Structural basis of sRNA RsmZ regulation of Pseudomonas aeruginosa virulence

Jia, Xinyu; Pan, Zhiling; Yuan, Yang; Luo, Bingnan; Luo, Yongbo; Mukherjee, Sunandan; Jia, Guowen; Liu, Liu; Ling, Xiaobin; Yang, Xiting; Miao, Zhichao; Wei, Xiawei; Bujnicki, Janusz M.; Zhao, Kelei; Su, Zhaoming

doi:10.1038/s41422-023-00786-3

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…A previous study on the RsmZ‐E complex structure based on nuclear magnetic resonance and electron paramagnetic resonance from P. fluorescens revealed similar 3D architecture of a protein‐binding site consisting of SL2 and SL3 compared to our RsmZ‐A complex structures from P. aeruginosa 79 . Intriguingly, top‐ranked TM‐score and GDT‐TS predictions from CoDock (R1189TS444_3), Venclovas (R1189TS494_3), Kiharalab_Server (R1189TS131_2), and RNApolis (R1189TS081_1) for target R1189 generated a more accurate secondary structure with the inclusion of SL4 (Figure 5H), and successfully retained the architecture of protein‐binding sites SL2 and SL3, albeit the RMSD values were slightly worse than the Yang groups' predictions and the rest of the protein‐binding sites were not accurately predicted (Figure 5I,J).…”

Section: Resultssupporting

confidence: 68%

“…In addition, we observed another conformation of RsmZ in complex with two RsmA homodimers (RsmZ‐A 2 ) at 4.60 Å resolution (PDB: 7YR6 CASP: RT1190 Table 1), with the binding site between SL2 and SL3 unoccupied and a subtle change of 6.5° in SL ter. 79 …”

Section: Resultsmentioning

confidence: 99%

“…A previous study on the RsmZ-E complex structure based on nuclear magnetic resonance and electron paramagnetic resonance from P. fluorescens revealed similar 3D architecture of a proteinbinding site consisting of SL2 and SL3 compared to our RsmZ-A complex structures from P. aeruginosa. 79…”

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confidence: 99%

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RNA target highlights in CASP15: Evaluation of predicted models by structure providers

et al. 2023

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The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA‐only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x‐ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA–protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.

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Section: Resultssupporting

confidence: 68%

Section: Resultsmentioning

confidence: 99%

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RNA target highlights in CASP15: Evaluation of predicted models by structure providers

et al. 2023

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“…We have not submitted any models for these targets because our group was involved in the determination of these RNP complex structures 39 …”

Section: Resultsmentioning

confidence: 99%

RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints

Baulin,

Mukherjee,

Moafinejad

et al. 2023

Proteins

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Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non‐canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non‐canonical base pairs.

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“…A recent work finished by Jia et al. (2023) has reported a high‐resolution structure (overall 3.8 Å) of full‐length repressor of secondary metabolites (Rsm) Z sRNA binding with RsmA protein of Pseudomonas aeruginosa . This finding provided us new insights about the structural basis of bacterial sRNA and related proteins.…”

Section: Protein‐binding Small Noncoding Rnamentioning

confidence: 99%

Small noncoding RNA in Streptococci: From regulatory functions to drug development

Yang

Wang

Liu

et al. 2023

Molecular Oral Microbiology

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Streptococci are a genus of gram‐positive coccus of spherical bacteria, including many commensal bacteria and opportunistic pathogens that threaten the public health system. Small noncoding RNAs (sRNAs) are a class of noncoding RNAs regulating gene expression via various regulatory mechanisms, which have been illustrated to play vital roles in regulations of virulence factor expressions. Recent advances in sequencing technology and bioinformatic analysis facilitated discovery of a myriad of sRNAs from pathogenic bacteria, revealing a variety of unique features that contribute to gene expressions and virulence regulations. Although various research studies have reported the regulatory functions of sRNAs in the virulence of bacterial species of the genus Streptococci, the common features of sRNAs in the pathogenesis of Streptococci remain unclear. This blocks the development of novel antistreptococcal antibiotics and antibacterial strategies. Here, we summarize the fundamental roles of Streptococcal sRNAs in pathogenic regulations, which advance mechanistic understanding of streptococcal infection associated diseases. Moreover, we discuss the prospects of sRNA acting as drug targets to combat bacterial antibiotic resistance.

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Structural basis of sRNA RsmZ regulation of Pseudomonas aeruginosa virulence

Cited by 8 publications

References 14 publications

RNA target highlights in CASP15: Evaluation of predicted models by structure providers

RNA target highlights in CASP15: Evaluation of predicted models by structure providers

RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints

Small noncoding RNA in Streptococci: From regulatory functions to drug development

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