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# Phytopathogen Effector Biology in the Burgeoning AI Era

Darcy Adam Bain, Sylvain Raffaele

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Darcy Adam Bain, Sylvain Raffaele. Phytopathogen Effector Biology in the Burgeoning AI Era. Annual Review of Phytopathology, 2025, 63 (1), pp.63-88. 10.1146/annurev-phyto-121823-081033.1.1

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*Annual Review of Phytopathology*

# Phytopathogen Effector Biology in the Burgeoning AI Era

Darcy Adam Bain Jones and Sylvain Raffaele

Laboratoire des Interactions Plantes-Microbes-Environnement (LIPME), INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, France; email: darcy.jones@inrae.fr, sylvain.raffaele@inrae.fr

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Annu. Rev. Phytopathol. 2025. 63:63–88

First published as a Review in Advance on
June 20, 2025

The *Annual Review of Phytopathology* is online at
phyto.annualreviews.org

https://doi.org/10.1146/annurev-phyto-121823-081033

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Keywords

machine learning, bioinformatics, effector, evolution

Abstract

Plant pathogens secrete effectors to facilitate infection and manipulate host physiological and immune responses. Effector proteins are challenging to characterize because of their sequence and functional diversity, rapid evolution, and host-specific interactions. Recent advances in artificial intelligence (AI), particularly in protein biology, offer new opportunities for identifying and characterizing effector proteins and understanding their evolutionary processes. This review discusses recent progress in applying AI to effector biology, focusing on identification, functional characterization, and evolution. Key areas include subcellular localization prediction, protein structural modeling with tools like AlphaFold, and the use of pretrained protein language models. AI promises to complement existing experimental and computational approaches and further accelerate the investigation of effector protein functions and their evolutionary histories, even in the absence of clear sequence similarity or known functional domains.
# 1. INTRODUCTION

Plant pathogens secrete proteins, metabolites, and other molecules during infection to cause disease. A subset of these secreted molecules that directly manipulate the physiology of host plants are collectively known as effectors (155). Effectors are a major focus of plant pathology research because their identification, molecular function, and evolution can directly inform disease dynamics and plant breeding for disease resistance. The characterization of the effector complement of a pathogen is, however, notoriously difficult. These molecules are indeed often numerous, functionally diverse, and evolutionarily plastic. Effectors typically contribute to virulence by suppressing plant immune responses and building a niche for the pathogen to exploit the plant's resources and outcompete other microbes. This can be achieved through a myriad of molecular functions involving interactions with host target molecules. Among the well-described effector activities are masking microbial or damage-associated molecular signatures (158), interference with host immune signaling (61) or host defense (17, 111), manipulation of host physiology (119), and the elicitation of host cell death by necrotrophic pathogens (toxins, necrotrophic effectors) (91). Effectors can also be a liability to pathogens because they can be recognized as nonself molecular signatures by plant immune receptors at the cell surface (by receptor-like proteins) or intracellularly (by nucleotide-binding leucine-rich repeat receptors) (104). The recognition of these pathogen avirulence effectors by plant immune receptors can elicit host resistance responses, including hypersensitive cell death. Virulence and avirulence exert a strong selective pressure on reproductively active pathogen and plant populations so that effectors and their targets or cognate receptors are constantly evolving to evade or regain interactions (126). Over many cycles, this often leads to distinct proteins with highly specific protein-protein interactions (PPIs) between a single host and pathogen (21). This rapid evolution and functional diversity of effectors pose a significant challenge to researchers attempting to identify effectors, predict their molecular and phenotypic functions, and investigate their evolutionary history.

The past decade has seen an explosion of artificial intelligence (AI) applications in protein biology, enabled by the enormous amount of public sequence data and advances in neural networks (NNs). There have been major advances in the performance of protein functional annotation (15), localization prediction (150), and tertiary structure modeling tools (2). Given the sequence and functional diversity of effectors, AI promises a new avenue to investigate characteristic features of effector proteins even in the absence of obvious sequence similarity or known functional domains, and with softer prior assumptions. Here, we review the current and potential applications of AI in effector protein biology, with a particular focus on effector identification, structure prediction, diversity, and evolution (Figure 1). Many examples focus on fungal effectors but are generally applicable to other plant pathology domains.

# 2. THE PROMISES OF MACHINE LEARNING IN PROTEIN BIOLOGY

## 2.1. A Brief Introduction to Machine Learning

AI encompasses any computational method that models data and generates outputs to summarize information or synthesize new data. In popular usage, AI often refers to generative models trained on diverse sources to perform a range of tasks for which they were not explicitly trained (e.g., ChatGPT). Machine learning (ML) focuses on targeted tasks by learning patterns in specific data sets and using trained models to make predictions or summaries. Typically, ML is trained on user-labeled data sets defining a ground truth, enabling the model to predict labels on new data—for example, classifying proteins based on their sequences or predicting disease severity from environmental and genetic data. Tasks using labeled data are called supervised learning. ML also includes
unsupervised learning, which identifies relationships within data without prior labels, using tech-
niques like dimensionality reduction [e.g., principal component analysis (PCA)] or clustering. The
mathematics behind ML and statistical modeling are often similar, blurring distinctions when
classical methods like linear regression or hidden Markov models (HMMs) are applied. The key
difference lies in their objectives: ML prioritizes accurate predictions without necessarily focusing
on interpretability, whereas statistics emphasizes understanding what the model has learned ac-
cording to statistical theory. ML can forgo some explicit assumptions required for causal statistical
inference, allowing for more complex models as long as predictive performance remains high.

AI is expected to transform numerous aspects of scientific research, including literature
analysis, predictive modeling, and guiding scientific experimental design (166). For accessible
introductions to the major concepts in ML, we refer readers to recent reviews by Greener et al.
(44) and Pichler & Hartig (113). ML techniques such as linear models, tree ensembles (e.g.,
random forests), and support vector machines proved highly performant for many problems

**Support vector machines:** supervised machine learning methods to find the best boundaries between data subsets

<figure><img src="image_7.png" /><figcaption>(Caption for Figure 1 appears on following page)</figcaption></figure>
An overview of effector-driven research in plant pathology showing key contributions from artificial intelligence (AI) tools. We classified the contribution of AI to effector biology into sequence-based, structure-based, and diversity-based insights according to the type of effector knowledge they exploit and generate. Relevant experimental data and AI tools are indicated in colored boxes. Sequence-based approaches primarily use genome and RNA sequencing data to perform gene prediction and annotation tasks, including effector identification. The low conservation of effector genes, their short size, conditional expression, and extreme functional diversity make this step challenging, justifying the recourse to AI tools. Elaborate and dedicated experimental and AI methods provide insights into the molecular functions of effectors using sequence information, which can be completed using structure-based information. Structure-based discovery exploits effector protein 3D structures determined experimentally or via AI tools to refine molecular function predictions, a major goal being the identification of host target proteins and binding interfaces. The limited repertoire of experimentally determined effector structures, lack of sequence alignment, and presence of intrinsically disordered segments are bottlenecks of analysis at this stage. The identification of effector sequence and structure enables the determination of effector families, the taxonomic distribution of effectors, and diversity-based analyses. This step relies on sequence information for diversity panels, population genomics, and ecology information. In combination with field sequencing, epidemiology, and plant diversity data, AI methods open perspectives for the prediction of effector variant effects, disease dynamics simulations (including potential for resistance breakdown and virulent strain emergence), inference of past ecological histories, and predictions for trajectories under simulated selective constraints. Abbreviations: DGMs, deep generative models; EM, electron microscopy; ESM, Facebook's evolutionary scale modeling neural networks; GANs, generative adversarial networks; HMMs, hidden Markov models; NMR, nuclear magnetic resonance; LOR, loss of resistance; LOS, loss of susceptibility; PLMs, protein language models; PPI, protein-protein interaction; TT3D, Topsy-Turvy 3D.

encountered in phytopathology (142). However, NNs have dominated the field of complex modeling tasks in recent years. The following primer first presents the basic concepts necessary to understand how and why NNs are so successful in protein biology and then describes how they can transform effector research in the post-genomic era.

## 2.2. From Linear Models to Neural Nets

Biologists commonly use linear regression to assess the impact of explanatory variables on a response variable. By applying a nonlinear function like a sigmoid transformation to the output, linear models can also be used to perform classification, leading to models like logistic regression. These generalized linear models use link functions to transform implicitly normally distributed predictions into different distributions. NNs use similar activation functions to capture complex nonlinear relationships between variables and outputs. A basic NN consists of layers containing numerous linear models with activation functions, with multiple layers stacked together, each taking the outputs of previous layers as inputs (Figure 2a). Deep NNs with several layers can make predictions from extremely complex inputs. This ability to stack and combine layers in intricate architectures makes NNs particularly powerful and adaptable. Intermediate results from middle/ hidden layers in a neural network, called embeddings, can be interrogated to understand what the model is learning. An embedding with three numbers can be considered as a point in 3D space; embeddings with similar values are close together in a 3D scatterplot, and trends along an axis of the embedding may indicate the presence or absence of a feature (e.g., a protein functional domain). Readers familiar with PCA may consider embeddings as loosely analogous to components but without the guarantees about variance.

NNs learn in an iterative process. During training, weights are initialized with random values and used to make a prediction, which results in a loss statistic that indicates how bad the prediction is (e.g., mean squared error). The weights are then modified in small increments to reduce the loss by a process called gradient error back-propagation, and this cycle is repeated many times until the loss is acceptably small (125).

The modern success of NNs is attributable to increased data and computational power as well as to advances in training larger deeper models. Two model architectures currently dominate large

**Embeddings:**
data represented as
numerical vectors,
placing features with
similar properties close
together in a shared
vector space
NNs and are relevant to protein modeling: convolutional NNs (CNNs) (42), which apply numerous small models in a sliding window across an input sequence or image, and transformers (161), which are used in most modern complex NNs, including ChatGPT and AlphaFold2. Transformers are built around a mechanism called attention, which enables a model to dynamically identify information relevant to the ML task (Figure 2b). Briefly, in attention we search a set of key embeddings, such as a sequence of embedded amino acids (AAs), with a query embedding (a single AA embedding), and compute the similarity between each pair. The similarity of each key is then used to scale its corresponding value embedding, and the sum of all weighted values forms a new attention output embedding for the query. The values with key embeddings that are more similar

**Transformers:**
neural networks using attention to model relationships in sequential data by comparing learned embeddings to identify and retrieve relevant information

<figure><img src="image_8.png" /></figure>

(Caption for Figure 2 appears on following page)
An overview of protein language models and their applications. (a) Multilayer perceptron, a basic neural network (NN) composed of numerous linear models with nonlinear transformations. Each of the three values in the embedding is the sum of the inputs multiplied by their weights. A fixed nonlinear activation function adds additional complexity to this hidden layer, and the resulting activations are fed into a final linear model to make a prediction. Weights are initially random but are iteratively updated to minimize the difference between the predicted and true outputs (the loss). (b) A simplified representation of a self-attention NN layer and self-supervised-learning training objective for a protein sequence. In this example, the third amino acid (AA) is artificially masked, and the network is asked to predict the original unmasked AA. An input embedding of the AAs is converted into new query, key, and value embeddings by sliding three separate NNs over the input sequence. The query and key embeddings are matrix multiplied to construct an attention matrix, which highlights information relevant to each AA based on the similarity of each column in the query and key matrices. The box in the attention matrix shows the similarity of each key to the query for our masked AA. These attention values are used as weights to scale each column in the value embeddings, and the row-wise sum forms the attention output for each AA. Multiple self-attention layers are usually trained, another sliding NN combines all the attention output embeddings into a new embedding, and the value of the masked AA can be predicted and used to update the NN weights. The row-wise averages of AA-level embeddings can be used as a representative whole protein-level embedding. (c) A plot of t-distributed stochastic neighbor embeddings (TSNE) compressing ESM2 protein level embeddings into two dimensions for the whole *Blumeria graminis* f. sp. *hordei* proteome, which shows that effector proteins and RALPH family effectors are generally close together in the embedded space. (d) These protein embeddings can be used as inputs for other data analysis tools like independent component analysis (ICA). (e) One ESM2 attention matrix for the *Blumeria graminis* f. sp. *tritici* (Bgt) effector AvrPm2 sequence, which closely resembles the protein structure. (f) A summarized attention importance for each residue, highlighting many of the conserved sites described by Cao et al. (22). (g) A naive fine-tuned model to predict effectors in the Bgt genome was trained and Shapley additive explanations (SHAP) feature importance scores for a positive effector prediction of AvrPm2 were computed for each AA, highlighting residues in the loop regions around β-sheet 3. DT, PD, QGG, and SP in panels f and g indicate the identities of amino acids highlighted by peaks in SHAP and attention flow scores.

to the query embedding contribute more to the attention output. In self-attention the key, query, and value embeddings are all learned from the same input AA sequence with separate NN layers, and all individual AA residues are used as queries to find relevant information in the other AAs. Because the embeddings are learned from separate NN layers, the NNs generating the key and query embeddings determine what regions of the AA sequence are of interest, and the NNs generating the values determine the information extracted from those regions. In cross-attention, we instead compare two sequences, where the query embeddings from one sequence retrieve information from the pairs of key-value embeddings in another sequence. Multiheaded attention layers are composed of numerous attention layers, which can each learn different query, key, and value transformations to identify and retrieve multiple sources of relevant information (161). The key advantages of transformers are their applicability to diverse problems (particularly in protein, language, and image modeling), increased ability to identify relationships between features that are far apart in the input (e.g., an AA at the end of the sequence with one at the beginning), and speed of training compared with some other language modeling architectures (161).

## 2.3. Pretrained Protein Neural Networks

The modular and generalizable nature of NNs means that they are well suited to pretraining methods like transfer learning and self-supervised learning. In transfer learning, a model is first pretrained on a very large general data set before being fine-tuned to learn a more specific task that might have fewer available training data. For example, a model might be pretrained using a large data set of diverse images and then repurposed to distinguish images of diseased leaves from nondiseased leaves. Pretraining using a large initial data set allows a model to learn the general structure of a data type (e.g., what a human face or leaf looks like), which may not be possible with a smaller data set.

In self-supervised learning, an AI model receives an artificially noisy version of a sequence (e.g., part of a protein sequence is considered as missing data) and is trained to recover the original non-noisy input (e.g., predict the missing AAs, typically called denoising) (Figure 2b). In cases where

Self-supervised
learning:
learning from
unlabeled data by
predicting original
values from slightly
modified inputs (e.g.,
imputing missing
amino acids)
very large amounts of unlabeled data exist (e.g., images, protein sequences), this is an effective way to learn complex relationships between spatially related features (11). Numerous self-supervised pretrained protein language models (PLMs) have been developed using similar methods, of which the most used currently are Facebook's evolutionary scale models (ESMs) (52, 84) and ProtT5 (33).

## 2.4. Fine-Tuning and Other Perspectives Offered by Protein Pretrained Neural Networks

Pretrained PLMs can learn very complex information about sequences without being explicitly trained for those tasks, including information relevant to protein evolution, structure, and function (130). These large models can be fine-tuned for specific problems with fewer available data; for example, ESM2 is used in the ESMFold protein structure prediction tool (84), and ProtT5 is used in the CATHe tool for remote homology detection with much greater sensitivity than HMM-based methods (102). Similarly, fine-tuned PLMs are used in many of the latest protein localization tools used in effector protein prediction (discussed below).

Some models combine self-supervised learning with an additional task to learn more general data representations. ProteinBERT (18) was pretrained to denoise the sequence while simultaneously predicting gene ontology (GO) terms. Similarly, ProstT5 (53) was pretrained to predict summarized structural information from sequences in AlphaFoldDB (160). Recently, ESM3 (52) was trained to bidirectionally predict sequence, tertiary structure, and InterPro functional annotation, meaning that users can both predict a protein's function or structure given its sequence and generate novel protein sequences that satisfy a particular function or structure. This multi-objective (a.k.a. multi-modal) training explicitly forces models to also learn specific aspects about protein biology, improving performance on downstream fine-tuned applications.

Recent progress in protein language modeling has focused on reducing model size, optimizing pretrained models for fine-tuning, and identifying limitations. Elnaggar et al. (32) attempted to identify the fundamental requirements of pretrained protein models and developed Ankh to focus on enhanced fine-tuning applicability while also reducing speed and hardware requirements (32). Although PLM embeddings show early promise for modeling tasks, researchers are still learning the limitations of pretrained models and how different model architectures can capture various aspects of protein diversity (28).

## 2.5. Unsupervised Applications of Protein Embeddings

Pretrained models also yield embeddings that can be interrogated to identify useful latent information in proteins. Even simple distance metrics between embeddings can find low-identity protein homologs and cluster protein families (15). Ziegler et al. (181) use a variational autoencoder (VAE) NN to demonstrate that embeddings can accurately identify protein families, taxonomic distributions of protein families, and functional differences between different members in different dimensions of the embedding.

Embeddings recover information relevant to effector biology without any fine-tuning. Analysis of the *Blumeria graminis* f. sp. *bordei* DH14 proteome embedded by ESM2 (35M_UR50D version) clearly separates effectors, effector candidates (by EffectorP), and predicted secreted proteins (by SignalP 6.0) from the bulk of the proteome (Figure 2c). A similar approach is used in the effector prediction server Fungtion (81), which provides tree visualizations comparing effectors based on the similarity of their ESM1b embeddings. Embeddings can be further interrogated using methods like PCA or independent component analysis (ICA). For example, Figure 2d shows one ICA component of the *Blumeria* ESM2 embeddings, which distinguishes a group of effector-like proteins.

**Protein language models (PLMs):** modeling protein sequences as language to learn general properties and relationships between sequence parts; often pretrained using self-supervised learning

**Autoencoder:** unsupervised neural network that compresses input data into restricted embeddings and then reconstructs them, forcing the embedding to summarize common patterns
Heatmap plots of attention matrices from PLMs can highlight important aspects of individual proteins. Attention matrices often show high values at rows and columns corresponding to functionally relevant sites, including protein binding and functional domains, and high attention values for pairs of residues can also indicate their proximity in 3D space (163). **Figure 2e** shows one attention matrix from the last layer of ESM2 that highlights information relevant to embeddings of the AvrPm2 effector sequence, in which the locations of high attention scores recover known secondary and tertiary structure features of the effector. The overall attention for each position in a protein sequence throughout a whole transformer model can be approximated as network flow paths through the attention layers (1). **Figure 2f** shows estimated attention flow through the whole ESM2 network for AvrPm2, which indicates high overall attention at the predicted signal peptide cut site, the cysteines, and several of the other conserved sites described by Cao et al. (22). InterPLM has also shown early promise in interrogating PLM residue embeddings using sparse autoencoders (136). Methods are available to interpret what supervised models have learned by identifying which parts of a protein are important for making a prediction (86). **Figure 2g** shows Shapley scores (86) indicating important sites for positively predicting AvrPm2 as an effector, using a naive fine-tuned ESM2 NN trained to predict *Blumeria* effector candidates, secreted proteins, and nonsecreted proteins. Although the AvrPm2 MLA-binding region encompasses beta sheets β1, β2, and β6 (88), the regions important for binding in structurally similar AVRA10 and AVRA22 are in the region corresponding to AvrPm2 β3 and β4 with high Shapley scores (22). GradCAM (133) was similarly used to interrogate the effector prediction tool DeepRedEff (72). These examples illustrate how interpretable AI may be used to investigate features of effector proteins in combination with expert knowledge and independent data to avoid overinterpretation (100). Pretrained PLMs are already the foundations of many state-of-the-art protein prediction tools discussed throughout this review, and we expect that this trend will continue.

# 3. SEQUENCE-BASED INSIGHTS: EFFECTOR IDENTIFICATION AND FUNCTION PREDICTION

## 3.1. Gene Prediction

Erroneous prediction of start codons, gene fusions, or splits may impact protein property prediction (e.g., signal peptides, domain presence, or absence) and the identification of protein families and their evolutionary histories. Beyond RNAseq, experimental methods that can enhance accurate coding sequence prediction, such as ribosome profiling (19), have received little attention in plant pathology. Nevertheless, these specialized methods have highlighted cases of complex translational regulation and unusual gene structures (66, 165). Current gene prediction tools fit an HMM to learn gene structure relevant to a few species (40). Deep-learning methods for gene prediction have been recently developed from high-level taxonomic groups to benefit from more training data and learn more diverse gene patterns. Tiberius, for example, was trained to predict mammalian genes without any experimental evidence (e.g., RNAseq, protein alignments) and outperforms current state-of-the-art evidence-guided models (39). Helixer adopts a similar approach and has models available for bacteria and fungi (56), although improved accuracy over classical HMMs for these taxonomic groups remains to be demonstrated. ML can also be used to evaluate existing gene predictions. Genes encoding proteins whose structures can be predicted confidently using tools like AlphaFold can be a useful indicator of protein validity and may be particularly helpful when evaluating multiple gene models or isoforms (140). Similarly, Psauron uses ML to evaluate how likely a predicted open reading frame is to encode a real protein based on observed codon usage and is applicable to all eukaryotes (141).
## 3.2. Effector Identification

For most bacterial pathogens, the primary task in effector identification is identifying proteins secreted by the type III alternative secretion system, which are directly injected into the host cell (85). Fungal and oomycete effectors have no such specialized secretion pathway, and effector protein sequences tend to be more divergent, so additional steps are required to predict known and novel effectors. Most oomycete effectors possess either an RxLR motif (167), WY-domain(s) (171), or a CRN motif (131), which can be detected with sequence profile methods like EffectorO (105) and EffectR (148).

Most fungal effectors lack any characterized functional domains, and researchers search instead for indirect features required for effector function (e.g., secretion signals) or commonly observed characteristics (e.g., small size, cysteine content) (85). As exceptions to these assumed common effector properties started to accumulate, EffectorP, the first ML application specific to effector discovery, was developed to make predictions balancing competing protein characteristics (146). Numerous other ML effector prediction tools have been developed since (85), including refined versions of EffectorP (143, 144), models tailored to oomycete effectors (105, 177), new tools for host subcellular localization prediction (145) and motif identification and clustering (20), and meta-models combining numerous effector prediction software (63). These examples all use tailored features that reflect prior assumptions about effector proteins (e.g., cysteine content, protein size, overall protein charge). DeepRedEff (72) broke this trend by using multiple NN architectures to learn features directly from the protein sequence. Similarly, Effector-GAN (generative adversarial network) (168) used a mixture of pretrained protein embeddings and more traditional protein properties' summary statistics. Fungtion (81) uses the ESM1b pretrained embeddings described earlier as input features for classification. Because the primary complexity of the model comes from the pretrained embeddings, Fungtion can model complex representations of effector data sets with a reduced risk of overfitting. In the case of newer NN methods like DeepRedEff and Fungtion, the explicit prior assumptions about the properties of effectors (e.g., size, cysteine content) are eliminated, and features are entirely informed by the chosen training data and model architecture.

Selection of training data has a major impact on the model performance and its biases. All effector prediction tools are limited by the relatively small number of known effectors available for use as positive training examples and the difficulty selecting representative negative data that exclude effector-like proteins of interest, which both carry the risk that a model will overfit to the training data and perform poorly on previously unseen proteins (effectors or otherwise) (142). Several approaches have been adopted to account for the small number of positive effector training examples. For example, EffectorP 2 and 3 (143, 144) and Fungtion (81) construct an ensemble of models using different balanced subsets of the negative data. Predector uses loss weighting so that negative and positive samples have a proportionally equal impact on the model training (63). Naive combinations of multiple effector prediction methods to form candidate lists often yield an impractical number of candidates or overlook interesting proteins. Meta-prediction approaches have been used to enhance prioritization, either by clustering candidates based on shared characteristics (129) or ranking them so that candidates with more effector characteristics appear at the top of a sorted list (e.g. Predector). This helps streamline subsequent manual curation and experimental validation. More recently, Effector-GAN (168) used synthetic data from a GAN to artificially increase the number of positive (effector) training samples, but the benefit to the model performance requires further evaluation.

Another strategy consists of assigning microbe lifestyles based on genome information before predicting lifestyle-associated genes. bacLIFE uses gene clustering and ML to infer lifestyles of

**Generative adversarial network (GAN):** two competing neural networks trained simultaneously; a generator synthesizes samples to fool the discriminator distinguishing real from generated samples
bacterial genomes and subsequently identify hundreds of genes potentially associated with plant-
pathogenic lifestyles, not limited to effector genes (45). CATASrophy uses the complement of
carbohydrate-active enzyme genes in fungal genomes to classify lifestyles, suggesting such an
approach might be possible with eukaryotes (50).

**3.3. Molecular Function Prediction**

ML has played a leading role in the genomics era of plant pathology research, with the prediction of secretion signals, transmembrane domains, and subcellular localization being standard practice in effector discovery (85). All the current state-of-the-art subcellular localization prediction tools use the same pretrained PLMs described above, including SignalP (150), DeepTMHMM (47), TMBed (14), and DeepLoc (153). The recent discovery of fungal effectors with antimicrobial activity, enabling pathogens to antagonize members of the plant microbiota (139), prompted the creation of the AMAPEC (antimicrobial activity prediction for effector candidates) model to predict this type of molecular function. A literature-curated set of 152 fungal antimicrobial proteins was used to train a classifier that identified novel potential antibiotics across diverse fungal lineages (95).

General ML protein functional prediction tools developed for the Critical Assessment of Functional Annotation challenge have improved annotation by integrating multiple sources of information (173), PLM sequence embeddings (73), and protein structural information (42). These serve as proof of concept for the next generation of protein functional annotation tools and may also help curate existing resources. For example, Bileschi et al. (15) developed a novel CNN to predict Pfam annotations and observed improved detection accuracy and specificity compared to the current standard HMMER. Moreover, they found that clustering the learned protein embeddings could effectively identify novel protein and domain families. The group later released a usable tool, ProteInfer, for GO term prediction using a similar model (127). This highlights the potential of large-scale ML and protein embeddings to provide functional predictions for proteins like effectors, which often lack known functional domains or have cryptic patterns that cannot be detected by HMMs alone.

**4. STRUCTURE-BASED INSIGHTS: FAMILIES AND INTERACTIONS**

Fungal effectors typically lack the sequence similarity and characterized functional domains re-
quired for the computational analysis of the function and evolution of proteins. Experimental
determination of structures for filamentous pathogen effectors revealed 3D fold similarity despite
highly divergent sequences, defining the WY-fold (171), MAX (*Magnaporthe* AVRs and ToxB-like)
(25), RALPH (RNase-like proteins associated with haustoria) (117), and LARS (Leptosphaeria
avirulence and suppressing) families (76). The cost of protein structural determination is pro-
hibitive for high-throughput protein family identification. Sequence-based methods, including
cysteine spacing (60) and HMMs (62), attempted to identify families of effector-like proteins,
but only the success of AlphaFold2 (65) in predicting protein tertiary structure opened the pos-
sibility of systematically identifying effector families and distant homologs at the genome scale
*(Figure 3a)*. We introduce here some of the key methodological advances made in computational
structure analysis and how they are advancing our understanding of effectors.

**4.1. Structure Prediction**

Structure prediction methods broadly fall into template-free and template-based approaches.
Template-free methods predict structures using physical principles and statistical patterns from
the protein sequence itself (29), whereas template-based methods leverage structural information
<figure><img src="image_9.png" /><figcaption>Figure 3</figcaption></figure>

Insights into effector diversity, functioning, and evolution enabled by artificial intelligence-based structural genomics. (a) The prediction of effector 3D structures using AlphaFold2 revealed families of sequence unrelated structurally similar (SUSS) effector candidates. Predicted structures for five effector candidates from *Sclerotinia sclerotiorum* are shown as ribbon diagrams oriented according to a 3D structural alignment. The connectors indicate pairwise percent identity in a multiple sequence alignment. Sequence comparisons alone were not sufficient to indicate possible functional and evolutionary relationships between these proteins. (b) Frustration, the unfavorable thermodynamic configuration of amino acids in folded proteins, coincides with effector-target interface in the *Magnaporthe oryzae* AVR-PikF-*Oryza sativa* OsHIPP19 complex. The structure complex (PDB:7B1I) was determined experimentally (87) with the residue frustration index (FI) mapped on the surface of AVR-PikF model. (c) A surface frustration-structure similarity trade-off in the evolution of SUSS effector families suggested by ancestral reconstructions and phylogenetic analyses of several SUSS family subclades (27). An increase in the mutation load over time alters ancestral effector structures in two major antagonistic ways: either by generating variant structure (high structural divergence) with high thermodynamic stability or by maintaining analogous structure (low structural divergence) showing higher surface frustration patterns. This model may explain how SUSS effector families emerged and maintained biological activity over long evolutionary periods in different environments.

from similar proteins found in databases like PDB or SCOP (169). Both approaches often con-
struct a contact map storing the physical distance or likelihood of proximity between residue pairs
as a square matrix (34). Coevolutionary structure prediction methods utilize the observation that
covarying residues in a multiple sequence alignment (MSA) are likely to be in contact within the
tertiary structures and can be used to construct contact maps (64, 98). Although coevolutionary
methods require numerous and diverse sequence homologs for accurate covariance estimation,
even sequences with very low similarity can be useful for contact map prediction (107).

The integration of AI in structure prediction came to prominence with AlphaFold2's signifi-
cant performance gains (65). AlphaFold2 primarily relies on coevolutionary information but can
also initialize a candidate contact map from homology templates. Briefly, AlphaFold2 takes an
input MSA converted to a numeric representation and an initial contact map, and iteratively re-
fines them both using an attention-based architecture called the Evoformer. Multiple Evoformer
layers repeatedly query the MSA representation to retrieve different information relevant to im-
proving the contact map predictions. Finally, structures are predicted from the contact map using
another series of attention-based layers. RoseTTAfold (6) and AlphaFold3 (2) employ broadly
similar approaches but differ in how they refine contact maps and generate the 3D structure.

Because AlphaFold2, AlphaFold3, and RoseTTAfold primarily use coevolutionary informa-
tion, their performance can significantly decline when the input MSA contains too few sequences
or insufficient diversity (65, 107). ESMFold addresses this limitation by replacing the MSA and in-
put encoding model with a pretrained PLM (ESM2) and predicts contact maps from the attention
matrices (84), which significantly reduces the runtime and avoids issues associated with poor-
quality MSAs. Although ESMFold's accuracy is slightly lower than AlphaFold2 and RoseTTAfold
under normal operation, it is notably higher when few or no homologous sequences are present
in the MSA (84).

## 4.2. Novel Effector Families and Their Functional and Evolutionary Diversity

Numerous studies have utilized protein structural predictions to identify novel sequence-
unrelated structurally similar (SUSS) families of fungal effectors, including KP4-like (24, 27, 154);
KP6-like (24, 123, 154, 174); ZiF (zinc-finger)-fold (26); Mig1-like and AvrSen1-like (99); and
AvrSr35-like, AvrSr50-like, Tin2-like, and ART (ADP-ribosyltransferases) (134). Expansions of
SUSS families have also been identified in new taxa, including MAX in *Venturia inaequalis* (123)
and FOLD (Fol dual-domain) in *Rhizophagus irregularis* (151). Most discovered families are ex-
panded in a small number of genomes and are present at lower frequencies in more distant taxa.
These include the LARS (*Leptosphaeria*), TIN2-like (*Ustilago*), MAX (*Magnaporthe*), and RALPH
(*Blumeria*) families. The *V. inaequalis* genome notably exhibits expanded families of LARS, MAX,
and ToxA-like proteins (123). Similar patterns are observed in oomycete RxLR effectors, which
are considerably expanded in many Peronosporaceae and *Pythium* species compared to other
oomycete pathogens (128). In contrast, de Guillen et al. (24) showed that structural homologs
of the antifungal proteins KP4 and KP6 are widespread in fungi and KP4-like proteins are also
found in plants.

These studies suggest that pathogen effectors share more commonalities than previously
thought and that effector families likely share common ancestors that have been duplicated and
diversified to acquire new functions while retaining similar structures (27, 135). Most SUSS fam-
ilies do not have a known conserved function, but numerous effectors appear to have emerged by
neofunctionalization of functionally conserved secreted protein families like antimicrobial yeast
killer toxins (154), antimicrobial defensins (123), ADP-ribose transferases (134), and hydrophobins
(135). Some effectors like *Zymoseptoria* Zt80707 gained function by acquiring a signal peptide
(116). Despite structural similarities, family members can exhibit vastly different functions and
host localizations. For example, oomycete WY-domain effector activities range from avirulence
elicitation (31) to immune signaling suppression via host MAP-kinase interaction (70). The KP6-
like family includes both antifungal toxins and the fungal effectors AvrLm6, AvrLm10A, SIX5,
and BAS4 (24). Similarly, the MAX family includes both host-intracellular avirulence effectors with heavy metal-associated (HMA)-domain binding activity (46) and the apoplastic ToxB toxin from *Pyrenophora* (37). Within species, expanded effector families also exhibit diverse activities. In *Magnaporthe*, MAX effector sequence diversity appears to be associated with host preference (77), and 20 subfamilies containing the core MAX fold have been described as possessing additional structural features and distinct surface properties (74).

Structural prediction may not be necessary to identify remote homology with comparable sensitivity to structure comparison. Predicting FoldSeek 3Di protein representations (159) from PLMs as proposed in ProstT5 (53) can dramatically increase the speed and sensitivity of remote homology detection compared to HMMER3 when the sequence identity falls below 20%. Distant homologs can also be identified by protein-level embedding searches using tools like DeepBlast (48). These methods could significantly improve high-throughput effector family identification and functional annotation transfer in the future.

Another common trend observed in effector structure prediction research is the prevalence of intrinsically disordered regions (IDRs) lacking stable tertiary structures. This is an inherent limitation of structural studies, and overfocusing on protein structures may overlook an important feature of pathogen virulence factors (23). However, AlphaFold can reliably predict IDRs and transient structures within IDRs (4, 114). Further interrogation of proteins with low overall confidence but high local confidence may be fruitful for disordered effector candidates. PLM embeddings may also help interrogate virulence-related information in IDRs, as they do not require protein structural information.

The progress in identifying effector families enabled by AI-based structural predictions is a significant advancement in effector biology, but the expected functional diversity within families and potential correlations with taxonomy or pathogen lifestyle remain largely unexplored. Predicting novel functional domains in effectors presents a promising new avenue of investigation with clear experimental implications (3, 41).

### 4.3. Effector Interaction with Plant Proteins

Effectors with similar structures can exhibit different functions, interacting sites, and host subcellular localizations (22, 106). The improved accuracy of protein structure prediction may enable the prediction of pathogen-host protein interactions, with a major challenge being the detection or design of candidate cognate immune receptors for key effectors (Figure 1). Large-scale experimental PPI studies such as co-immunoprecipitation (112) and cDNA library yeast-two-hybrid (Y2H) screens (43, 69, 93) have shown promise in identifying plant-interacting partners of effector candidates. Identifying specific interacting proteins and interfacial regions remains a slow process, relying on techniques like cryo-EM (22) and protein truncations (88). Computational prediction of pathogen-plant interacting protein pairs can complement these experimental methods by refining sets of candidate PPIs (110) or as de novo tools to refine candidate effectors before experimental validation.

#### 4.3.1. Protein-protein interaction prediction.
Protein interaction prediction methods specialize in determining whether two proteins are likely to interact but do not necessarily identify the interacting sites. PPI annotations can be transferred to new proteins by identifying orthologs of conserved interacting proteins (interlogs) (78) or pairs of known interacting domains (147); however, the accuracy of annotation transfer degrades with evolutionary distance (96). Recent NNs that directly use protein sequences have reported significantly improved performance (51, 137, 138). These models are generally constructed as twinned networks, where both proteins pass through an identical encoding NN, their embeddings are combined, and a final classifier layer
predicts interaction. Just like in structure prediction, coevolutionary variance between the two
proteins is also a reliable indicator of interacting protein regions and is often used by the latest
NN PPI predictors (49). Some models such as DPPI (51) use coevolutionary information from
sequence homology searches, whereas others such as D-SCRIPT (138) use pretrained sequence
embeddings. Uniquely, D-SCRIPT and its descendant Topsy-Turvy models (137) combine the
twinned embeddings into a predicted contact map, which is then used for PPI prediction. A re-
cent benchmark of NN-based PPI prediction tools by Barnett et al. (13) demonstrated that the
reported accuracy of these models is significantly inflated because of flawed evaluation and hy-
pothesized that models are simply identifying similar sequence pairs to positive training samples.
They report that a comparatively simple method, SPRINT (82), based on local sequence align-
ment and PPI network topology often outperforms sequence-based NN methods, particularly
when test data sets diverge significantly from the training data set. This highlights both the dif-
ficulties of developing truly de novo PPI prediction tools and the significant impact of data set
selection on model performance and evaluation.

Despite these overall advances, large-scale PPI prediction has had limited impact on plant pathogen research so far. Most PPI prediction studies in effector biology have used homology transfer and attempted to construct databases of candidate PPIs, such as *Ralstonia-Arabidopsis* (83), *Magnaporthe*-rice (178), and *Ustilaginoidea virens*-rice PPIs (175). Specialized tools like DeepHPI (68) and AraPathogen (80) have been developed for host-pathogen PPI prediction, but generalizing models across multiple host-pathogen pathosystems and species remains challenging. Nevertheless, computational models are most valuable when combined with experimental data and critical biological interpretation. For example, the experimental demonstration that several extracellular effectors inhibit plant proteases prompted an AlphaFold-Multimer (35) search for putative interactions between 1,879 effector candidates from multiple pathogens and 6 defense-related hydrolases from tomato. Four novel inhibitors of P69b hydrolase were then validated by activity labeling (57). Although the literature in this area is sparse, expert interrogation of predicted plant-pathogen PPIs may yield valuable insights and inform future experiments in effector research (58).

PPI networks integrating multiple sources of evidence (e.g., gene co-expression, protein co-localization, experimental and computational PPI predictions) can be refined using graph theory to infer missing or false PPI predictions using local network topology (79, 110). Because pathogens often target similar host pathways (170), collecting and comparing multiple pathogen-host PPI networks may enhance our understanding of effector functions across different hosts or lifestyles. Analyzing host PPI networks, even without pathogen-interacting partners, may identify resistance hubs and candidate effectors that manipulate resistance signaling pathways (97). Several small pathogen-host interaction databases exist (5, 67, 180), and tools like PPIntegrator (92) have been developed to integrate multiple sources of host-pathogen PPI relevant information and predict novel interacting pairs from networks. Established PPI databases like IntAct and BioGRID also serve as valuable resources.

**4.3.2. Effector binding site prediction.** Protein binding regions often exhibit physico-
chemical biases in their surface exposed residues and can sometimes be identified independently
of their interacting partner (120). Tools that identify these binding regions are generally focused
on drug design (108), but again NNs have been developed to predict protein binding hot spots
from sequences (59) and structures (156).

The common observations of effectors with membrane-binding motifs (162), pseudo-enzymes (38), surface frustration (27), bioactive charged patches (176), IDRs (89), and variations in binding site locations within effector families (22, 46) suggest that binding site prediction may be
another useful tool when investigating effector families and effector candidates independently of plant-interacting partners. For example, Waksman et al. (164) predicted binding sites in the green peach aphid using ScanNet (156) and identified candidate protein interacting regions within small patches of hydrophobic residues of disordered proteins, and in several enzymes away from their canonical active sites. In the complex of *Magnaporthe oryzae* AVR-PikF MAX effector with the HMA domain of its rice target OsHIPP19 (PDB:7B1I) (87), a patch of frustrated residues at the surface of the effector corresponds with the binding interface (Figure 3b). Surface frustrated residues are generally more prone to establish intermolecular interactions and could serve to identify candidate binding interfaces (27). Likewise, surface stickiness indicates amino acid propensity to interact with other proteins and can be visualized using tools like SURFMAP (132). Analysis of candidate binding sites may be particularly useful in identifying alternative functions of pseudo-enzyme effector candidates.

**4.3.3. Effector docking studies.** Protein docking methods take two protein structures and attempt to find possible interaction configurations between them by minimizing an energy function based on steric and physicochemical properties (109). Most docking literature relevant to plant pathology to date has focused on identifying metabolites that may inhibit effector activity (115). However, docking has been used to model a putative interaction between the *Melampsora larici-populina* effector Mlp37347 and *Arabidopsis* GAD1, which had previously been demonstrated in orthologs with Y2H studies (118). Rozano et al. (124) benchmarked classical docking approaches against known binding structures of MAX effectors and plant HMA domain proteins. They recovered near-native conformations but required optimized docking parameters that may not translate well to other effector-receptor interactions. Protein docking is not necessarily a reliable method of identifying interacting protein pairs (157), and for nonexperts it may be more suitable for investigating known interactions than de novo PPI prediction.

**4.3.4. Fold-and-Dock.** AlphaFold2 can predict multimeric protein structures by artificially linking two input sequences with a long stretch of glycine residues (71). This fold-and-dock approach was extended by manipulating how AlphaFold2 interprets sequence distance, effectively creating a space without introducing implicit covalent bonds (6, 35). A benchmark of AlphaFold2-based fold-and-dock methods showed that AlphaFold2 showed a significant improvement over regular docking approaches (e.g., ZDOCK), even for transient PPI structures (172). AlphaFold3 improved the accuracy of fold-and-dock protein modeling further and introduced the ability to incorporate additional molecule types (e.g., ions, DNA) (2).

A comparison of predicted complexes of the effector-receptor pairs Sr35-AvrSr35, RPP1-ATR1, and Roq1-XopQ with their known cryo-EM structures indicated that AlphaFold2-multimer can predict the general interacting regions but could not accurately predict specific atomic interactions or the residues important for the binding interface (36). AlphaFold3 was also unable to recover the native conformation of the cryo-EM structure of the *Blumeria RALPH* effector AvrA13 interaction with barley MLA13 (75), which was reflected by a low confidence score. AlphaFold3 iLDDT scores may help separate real from spurious PPIs (2, 90). However, a benchmark of effector-NLR interaction predictions using AlphaFold2-Multimer found no difference in AlphaFold confidence scores between known interacting pairs and a set of negative controls (36), suggesting that docking confidence scores alone cannot reliably identify true interacting pairs. Overall, fold-and-dock approaches appear to be valuable for evaluating coarse structural interfaces when an interaction is known to occur, but they may not yet be reliable for predicting whether two proteins interact. Dedicated tools for PPI prediction or postprocessing of binding affinity scores (36) may be more viable for high-throughput prediction of plant-pathogen protein interactions.

**Parameters:**

variables in an ML model that are trained; their learned values are called weights or coefficients
# 5. DIVERSITY-BASED INSIGHTS: SITE, GENE, AND POPULATION EVOLUTION

The democratization of genome sequencing gives access to the diversity of pathogen species and genetic variation in pathogen populations. The sequencing of herbarium samples also provides information on the evolutionary history of a few preserved strains. Yet the available genomic information does not directly inform on processes that generated extant diversity patterns, such as organism dispersal and ecological constraints. ML methods are being developed to fill this gap, and effector evolution could be a key feature in predicting population and evolutionary dynamics.

## 5.1. Detection of Sites Under Selection and Variants Evading Immunity

Selection analyses within pathogen populations often reveal that genes under positive selection have virulence functions (149). Assessing selection signatures in the context of protein 3D structures can help identify molecular functions underlying these signatures (116, 135). However, the extreme diversity of effector proteins and their complex interactions with hosts complicate long-term evolutionary analyses. Emerging ML methods can estimate site-specific evolutionary rates without sequence alignments, which is valuable for understanding functionally relevant sites in orphan effector proteins (101). Structure prediction tools or specialized tools like DDMut can predict the effect of point mutations on protein stability or PPIs (179), aiding in designing mutagenesis experiments for functional characterization. For example, Khan et al. (69) used AlphaFold2 to predict structures of *Ustilago* effectors Tip6 and Tip7 in complex with maize TOPLESS protein RELK2, introduce targeted mutations at predicted binding sites, and identify interacting residues via Y2H assays. Alternatively, exploring why and how predicted energetically suboptimal residues are conserved in effector proteins can provide insights into natural diversity (30).

With an aim to anticipate pathogen evolution, GAN architectures have been used to predict the evolution of viral populations. For instance, VirusHound-I was trained to predict viral proteins that evade the host immune system (9). Computational exploration of point mutations may help identify avirulence effector variants that evade host resistance recognition or discover novel pseudo-enzyme effector candidates. Reciprocally, this strategy could help identify effector targets in plant germplasm with altered binding capabilities, giving rise to loss-of-resistance or loss-of-susceptibility useful in crop breeding (Figure 1).

## 5.2. Protein Ancestral Sequence Reconstruction and Evolutionary Trajectories

Beyond individual mutations, analyzing selection history and evolutionary rates at the protein level can illuminate historical evolutionary events, including speciation and selective pressures. Classical methods involve selection indices or simulating evolutionary events to explain observed gene trees or ancestral recombination graphs (54). Ancestral sequence reconstruction predicts ancient ortholog sequences based on observed protein family sequences, aiding in identifying past selection events or functional divergence points in gene trees (94). Integrating these methods with structural predictions may help explain effector SUSS family histories, although extreme sequence divergence may hinder accurate phylogenetic reconstruction at the family level. The analysis of structural similarity and surface frustration in SUSS subfamilies revealed an evolutionary trade-off between effector structure stability and thermodynamic stability (Figure 3c). Surface frustration therefore appears as a likely driver of effectors’ evolvability by enabling new molecular interactions and preserving the overall 3D fold following functional switches (27).

Embeddings from PLMs capture evolutionarily relevant information about protein taxonomic and functional diversity, aiding in understanding protein family evolutionary histories (122). Generative models like VAEs can synthesize new proteins near targeted embedded proteins,
and resampling many synthetic proteins across an entire protein family estimates the family's
evolutionary history as a distribution in the embedded space (181). Paths through this embedded
distribution can identify functional and taxonomic divergence points. Similarly, constructing di-
rected protein similarity networks from protein embeddings can reveal distant evolutionary roots,
order proteins in evolutionary pseudotime, and identify functional divergence branching points
(55). Using modern protein structural prediction and embeddings to predict ancestral states and
evolutionary histories of effectors may enable the plant pathology community to understand
common patterns of effector evolution and their contributions to species-level evolution.

**5.3. Looking into the Past and Future of Plant Diseases with Genomic Data**

The coevolution between host and viruses impacts genomic sequences, enabling ML-based pre-
diction of interactions between virus-host pairs at the species level from genomic sequences
(7). Species interactions may result in correlated demographic histories that can be revealed by
ML methods. For instance, an approximate Bayesian computation with random forests served to
reconstruct the past demography of monarch butterflies and their milkweed host plant, revealing
two events of population expansion for both species over the past 20,000 years (16). Similarly,
the coevolution between pathogen effectors and their host targets may enable the prediction of
pathogen host range, cryptic hosts, and vulnerable genotypes and reconstruct past episodes of
adaptation to diverse pathogen and host populations based on effector data. In another study,
an ML approach combined with phylogenetic reconstruction identified diurnal air temperature
range, soil sand content, and plant size as major drivers in cactus diversification (152). Similar ap-
proaches targeted at plant-pathogen interactions may help identify climatic factors that strongly
influence disease incidence and evolution.

A convergence of classical evolutionary theory and large unsupervised ML models is underway, exemplified by NN adaptations of fundamental tools for multiple sequence alignment (learnMSA) (8) and phylogenetic inference (103). These developments along with rapid simulations and re-sampling techniques made possible with ML may enable plant pathology researchers to predict historical selection events or simulate pathogen populations (121). NNs in combination with phylogenetic models have started to generate realistic descendance predictions at the sequence level for pathogens with small genomes. MutaGAN, for example, was trained using phylogenetic mod- els of protein evolution to predict the dynamics of influenza populations at the nucleotide level (12). Adapting these approaches to plant disease may enable the prediction of emerging aggressive strains and of the ecological contexts from which they are likely to emerge.

**6. CONCLUSIONS**

AI has already transformed effector research, and this shall continue as new user-accessible meth-
ods become available. Pretrained language models are expected to become commonplace tools for
investigating protein function and diversity, with potential applications extending to genomic anal-
yses using DNA language models like GPN (10). Unfortunately, many of the advanced AI tools
remain inaccessible to nonspecialists or exist only as ephemeral web services. This review fo-
cused on concepts and ML applications to best help researchers navigate the rapidly developing
landscape of methods with possible applications in plant pathology.

Tools for prediction of protein subcellular localization, effector characteristics, functional an-
notation, and PPIs are invaluable for identifying and refining sets of effector candidates. The
integration of PLMs and structural predictions into these tools has notably improved their perfor-
mance, aiding in the understanding of the diverse landscape of effector proteins and their activities.
AI tools accelerate and refine the generation of hypotheses on phytopathogen effector functions
that require in planta experimental validation, driving conceptual and practical advances in plant pathology.

Structural prediction has already had a major impact on effector research since the release of AlphaFold2 in 2021. The identification of novel SUSS effector families and effectors arising from neofunctionalization of conserved protein families has offered new perspectives on their functional diversity and evolutionary histories. Although the origins of plant-pathogen effector proteins have been elusive, protein embeddings and structural predictions may enable the investigation of their ancient evolutionary histories and their influence on host preferences or pathogen lifestyles.

Overall, AI offers new perspectives and improved techniques to advance effector biology and plant pathology. Evolutionary analyses of effectors using new ML methods may provide insight into their origins and effects on species evolution. Despite significant improvements observed in structural and functional predictions, experimental research remains essential. Nevertheless, high-throughput identification of effector candidates, effector families, and potential pathogen-host interactions continues to improve and accelerate our understanding of effectors.

## DISCLOSURE STATEMENT

The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

## ACKNOWLEDGMENTS

D.A.B.J. is supported by a post-doc grant from the INRAE division "Santé des plantes et environnement." S.R. is member of the "Laboratoire d'Excellence (LABEX)" TULIP (ANR-10-LABX-41) and the "École Universitaire de Recherche (EUR)" TULIP-GS (ANR-18-EURE-0019).

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