5.3% on SHS148K. Further analysis of the generalization ability and patterns of predicted PPIs also demonstrates our model's generalizability and robustness to the imbalanced nature of PPI datasets. The source code of this work is publicly available at https://github.com/ttan6729/BaPPI.

Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network.

Nucleotide-binding leucine-rich repeat (NLR) family is a class of immune receptors capable of detecting and defending against pathogen invasion. They have been widely used in crop breeding. Notably, the correspondence between NLRs and effectors (CNE) determines the applicability and effectiveness of NLRs. Unfortunately, CNE data is very scarce. In fact, we've found a substantial 91291 NLRs confirmed via wet experiments and bioinformatics methods but only 387 CNEs are recognized, which greatly restricts the potential application of NLRs. We propose a deep learning algorithm called ProNEP to identify NLR-effector pairs in a high-throughput manner. Specifically, we conceptualized the CNE prediction task as a protein-protein interaction (PPI) prediction task. Then, ProNEP predicts the interaction between NLRs and effectors by combining the transfer learning with a bilinear attention network. ProNEP achieves superior performance against state-of-the-art models designed for PPI predictions. Based on ProNEP, we conduct extensive identification of potential CNEs for 91291 NLRs. With the rapid accumulation of genomic data, we expect that this tool will be widely used to predict CNEs in new species, advancing biology, immunology, and breeding. The ProNEP is available at http://nerrd.cn/#/prediction. The project code is available at https://github.com/QiaoYJYJ/ProNEP.

Identification of Homeobox Transcription Factors in a Dimorphic Fungus Talaromyces marneffei and Protein-Protein Interaction Prediction of RfeB.

Talaromyces marneffei is a thermally dimorphic fungus that can cause life-threatening systemic mycoses, particularly in immunocompromised individuals. Fungal homeobox transcription factors control various developmental processes, including the regulation of sexual reproduction, morphology, metabolism, and virulence. However, the function of homeobox proteins in T. marneffei has not been fully explored. Here, we searched the T. marneffei genome for the total homeobox transcription factors and predicted their biological relevance by performing gene expression analysis in different cell types, including conidia, mycelia, yeasts, and during phase transition. RfeB is selected for further computational analysis since (i) its transcripts were differentially expressed in different phases of T. marneffei, and (ii) this protein contains the highly conserved protein-protein interaction region (IR), which could be important for pathobiology and have therapeutic application. To assess the structure-function of the IR region, in silico alanine substitutions were performed at three-conserved IR residues (Asp276, Glu279, and Gln282) of RfeB, generating a triple RfeB mutated protein. Using 3D modeling and molecular dynamics simulations, we compared the protein complex formation of wild-type and mutated RfeB proteins with the putative partner candidate TmSwi5. Our results demonstrated that the mutated RfeB protein exhibited increased free binding energy, elevated protein compactness, and a reduced number of atomic contacts, suggesting disrupted protein stability and interaction. Notably, our model revealed that the IR residues primarily stabilized the RfeB binding sites located in the central region (CR). This computational approach for protein mutagenesis could provide a foundation for future experimental studies on the functional characterization of RfeB and other homeodomain-containing proteins in T. marneffei.

Genome-wide identification, expression pattern and interacting protein analysis of INDETERMINATE DOMAIN (IDD) gene family in Phalaenopsis equestris

The plant-specific INDETERMINATE DOMAIN (IDD) gene family is important for plant growth and development. However, a comprehensive analysis of the IDD family in orchids is limited. Based on the genome data of Phalaenopsis equestris, the IDD gene family was identified and analyzed by bioinformatics methods in this study. Ten putative P. equestris IDD genes (PeIDDs) were characterized and phylogenetically classified into two groups according to their full amino acid sequences. Protein motifs analysis revealed that overall structures of PeIDDs in the same group were relatively conserved. Its promoter regions harbored a large number of responsive elements, including light responsive, abiotic stress responsive elements, and plant hormone cis-acting elements. The transcript level of PeIDD genes under cold and drought conditions, and by exogenous auxin (NAA) and abscisic acid (ABA) treatments further confirmed that most PeIDDs responded to various conditions and might play essential roles under abiotic stresses and hormone responses. In addition, distinct expression profiles in different tissues/organs suggested that PeIDDs might be involved in various development processes. Furthermore, the prediction of protein-protein interactions (PPIs) revealed some PeIDDs (PeIDD3 or PeIDD5) might function via cooperating with chromatin remodeling factors. The results of this study provided a reference for further understanding the function of PeIDDs.

Evolution of Apoptotic Signaling Pathways Within Lophotrochozoans.

Apoptosis is the main form of regulated cell death in metazoans. Apoptotic pathways are well characterized in nematodes, flies, and mammals, leading to a vision of the conservation of apoptotic pathways in metazoans. However, we recently showed that intrinsic apoptosis is in fact divergent among metazoans. In addition, extrinsic apoptosis is poorly studied in non-mammalian animals, making its evolution unclear. Consequently, our understanding of apoptotic signaling pathways evolution is a black box which must be illuminated by extending research to new biological systems. Lophotrochozoans are a major clade of metazoans which, despite their considerable biological diversity and key phylogenetic position as sister group of ecdysozoans (i.e. flies and nematodes), are poorly explored, especially regarding apoptosis mechanisms. Traditionally, each apoptotic signaling pathway was considered to rely on a specific initiator caspase, associated with an activator. To shed light on apoptosis evolution in animals, we explored the evolutionary history of initiator caspases, caspase activators, and the BCL-2 family (which control mitochondrial apoptotic pathway) in lophotrochozoans using phylogenetic analysis and protein interaction predictions. We discovered a diversification of initiator caspases in molluscs, annelids, and brachiopods, and the loss of key extrinsic apoptosis components in platyhelminths, along with the emergence of a clade-specific caspase with an ankyrin pro-domain. Taken together, our data show a specific history of apoptotic actors' evolution in lophotrochozoans, further demonstrating the appearance of distinct apoptotic signaling pathways during metazoan evolution.

Exploring the Effect of Fidgetin-Like 1 on Colorectal Cancer Through Tissue Chip and In Vitro Experiments

Fidgetin-like 1 (FIGNL1) is extensively overexpressed in a variety of cancers. It facilitates non‑small cell lung cancer tumor cell proliferation and hepatocellular carcinoma formation due to abnormal DNA repair. Clinically relevant data indicates that its high expression is linked with the poor prognosis of patients with renal clear-cell carcinoma, low-grade gliomas, and hepatocellular carcinoma. Nevertheless, the scope of FIGNL1’s involvement in cancer, particularly colorectal cancer (CRC), remains unclear. To investigate the function of FIGNL1 in CRC. Cell culture study. The TCGA database and immunohistochemistry analysis were employed to investigate FIGNL1 expression in CRC tissue. A cell viability assay was performed using the Cell Counting Kit-8. The cell migration and invasion were evaluated using the transwell assay. Small interfering RNA (siRNA) transfection was conducted to knockdown FIGNL1 expression. Infection with FIGNL1 overexpression lentivirus was performed to promote FIGNL1 overexpression. The STRING database was employed for predicting protein interaction. FIGNL1 was substantially upregulated in human CRC tissues and was associated with TNM stages and lymph node metastasis in patients. The inhibition of CRC cell proliferation, migration, and invasion in Caco-2 cells was achieved by silencing FIGNL1 using siRNA. Additional investigations suggested that FIGNL1 overexpression could promote CRC cell proliferation, migration, and invasion via P38 signaling pathway activation in Colo-205 cells. Subsequent experiments demonstrated that FIGNL1-mediated P38 phosphorylation was contingent upon SPIDR interaction. These results implied that FIGNL1 was a potential anticancer drug target, which also offered a novel strategy for future CRC treatment.

Low CD3 level is a risk factor for amyotrophic lateral sclerosis: a Mendelian randomization study

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease characterized by neuronal degeneration of the spinal cord and brain and believed to be related to the immune system. In this study, our aim is to use Mendelian randomization (MR) to search for immune markers related to ALS. A total of 731 immune cell traits were included in this study. MR analysis was used to identify the causality between 731 immune cell traits (with 3,757 Europeans) and ALS (with 138,086 Europeans). Colocalization analysis was used to verify the found causality, protein–protein interaction prediction was used to look for the interacting proteins that are known to be involved in ALS. We found low expression levels of CD3 on central memory CD8+ T cell is risk factor for ALS (OR = 0.90, 95% CI: 0.86–0.95, P = 0.0000303). CD3 can interact with three ALS-related proteins: VCP, HLA-DRA and HLA-DRB5, which are associated with adaptive immune response. Our study reported for the first time that low-level CD3 is a risk factor for ALS and the possible mechanism, which could provide a potential strategy for ALS diagnosis and therapy.

Genome-Wide Identification and Expression Analysis of the Cyclic Nucleotide-Gated Channel Gene Family in Zoysia japonica under Salt Stress.

Salt stress severely inhibits plant growth. Understanding the mechanism of plant salt tolerance is highly important to improving plant salt tolerance. Previous studies have shown that nonselective cyclic nucleotide-gated ion channels (CNGCs) play an important role in plant salt tolerance. However, current research on CNGCs mainly focuses on CNGCs in glycophytic plants, and research on CNGCs in halophytes that exhibit special salt tolerance strategies is still scarce. This study used the halophilic plant Zoysia japonica, an excellent warm-season turfgrass, as the experimental material. Through bioinformatics analysis, 18 members of the CNGC family were identified in Zoysia japonica; they were designated ZjCNGC1 through ZjCNGC18 according to their scaffold-level chromosomal positions. ZjCNGCs are divided into four groups (I-IV), with the same groups having differentiated protein-conserved domains and gene structures. ZjCNGCs are unevenly distributed on 16 scaffold-level chromosomes. Compared with other species, the ZjCNGCs in Group III exhibit obvious gene expansion, mainly due to duplication of gene segments. The collinearity between ZjCNGCs, OsCNGCs, and SjCNGCs suggests that CNGCs are evolutionarily conserved among gramineous plants. However, the Group III ZjCNGCs are only partially collinear with OsCNGCs and SjCNGCs, implying that the expansion of Group III ZjCNGC genes may have been an independent event occurring in Zoysia japonica. Protein interaction prediction revealed that ZjCNGCs, calcium-dependent protein kinase, H+-ATPase, outwardly rectifying potassium channel protein, and polyubiquitin 3 interact with ZjCNGCs. Multiple stress response regulatory elements, including those involved in salt stress, are present on the ZjCNGC promoter. The qPCR results revealed differences in the expression patterns of ZjCNGCs in different parts of the plant. Under salt stress conditions, the expression of ZjCNGCs was significantly upregulated in roots and leaves, with ZjCNGC8 and ZjCNGC13 showing the greatest increase in expression in the roots. These results collectively suggest that ZjCNGCs play an important role in salt tolerance and that their expansion into Group III may be a special mechanism underlying the salt tolerance of Zoysia japonica.

Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process

Protein–protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks’ weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.

ESMDNN-PPI: a new protein–protein interaction prediction model developed with protein language model of ESM2 and deep neural network

ESMDNN-PPI: a new protein–protein interaction prediction model developed with protein language model of ESM2 and deep neural network