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At least 19 recordsLinked to original sources

PubMed2025

AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets.

We analyzed four cases from the CASP15 experiment with low prediction accuracy and compared AlphaFold2, SPINE-X, and Seder on these cases. We find that overall, AlphaFold2 performs better than SPINE-X in predicting secondary structure (SS) and solvent accessible surface area (ASA). For some cases, SPINE-X better predicts sheet and coil regions. We also find that AlphaFold2 is better than Seder in selecting the best matching tertiary structure model for one case and is worse in another case. For two cases Alphafold2 and Seder selected the same models. From the cases presented here, it appears that AlphaFold2 predicts more compact structures than the native one. We find that while, as widely reported, AlphaFold2 significantly improved protein tertiary structure prediction, there are cases, such as the four presented here, for which the tertiary structure prediction could still be significantly enhanced. The source code, license, and documentation for SPINE-X and Seder are available from Research and Information Systems, LLC at http://mamiris.com .

Software↗

PubMed2025

Integrating AlphaFold2 models and clinical data to improve the assessment of Short Linear Motifs (SLiMs) and their variants' pathogenicity.

Short Linear Motifs (SLiMs) are protein functionally relevant regions that mediate reversible protein-protein interactions. Variants that disrupt SLiMs can lead to numerous Mendelian diseases. Although various bioinformatic tools have been developed to identify SLiMs, most suffer from low specificity. In our previous work, we demonstrated that integrating sequence variant information with structural analysis can enhance the prediction of true functional SLiMs while simultaneously generating tolerance matrices that indicate whether each of the 19 possible single amino acid substitutions (SASs) is tolerated. However, the scarcity of representative crystallographic structures of SLiM-receptor complexes posed a significant limitation. In this study, we demonstrate that these interactions can be modeled using AlphaFold2 (AF2) to generate high-quality structures that serve as input for our MotSASi method. These AF2-derived structures show robust performance, both in reproducing known structures deposited in the Protein Data Bank (PDB) and in reflecting the deleterious effects of known sequence variants. This updated version of MotSASi expands the repertoire of high-confidence predicted SLiMs and provides a comprehensive catalog of variants located within SLiMs, along with their respective deleteriousness assessments. When compared to AlphaMissense, MotSASi demonstrates superior performance in predicting variant deleteriousness. By contributing to the accurate identification and interpretation of variants, this work aligns with ACMG/AMP standards and aims to improve diagnostic rates in clinical genomics.

Humans↗

PubMed2026

Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.

Predicting accurate protein structures is essential for understanding molecular mechanisms, interpreting the impact of sequence variation, and supporting translational applications ranging from drug discovery to clinical genomics. Recent advances in deep-learning-based predictors such as AlphaFold2, OpenFold, and AlphaFold3 have transformed structural biology, enabling routine in silico modeling even for challenging or previously uncharacterized proteins. However, systematic benchmarking of these tools-especially for novel targets and single amino acid variants-remains limited. Conventional global metrics often fail to capture biologically meaningful discrepancies. By evaluating multiple implementations of AlphaFold2 and OpenFold, together with ColabFold and the AlphaFold3 server, across 10 different proteins and 222 single amino acid protein variants encompassing a wide range of sizes, structures, and functions, we show that although widely used global indicators-like mean pLDDT, pTM-score, and RMSD-frequently suggest comparable performance, substantial local-level differences remain elusive. To address this gap, we introduce a comparative framework leveraging Bland-Altman agreement analysis, to evaluate per-residue Cα-confidence differences and Per-Residue profiles (PRPs), complemented by Uniform Manifold Approximation and Projection (UMAP). This approach reveals marked localized divergences, particularly within flexible or intrinsically disordered regions, where both predictor choice and single-residue substitutions trigger the largest conformational shifts. We further demonstrate that using reduced homology databases has minimal impact on predicted structural quality, offering computationally efficient alternatives. Collectively, our findings underscore the importance of integrating global and residue-specific evaluations to more accurately assess robustness, agreement, and practical usability across contemporary protein structure prediction methods.

Proteins↗

PubMed2025

In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions.

Plant Nucleotide-binding leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification of a myriad of NLRs in numerous agriculturally important plant species. However, deciphering which NLRs recognize specific pathogen effectors remains challenging. Predicting NLR-effector interactions in silico will provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR-effector interactions reported in literature. Binding affinities- and energies were predicted using 97 machine learning models from Area-Affinity. We show that AlphaFold2-Multimer predicted structures have acceptable accuracy and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol-1, depending on the Area-Affinity model used. For 2427 "forced" NLR-effector complexes, these estimates showed larger variability, enabling identification of novel NLR-effector interactions with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for "true" interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all pathosystems. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for plant-pathogen resistance, advancing our understanding of plant immunity.

Plant Proteins↗

PubMed2025

In silico prediction of the impact of genomic variations in the small conductance calcium activated potassium channel SK3 structure and function.

The small-conductance calcium-activated potassium channel SK3, encoded by the KCNN3 gene, plays a critical role in regulating dopaminergic neuron (DN) firing patterns by modulating after hyperpolarization currents. SK3 dysfunction has been implicated in neuropsychiatric and neurodegenerative disorders. We analyzed structural and functional consequences of KCNN3 splicing and genetic variation. Alternative splicing variants of the KCNN3 gene were retrieved from the Ensembl database and aligned using T-Coffee, manually inspected and curated. Protein domains were identified with Pfam 35.0, SMART 9.0, and InterPro 98.0, and visualized. An AlphaFold2 model of SK3 full-length protein (UniProt: Q9UGI6) used as reference and structural models of its splicing variants were predicted with ColabFold. Functional domains (S1-S6 transmembrane helices, H5 pore loop, and calmodulin-binding) were defined and superimposed onto the AlphaFold2 reference. Domain integrity was assessed based on completeness of all expected residue indices within each functional region. SNPs and CNVs across all coding KCNN3 splicing variants were analyzed, classified, and filtered to isolate pathogenic variants prioritizing non-synonymous amino acid substitutions. Differential variant impacts across splicing isoforms were assessed by mapping variant positions to individual transcript protein sequences and used to predict functional consequences. Two long and two short splicing variants are known. Short variants lack the motif required for potassium channels. Pathogenic variants result from missense mutations resulting in amino acid substitutions. In all cases, the consequential effects depend on the specific location and role of the amino acid being changed.

SK3 channels↗

PubMed2026

A conserved antioxidant defense at the endoplasmic reticulum membrane.

Oxidative protein folding in the endoplasmic reticulum (ER) is essential for eukaryotic cells yet generates hydrogen peroxide (H2O2), a reactive oxygen species. The ER-transmembrane protein that supports ER proteostasis and guards the cytosol for antioxidant defense remains unidentified. Here, we combine AlphaFold2 and functional screens in C. elegans to discover a previously uncharacterized and evolutionarily conserved protein ERGU-1 that fulfills these roles. Deleting ERGU-1 upregulates H2O2 and NRF2/SKN-1-dependent gene expression. ERGU-1 deficiency also impairs organismal reproduction and behavioral responses to H2O2. Both C. elegans ERGU-1 and human homolog TMEM161B localize to ER membranes, forming reticular networks. Human and Drosophila homologs of ERGU-1 rescue C. elegans mutant phenotypes, demonstrating ancient and conserved functions. In addition, purified ERGU-1 and TMEM161B exhibit redox-modulated oligomeric states. Together, our results reveal an ER-membrane-specific machinery, suggesting a conserved mechanism for maintaining ER redox homeostasis and proteostasis in animal cells.

Animals↗

PubMed2025

Modeling Alternative Conformational States in CASP16.

The CASP16 Ensemble Prediction experiment assessed advances in methods for modeling proteins, nucleic acids, and their complexes in multiple conformational states. Targets included systems with experimental structures determined in two or three states, evaluated by direct comparison to experimental coordinates, as well as domain-linker-domain (D-L-D) targets assessed against statistical models from NMR and SAXS data. This paper focuses on the former class of multi-state targets. Ten ensembles were released as community challenges, including ligand-induced conformational changes, protein-DNA complexes, a trimeric protein, a stem-loop RNA, and multiple oligomeric states of a single RNA. For five targets, some groups produced reasonably accurate models of both reference states (best TM-score >0.75). However, with the exception of one protein-ligand complex (T1214), where an apo structure was available as a template, predictors generally failed to capture key structural details distinguishing the states. Overall, accuracy was significantly lower than for single-state targets in other CASP experiments. The most successful approaches generated multiple AlphaFold2 models using enhanced multiple sequence alignments and sampling protocols, followed by model quality based selection. While the AlphaFold3 server performed well on several targets, individual groups outperformed it in specific cases. By contrast, predictions for one protein-DNA complex, three RNA targets, and multiple oligomeric RNA states consistently fell short (TM-score <0.75). These results highlight both progress and persistent challenges in multi-state prediction. Despite recent advances, accurate modeling of conformational ensembles, particularly RNA and large multimeric assemblies, remains a critical frontier for structural biology.

AlphaFold2↗

PubMed2026

Characterization of a Ku-binding motif in the C-terminal region of RAG2.

We applied an unsupervised interactome analysis with the RAG2 C-terminal region (R2CT) in v-abl pro-B cells undergoing V(D)J recombination. Mass-spectrometry analyses showed that Ku70 and Ku80 were among the top 10 hits. To further strengthen these observations, we performed Proximity Ligation Assay (PLA) and characterize the existence of a GFP-R2CT-Ku complex formation in cellulo. The interaction of several partners with Ku70/80 (Ku) through Ku-binding motifs (KBMs) in their sequences governs their enrolment in NHEJ repair complexes. Through sequence analysis, we identified a KBM within R2CT (R-KBM, amino acids 589-527). We confirmed by calorimetry a specific micromolar interaction between this RAG2 region and Ku70/80/DNA complex. The RAG2 motif KBM can be subdivided in two conserved parts that have no interaction individually. AlphaFold2 prediction coupled with molecular dynamic simulations indicate that the C-terminal part of the RAG2 motif interacts with Ku80 on the same site than the NHEJ factor XLF. These in silico analyses indicated that the N-terminal part of the RAG2 motif interacts with DNA adjacent to Ku with a major role of the K503 residue in agreement with disruption of the interaction observed with the K503E mutant. This study further extends the large ensemble of proteins recruited at DSBs by KBM motifs and substantiates the model of a tight coupling between DNA breakage and repair during V(D)J recombination, mediated by the Ku-RAG2 C-terminus interaction.

Ku Autoantigen↗

PubMed2026

In silico genome mining and characterization of putative horse feces-derived bacterial phytases as potential monogastric animal feed additive candidates.

Phytic acid exerts a significant antinutritional effect in poultry, swine, and fish, which can be mitigated by supplementing monogastric feeds with efficient microbial phytases. Accordingly, mining bacterial genomes for novel phytases represents a strategic computational approach to identifying candidates for improving monogastric animal nutrition. In this study, 162 bacterial genomes associated with horse feces were systematically mined using an in silico pipeline to identify and characterize putative phytases.A total of 69 non-redundant sequences were identified and classified as histidine acid phytase (HAPhy) or protein tyrosine phosphatase-like phytase (PTPLPhy). HAPhys were detected in the genomes of Escherichia coli, Klebsiella pneumoniae, Salmonella enterica, Acinetobacter baumannii, and Cutibacterium equinum, whereas PTPLPhys were found in K. pneumoniae, Limosilactobacillus reuteri, Pediococcus acidilactici, Bifidobacterium pseudolongum, and Prescottella equi. Principal component analysis identified glucose-1-phosphatase (CAJ1242485.1) and bifunctional acid phosphatase (NHR17779.1) as the HAPhy candidates exhibiting the most favorable predicted physicochemical properties for potential feed applications. Similarly, among the PTPLPhys, protein tyrosine phosphatase (UNQ40438.1) and a hypothetical protein (CAJ1246072.1) showed the most favorable computational profiles. Biosafety analysis identified potential virulence factors, indicating that sources should be screened prior to feed application. High-quality AlphaFold2 models were obtained for these phytases (90.9-97.2). Molecular docking analysis showed that NHR17779.1 exhibited the strongest binding to phytic acid, whereas CAJ1246072.1 demonstrated the weakest interaction. Overall, this study identifies the horse fecal microbiota as a diverse source of putative phytases that may serve as promising targets for genetic and protein engineering; however, further in vitro and in vivo studies are essential to validate the enzymatic activity and industrial efficacy of these computational candidates.

Bacterial phytase↗

PubMed2025

Insights into the Catalytic Activity of a Metagenome-Derived Urethanase.

The discovery of urethanases shows an opportunity to access the biotechnological recycling of polyurethane-based plastics (PURs), widely used in the manufacture of everyday materials. However, the mechanistic understanding of these enzymes remains under debate. In this work, we report a QM/MM-based mechanistic study of the metagenome-derived urethanase UMG-SP2 catalyzing the degradation of a urethane-like model compound, 4-nitrophenyl benzylcarbamate (pNC). A high-quality structural model generated with AlphaFold2, prior to the availability of the crystal structure, accurately captured the Ser-Ser-Lys catalytic triad characteristic of amidase signature enzymes. Highly accurate constant-pH nonequilibrium molecular dynamics and Monte Carlo (neMD/MC) simulations provided the full titration curve of active site Lys, explaining the need for alkaline media for the enzyme to be active. The generation of the free energy landscape, obtained by means of free energy perturbation methods with the M06-2X DFT functional describing the QM region of the full system, reveals an esterase-like three-step mechanism of UMG-SP2, i.e., acylation, hydrolysis, and decarboxylation, with all steps being kinetically feasible. Our computational results show very good agreement with experimental kinetic data, with a calculated free energy barrier of 21.2 kcal·mol-1 for the rate-determining step compared to 22.9 kcal·mol-1 derived from the experimentally measured turnover frequency (TOF). The present results also open the door for the final decarboxylation occurring in the solution after the release of the product of the hydrolysis step or within the active site. These findings provide an atomistic insight into the urethanase function and establish a robust framework for the future design of biocatalysts targeting polyurethane degradation.

Metagenome↗

PubMed2026

Analysis of structure and conservation for supporting functional evaluation of PMS2 missense variants.

Germline defects in mismatch repair (MMR) genes are known to significantly increase the risk of developing certain types of cancers, notably colorectal and endometrial cancers. These conditions are characterized under Lynch syndrome. Accurate diagnosis of this predisposition, along with meaningful predictive testing for family members, necessitates the identification of pathogenic variants. However, classifying small coding genetic variants identified in cancer patients is very challenging, specifically in the case of PMS2 variants, since PMS2 pathogenic variants display a lower penetrance and less severe phenotype and therefore a lower tumor burden in affected families. We have assembled clinical data on four PMS2 missense variants of uncertain significance (VUS) identified in 23 patients (p.(Asp286Gly), p.(Asn335Ser), p.(Ile679Thr) and p.(Arg799Trp)). For these variants, functional testing was performed (RNA splicing, protein stability and catalytic activity). Since many protein ortholog sequences and accurate predictive models from AlphaFold2 are available, we also included a systematic analysis of residue conservation and structural role (ConStruct assessment). Overall, our findings indicate that p.(Asp286Gly) and p.(Arg799Trp) behave similarly to wild-type PMS2 and are thus probably neutral. In contrast, p.(Asn335Ser) and p.(Ile679Thr) conferred defects in protein expression or MMR activity. These could be explained by the relevant roles of these amino acids in MLH1-PMS2-N-terminal dimerization (p.Asn335) and C-terminal dimerization (p.Ile679). Our data thus suggest that p.(Asp286Gly) and p.(Arg799Trp) are benign, while the tumor risk in the other two variants remains to be established. Taken together, we suggest roadmaps for the individualized evaluation of difficult uncertain variants by comprising information from all available sources.

Humans↗

PubMed2026

Rapidly evolving aphid gall effector proteins exhibit saposin-like folds.

Many insects manipulate plants by injecting effector proteins. In one extreme example of this molecular "hijacking," Hormaphis cornu aphids inject bicycle proteins into Hamamelis virginiana, contributing to the development of novel organs called galls. Bicycle proteins share no amino acid sequence similarity with proteins of known function. Here, we report the crystal structures of two divergent bicycle proteins. Both proteins contain saposin-like folds: one with multiple disulfide bonds exhibits a swapped domain topology; the other has no disulfide bonds and possesses two distinct, tandem domains. To explore the structural evolution of bicycle proteins, we attempted to predict bicycle protein structures with Alphafold2 (AF2) and other deep learning programs. While AF2 did not recover the two experimental structures using existing databases, it succeeded when provided with multiple sequence alignments (MSAs) of protein sequences from newly sequenced closely related species. Using this approach, we generated 2,400 high-confidence bicycle protein predictions from seven aphid species. While all aphid bicycle proteins contain predicted saposin-like folds, they display a vast diversity of structural and physicochemical properties. While this diversity thwarts prediction of conserved functions encoded in structure, it suggests that bicycle proteins have evolved to target diverse plant processes and/or to evade plant immune surveillance. Our extension of AF2 with custom MSAs of proteins from closely related species provides a generalizable, powerful approach for predicting structures of rapidly evolving protein families.

Animals↗

PubMed2025

BAV-LLPS: a database of bacterial, archaea, and virus liquid-liquid phase separation proteins.

MOTIVATION: Liquid-liquid phase separation (LLPS) is a key process underlying the formation of biomolecular condensates, such as membrane-less organelles, that compartmentalize biochemical processes inside the cells. While LLPS has been extensively studied in eukaryotes, its role in bacteria, archaea, and viruses remains far less characterized. Recent studies in bacteria have revealed that LLPS-driven condensates play critical roles in RNA processing, stress response, and pathogenicity. Similarly, many viruses exploit LLPS to facilitate crucial steps in their infection cycles, including viral entry, genome replication, assembly, and host immune evasion. RESULTS: In this work, we introduce a hand-curated database of LLPS proteins from bacteria, archaea, and viruses (BAV-LLPS Database). This resource, extended through sequence similarity searches, comprises over 5000 proteins and integrates diverse data including biological annotations, sequence features, predicted disordered regions, LLPS per site probability, and AlphaFold2-based structural models. Additionally, our web server enables users to explore both the curated and homologous derived datasets, providing a platform to uncover evolutionary relationships and intrinsic and differential properties of LLPS proteins across various taxonomic groups. This work seeks to deepen our understanding of LLPS mechanisms beyond eukaryotic organisms, emphasizing their significance across diverse life forms. It also aims to foster the development of specialized predictive tools that will facilitate the exploration and characterization of LLPS processes in a wide array of living organisms, thereby contributing to advancements in both fundamental biological research and applied biomedical sciences. AVAILABILITY AND IMPLEMENTATION: BAV-LLPS DB is freely accessible at https://bav-llps-db.bioinformatica.org/. The data can be retrieved from the website. The source code of the database can be downloaded from https://bav-llps-db.bioinformatica.org/download.

Databases, Protein↗

PubMed2026

PMGen: from peptide-MHC structure prediction to peptide generation.

MOTIVATION: Accurate structural modeling of peptide-major histocompatibility complex (pMHC) complexes is essential for structure-driven immunotherapy design, yet current prediction tools suffer from narrow class coverage, restricted peptide lengths, insufficient accuracy, and a lack of built-in structure-aware peptide sampling. Consequently, most mimotope and altered peptide ligand designs rely solely on sequence substitution, leaving spatial and biophysical insights from pMHC structures largely unexploited. RESULTS: We introduce peptide-MHC generator (PMGen), an integrated framework for structure prediction and structure-guided design of variable-length peptides across MHC Class I and II. PMGen enforces anchor constraints within AlphaFold2 through two complementary strategies, initial guess and template engineering, achieving state-of-the-art structural fidelity without model fine-tuning. On a comprehensive benchmark, PMGen outperforms all existing methods, yielding median peptide-core Cα RMSDs of 0.62 Å for MHC-I and 0.33 Å for MHC-II. We show that PMGen can recover incorrectly predicted anchor positions and that AlphaFold pLDDT scores enable sequence-independent binding-core identification. Applied to a published neoantigen/wild-type pair, PMGen accurately captures mutation-induced conformational changes. Beyond structure prediction, we show that ProteinMPNN sampling on PMGen-predicted backbones yields higher affinity peptides while preserving the parental 3D conformation. Using PMGen to generate 63 817 high-confidence pMHC structures as training data, we further improve ProteinMPNN's peptide sequence recovery from 0.14 to 0.64 on a test set of 85 unseen MHC-I alleles, highlighting the value of accurate predicted structures for downstream machine learning tasks. AVAILABILITY AND IMPLEMENTATION: PMGen is freely available at https://github.com/soedinglab/PMGen, with an interactive Colab notebook at https://colab.research.google.com/github/soedinglab/PMGen/blob/master/colab.ipynb.

Peptides↗

PubMed2025

Transposable Elements Drive Regulatory and Functional Innovation of F-box Genes.

Protein domains of transposable elements (TEs) and viruses increase the protein diversity of host genomes by recombining with other protein domains. By screening 10 million eukaryotic proteins, we identified several domains that define multicopy gene families and frequently co-occur with TE/viral domains. Among these, a Tc1/Mariner transposase helix-turn-helix (HTH) domain was captured by F-box genes in the Caenorhabditis genus, creating a new class of F-box genes. For specific members of this class, like fbxa-215, we found that the HTH domain is required for diverse processes including germ granule localization, fertility, and thermotolerance. Furthermore, we provide evidence that Heat Shock Factor 1 (HSF-1) mediates the transcriptional integration of fbxa-215 into the heat shock response by binding to Helitron TEs directly upstream of the fbxa-215 locus. The interactome of HTH-bearing F-box factors suggests roles in post-translational regulation and proteostasis, consistent with established functions of F-box proteins. Based on AlphaFold2 multimer proteome-wide screens, we propose that the HTH domain may diversify the repertoire of protein substrates that F-box factors regulate post-translationally. We also describe an independent capture of a TE domain by F-box genes in zebrafish. In conclusion, we identify two independent TE domain captures by F-box genes in eukaryotes and provide insights into how these novel proteins are integrated within host gene regulatory networks.

Animals↗

PubMed2025

Deep learning-based assessment of missense variants in the COG4 gene presented with bilateral congenital cataract.

OBJECTIVE: We compared the protein structure and pathogenicity of clinically relevant variants of the COG4 gene with AlphaFold2 (AF2), Alpha Missense (AM), and ThermoMPNN for the first time. METHODS AND ANALYSIS: The sequences of clinically relevant Cog4 missense variants (one novel identified p.Y714F and three pre-existing p.G512R, p.R729W and p.L769R from Uniprot Q9H9E3) were imported into AF2 for protein structural prediction, and the pathogenicity was estimated using AM and ThermoMPNN. Different pathogenicity metrics were aggregated with principal component analysis (PCA) and further analysed at three levels (amino acid position, substitution and post-translation) based on all possible Cog4 missense variants (n=14 915). RESULTS: Localised protein structural impact including change of conformation and amino acid polarity, breakage of hydrogen bond and salt-bridge, and formation of alpha-helix were identified among clinically relevant Cog4 variants. The global structural comparison with multidimensional scaling demonstrated variants with similar protein structures (AF2) tended to exhibit similar clinical and biological phenotypes. The Cog4 p.Y714F variant exhibited greater protein structural similarity to mutated Cog4 found in Saul‒Wilson syndrome (p.G512R) and shared similar clinical phenotype (congenital cataract and psychomotor retardation). PCA of included pathogenic metrics demonstrated p.Y714F occurred at a critical position in Cog4 amino acid sequence with disrupted post-translational phosphorylation. CONCLUSION: Deep learning algorithms, including AF2, AM and ThermoMPNN, can be useful for evaluating variant of uncertain significance (VUS) by structural and pathogenicity prediction. Despite classified as VUS (American College of Medical Genetics and Genomics criteria: PM1, PP4), the pathogenicity in this Cog4 variant cannot be ruled out and warrants further investigation.

Mutation, Missense↗

PubMed2026

Conserved protein folds underpin the diversification of secreted proteins in a fungal pathogen.

BACKGROUND: During host colonization, fungal plant pathogens secrete effector-like proteins that alter host cell physiology and target plant-associated microbes. However, rapid evolution and low sequence conservation hinder the study and characterization of these proteins. The fungus Zymoseptoria passerinii infects Hordeum spp. and includes lineages adapted to wild and domesticated barley. To date, the evolution of effector-like proteins in this species has not been addressed. RESULTS: We combined multiple structure-based and network analyses to unravel the secretome of Z. passerinii. We first compared AlphaFold2 and ESMFold predictions to establish the baseline for structural analyses. We identified 72 structural clusters in the secretome, revealing fold-level relationships across divergent sequences. We showed that effector-like proteins with predicted host immune-interfering functions evolved from a limited group of protein folds, whereas proteins with predicted antimicrobial properties were distributed across fold groups. Physicochemical comparisons indicate that putative antimicrobial effectors predominantly emerged through amino acid replacements on common effector-enriched scaffolds in Z. passerinii, reconfiguring surface charge and electrostatics. We analyzed intra- and interspecific variation in selected effector-enriched families by comparing Z. passerinii proteins and homologs across the genus Zymoseptoria. We describe constrained core folds, with local variation in loop and surface-exposed regions, consistent with fold stability while still enabling protein diversification. We further report that putative antimicrobial effector homologs are broadly distributed across the genus despite sequence divergence. CONCLUSIONS: The secretome of Z. passerinii is organized around common structural folds that support diverse biological roles, including host manipulation and host-associated microbial interactions. Conserved scaffolds combined with surface and physicochemical variation likely contribute to rapid adaptive evolution of effector-like proteins in Z. passerinii.

Fungal Proteins↗

PubMed2025

The structural features and immunological role of biomphalysins in the snail Biomphalaria glabrata.

Biomphalysins are β-Pore Forming Toxins (β-PFT) identified in the planorbid Biomphalaria glabrata that belong to the aerolysin-like protein family. Despite potentially diverse biochemical activities, very few eukaryotic aerolysin-related proteins have been extensively studied. Most of the data refers to their discovery in genomes or to transcriptional activity. The involvement of biomphalysins in the immune response of Biomphalaria glabrata has been studied previously, especially regarding biomphalysin 1, which can bind and kill Schistosoma mansoni mother sporocysts. However, the repartition of biomphalysin 1 protein in B. glabrata has yet to be defined. The transcriptional behavior of the 22 other biomphalysin genes following immune challenge also remains uncharacterized. Therefore, herein, we investigate for the first time the tissular distribution of biomphalysin 1 (and 2) in B. glabrata by histological and cytological analyses through immunofluorescence approaches, notably unveiling unexpected tissue location that are involved in biomphalysin 1 synthesis. Structural predictions of the 23 members of the family have been updated using predictions based on aminoacyl spatial pair representation (AlphaFold2), highlighting unique features of the small lobe. In addition, mass spectrometry-based proteomic data more precisely predicted the regions of post-translational cleavage of biomphalysin 1. Transcriptional activity of the biomphalysin genes was explored, after which the plasmatic presence of the biomphalysin proteins was investigated in naive and S. mansoni-infected snails. The ability of native biomphalysin 1 (and 2) to bind several cell types was also investigated and correlated with the lytic ability of plasma toward the exposed cells, highlighting the central role occupied by biomphalysin 1 (and 2) in the humoral immunity of B. glabrata.

Biomphalaria↗