Structural basis of TMPRSS2 zymogen activation and recognition by the HKU1 seasonal coronavirus.

The human seasonal coronavirus HKU1-CoV, which causes common colds worldwide, relies on the sequential binding to surface glycans and transmembrane serine protease 2 (TMPRSS2) for entry into target cells. TMPRSS2 is synthesized as a zymogen that undergoes autolytic activation to process its substrates. Several respiratory viruses, in particular coronaviruses, use TMPRSS2 for proteolytic priming of their surface spike protein to drive membrane fusion upon receptor binding. We describe the crystal structure of the HKU1-CoV receptor binding domain in complex with TMPRSS2, showing that it recognizes residues lining the catalytic groove. Combined mutagenesis of interface residues and comparison across species highlight positions 417 and 469 as determinants of HKU1-CoV host tropism. The structure of a receptor-blocking nanobody in complex with zymogen or activated TMPRSS2 further provides the structural basis of TMPRSS2 activating conformational change, which alters loops recognized by HKU1-CoV and dramatically increases binding affinity.

Cryo-EM Structure of the Human Cannabinoid Receptor CB2-Gi Signaling Complex.

Drugs selectively targeting CB2 hold promise for treating neurodegenerative disorders, inflammation, and pain while avoiding psychotropic side effects mediated by CB1. The mechanisms underlying CB2 activation and signaling are poorly understood but critical for drug design. Here we report the cryo-EM structure of the human CB2-Gi signaling complex bound to the agonist WIN 55,212-2. The 3D structure reveals the binding mode of WIN 55,212-2 and structural determinants for distinguishing CB2 agonists from antagonists, which are supported by a pair of rationally designed agonist and antagonist. Further structural analyses with computational docking results uncover the differences between CB2 and CB1 in receptor activation, ligand recognition, and Gi coupling. These findings are expected to facilitate rational structure-based discovery of drugs targeting the cannabinoid system.

Transcription Elongation Can Affect Genome 3D Structure.

How transcription affects genome 3D organization is not well understood. We found that during influenza A (IAV) infection, rampant transcription rapidly reorganizes host cell chromatin interactions. These changes occur at the ends of highly transcribed genes, where global inhibition of transcription termination by IAV NS1 protein causes readthrough transcription for hundreds of kilobases. In these readthrough regions, elongating RNA polymerase II disrupts chromatin interactions by inducing cohesin displacement from CTCF sites, leading to locus decompaction. Readthrough transcription into heterochromatin regions switches them from the inert (B) to the permissive (A) chromatin compartment and enables transcription factor binding. Data from non-viral transcription stimuli show that transcription similarly affects cohesin-mediated chromatin contacts within gene bodies. Conversely, inhibition of transcription elongation allows cohesin to accumulate at previously transcribed intragenic CTCF sites and to mediate chromatin looping and compaction. Our data indicate that transcription elongation by RNA polymerase II remodels genome 3D architecture.

Manipulating the 3D organization of the largest synthetic yeast chromosome.

Whether synthetic genomes can power life has attracted broad interest in the synthetic biology field. Here, we report de novo synthesis of the largest eukaryotic chromosome thus far, synIV, a 1,454,621-bp yeast chromosome resulting from extensive genome streamlining and modification. We developed megachunk assembly combined with a hierarchical integration strategy, which significantly increased the accuracy and flexibility of synthetic chromosome construction. Besides the drastic sequence changes, we further manipulated the 3D structure of synIV to explore spatial gene regulation. Surprisingly, we found few gene expression changes, suggesting that positioning inside the yeast nucleoplasm plays a minor role in gene regulation. Lastly, we tethered synIV to the inner nuclear membrane via its hundreds of loxPsym sites and observed transcriptional repression of the entire chromosome, demonstrating chromosome-wide transcription manipulation without changing the DNA sequences. Our manipulation of the spatial structure of synIV sheds light on higher-order architectural design of the synthetic genomes.

A B-Cell-Specific Enhancer Orchestrates Nuclear Architecture to Generate a Diverse Antigen Receptor Repertoire.

The genome is organized into topologically associated domains (TADs) that enclose smaller subTADs. Here, we identify and characterize an enhancer that is located in the middle of the V gene region of the immunoglobulin kappa light chain (Igκ) locus that becomes active preceding the stage at which this locus undergoes V(D)J recombination. This enhancer is a hub of long-range chromatin interactions connecting subTADs in the V gene region with the recombination center at the J genes. Deletion of this element results in a highly altered long-range chromatin interaction pattern across the locus and, importantly, affects individual V gene utilization locus-wide. These results indicate the existence of an enhancer-dependent framework in the Igκ locus and further suggest that the composition of the diverse antibody repertoire is regulated in a subTAD-specific manner. This enhancer thus plays a structural role in orchestrating the proper folding of the Igκ locus in preparation for V(D)J recombination.

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

The Evf2 Ultraconserved Enhancer lncRNA Functionally and Spatially Organizes Megabase Distant Genes in the Developing Forebrain.

Gene regulation requires selective targeting of DNA regulatory enhancers over megabase distances. Here we show that Evf2, a cloud-forming Dlx5/6 ultraconserved enhancer (UCE) lncRNA, simultaneously localizes to activated (Umad1, 1.6 Mb distant) and repressed (Akr1b8, 27 Mb distant) chr6 target genes, precisely regulating UCE-gene distances and cohesin binding in mouse embryonic forebrain GABAergic interneurons (INs). Transgene expression of Evf2 activates Lsm8 (12 Mb distant) but fails to repress Akr1b8, supporting trans activation and long-range cis repression. Through both short-range (Dlx6 antisense) and long-range (Akr1b8) repression, the Evf2-5'UCE links homeodomain and mevalonate pathway-regulated enhancers to IN diversity. The Evf2-3' end is required for long-range activation but dispensable for RNA cloud localization, functionally dividing the RNA into 3'-activator and 5'UCE repressor and targeting regions. Together, these results support that Evf2 selectively regulates UCE interactions with multi-megabase distant genes through complex effects on chromosome topology, linking lncRNA-dependent topological and transcriptional control with interneuron diversity and seizure susceptibility.

Contribution of the IGCR1 regulatory element and the 3'Igh CTCF-binding elements to regulation of Igh V(D)J recombination.

Immunoglobulin heavy chain variable region exons are assembled in progenitor-B cells, from VH, D, and JH gene segments located in separate clusters across the Igh locus. RAG endonuclease initiates V(D)J recombination from a JH-based recombination center (RC). Cohesin-mediated extrusion of upstream chromatin past RC-bound RAG presents Ds for joining to JHs to form a DJH-RC. Igh has a provocative number and organization of CTCF-binding elements (CBEs) that can impede loop extrusion. Thus, Igh has two divergently oriented CBEs (CBE1 and CBE2) in the IGCR1 element between the VH and D/JH domains, over 100 CBEs across the VH domain convergent to CBE1, and 10 clustered 3'Igh-CBEs convergent to CBE2 and VH CBEs. IGCR1 CBEs segregate D/JH and VH domains by impeding loop extrusion-mediated RAG-scanning. Downregulation of WAPL, a cohesin unloader, in progenitor-B cells neutralizes CBEs, allowing DJH-RC-bound RAG to scan the VH domain and perform VH-to-DJH rearrangements. To elucidate potential roles of IGCR1-based CBEs and 3'Igh-CBEs in regulating RAG-scanning and elucidate the mechanism of the ordered transition from D-to-JH to VH-to-DJH recombination, we tested effects of inverting and/or deleting IGCR1 or 3'Igh-CBEs in mice and/or progenitor-B cell lines. These studies revealed that normal IGCR1 CBE orientation augments RAG-scanning impediment activity and suggest that 3'Igh-CBEs reinforce ability of the RC to function as a dynamic loop extrusion impediment to promote optimal RAG scanning activity. Finally, our findings indicate that ordered V(D)J recombination can be explained by a gradual WAPL downregulation mechanism in progenitor-B cells as opposed to a strict developmental switch.

Structural and biochemical characterization of Arabidopsis alcohol dehydrogenases reveals distinct functional properties but similar redox sensitivity.

Alcohol dehydrogenases (ADHs) are a group of zinc-binding enzymes belonging to the medium-length dehydrogenase/reductase (MDR) protein superfamily. In plants, these enzymes fulfill important functions involving the reduction of toxic aldehydes to the corresponding alcohols (as well as catalyzing the reverse reaction, i.e., alcohol oxidation; ADH1) and the reduction of nitrosoglutathione (GSNO; ADH2/GSNOR). We investigated and compared the structural and biochemical properties of ADH1 and GSNOR from Arabidopsis thaliana. We expressed and purified ADH1 and GSNOR and determined two new structures, NADH-ADH1 and apo-GSNOR, thus completing the structural landscape of Arabidopsis ADHs in both apo- and holo-forms. A structural comparison of these Arabidopsis ADHs revealed a high sequence conservation (59% identity) and a similar fold. In contrast, a striking dissimilarity was observed in the catalytic cavity supporting substrate specificity and accommodation. Consistently, ADH1 and GSNOR showed strict specificity for their substrates (ethanol and GSNO, respectively), although both enzymes had the ability to oxidize long-chain alcohols, with ADH1 performing better than GSNOR. Both enzymes contain a high number of cysteines (12 and 15 out of 379 residues for ADH1 and GSNOR, respectively) and showed a significant and similar responsivity to thiol-oxidizing agents, indicating that redox modifications may constitute a mechanism for controlling enzyme activity under both optimal growth and stress conditions.

Structure-function relationships underpin disulfide loop cleavage-dependent activation of Legionella pneumophila lysophospholipase A PlaA.

Legionella pneumophila, the causative agent of a life-threatening pneumonia, intracellularly replicates in a specialized compartment in lung macrophages, the Legionella-containing vacuole (LCV). Secreted proteins of the pathogen govern important steps in the intracellular life cycle including bacterial egress. Among these is the type II secreted PlaA which, together with PlaC and PlaD, belongs to the GDSL phospholipase family found in L. pneumophila. PlaA shows lysophospholipase A (LPLA) activity which increases after secretion and subsequent processing by the zinc metalloproteinase ProA within a disulfide loop. Activity of PlaA contributes to the destabilization of the LCV in the absence of the type IVB-secreted effector SdhA. We here present the 3D structure of PlaA which shows a typical α/ß-hydrolase fold and reveals that the uncleaved disulfide loop forms a lid structure covering the catalytic triad S30/D278/H282. This leads to reduction of substrate access before activation; however, the catalytic site gets more accessible when the disulfide loop is processed. After structural modeling, a similar activation process is suggested for the GDSL hydrolase PlaC, but not for PlaD. Furthermore, the size of the PlaA substrate-binding site indicated preference toward phospholipids comprising ~16 carbon fatty acid residues which was verified by lipid hydrolysis, suggesting a molecular ruler mechanism. Indeed, mutational analysis changed the substrate profile with respect to fatty acid chain length. In conclusion, our analysis revealed the structural basis for the regulated activation and substrate preference of PlaA.

Acinetobacter baumannii satellite phage Aci01-2-Phanie depends on a helper myophage for its multiplication.

We report the discovery of a satellite-helper phage system with a novel type of dependence on a tail donor. The Acinetobacter baumannii satellite podovirus Aci01-2-Phanie (short name Phanie) uses a phage phi29-like DNA replication and packaging mode. Its linear 11,885 bp dsDNA genome bears 171 bp inverted terminal repeats (ITR). Phanie is related to phage DU-PP-III from Pectobacterium and to members of the Astrithrvirus from Salmonella enterica. Together, they form a new clade of phages with 27% to 30% identity over the whole genome. Detailed 3D protein structure prediction and mass spectrometry analyses demonstrate that Phanie encodes its capsid structural genes and genes necessary to form a short tail. However, our study reveals that Phanie virions are non-infectious unless they associate with the contractile tail of an unrelated phage, Aci01-1, to produce chimeric myoviruses. Following the coinfection of Phanie with myovirus Aci01-1, hybrid viral particles composed of Phanie capsids and Aci01-1 contractile tails are assembled together with Phanie and Aci01-1 particles.IMPORTANCEThere are few reported cases of satellite-helper phage interactions but many more may be yet undiscovered. Here we describe a new mode of satellite phage dependence on a helper phage. Phanie, like phage phi29, replicates its linear dsDNA by a protein primed-mechanism and protects it inside podovirus-like particles. However, these particles are defective, requiring the acquisition of the tail from a myovirus helper for production of infectious virions. The formation of chimeras between a phi29-like podovirus and a helper contractile tail reveals an unexpected association between very different bacterial viruses.

CATAD: exploring topologically associating domains from an insight of core-attachment structure.

Identifying topologically associating domains (TADs), which are considered as the basic units of chromosome structure and function, can facilitate the exploration of the 3D-structure of chromosomes. Methods have been proposed to identify TADs by detecting the boundaries of TADs or identifying the closely interacted regions as TADs, while the possible inner structure of TADs is seldom investigated. In this study, we assume that a TAD is composed of a core and its surrounding attachments, and propose a method, named CATAD, to identify TADs based on the core-attachment structure model. In CATAD, the cores of TADs are identified based on the local density and cosine similarity, and the surrounding attachments are determined based on boundary insulation. CATAD was applied to the Hi-C data of two human cell lines and two mouse cell lines, and the results show that the boundaries of TADs identified by CATAD are significantly enriched by structural proteins, histone modifications, transcription start sites and enzymes. Furthermore, CATAD outperforms other methods in many cases, in terms of the average peak, boundary tagged ratio and fold change. In addition, CATAD is robust and rarely affected by the different resolutions of Hi-C matrices. Conclusively, identifying TADs based on the core-attachment structure is useful, which may inspire researchers to explore TADs from the angles of possible spatial structures and formation process.

Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model.

In recent years, protein structure problems have become a hotspot for understanding protein folding and function mechanisms. It has been observed that most of the protein structure works rely on and benefit from co-evolutionary information obtained by multiple sequence alignment (MSA). As an example, AlphaFold2 (AF2) is a typical MSA-based protein structure tool which is famous for its high accuracy. As a consequence, these MSA-based methods are limited by the quality of the MSAs. Especially for orphan proteins that have no homologous sequence, AlphaFold2 performs unsatisfactorily as MSA depth decreases, which may pose a barrier to its widespread application in protein mutation and design problems in which there are no rich homologous sequences and rapid prediction is needed. In this paper, we constructed two standard datasets for orphan and de novo proteins which have insufficient/none homology information, called Orphan62 and Design204, respectively, to fairly evaluate the performance of the various methods in this case. Then, depending on whether or not utilizing scarce MSA information, we summarized two approaches, MSA-enhanced and MSA-free methods, to effectively solve the issue without sufficient MSAs. MSA-enhanced model aims to improve poor MSA quality from the data source by knowledge distillation and generation models. MSA-free model directly learns the relationship between residues on enormous protein sequences from pre-trained models, bypassing the step of extracting the residue pair representation from MSA. Next, we evaluated the performance of four MSA-free methods (trRosettaX-Single, TRFold, ESMFold and ProtT5) and MSA-enhanced (Bagging MSA) method compared with a traditional MSA-based method AlphaFold2, in two protein structure-related prediction tasks, respectively. Comparison analyses show that trRosettaX-Single and ESMFold which belong to MSA-free method can achieve fast prediction ($\sim\! 40$s) and comparable performance compared with AF2 in tertiary structure prediction, especially for short peptides, $\alpha $-helical segments and targets with few homologous sequences. Bagging MSA utilizing MSA enhancement improves the accuracy of our trained base model which is an MSA-based method when poor homology information exists in secondary structure prediction. Our study provides biologists an insight of how to select rapid and appropriate prediction tools for enzyme engineering and peptide drug development. CONTACT: guofei@csu.edu.cn, jj.tang@siat.ac.cn.

Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

Impact of human CD95 mutations on cell death and autoimmunity: a model.

CD95/Fas/APO-1 can trigger apoptotic as well as nonapoptotic pathways in immune cells. CD95 signaling in humans can be inhibited by several mechanisms, including mutations in the gene encoding CD95. CD95 mutations lead to autoimmune disorders, such as autoimmune lymphoproliferative syndrome (ALPS). Gaining further insight into the reported mutations of CD95 and resulting alterations of its signaling networks may provide further understanding of their presumed role in certain autoimmune diseases. For illustrative purposes and to better understand the potential outcomes of CD95 mutations, here we assign their positions to the recently determined 3D structures of human CD95. Based on this, we make certain predictions and speculate on the putative role of CD95 mutation defects in CD95-mediated signaling for certain autoimmune diseases.

Oligomeric Structure of Yeast and Other Invertases Governs Specificity.

Invertases, or ß-fructofuranosidases, are metabolic enzymes widely distributed among plants and microorganisms that hydrolyze sucrose and release fructose from various substrates. Invertase was one of the earliest discovered enzymes, first investigated in the mid-nineteenth century, becoming a classical model used in the primary biochemical studies on protein synthesis, activity, and the secretion of glycoproteins. However, it was not until 20 years ago that a member of this family of enzymes was structurally characterized, showing a bimodular arrangement with a ß-propeller catalytic domain, and a ß-sandwich domain with unknown function. Since then, many studies on related plant and fungal enzymes have revealed them as basically monomeric. By contrast, all yeast enzymes in this family that have been characterized so far have shown sophisticated oligomeric structures mediated by the non-catalytic domain, which is also involved in substrate binding, and how this assembly determines the particular specificity of each enzyme. In this chapter, we will review the available structures of yeast invertases to elucidate the mechanism regulating oligomer formation and compare them with other reported dimeric invertases in which the oligomeric assembly has no apparent functional implications. In addition, recent work on a new family of invertases with absolute specificity for the α-(1,2)-bond of sucrose found in cyanobacteria and plant invertases is highlighted.

Quantitative 3D electron microscopy characterization of mitochondrial structure, mitophagy, and organelle interactions in murine atrial fibrillation.

Atrial fibrillation (AF) is the most common clinical arrhythmia, however there is limited understanding of its pathophysiology including the cellular and ultrastructural changes rendered by the irregular rhythm, which limits pharmacological therapy development. Prior work has demonstrated the importance of reactive oxygen species (ROS) and mitochondrial dysfunction in the development of AF. Mitochondrial structure, interactions with other organelles such as sarcoplasmic reticulum (SR) and T-tubules (TT), and degradation of dysfunctional mitochondria via mitophagy are important processes to understand ultrastructural changes due to AF. However, most analysis of mitochondrial structure and interactome in AF has been limited to two-dimensional (2D) modalities such as transmission electron microscopy (EM), which does not fully visualize the morphological evolution of the mitochondria during mitophagy. Herein, we utilize focused ion beam-scanning electron microscopy (FIB-SEM) and perform reconstruction of three-dimensional (3D) EM from murine left atrial samples and measure the interactions of mitochondria with SR and TT. We developed a novel 3D quantitative analysis of FIB-SEM in a murine model of AF to quantify mitophagy stage, mitophagosome size in cardiomyocytes, and mitochondrial structural remodeling when compared with control mice. We show that in our murine model of spontaneous and continuous AF due to persistent late sodium current, left atrial cardiomyocytes have heterogenous mitochondria, with a significant number which are enlarged with increased elongation and structural complexity. Mitophagosomes in AF cardiomyocytes are located at Z-lines where they neighbor large, elongated mitochondria. Mitochondria in AF cardiomyocytes show increased organelle interaction, with 5X greater contact area with SR and are 4X as likely to interact with TT when compared to control. We show that mitophagy in AF cardiomyocytes involves 2.5X larger mitophagosomes that carry increased organelle contents. In conclusion, when oxidative stress overcomes compensatory mechanisms, mitophagy in AF faces a challenge of degrading bulky complex mitochondria, which may result in increased SR and TT contacts, perhaps allowing for mitochondrial Ca2+ maintenance and antioxidant production.

Facilitation of Symplastic Effector Protein Mobility by Paired Effectors Is Conserved in Different Classes of Fungal Pathogens.

It has been discovered that plant pathogens produce effectors that spread via plasmodesmata (PD) to allow modulation of host processes in distal uninfected cells. Fusarium oxysporum f. sp. lycopersici (Fol) facilitates effector translocation by expansion of the size-exclusion limit of PD using the Six5/Avr2 effector pair. How other fungal pathogens manipulate PD is unknown. We recently reported that many fungal pathogens belonging to different families carry effector pairs that resemble the SIX5/AVR2 gene pair from Fol. Here, we performed structural predictions of three of these effector pairs from Leptosphaeria maculans (Lm) and tested their ability to manipulate PD and to complement the virulence defect of a Fol SIX5 knockout mutant. We show that the AvrLm10A homologs are structurally related to FolSix5 and localize at PD when they are expressed with their paired effectors. Furthermore, these effectors were found to complement FolSix5 function in cell-to-cell mobility assays and in fungal virulence. We conclude that distantly related fungal species rely on structurally related paired effector proteins to manipulate PD and facilitate effector mobility. The wide distribution of these effector pairs implies Six5-mediated effector translocation to be a conserved propensity among fungal plant pathogens. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.