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Understanding the biological functions of Puccinia striiformis f. sp. tritici (Pst) effectors is fundamental for uncovering the mechanisms of pathogenicity and variability, thereby paving the way for developing durable and effective control strategies for stripe rust. However, due to the lack of an efficient genetic transformation system in Pst, progress in effector function studies has been slow. Here, we modeled the structures of 15,201 effectors from twelve Pst races or isolates, a Puccinia striiformis isolate, and one Puccinia striiformis f. sp. hordei isolate using AlphaFold2. Of these, 8,102 folds were successfully predicted, and we performed sequence- and structure-based annotations of these effectors. These effectors were classified into 410 structure clusters and 1,005 sequence clusters. Sequence lengths varied widely, with a concentration between 101-250 amino acids, and motif analysis revealed the presence of known effector motifs such as [Y/F/W]xC and RxLR. Subcellular localization predictions indicated a predominant cytoplasmic localization, with notable chloroplast and nuclear presence. Clear annotations based on sequence and structure included superoxide dismutase and trehalose-6-phosphate phosphatase. A common feature observed was the formation of similar structures from different sequences. In our study, one of the comparative structural analyses revealed a new structure family with a core structure of four helices, including Pst27791, PstGSRE4, and PstSIE1, which target key wheat immune pathway proteins, impacting the host immune function. Further comparative structural analysis showed similarities between Pst effectors and effectors from other pathogens such as AvrSr35, AvrSr50, Zt-KP4-1, and MoHrip2, highlighting convergent evolutionary strategies. This comprehensive analysis provides novel insights into Pst effectors' structural and functional characterization, advancing our understanding of Pst pathogenicity and evolution.
Understanding the biological functions of Puccinia striiformis f. sp. tritici (Pst) effectors is fundamental for uncovering the mechanisms of pathogenicity and variability, thereby paving the way for developing durable and effective control strategies for stripe rust. However, due to the lack of an efficient genetic transformation system in Pst, progress in effector function studies has been slow. Here, we modeled the structures of 15,201 effectors from twelve Pst races or isolates, a Puccinia striiformis isolate, and one Puccinia striiformis f. sp. hordei isolate using AlphaFold2. Of these, 8,102 folds were successfully predicted, and we performed sequence- and structure-based annotations of these effectors. These effectors were classified into 410 structure clusters and 1,005 sequence clusters. Sequence lengths varied widely, with a concentration between 101-250 amino acids, and motif analysis revealed the presence of known effector motifs such as [Y/F/W]xC and RxLR. Subcellular localization predictions indicated a predominant cytoplasmic localization, with notable chloroplast and nuclear presence. Clear annotations based on sequence and structure included superoxide dismutase and trehalose-6-phosphate phosphatase. A common feature observed was the formation of similar structures from different sequences. In our study, one of the comparative structural analyses revealed a new structure family with a core structure of four helices, including Pst27791, PstGSRE4, and PstSIE1, which target key wheat immune pathway proteins, impacting the host immune function. Further comparative structural analysis showed similarities between Pst effectors and effectors from other pathogens such as AvrSr35, AvrSr50, Zt-KP4-1, and MoHrip2, highlighting convergent evolutionary strategies. This comprehensive analysis provides novel insights into Pst effectors' structural and functional characterization, advancing our understanding of Pst pathogenicity and evolution.
Wnt signaling is involved in embryo development and cancer. The binding between the DIX domains of Axin1/2, Dishevelled1/2/3, and Coiled-coil-DIX1 is essential for Wnt/β-catenin signaling. Structural and biological studies have revealed that DIX domains are polymerized through head-to-tail interface interactions, which are indispensable for activating β-catenin Wnt signaling. Although different isoforms of Dvl and Axin proteins display both redundant and specific functions in Wnt signaling, the specificity of DIX-mediated interactions remains unclear due to technical challenges. Using AlphaFold2(AF2), we predict the structures of 6 homodimers and 22 heterodimers of DIX domains without templates and compare them with the reported X-ray complex structures. PRODIGY is used to calculate the binding affinities of these DIX complexes. Our results show that the Axin2 DIX homodimer has a stronger binding affinity than the Axin1 DIX homodimer. Among Dishevelled (Dvl) proteins, the binding affinity of the Dvl1 DIX homodimer is stronger than that of Dvl2 and Dvl3. The Coiled-coil-DIX1(Ccd1) DIX homodimer shows weaker binding than the Axin1 DIX homodimer. Generally, heterodimer interactions tend to be stronger than those of homodimers. Our findings provide insights into the mechanism of the Wnt signaling pathway and highlight the potential of AF2 and PRODIGY for studying protein–protein interactions in signaling pathways.
Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture the effects of single point mutations that induced significant structural changes. We examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric hotspots that correspond to state-switching mutational sites which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.
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