Phosphatidic acid-regulated SOS2 controls sodium and potassium homeostasis in Arabidopsis under salt stress.

The maintenance of sodium/potassium (Na+ /K+ ) homeostasis in plant cells is essential for salt tolerance. Plants export excess Na+ out of cells mainly through the Salt Overly Sensitive (SOS) pathway, activated by a calcium signal; however, it is unknown whether other signals regulate the SOS pathway and how K+ uptake is regulated under salt stress. Phosphatidic acid (PA) is emerging as a lipid signaling molecule that modulates cellular processes in development and the response to stimuli. Here, we show that PA binds to the residue Lys57 in SOS2, a core member of the SOS pathway, under salt stress, promoting the activity and plasma membrane localization of SOS2, which activates the Na+ /H+ antiporter SOS1 to promote the Na+ efflux. In addition, we reveal that PA promotes the phosphorylation of SOS3-like calcium-binding protein 8 (SCaBP8) by SOS2 under salt stress, which attenuates the SCaBP8-mediated inhibition of Arabidopsis K+ transporter 1 (AKT1), an inward-rectifying K+ channel. These findings suggest that PA regulates the SOS pathway and AKT1 activity under salt stress, promoting Na+ efflux and K+ influx to maintain Na+ /K+ homeostasis.

Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM.

The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.

Isolation of SARS-CoV-2-related coronavirus from Malayan pangolins.

The current outbreak of coronavirus disease-2019 (COVID-19) poses unprecedented challenges to global health1. The new coronavirus responsible for this outbreak-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-shares high sequence identity to SARS-CoV and a bat coronavirus, RaTG132. Although bats may be the reservoir host for a variety of coronaviruses3,4, it remains unknown whether SARS-CoV-2 has additional host species. Here we show that a coronavirus, which we name pangolin-CoV, isolated from a Malayan pangolin has 100%, 98.6%, 97.8% and 90.7% amino acid identity with SARS-CoV-2 in the E, M, N and S proteins, respectively. In particular, the receptor-binding domain of the S protein of pangolin-CoV is almost identical to that of SARS-CoV-2, with one difference in a noncritical amino acid. Our comparative genomic analysis suggests that SARS-CoV-2 may have originated in the recombination of a virus similar to pangolin-CoV with one similar to RaTG13. Pangolin-CoV was detected in 17 out of the 25 Malayan pangolins that we analysed. Infected pangolins showed clinical signs and histological changes, and circulating antibodies against pangolin-CoV reacted with the S protein of SARS-CoV-2. The isolation of a coronavirus from pangolins that is closely related to SARS-CoV-2 suggests that these animals have the potential to act as an intermediate host of SARS-CoV-2. This newly identified coronavirus from pangolins-the most-trafficked mammal in the illegal wildlife trade-could represent a future threat to public health if wildlife trade is not effectively controlled.

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network.

While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins makes these methods ineffective in dealing with novel proteins. The advent of AlphaFold2 presents a significant opportunity and also a challenge to predict PPIs in a straightforward way based on monomer structures while controlling bias from protein sequences. In this work, we established Structure and Graph-based Predictions of Protein Interactions (SGPPI), a structure-based DL framework for predicting PPIs, using the graph convolutional network. In particular, SGPPI focused on protein patches on the protein-protein binding interfaces and extracted the structural, geometric and evolutionary features from the residue contact map to predict PPIs. We demonstrated that our model outperforms traditional machine learning methods and state-of-the-art DL-based methods using non-representation-bias benchmark datasets. Moreover, our model trained on human dataset can be reliably transferred to predict yeast PPIs, indicating that SGPPI can capture converging structural features of protein interactions across various species. The implementation of SGPPI is available at https://github.com/emerson106/SGPPI.

AraPathogen2.0: An Improved Prediction of Plant-Pathogen Protein-Protein Interactions Empowered by the Natural Language Processing Technique.

Plant-pathogen protein-protein interactions (PPIs) play crucial roles in the arm race between plants and pathogens. Therefore, the identification of these interspecies PPIs is very important for the mechanistic understanding of pathogen infection and plant immunity. Computational prediction methods can complement experimental efforts, but their predictive performance still needs to be improved. Motivated by the rapid development of natural language processing and its successful applications in the field of protein bioinformatics, here we present an improved XGBoost-based plant-pathogen PPI predictor (i.e., AraPathogen2.0), in which sequence encodings from the pretrained protein language model ESM2 and Arabidopsis PPI network-related node representations from the graph embedding technique struc2vec are used as input. Stringent benchmark experiments showed that AraPathogen2.0 could achieve a better performance than its precedent version, especially for processing the test data set with novel proteins unseen in the training data.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

Structural insights into the interactions and epigenetic functions of human nucleic acid repair protein ALKBH6.

Human AlkB homolog 6, ALKBH6, plays key roles in nucleic acid damage repair and tumor therapy. However, no precise structural and functional information are available for this protein. In this study, we determined atomic resolution crystal structures of human holo-ALKBH6 and its complex with ligands. AlkB members bind nucleic acids by NRLs (nucleotide recognition lids, also called Flips), which can recognize DNA/RNA and flip methylated lesions. We found that ALKBH6 has unusual Flip1 and Flip2 domains, distinct from other AlkB family members both in sequence and conformation. Moreover, we show that its unique Flip3 domain has multiple unreported functions, such as discriminating against double-stranded nucleic acids, blocking the active center, binding other proteins, and in suppressing tumor growth. Structural analyses and substrate screening reveal how ALKBH6 discriminates between different types of nucleic acids and may also function as a nucleic acid demethylase. Structure-based interacting partner screening not only uncovered an unidentified interaction of transcription repressor ZMYND11 and ALKBH6 in tumor suppression but also revealed cross talk between histone modification and nucleic acid modification in epigenetic regulation. Taken together, these results shed light on the molecular mechanism underlying ALKBH6-associated nucleic acid damage repair and tumor therapy.

Gene coexpression network during ontogeny in the yellow fever mosquito, Aedes aegypti.

BACKGROUND: The behaviors and ontogeny of Aedes aegypti are closely related to the spread of diseases caused by dengue (DENV), chikungunya (CHIKV), Zika (ZIKV), and yellow fever (YFV) viruses. During the life cycle, Ae. aegypti undergoes drastic morphological, metabolic, and functional changes triggered by gene regulation and other molecular mechanisms. Some essential regulatory factors that regulate insect ontogeny have been revealed in other species, but their roles are still poorly investigated in the mosquito. RESULTS: Our study identified 6 gene modules and their intramodular hub genes that were highly associated with the ontogeny of Ae. aegypti in the constructed network. Those modules were found to be enriched in functional roles related to cuticle development, ATP generation, digestion, immunity, pupation control, lectins, and spermatogenesis. Additionally, digestion-related pathways were activated in the larvae and adult females but suppressed in the pupae. The integrated protein‒protein network also identified cilium-related genes. In addition, we verified that the 6 intramodular hub genes encoding proteins such as EcKinase regulating larval molt were only expressed in the larval stage. Quantitative RT‒PCR of the intramodular hub genes gave similar results as the RNA-Seq expression profile, and most hub genes were ontogeny-specifically expressed. CONCLUSIONS: The constructed gene coexpression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. Ultimately, these findings will be key in identifying potential molecular targets for disease control.

Sympatric Recombination in Zoonotic Cryptosporidium Leads to Emergence of Populations with Modified Host Preference.

Genetic recombination plays a critical role in the emergence of pathogens with phenotypes such as drug resistance, virulence, and host adaptation. Here, we tested the hypothesis that recombination between sympatric ancestral populations leads to the emergence of divergent variants of the zoonotic parasite Cryptosporidium parvum with modified host ranges. Comparative genomic analyses of 101 isolates have identified seven subpopulations isolated by distance. They appear to be descendants of two ancestral populations, IIa in northwestern Europe and IId from southwestern Asia. Sympatric recombination in areas with both ancestral subtypes and subsequent selective sweeps have led to the emergence of new subpopulations with mosaic genomes and modified host preference. Subtelomeric genes could be involved in the adaptive selection of subpopulations, while copy number variations of genes encoding invasion-associated proteins are potentially associated with modified host ranges. These observations reveal ancestral origins of zoonotic C. parvum and suggest that pathogen import through modern animal farming might promote the emergence of divergent subpopulations of C. parvum with modified host preference.

PlaASDB: a comprehensive database of plant alternative splicing events in response to stress.

BACKGROUND: Alternative splicing (AS) is a co-transcriptional regulatory mechanism of plants in response to environmental stress. However, the role of AS in biotic and abiotic stress responses remains largely unknown. To speed up our understanding of plant AS patterns under different stress responses, development of informative and comprehensive plant AS databases is highly demanded. DESCRIPTION: In this study, we first collected 3,255 RNA-seq data under biotic and abiotic stresses from two important model plants (Arabidopsis and rice). Then, we conducted AS event detection and gene expression analysis, and established a user-friendly plant AS database termed PlaASDB. By using representative samples from this highly integrated database resource, we compared AS patterns between Arabidopsis and rice under abiotic and biotic stresses, and further investigated the corresponding difference between AS and gene expression. Specifically, we found that differentially spliced genes (DSGs) and differentially expressed genes (DEG) share very limited overlapping under all kinds of stresses, suggesting that gene expression regulation and AS seemed to play independent roles in response to stresses. Compared with gene expression, Arabidopsis and rice were more inclined to have conserved AS patterns under stress conditions. CONCLUSION: PlaASDB is a comprehensive plant-specific AS database that mainly integrates the AS and gene expression data of Arabidopsis and rice in stress response. Through large-scale comparative analyses, the global landscape of AS events in Arabidopsis and rice was observed. We believe that PlaASDB could help researchers understand the regulatory mechanisms of AS in plants under stresses more conveniently. PlaASDB is freely accessible at http://zzdlab.com/PlaASDB/ASDB/index.html .

Current status and future perspectives of computational studies on human-virus protein-protein interactions.

The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the development of highly effective drugs to break the transmission of viral infectious diseases. Recent years have witnessed the rapid accumulation of experimentally identified human-virus PPI data, which provides an unprecedented opportunity for bioinformatics studies revolving around human-virus PPIs. In this article, we provide a comprehensive overview of computational studies on human-virus PPIs, especially focusing on the method development for human-virus PPI predictions. We briefly introduce the experimental detection methods and existing database resources of human-virus PPIs, and then discuss the research progress in the development of computational prediction methods. In particular, we elaborate the machine learning-based prediction methods and highlight the need to embrace state-of-the-art deep-learning algorithms and new feature engineering techniques (e.g. the protein embedding technique derived from natural language processing). To further advance the understanding in this research topic, we also outline the practical applications of the human-virus interactome in fundamental biological discovery and new antiviral therapy development.

HVIDB: a comprehensive database for human-virus protein-protein interactions.

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

MOTIVATION: To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human-virus protein-protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance. RESULTS: To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e. 'frozen' type and 'fine-tuning' type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Finally, we utilize the 'frozen' type transfer learning approach to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. AVAILABILITY AND IMPLEMENTATION: The source codes and datasets are available at https://github.com/XiaodiYangCAU/TransPPI/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ASTool: An Easy-to-Use Tool to Accurately Identify Alternative Splicing Events from Plant RNA-Seq Data.

Alternative splicing (AS) is an essential co-transcriptional regulatory mechanism in eukaryotes. The accumulation of plant RNA-Seq data provides an unprecedented opportunity to investigate the global landscape of plant AS events. However, most existing AS identification tools were originally designed for animals, and their performance in plants was not rigorously benchmarked. In this work, we developed a simple and easy-to-use bioinformatics tool named ASTool for detecting AS events from plant RNA-Seq data. As an exon-based method, ASTool can detect 4 major AS types, including intron retention (IR), exon skipping (ES), alternative 5' splice sites (A5SS), and alternative 3' splice sites (A3SS). Compared with existing tools, ASTool revealed a favorable performance when tested in simulated RNA-Seq data, with both recall and precision values exceeding 95% in most cases. Moreover, ASTool also showed a competitive computational speed and consistent detection results with existing tools when tested in simulated or real plant RNA-Seq data. Considering that IR is the most predominant AS type in plants, ASTool allowed the detection and visualization of novel IR events based on known splice sites. To fully present the functionality of ASTool, we also provided an application example of ASTool in processing real RNA-Seq data of Arabidopsis in response to heat stress.

Characterization of Dense Granule Metalloproteinase INS-16 in Cryptosporidium parvum.

The protozoan pathogen Cryptosporidium parvum infects intestinal epithelial cells and causes diarrhea in humans and young animals. Among the more than 20 genes encoding insulinase-like metalloproteinases (INS), two are paralogs with high sequence identity. In this study, one of them, INS-16 encoded by the cgd3_4270 gene, was expressed and characterized in a comparative study of its sibling, INS-15 encoded by the cgd3_4260 gene. A full-length INS-16 protein and its active domain I were expressed in Escherichia coli, and antibodies against the domain I and an INS-16-specific peptide were produced in rabbits. In the analysis of the crude extract of oocysts, a ~60 kDa fragment of INS-16 rather than the full protein was recognized by polyclonal antibodies against the specific peptide, indicating that INS-16 undergoes proteolytic cleavage before maturation. The expression of the ins-16 gene peaked at the invasion phase of in vitro C. parvum culture, with the documented expression of the protein in both sporozoites and merozoites. Localization studies with antibodies showed significant differences in the distribution of the native INS-15 and INS-16 proteins in sporozoites and merozoites. INS-16 was identified as a dense granule protein in sporozoites and macrogamonts but was mostly expressed at the apical end of merozoites. We screened 48 candidate INS-16 inhibitors from the molecular docking of INS-16. Among them, two inhibited the growth of C. parvum in vitro (EC50 = 1.058 µM and 2.089 µM). The results of this study suggest that INS-16 may have important roles in the development of C. parvum and could be a valid target for the development of effective treatments.

The hybrid protein interactome contributes to rice heterosis as epistatic effects.

Heterosis is the phenomenon in which hybrid progeny exhibits superior traits in comparison with those of their parents. Genomic variations between the two parental genomes may generate epistasis interactions, which is one of the genetic hypotheses explaining heterosis. We postulate that protein-protein interactions specific to F1 hybrids (F1 -specific PPIs) may occur when two parental genomes combine, as the proteome of each parent may supply novel interacting partners. To test our assumption, an inter-subspecies hybrid interactome was simulated by in silico PPI prediction between rice japonica (cultivar Nipponbare) and indica (cultivar 9311). Four-thousand, six-hundred and twelve F1 -specific PPIs accounting for 20.5% of total PPIs in the hybrid interactome were found. Genes participating in F1 -specific PPIs tend to encode metabolic enzymes and are generally localized in genomic regions harboring metabolic gene clusters. To test the genetic effect of F1 -specific PPIs in heterosis, genomic selection analysis was performed for trait prediction with additive, dominant and epistatic effects separately considered in the model. We found that the removal of single nucleotide polymorphisms associated with F1 -specific PPIs reduced prediction accuracy when epistatic effects were considered in the model, but no significant changes were observed when additive or dominant effects were considered. In summary, genomic divergence widely dispersed between japonica and indica rice may generate F1 -specific PPIs, part of which may accumulatively contribute to heterosis according to our computational analysis. These candidate F1 -specific PPIs, especially for those involved in metabolic biosynthesis pathways, are worthy of experimental validation when large-scale protein interactome datasets are generated in hybrid rice in the future.

Tobacco Necrosis Virus-AC Single Coat Protein Amino Acid Substitutions Determine Host-Specific Systemic Infections of Nicotiana benthamiana and Soybean.

Plant viruses often infect several distinct host species. Sometimes, viruses can systemically infect a specific host whereas, in other cases, only local infections occur in other species. How viral and host factors interact to determine systemic infections among different hosts is largely unknown, particularly for icosahedral positive-stranded RNA viruses. The Tobacco necrosis virus-A Chinese isolate belongs to the genus Alphanecrovirus in the family Tombusviridae. In this study, we investigated variations in systemic infections of tobacco necrosis virus-AC (TNV-AC) in Nicotiana benthamiana and Glycine max (soybean) by alanine-scanning mutagenesis of the viral coat protein (CP), which is essential for systemic movement of TNV-AC. We found that three amino acids, R169, K177, and Q233, are key residues that mediate varying degrees of systemic infections of N. benthamiana and soybean. Further analysis revealed that variations in systemic trafficking of TNV-AC CP mutants in N. benthamiana and soybean are associated with virion assembly and stability. The CP amino acids K177 and Q233 are highly conserved among all TNV-A isolates and are replaced by Q and K in the TNV-D isolates. We demonstrated that systemic infectivity of either TNV-AC K177A and Q233A or K177Q and Q233K mutants are correlated with the binding affinity of the mutated CPs to the host-specific Hsc70-2 protein. These results expand our understanding of host-dependent long-distance movement of icosahedral viruses in plants.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Prp19-associated splicing factor Cwf15 regulates fungal virulence and development in the rice blast fungus.

The splicing factor Cwf15 is an essential component of the Prp19-associated component of the spliceosome and regulates intron splicing in several model species, including yeasts and human cells. However, the roles of Cwf15 remain unexplored in plant pathogenic fungi. Here, we report that MoCWF15 in the rice blast fungus Magnaporthe oryzae is non-essential to viability and important to fungal virulence, growth and conidiation. MoCwf15 contains a putative nuclear localization signal (NLS) and is localized into the nucleus. The NLS sequence but not the predicted phosphorylation site or two sumoylation sites was essential for the biological functions of MoCwf15. Importantly, MoCwf15 physically interacted with the Prp19-associated splicing factors MoCwf4, MoSsa1 and MoCyp1, and negatively regulated protein accumulations of MoCyp1 and MoCwf4. Furthermore, with the deletion of MoCWF15, aberrant intron splicing occurred in near 400 genes, 20 of which were important to the fungal development and virulence. Taken together, MoCWF15 regulates fungal growth and infection-related development by modulating the intron splicing efficiency of a subset of genes in the rice blast fungus.

Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods.

The identification of plant-pathogen protein-protein interactions (PPIs) is an attractive and challenging research topic for deciphering the complex molecular mechanism of plant immunity and pathogen infection. Considering that the experimental identification of plant-pathogen PPIs is time-consuming and labor-intensive, computational methods are emerging as an important strategy to complement the experimental methods. In this work, we first evaluated the performance of traditional computational methods such as interolog, domain-domain interaction and domain-motif interaction in predicting known plant-pathogen PPIs. Owing to the low sensitivity of the traditional methods, we utilized Random Forest to build an inter-species PPI prediction model based on multiple sequence encodings and novel network attributes in the established plant PPI network. Critical assessment of the features demonstrated that the integration of sequence information and network attributes resulted in significant and robust performance improvement. Additionally, we also discussed the influence of Gene Ontology and gene expression information on the prediction performance. The Web server implementing the integrated prediction method, named InterSPPI, has been made freely available at http://systbio.cau.edu.cn/intersppi/index.php. InterSPPI could achieve a reasonably high accuracy with a precision of 73.8% and a recall of 76.6% in the independent test. To examine the applicability of InterSPPI, we also conducted cross-species and proteome-wide plant-pathogen PPI prediction tests. Taken together, we hope this work can provide a comprehensive understanding of the current status of plant-pathogen PPI predictions, and the proposed InterSPPI can become a useful tool to accelerate the exploration of plant-pathogen interactions.