Prediction Of Protein-protein Interactions Research Articles

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

Genome-wide identification of oxidosqualene cyclase genes regulating natural rubber in Taraxacum kok-saghyz.

Nine TkOSC genes have been identified by genome-wide screening. Among them, TkOSC4-6 might be more crucial for natural rubber biosynthesis in Taraxacum kok-saghyz roots. Taraxacum kok-saghyz Rodin (TKS) roots contain large amounts of natural rubber, inulin, and valuable metabolites. Oxidosqualene cyclase (OSC) is a key member for regulating natural rubber biosynthesis (NRB) via the triterpenoid biosynthesis pathway. To explore the functions of OSC on natural rubber producing in TKS, its gene family members were identified in TKS genome via genome-wide screening. Nine TkOSCs were identified, which were mainly distributed in the cytoplasm. Their family genes experienced a neutral selection during the evolution process. Overall sequence homology analysis OSC proteins revealed 80.23% similarity, indicating a highly degree of conservation. Pairwise comparisons showed a multiple sequence similarity ranging from 57% to 100%. Protein interaction prediction revealed that TkOSCs may interact with baruol synthase, sterol 1,4-demethylase, lupeol synthase and squalene epoxidase. Phylogenetic analysis showed that OSC family proteins belong to two branches. TkOSC promoter regions contain cis-acting elements related to plant growth, stress response, hormones response and light response. Protein accumulation analysis demonstrated that TkOSC4, TkOSC5 and TkOSC6 proteins had strong expression levels in the root, latex and plumular axis. Comparison of gene expression patterns showed TkOSC1, TkOSC4, TkOSC5, TkOSC6, TkOSC7, TkOSC8 and TkOSC9 might be important in regulating NRB. Combination of gene and protein results revealed TkOSC4-6 might be more crucial, and the data might contribute to a more profound understanding of the roles of OSCs for NRB in TKS roots.

Accurate and efficient protein embedding using multi-teacher distillation learning.

Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due to their large number of parameters, which can reach millions or even billions. The growing availability of large-scale protein datasets and the need for efficient analysis tools have created a pressing demand for efficient protein embedding methods. We propose a novel protein embedding approach based on multi-teacher distillation learning, which leverages the knowledge of multiple pre-trained protein embedding models to learn a compact and informative representation of proteins. Our method achieves comparable performance to state-of-the-art methods while significantly reducing computational costs and resource requirements. Specifically, our approach reduces computational time by ∼70% and maintains ±1.5% accuracy as the original large models. This makes our method well-suited for large-scale protein analysis and enables the bioinformatics community to perform protein embedding tasks more efficiently. The source code of MTDP is available via https://github.com/KennthShang/MTDP.

Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention.

The physiological activities within cells are mainly regulated through protein-protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated.

Homeodomain leucine zipper (HD-ZIP) gene family in Apostasia shenzhenica Z. J. Liu & L. J. Chen (Orchidaceae): In silico identification and characterization

The homeodomain leucine zipper (HD-ZIP) gene family constitutes an important class of transcription factors found in plants that play crucial roles in various aspects of growth, development and stress regulation. Despite this, there have been minuscule reports on identification and characterization of HD-ZIP gene family in orchids. The present study identifies 31 putative genes (AsHD-ZIP) in Apostasia shenzhenica, using in silico approaches. All of the identified transcription factors were grouped into four subfamilies (HD-ZIP I to IV) based on the distribution of various conserved domains and motifs i.e., HD-ZIP I (homeodomain and leucine zipper), HD-ZIP II (homeodomain, leucine zipper and CPSCE), HD-ZIP III (homeodomain, leucine zipper, MEKHLA and START) and HD-ZIP IV (homeodomain, leucine zipper and START). Prediction of physicochemical properties, protein structures, protein-protein interactions, gene ontology, and gene structures further validated this classification. Phylogenetic analysis showed clustering of all subgroups separately along with their orthologs in Arabidopsis thaliana, Oryza sativa, Dendrobium catenatum and Phalaenopsis equestris. Furthermore, spatio-temporal expression profiling and presence of cis-regulatory elements confirmed tissue specific expression. As HD-ZIP genes are vital players in regulation of plant growth and development as well as in biotic and abiotic stress responses, this study provides a foundation for genetic improvement in orchids through functional validation and characterization of HD-ZIP gene family.

A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.

The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer and RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower quality display significant structural biases at the level of tertiary motifs (TERMs) toward having fewer structural matches in non-antibody-containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that the scarcity of interfacial geometry data in the structural database may currently limit the application of ML to the prediction of antibody-antigen interactions.

Predicting the Dynamic Interaction of Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) participate in various biological processes. Interactions involving IDPs are usually dynamic and are affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-embedded LSTM network, to capture the dynamic nature of IDP-involved interactions and predict their behaviors. This framework models multiple conformations of IDP as a dynamic graph, which can effectively describe the fluctuation of its flexible conformation. The dynamic interaction between IDPs is studied, and the data sets of IDP conformations and their interactions are obtained through atomistic molecular dynamic (MD) simulations. Residues of IDP are encoded through a series of features including their frustration. GSALIDP can effectively predict the interaction sites of IDP and the contact residue pairs between IDPs. Its performance in predicting IDP interactions is on par with or even better than the conventional models in predicting the interaction of structural proteins. To the best of our knowledge, this is the first model to extend the protein interaction prediction to IDP-involved interactions.

Funnel graph neural networks with multi-granularity cascaded fusing for protein–protein interaction prediction

The identification of potential protein–protein interactions (PPIs) between humans and viruses is crucial for comprehending viral infection and disease mechanisms at the molecular level. Recently, graph neural networks (GNNs) have emerged as a promising approach to expedite PPI identification. However, GNNs often suffer from over-smoothing when capturing high-order neighbor information. To tackle this issue and effectively capture implicit collaborative information from multi-hop neighbors, we propose FGNN, Funnel Graph Neural Networks with Multi-Granularity Cascaded Fusing, facilitating the distillation of information in a funnel-like manner. Specifically, it enables the mapping of information flow from the full graph to subgraphs and ultimately to nodes. By regarding subgraphs as bridges connecting higher-order neighbors, we ensure the projection of multi-hop neighbors into the same subspace, thereby achieving a comprehensive mapping of the full graph into subgraphs. Moreover, we employ an encoder equipped with a multi-head attention mechanism to effectively map subgraphs onto nodes, facilitating further refinement and compression of information derived from high-order neighbors. FGNN can effectively capture high-order neighbor information whilst relieving over-smoothing. Extensive experiments demonstrate FGNN is superior to the state-of-the-art methods in terms of AUC value. The achieved improvements in the four cardiovascular disease datasets are 7.96 %, 2.3 %, 2.49 %, and 0.82 %, respectively.

Genome-wide analysis of the NYN domain gene family in Brassica napus and its function role in plant growth and development

The NYN domain gene family consists of genes that encode ribonucleases that are characterized by a newly identified NYN domain. Members of the family were widely distributed in all life kingdoms and play a crucial role in various RNA regulation processes, although the wide genome overview of the NYN domain gene family is not yet available in any species. Rapeseed (Brassica napus L.), a polyploid model species, is an important oilseed crop. Here, the phylogenetic analysis of these BnaNYNs revealed five distinct groups strongly supported by gene structure, conserved domains, and conserved motifs. The survey of the expansion of the gene family showed that the birth of BnaNYNs is explained by various duplication events. Furthermore, tissue-specific expression analysis, protein–protein interaction prediction, and cis-element prediction suggested a role for BnaNYNs in plant growth and development. Interestingly, the data showed that three tandem duplicated BnaNYNs (TDBs) exhibited distinct expression patterns from those other BnaNYNs and had a high similarity in protein sequence level. Furthermore, the analysis of one of these TDBs, BnaNYN57, showed that overexpression of BnaNYN57 in Arabidopsis thaliana and B. napus accelerated plant growth and significantly increased silique length, while RNA interference resulted in the opposite growth pattern. It suggesting a key role for the TDBs in processes related to plant growth and development.

Computational analysis of pathogen-host interactome for fast and low-risk in-silico drug repurposing in emerging viral threats like Mpox

Monkeypox (Mpox), a zoonotic illness triggered by the monkeypox virus (MPXV), poses a significant threat since it may be transmitted and has no cure. This work introduces a computational method to predict Protein–Protein Interactions (PPIs) during MPXV infection. The objective is to discover prospective drug targets and repurpose current potential Food and Drug Administration (FDA) drugs for therapeutic purposes. In this work, ensemble features, comprising 2–5 node graphlet attributes and protein composition-based features are utilized for Deep Learning (DL) models to predict PPIs. The technique that is used here demonstrated an excellent prediction performance for PPI on both the Human Integrated Protein–Protein Interaction Reference (HIPPIE) and MPXV-Human PPI datasets. In addition, the human protein targets for MPXV have been identified accurately along with the detection of possible therapeutic targets. Furthermore, the validation process included conducting docking research studies on potential FDA drugs like Nicotinamide Adenine Dinucleotide and Hydrogen (NADH), Fostamatinib, Glutamic acid, Cannabidiol, Copper, and Zinc in DrugBank identified via research on drug repurposing and the Drug Consensus Score (DCS) for MPXV. This has been achieved by employing the primary crystal structures of MPXV, which are now accessible. The docking study is also supported by Molecular Dynamics (MD) simulation. The results of our study emphasize the effectiveness of using ensemble feature-based PPI prediction to understand the molecular processes involved in viral infection and to aid in the development of repurposed drugs for emerging infectious diseases such as, but not limited to, Mpox. The source code and link to data used in this work is available at: https://github.com/CMATERJU-BIOINFO/In-Silico-Drug-Repurposing-Methodology-To-Suggest-Therapies-For-Emerging-Threats-like-Mpox.

Genome-Wide Identification, Evolution, and Expression Analysis of the Dirigent Gene Family in Cassava (Manihot esculenta Crantz)

Dirigent (DIR) genes play a pivotal role in plant development and stress adaptation. Manihot esculenta Crantz, commonly known as cassava, is a drought-resistant plant thriving in tropical and subtropical areas. It is extensively utilized for starch production, bioethanol, and animal feed. However, a comprehensive analysis of the DIR family genes remains unexplored in cassava, a crucial cash and forage crop in tropical and subtropical regions. In this study, we characterize a total of 26 cassava DIRs (MeDIRs) within the cassava genome, revealing their uneven distribution across 13 of the 18 chromosomes. Phylogenetic analysis classified these genes into four subfamilies: DIR-a, DIR-b/d, DIR-c, and DIR-e. Comparative synteny analysis with cassava and seven other plant species (Arabidopsis (Arabidopsis thaliana), poplar (Populus trichocarpa), soybean (Glycine max), tomato (Solanum lycopersicum), rice (Oryza sativa), maize (Zea mays), and wheat (Triticum aestivum)) provided insights into their likely evolution. We also predict protein interaction networks and identify cis-acting elements, elucidating the functional differences in MeDIR genes. Notably, MeDIR genes exhibited specific expression patterns across different tissues and in response to various abiotic and biotic stressors, such as pathogenic bacteria, cadmium chloride (CdCl2), and atrazine. Further validation through quantitative real-time PCR (qRT-PCR) confirmed the response of DIR genes to osmotic and salt stress. These findings offer a comprehensive resource for understanding the characteristics and biological functions of MeDIR genes in cassava, enhancing our knowledge of plant stress adaptation mechanisms.

The Inhibitory Effects of Maclurin on Fatty Acid Synthase and Adipocyte Differentiation.

Obesity is a complex health condition characterized by excessive adipose tissue accumulation, leading to significant metabolic disturbances such as insulin resistance and cardiovascular diseases. Fatty acid synthase (FAS), a key enzyme in lipogenesis, has been identified as a potential therapeutic target for obesity due to its role in adipocyte differentiation and lipid accumulation. This study employed a multidisciplinary approach involving in silico and in vitro analyses to investigate the anti-adipogenic properties of maclurin, a natural phenolic compound derived from Morus alba. Using SwissDock software (ChEMBL version 23), we predicted protein interactions and demonstrated a high probability (95.6%) of maclurin targeting FAS, surpassing the interaction rates of established inhibitors like cerulenin. Docking simulations revealed maclurin's superior binding affinity to FAS, with a binding score of -7.3 kcal/mol compared to -6.7 kcal/mol for cerulenin. Subsequent in vitro assays confirmed these findings, with maclurin effectively inhibiting FAS activity in a concentration-dependent manner in 3T3-L1 adipocytes, without compromising cell viability. Furthermore, maclurin treatment resulted in significant reductions in lipid accumulation and the downregulated expression of critical adipogenic genes such as PPARγ, C/EBPα, and FAS, indicating the suppression of adipocyte differentiation. Maclurin shows potential as a novel FAS inhibitor with significant anti-adipogenic effects, offering a promising therapeutic avenue for the treatment and prevention of obesity.

Integrated metabolomic and transcriptomic analysis reveals biosynthesis mechanism of flavone and caffeoylquinic acid in chrysanthemum

BackgroundChrysanthemum morifolium ‘HangBaiJu’, a popular medicinal and edible plant, exerts its biological activities primarily through the presence of flavones and caffeoylquinic acids (CQAs). However, the regulatory mechanism of flavone and CQA biosynthesis in the chrysanthemum capitulum remains unclear.ResultsIn this study, the content of flavones and CQAs during the development of chrysanthemum capitulum was determined by HPLC, revealing an accumulation pattern with higher levels at S1 and S2 and a gradual decrease at S3 to S5. Transcriptomic analysis revealed that CmPAL1/2, CmCHS1/2, CmFNS, CmHQT, and CmHCT were key structural genes in flavones and CQAs biosynthesis. Furthermore, weighted gene co-expression correlation network analysis (WGCNA), k-means clustering, correlation analysis and protein interaction prediction were carried out in this study to identify transcription factors (TFs) associated with flavone and CQA biosynthesis, including MYB, bHLH, AP2/ERF, and MADS-box families. The TFs CmERF/PTI6 and CmCMD77 were proposed to act as upstream regulators of CmMYB3 and CmbHLH143, while CmMYB3 and CmbHLH143 might form a complex to directly regulate the structural genes CmPAL1/2, CmCHS1/2, CmFNS, CmHQT, and CmHCT, thereby controlling flavone and CQA biosynthesis.ConclusionsOverall, these findings provide initial insights into the TF regulatory network underlying flavones and CQAs accumulation in the chrysanthemum capitulum, which laid a theoretical foundation for the quality improvement of C. morifolium ‘HangBaiJu’ and the high-quality development of the industry.

Genome-Wide Identification, Expression, and Interaction Analysis of the Auxin Response Factor and AUX/IAA Gene Families in Vaccinium bracteatum.

Auxin, a plant hormone, plays diverse roles in the modulation of plant growth and development. The transport and signal transduction of auxin are regulated by various factors involved in shaping plant morphology and responding to external environmental conditions. The auxin signal transduction is primarily governed by the following two gene families: the auxin response factor (ARF) and auxin/indole-3-acetic acid (AUX/IAA). However, a comprehensive genomic analysis involving the expression profiles, structures, and functional features of the ARF and AUX/IAA gene families in Vaccinium bracteatum has not been carried out to date. Through the acquisition of genomic and expression data, coupled with an analysis using online tools, two gene family members were identified. This groundwork provides a distinguishing characterization of the chosen gene families in terms of expression, interaction, and response in the growth and development of plant fruits. In our genome-wide search of the VaARF and VaIAA genes in Vaccinium bracteatum, we identified 26 VaARF and 17 VaIAA genes. We analyzed the sequence and structural characteristics of these VaARF and VaIAA genes. We found that 26 VaARF and 17 VaIAA genes were divided into six subfamilies. Based on protein interaction predictions, VaIAA1 and VaIAA20 were designated core members of VaIAA gene families. Moreover, an analysis of expression patterns showed that 14 ARF genes and 12 IAA genes exhibited significantly varied expressions during fruit development. Two key genes, namely, VaIAA1 and VaIAA20, belonging to a gene family, play a potentially crucial role in fruit development through 26 VaARF-IAAs. This study provides a valuable reference for investigating the molecular mechanism of fruit development and lays the foundation for further research on Vaccinium bracteatum.

Effectiveness and Efficiency: Label-Aware Hierarchical Subgraph Learning for Protein–Protein Interaction

The study of protein–protein interactions (PPIs) holds immense significance in understanding various biological activities, as well as in drug discovery and disease diagnosis. Existing deep learning methods for PPI prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they often experience a decline in performance in the real world. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes’ degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. The huge data growth of PPI leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose a label-aware hierarchical subgraph learning method (laruGL-PPI) that can effectively infer PPIs while being interpretable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments conducted across various scales of PPI datasets have conclusively demonstrated that the laruGL-PPI method surpasses the most advanced PPI prediction techniques currently available, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.

The role of GADD45G methylation in endometrial cancer: Insights into CDK1/CCNB1 activation and therapeutic opportunities.

Accumulating evidence suggests the significant involvement of GADD45G in the development of various cancers. This study investigates GADD45G's involvement and methylation status in endometrial cancer (EC), along with molecular mechanisms and potential therapies. The expression of GADD45G in EC tissues and controls was evaluated using RNA-seq, quantitative real-time polymerase chain reaction (qRT-PCR), and western blotting (WB). Methylation-specific PCR (MSP) evaluated GADD45G's methylation status. Protein-protein interaction (PPI) prediction identified potential interactors of GADD45G, and co-immunoprecipitation (co-IP) confirmed GADD45G interact with Cyclin-dependent kinase 1 (CDK1) and cyclin B1 (CCNB1). Several cell behavior assays were conducted in both in vitro and in vivo settings to comprehensively understand the impact of GADD45G dysregulation in EC. Our findings revealed a significant decrease in the expression of GADD45G in endometrial cancer tissues and cells, which was attributed to its methylation status. Reduced GADD45G expression correlated with increased invasive behaviors in EC cells. Furthermore, GADD45G negatively regulated CDK1 and CCNB1, promoting invasive behaviors at transcript and protein levels. This study demonstrated that the downregulation of GADD45G, mediated by methylation, facilitates the invasive behaviors of EC cells through interaction with the CDK1/CCNB1. These findings enhance understanding of the molecular mechanisms underlying endometrial cancer and suggest potential therapeutic strategies targeting GADD45G for treatment.

Genome-Wide Identification and Expression Analysis of the Sweet Cherry Whirly Gene Family.

Sweet cherry (Prunus avium) is one of the economically valuable horticultural fruit trees and it is widely cultivated throughout the world. Whirly (WHY) genes are a unique gene family with few members and have important biological functions in plant growth, development, and response to abiotic stress. This study utilized whole-genome identification to conduct a comprehensive analysis of the WHY genes in sweet cherry and examined their transcription levels in different tissues and under abiotic stress to explore their functions. Two WHY genes were identified in the sweet cherry genome and named PavWHY1 and PavWHY2, respectively, based on their homology with those in Arabidopsis thaliana. Both genes have theoretical isoelectric points greater than seven and are hydrophilic proteins, suggesting that they may be localized in plastids. The two genes are evolutionarily classified into two categories, with large differences in gene structure, and highly similar protein tertiary structures, and both have conserved domains of WHY. PavWHY1 and PavWHY2 are collinear with AtWHY1 and AtWHY2, respectively. The promoter sequence contains cis-acting elements related to hormones and abiotic stress, which are differentially expressed during flower bud differentiation, fruit development, and cold accumulation. qRT-PCR showed that PavWHY1 and PavWHY2 were differentially expressed in flower and fruit development and responded to low temperature and exogenous ABA treatment. The recombinant plasmid pGreenII-0800-Luc with the promoters of these two genes can activate luciferase expression in tobacco. Protein interaction predictions indicate that these gene products may interact with other proteins. This study reveals the molecular features, evolutionary relationships, and expression patterns of sweet cherry WHY genes, and investigates the activities of their promoters, which lays the foundation for further exploration of their biological functions and provides new insights into the WHY gene family in Rosaceae.

Genome-Wide Identification and Expression Characterization of the D27 Gene Family of Capsicum annuum L.

As a crucial member of the gene family involved in the biosynthesis of strigolactones, D27 plays an important regulatory role in plant branching and root development, which is essential for field management and yield increase in peppers (Capsicum annuum L.). To comprehensively understand the characteristics of the pepper D27 gene family, we identified three CaD27 genes. By analyzing their physicochemical properties, phylogenetic relationships, gene structures, promoters, and expression patterns in different tissues, the characteristics of the CaD27 gene family were revealed. The research results showed that these three CaD27 genes are located in three different chromosomes. Evolutionary analysis divided the members of CaD27 into three groups, and gene collinearity analysis did not find any duplicates, indicating the diversity and non-redundancy of the CaD27 gene family members. In addition, we identified and classified cis-elements in the promoter regions of CaD27 genes, with a relatively high proportion related to light and plant hormone responses. Expression pattern analysis showed that CaD27.1 is expressed in leaves, while CaD27.2 is expressed in roots, indicating tissue specificity. Furthermore, protein interaction predictions revealed an interaction between D27.2 and CCD7. This study provided important insights into the function and regulatory mechanisms of the CaD27 gene family and the role of strigolactones in plant growth and development.

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new 'quintuplet' neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID's orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.