Protein-protein Interactions In Plants Research Articles

Unlocking protein-protein interactions in plants: a comprehensive review of established and emerging techniques.

Protein-protein interactions orchestrate plant development and serve as crucial elements for cellular and environmental communication. Understanding these interactions offers a gateway to unravel complex protein networks that will allow a better understanding of nature. Methods for the characterization of protein-protein interactions have been around over 30 years, yet the complexity of some of these interactions has fueled the development of new techniques that provide a better understanding of the underlying dynamics. In many cases, the application of these techniques is limited by the nature of the available sample. While some methods require an in vivo set-up, others solely depend on protein sequences to study protein-protein interactions via an in silico set-up. The vast number of techniques available to date calls for a way to select the appropriate tools for the study of specific interactions. Here, we classify widely spread tools and new emerging techniques for the characterization of protein-protein interactions based on sample requirements while providing insights into the information that they can potentially deliver. We provide a comprehensive overview of commonly used techniques and elaborate on the most recent developments, showcasing their implementation in plant research.

Immunoprecipitation and FRET-FLIM to Determine Metabolons on the Plant ER.

Metabolons are protein complexes that contain all the enzymes necessary for a metabolic pathway but also scaffolding proteins. Such a structure allows efficient channeling of intermediate metabolites form one active site to the next and is highly advantageous for labile or toxic intermediates. Here we describe two methods currently used to identify metabolons via protein-protein interaction methodology: immunoprecipitations using GFP-Trap®_A beads to find novel interaction partners and potential metabolon components and FRET-FLIM to test for and quantify protein-protein interactions in planta.

The Split-Luciferase Complementation Assay to Detect and Quantify Protein-Protein Interactions in Planta.

Protein-protein interactions play a critical role in plant viral infection and defense responses against pathogens. This protocol provides a detailed and reliable methodology for investigating protein-protein interactions using a luciferase-based complementation assay that includes easy luminescence-based normalization within a single plate. The protocol includes step-by-step procedures, reagent lists, and considerations for data interpretation, ensuring robust and reproducible results. By following this protocol, researchers can advance on understanding of the crucial role of protein-protein interactions in plant viral infection and defense responses to other pathogen attacks.

TurboID-Based Proximity Labeling: A Method to Decipher Protein-Protein Interactions in Plants.

Proteins form complex networks through interaction to drive biological processes. Thus, dissecting protein-protein interactions (PPIs) is essential for interpreting cellular processes. To overcome the drawbacks of traditional approaches for analyzing PPIs, enzyme-catalyzed proximity labeling (PL) techniques based on peroxidases or biotin ligases have been developed and successfully utilized in mammalian systems. However, the use of toxic H2O2 in peroxidase-based PL, the requirement of long incubation time (16-24h), and higher incubation temperature (37°C) with biotin in BioID-based PL significantly restricted their applications in plants. TurboID-based PL, a recently developed approach, circumvents the limitations of these methods by providing rapid PL of proteins under room temperature. We recently optimized the use of TurboID-based PL in plants and demonstrated that it performs better than BioID in labeling endogenous proteins. Here, we describe a step-by-step protocol for TurboID-based PL in studying PPIs in planta, including Agrobacterium-based transient expression of proteins, biotin treatment, protein extraction, removal of free biotin, quantification, and enrichment of the biotinylated proteins by affinity purification. We describe the PL using plant viral immune receptor N, which belongs to the nucleotide-binding leucine-rich repeat (NLR) class of immune receptors, as a model. The method described could be easily adapted to study PPI networks of other proteins in Nicotiana benthamiana and provides valuable information for future application of TurboID-based PL in other plant species.

Regulation of light-harvesting systems in plants: Flexible conformation of light-harvesting complex II trimer depending on its binding site.

Photosynthesis is a fundamental energy conversion process that sustains the Earth's ecosystem. As the environment has changed, photosynthetic systems of photosynthetic organisms have been diversified and optimized. Recent studies indicate that photosynthesis has been designed not only for ‘efficiency’ in energy conversion but also for ‘flexibility' in sustaining itself in a changing environment. In particular, light-harvesting systems were found to have the greatest diversity and flexibility to cope with the changing light conditions. In this presentation, I would like to introduce my recent studies on the regulation of light-harvesting systems by protein-protein interactions in plants and green algae. Our results revealed that the conformation of a light-harvesting complex II (LHCII) trimer in the green alga Chlamydomonas reinhardtii was dependent on the binding site. We propose that this conformational flexibility of the LHCII trimer possibly modulates its light-harvesting properties by altering excitation-energy flow over the photosynthetic supercomplex.

AirID-Based Proximity Labeling for Protein-Protein Interaction in Plants.

In mammalian cells and plants, proximity labeling (PL) approaches using modified ascorbate peroxidase (APEX) or the Escherichia coli biotin ligase BirA (known as BioID) have proven successful in identifying protein-protein interactions (PPIs). APEX, BioID, and TurboID, a revised version of BioID have some restrictions in addition to being valuable technologies. The recently developed AirID, a novel version of BioID for proximity identification in protein-protein interactions, overcame these restrictions. Previously, AirID has been used in animal models, while the current study demonstrates the use of AirID in plants, and the results confirmed that AirID performs better in plant systems as compared to other PL enzymes such as BioID and TurboID for protein labeling that are proximal to the target proteins. Here is a step-by-step protocol for identifying protein interaction partners using AT4G18020 (APRR2) protein as a model. The methods describe the construction of vector, the transformation of construct through agroinfiltration, biotin transformation, extraction of proteins, and enrichment of biotin-labeled proteins through affinity purification technique. The results conclude that AirID is a novel and ideal enzyme for analyzing PPIs in plants. The method can be applied to study other proteins in plants.

DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.

The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.

CPIELA: Computational Prediction of Plant Protein-Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information.

Identification and characterization of plant protein–protein interactions (PPIs) are critical in elucidating the functions of proteins and molecular mechanisms in a plant cell. Although experimentally validated plant PPIs data have become increasingly available in diverse plant species, the high-throughput techniques are usually expensive and labor-intensive. With the incredibly valuable plant PPIs data accumulating in public databases, it is progressively important to propose computational approaches to facilitate the identification of possible PPIs. In this article, we propose an effective framework for predicting plant PPIs by combining the position-specific scoring matrix (PSSM), local optimal-oriented pattern (LOOP), and ensemble rotation forest (ROF) model. Specifically, the plant protein sequence is firstly transformed into the PSSM, in which the protein evolutionary information is perfectly preserved. Then, the local textural descriptor LOOP is employed to extract texture variation features from PSSM. Finally, the ROF classifier is adopted to infer the potential plant PPIs. The performance of CPIELA is evaluated via cross-validation on three plant PPIs datasets: Arabidopsis thaliana, Zea mays, and Oryza sativa. The experimental results demonstrate that the CPIELA method achieved the high average prediction accuracies of 98.63%, 98.09%, and 94.02%, respectively. To further verify the high performance of CPIELA, we also compared it with the other state-of-the-art methods on three gold standard datasets. The experimental results illustrate that CPIELA is efficient and reliable for predicting plant PPIs. It is anticipated that the CPIELA approach could become a useful tool for facilitating the identification of possible plant PPIs.

Methods for Detection of Protein Interactions with Plasmodesmata-Localized Reticulons.

Plant reticulon family proteins (RTN) tubulate the ER by dimerization and oligomerization, creating localized ER membrane tensions that result in membrane curvature. Two RTN ER-shaping proteins have been found in the plasmodesmata (PD) proteome which could potentially contribute to the formation of the desmotubule, an ER-derived structure that crosses primary PD and physically connects the ER of two cells. Here we describe two methods used to identify partners of two PD-resident reticulon proteins, RTN3 and RTN6 that are located in primary PD at cytokinesis in tobacco (Nicotiana tabacum): immunoprecipitations using GFP-Trap®_A beads to find novel interaction partners and FRET-FLIM to test for and quantify direct protein-protein interactions in planta.

The Use of Fluorescent Protein Fusions to Monitor the Unfolded Protein Response and Protein Foldase-Substrate Interactions in Plant Protoplasts.

Endoplasmic reticulum (ER) stress and the resulting unfolded protein response (UPR) are critical stress response pathways in eukaryotes. To study these types of interactions in plants, a wide range of methods have been used, including generation of transgenic plants, subcellular immunolocalization of protein foldases, and co-immunoprecipitation (co-IP) assays. Although these more time-consuming methods have been successfully implemented, there is a need for a versatile and rapid in vivo system to investigate ER stress and UPR. Here, we describe a transient expression system that uses plant protoplasts to define in vivo subcellular localizations and protein-protein interactions of protein foldases and their substrates fused to fluorescent protein reporters. This accurate and robust assay utilizes a variety of analyses, such as subcellular localization, FLIM-FRET, co-IP, mutagenesis, and RT-PCR in the genetically amenable Arabidopsis model system. We demonstrate the methodology by using the representative protein foldase, protein disulfide isomerase-9 (PDI9), as well as subcellular markers, secretory proteins, and dithiothreitol (DTT)-mediated induction of the UPR as monitored by RT-PCR. Together, these methods yield reliable high output results for investigating subcellular localization and protein-protein interactions in plants to decipher the UPR pathways.

A Powerful Method for Studying Protein-Protein Interactions in Plants: Coimmunoprecipitation (Co-IP) Assay.

With the continuous application of molecular biology technologies in the field of plant virology, growing evidence shows that the interactions between viral proteins or virus and host are essential in almost all steps of virus infection. Various methods used to study protein-protein interactions have been extensively adapted for plant virology research. Among them, coimmunoprecipitation (Co-IP) is considered as a standard and powerful tool by molecular biologists to analyze the occurrence of protein-protein interactions in living cells. This method can help us gain a deeper understanding of the functions of target proteins, which interact with other proteins or belong to some functional complexes. In this chapter, we detail a protocol for the Co-IP assay and highlight challenges associated with this method.

Prediction of Protein-Protein Interactions in Arabidopsis, Maize, and Rice by Combining Deep Neural Network With Discrete Hilbert Transform.

Protein–protein interactions (PPIs) in plants play an essential role in the regulation of biological processes. However, traditional experimental methods are expensive, time-consuming, and need sophisticated technical equipment. These drawbacks motivated the development of novel computational approaches to predict PPIs in plants. In this article, a new deep learning framework, which combined the discrete Hilbert transform (DHT) with deep neural networks (DNN), was presented to predict PPIs in plants. To be more specific, plant protein sequences were first transformed as a position-specific scoring matrix (PSSM). Then, DHT was employed to capture features from the PSSM. To improve the prediction accuracy, we used the singular value decomposition algorithm to decrease noise and reduce the dimensions of the feature descriptors. Finally, these feature vectors were fed into DNN for training and predicting. When performing our method on three plant PPI datasets Arabidopsis thaliana, maize, and rice, we achieved good predictive performance with average area under receiver operating characteristic curve values of 0.8369, 0.9466, and 0.9440, respectively. To fully verify the predictive ability of our method, we compared it with different feature descriptors and machine learning classifiers. Moreover, to further demonstrate the generality of our approach, we also test it on the yeast and human PPI dataset. Experimental results anticipated that our method is an efficient and promising computational model for predicting potential plant–protein interacted pairs.

FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method

Protein-protein interactions (PPIs) in plants are crucial for understanding biological processes. Although high-throughput techniques produced valuable information to identify PPIs in plants, they are usually expensive, inefficient, and extremely time-consuming. Hence, there is an urgent need to develop novel computational methods to predict PPIs in plants. In this article, we proposed a novel approach to predict PPIs in plants only using the information of protein sequences. Specifically, plants’ protein sequences are first converted as position-specific scoring matrix (PSSM); then, the fast Walsh–Hadamard transform (FWHT) algorithm is used to extract feature vectors from PSSM to obtain evolutionary information of plant proteins. Lastly, the rotation forest (RF) classifier is trained for prediction and produced a series of evaluation results. In this work, we named this approach FWHT-RF because FWHT and RF are used for feature extraction and classification, respectively. When applying FWHT-RF on three plants’ PPI datasets Maize, Rice, and Arabidopsis thaliana (Arabidopsis), the average accuracies of FWHT-RF using 5-fold cross validation were achieved as high as 95.20%, 94.42%, and 83.85%, respectively. To further evaluate the predictive power of FWHT-RF, we compared it with the state-of-art support vector machine (SVM) and K-nearest neighbor (KNN) classifier in different aspects. The experimental results demonstrated that FWHT-RF can be a useful supplementary method to predict potential PPIs in plants.

Visualizing protein-protein interactions in plants by rapamycin-dependent delocalization.

Identifying protein-protein interactions (PPIs) is crucial for understanding biological processes. Many PPI tools are available, yet only some function within the context of a plant cell. Narrowing down even further, only a few tools allow complex multi-protein interactions to be visualized. Here, we present a conditional in vivo PPI tool for plant research that meets these criteria. Knocksideways in plants (KSP) is based on the ability of rapamycin to alter the localization of a bait protein and its interactors via the heterodimerization of FKBP and FRB domains. KSP is inherently free from many limitations of other PPI systems. This in vivo tool does not require spatial proximity of the bait and prey fluorophores and it is compatible with a broad range of fluorophores. KSP is also a conditional tool and therefore the visualization of the proteins in the absence of rapamycin acts as an internal control. We used KSP to confirm previously identified interactions in Nicotiana benthamiana leaf epidermal cells. Furthermore, the scripts that we generated allow the interactions to be quantified at high throughput. Finally, we demonstrate that KSP can easily be used to visualize complex multi-protein interactions. KSP is therefore a versatile tool with unique characteristics and applications that complements other plant PPI methods.

Sequence-Based Prediction of Plant Protein-Protein Interactions by Combining Discrete Sine Transformation With Rotation Forest.

Protein-protein interactions (PPIs) in plants are essential for understanding the regulation of biological processes. Although high-throughput technologies have been widely used to identify PPIs, they are usually laborious, expensive, and suffer from high false-positive rates. Therefore, it is imperative to develop novel computational approaches as a supplement tool to detect PPIs in plants. In this work, we presented a method, namely DST-RoF, to identify PPIs in plants by combining an ensemble learning classifier-Rotation Forest (RoF) with discrete sine transformation (DST). Specifically, plant protein sequence is firstly converted into Position-Specific Scoring Matrix (PSSM). Then, the discrete sine transformation was employed to extract effective features for obtaining the evolutionary information of proteins. Finally, these optimal features were fed into the RoF classifier for training and prediction. When performed on the plant datasets Arabidopsis, Rice, and Maize, DST-RoF yielded high prediction accuracy of 82.95%, 88.82%, and 93.70%, respectively. To further evaluate the prediction ability of our approach, we compared it with 4 state-of-the-art classifiers and 3 different feature extraction methods. Comprehensive experimental results anticipated that our method is feasible and robust for predicting potential plant-protein interacted pairs.

Advances in the elucidation of nuclear proteins in the model plant Arabidopsis thaliana: based on protein interactions and bioinformatics analysis

ABSTRACT Plant protein–protein interaction (PPI) networks provide valuable information for the understanding of plant biological processes. In this study, we summarized and constructed the nuclear-protein interactome of Arabidopsis based on PPIs, and preliminarily analyzed the relationships among the main pathways involving these protein interactions. These proteins are involved mainly in the plant hormone signal transduction and plant-pathogen interaction, and these pathways are linked closely by co-pathway and nodal proteins. The key nodal proteins play important roles in communication among hormonal pathways. Further analysis revealed that all of these key nodal proteins were localized in multiple organelles. High-frequency protein multi-localization analysis suggested that the multi-location proteins may play key roles in organelle communication and plant growth. Thus, the screening and study of multi-location proteins may be an important research direction. Our findings can inform new research based on protein interactions and provide novel directions for plant proteome research.

Split-luciferase Complementation Imaging Assay to Study Protein-protein Interactions in Nicotiana benthamiana.

The experimental identification of protein-protein interactions (PPIs) is critical to understand protein function. Thus, a plethora of sensitive and versatile approaches have been developed to detect PPIs in vitro or in vivo, such as protein pull-down, yeast two-hybrid (Y2H), co-immunoprecipitation (co-IP), and bimolecular fluorescence complementation (BiFC) assays. The recently established split-luciferase complementation (Split-LUC) imaging assay has several advantages compared to other approaches to detect PPIs in planta: it is a relatively simple and fast method to detect PPIs in vivo; the results are quantitative, with high sensitivity and low background; it measures dynamic PPIs in real-time; and it requires limited experimental materials and instrumentation. In this assay, the amino-terminal and carboxyl-terminal halves of the luciferase enzyme are fused to two proteins of interest (POIs), respectively; the luciferase protein is reconstituted when two POIs interact with each other, giving rise to a measurable activity. Here, we describe a protocol for the Split-LUC imaging assay using a pair of modified gateway-compatible vectors upon Agrobacterium-mediated transient expression in Nicotiana benthamiana. With this setup, we have successfully confirmed a series of interactions among virus-plant proteins, virus-virus proteins, plant-plant proteins, or bacteria-plant proteins in N. benthamiana.

Rapid Identification of Protein-Protein Interactions in Plants.

Enzyme-enzyme interactions can be discovered by affinity purification mass spectrometry (AP-MS) under in vivo conditions. Tagged enzymes can either be transiently transformed into plant leaves or stably transformed into plant cells prior to AP-MS. The success of AP-MS depends on the levels and stability of the bait protein, the stability of the protein-protein interactions, and the efficiency of trypsin digestion and recovery of tryptic peptides for MS analysis. Unlike in-gel-digestion AP-MS, in which the gel is cut into pieces for several independent trypsin digestions, we uses a proteomics-based in-solution digestion method to directly digest the proteins on the beads following affinity purification. Thus, a single replicate within an AP-MS experiment constitutes a single sample for LC-MS measurement. In subsequent data analysis, normalized signal intensities can be processed to determine fold-change abundance (FC-A) scores by use of the SAINT algorithm embedded within the CRAPome software. Following analysis of co-sublocalization of "bait" and "prey," we suggest considering only the protein pairs for which the intensities were more than 2% compared with the bait, corresponding to FC-A values of at least four within-biological replicates, which we recommend as minimum. If the procedure is faithfully followed, experimental assessment of enzyme-enzyme interactions can be carried out in Arabidopsis within 3 weeks (transient expression) or 5 weeks (stable expression). © 2019 The Authors. Basic Protocol 1: Gene cloning to the destination vectors Alternate Protocol: In-Fusion or Gibson gene cloning protocol Basic Protocol 2: Transformation of baits into the plant cell culture or plant leaf Basic Protocol 3: Affinity purification of protein complexes Basic Protocol 4: On-bead trypsin/LysC digestion and C18 column peptide desalting and concentration Basic Protocol 5: Data analysis and quality control.

Split-TALE: A TALE-Based Two-Component System for Synthetic Biology Applications in Planta.

Transcription activator-like effectors (TALEs) are bacterial Type-III effector proteins from phytopathogenic Xanthomonas species that act as transcription factors in plants. The modular DNA-binding domain of TALEs can be reprogrammed to target nearly any DNA sequence. Here, we designed and optimized a two-component AND-gate system for synthetic circuits in plants based on TALEs. In this system, named split-TALE (sTALE), the TALE DNA binding domain and the transcription activation domain are separated and each fused to protein interacting domains. Physical interaction of interacting domains leads to TALE-reconstitution and can be monitored by reporter gene induction. This setup was used for optimization of the sTALE scaffolds, which result in an AND-gate system with an improved signal-to-noise ratio. We also provide a toolkit of ready-to-use vectors and single modules compatible with Golden Gate cloning and MoClo syntax. In addition to its implementation in synthetic regulatory circuits, the sTALE system allows the analysis of protein-protein interactions in planta.

Exploring the protein-protein interaction landscape in plants.

Protein-protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.