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Modeling Pseudomonas syringae ice-nucleation protein as a beta-helical protein.

ABSTRACT: Antifreeze proteins (AFPs) inhibit the growth of ice, whereas ice-nucleation proteins (INPs) promote its formation. Although the structures of several AFPs are known, the structure of INP has been modeled thus far because of the difficulty in determining membrane protein structures. Here, we present a novel model of an INP structure from Pseudomonas syringae based on comparison with two newly determined insect AFP structures. The results suggest that both this class of AFPs and INPs may have a similar beta-helical fold and that they could interact with water through the repetitive TXT motif. By theoretical arguments, we show that the distinguishing feature between an ice inhibitor and an ice nucleator lies in the size of the ice-interacting surface. For INPs, the larger surface area acts as a template that is larger than the critical ice embryo surface area required for growth. In contrast, AFPs are small enough so that they bind to ice and inhibit further growth without acting as a nucleator.

SUBMITTER: Graether SP

PROVIDER: S-EPMC1301312 | biostudies-other | 2001 Mar

REPOSITORIES: biostudies-other

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Modeling Pseudomonas syringae ice-nucleation protein as a beta-helical protein.

Graether S P SP Jia Z Z

Biophysical journal 20010301 3

Antifreeze proteins (AFPs) inhibit the growth of ice, whereas ice-nucleation proteins (INPs) promote its formation. Although the structures of several AFPs are known, the structure of INP has been modeled thus far because of the difficulty in determining membrane protein structures. Here, we present a novel model of an INP structure from Pseudomonas syringae based on comparison with two newly determined insect AFP structures. The results suggest that both this class of AFPs and INPs may have a s ...[more]

PMID: 11222281

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Project description:The ice nucleation protein (INP) of Pseudomonas syringae has gained scientific interest not only because of its pathogenicity of foliar necroses but also for its wide range of potential applications, such as in snow making, frozen food preparation, and surface-display system development. However, studies on the transport activity of INP remain lacking. In the present study, a newly identified INP-gene variant, inaQ, from a P. syringae MB03 strain was cloned. Its structural domains, signal sequences, and the hydrophilicity or hydrophobicity of each domain, were then characterized. The deduced amino acid sequence of InaQ shares similar protein domains with three P. syringae INPs, namely, InaK, InaZ, and InaV, which were identified as an N-terminal domain, a central repeating domain, and a C-terminal domain. The expression of the full-length InaQ and of various truncated variants was induced in Escherichia coli to analyze their transmembrane transport and surface-binding activities, while using the green fluorescence protein (GFP) as the fusion partner. With two transmembrane segments and a weak secretion signal, the N-terminal domain (InaQ-N) alone was found to be responsible for the transport process as well as for the binding to the outer membrane, whereas the C-terminal region was nonfunctional in protein transport. Increased membrane transport and surface-binding capacities were induced by a low isopropyl-?-D-thiogalactoside concentration (0.1 mmol/l) but not by culture temperatures (15 ºC to 37 ºC). Furthermore, by constructing the GFP-fused proteins with a single InaQ-N, as well as two and three tandemly aligned InaQ-N molecules, the transport and membrane-binding activities of these proteins were compared using Western blot analysis, immmunofluorescence microscopy, and assays of the GFP specific fluorescence intensity of subcellular fractions and flow cytometry, which showed that the increase of InaQ-N repeats resulted in a coordinated increase of the surface-immobilization efficiency. Therefore, the results of this study can serve as a molecular basis for improving the performance of INP-based cell surface-display systems.

Project description:Microbially-produced ice nucleating proteins (INpro) are unique molecular structures with the highest known catalytic efficiency for ice formation. Airborne microorganisms utilize these proteins to enhance their survival by reducing their atmospheric residence times. INpro also have critical environmental effects including impacts on the atmospheric water cycle, through their role in cloud and precipitation formation, as well as frost damage on crops. INpro are ubiquitously present in the atmosphere where they are emitted from diverse terrestrial and marine environments. Even though bacterial genes encoding INpro have been discovered and sequenced decades ago, the details of how the INpro molecular structure and oligomerization foster their unique ice-nucleation activity remain elusive. Using machine-learning based software AlphaFold 2 and trRosetta, we obtained and analysed the first ab initio structural models of full length and truncated versions of bacterial INpro. The modeling revealed a novel beta-helix structure of the INpro central repeat domain responsible for ice nucleation activity. This domain consists of repeated stacks of two beta strands connected by two sharp turns. One beta-strand is decorated with a TxT amino acid sequence motif and the other strand has an SxL[T/I] motif. The core formed between the stacked beta helix-pairs is unusually polar and very distinct from previous INpro models. Using synchrotron radiation circular dichroism, we validated the β-strand content of the central repeat domain in the model. Combining the structural model with functional studies of purified recombinant INpro, electron microscopy and modeling, we further demonstrate that the formation of dimers and higher-order oligomers is key to INpro activity. Using computational docking of the new INpro model based on rigid-body algorithms we could reproduce a previously proposed homodimer structure of the INpro CRD with an interface along a highly conserved tyrosine ladder and show that the dimer model agrees with our functional data. The parallel dimer structure creates a surface where the TxT motif of one monomer aligns with the SxL[T/I] motif of the other monomer widening the surface that interacts with water molecules and therefore enhancing the ice nucleation activity. This work presents a major advance in understanding the molecular foundation for bacterial ice-nucleation activity.

Project description:Pseudomonas syringae is a genetically diverse bacterial species complex responsible for numerous agronomically important crop diseases. Individual P. syringae isolates are assigned pathovar designations based on their host of isolation and the associated disease symptoms, and these pathovar designations are often assumed to reflect host specificity although this assumption has rarely been rigorously tested. Here we developed a rapid seed infection assay to measure the virulence of 121 diverse P. syringae isolates on common bean (Phaseolus vulgaris). This collection includes P. syringae phylogroup 2 (PG2) bean isolates (pathovar syringae) that cause bacterial spot disease and P. syringae phylogroup 3 (PG3) bean isolates (pathovar phaseolicola) that cause the more serious halo blight disease. We found that bean isolates in general were significantly more virulent on bean than non-bean isolates and observed no significant virulence difference between the PG2 and PG3 bean isolates. However, when we compared virulence within PGs we found that PG3 bean isolates were significantly more virulent than PG3 non-bean isolates, while there was no significant difference in virulence between PG2 bean and non-bean isolates. These results indicate that PG3 strains have a higher level of host specificity than PG2 strains. We then used gradient boosting machine learning to predict each strain's virulence on bean based on whole genome k-mers, type III secreted effector k-mers, and the presence/absence of type III effectors and phytotoxins. Our model performed best using whole genome data and was able to predict virulence with high accuracy (mean absolute error = 0.05). Finally, we functionally validated the model by predicting virulence for 16 strains and found that 15 (94%) had virulence levels within the bounds of estimated predictions. This study strengthens the hypothesis that P. syringae PG2 strains have evolved a different lifestyle than other P. syringae strains as reflected in their lower level of host specificity. It also acts as a proof-of-principle to demonstrate the power of machine learning for predicting host specific adaptation.

Dataset Information

Modeling Pseudomonas syringae ice-nucleation protein as a beta-helical protein.

Publications

Modeling Pseudomonas syringae ice-nucleation protein as a beta-helical protein.

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