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Ice-nucleating proteins (INPs) catalyze ice formation at high subzero temperatures, with major biological and environmental implications. While bacterial INPs have been structurally characterized, their counterparts in other organisms remain unknown. Here, we identify a new class of efficient INPs in fungi. These proteins are membrane-free, adopt β-solenoid folds, and multimerize to form large ice-binding surfaces, showing mechanistic parallels with bacterial INPs. Structural modeling, sequence analysis, and functional assays show they are encoded by orthologs of the bacterial InaZ gene, likely acquired via horizontal gene transfer. Our results demonstrate that distinct lineages have independently converged on a common molecular strategy to overcome the energetic barriers of ice formation. The discovery of cell-free INPs provides tools for freezing applications and reveals biophysical constraints on nucleation across life. 

Bacterial ice nucleating proteins (INPs) are exceptionally effective in promoting the kinetically hindered transition of water to ice. Their efficiency relies on the assembly of INPs into large functional aggregates, with the size of ice nucleation sites determining activity. Experimental freezing spectra have revealed two distinct, defined aggregate sizes, typically classified as class A and C ice nucleators (INs). Despite the importance of INPs and years of extensive research, the precise number of INPs forming the two aggregate classes, and their assembly mechanism have remained enigmatic. Here, we report that bacterial ice nucleation activity emerges from more than two prevailing aggregate species and identify the specific number of INPs responsible for distinct crystallization temperatures. We find that INP dimers constitute class C INs, tetramers class B INs, and hexamers and larger multimers are responsible for the most efficient class A activity. We propose a hierarchical assembly mechanism based on tyrosine interactions for dimers, and electrostatic interactions between INP dimers to produce larger aggregates. This assembly is membrane-assisted: Increasing the bacterial outer membrane fluidity decreases the population of the larger aggregates, while preserving the dimers. Inversely, Dulbecco’s Phosphate-Buffered Saline buffer increases the population of multimeric class A and B aggregates 200-fold and endows the bacteria with enhanced stability toward repeated freeze-thaw cycles. Our analysis suggests that the enhancement results from the better alignment of dimers in the negatively charged outer membrane, due to screening of their electrostatic repulsion. This demonstrates significant enhancement of the most potent bacterial INs. 

The heterogeneous nucleation of ice is an importantatmospheric process facilitated by a wide range of aerosols. Drop-freezingexperiments are key for the determination of the ice nucleation activity ofbiotic and abiotic ice nucleators (INs). The results of these experimentsare reported as the fraction of frozen droplets fice(T) as a functionof decreasing temperature and the corresponding cumulative freezing spectraNm(T) computed using Gabor Vali's methodology. The differential freezingspectrum nm(T) is an approximant to the underlying distribution ofheterogeneous ice nucleation temperatures Pu(T) that represents thecharacteristic freezing temperatures of all INs in the sample. However,Nm(T) can be noisy, resulting in a differential form nm(T) that is challenging to interpret. Furthermore, there is no rigorousstatistical analysis of how many droplets and dilutions are needed to obtaina well-converged nm(T) that represents the underlying distributionPu(T). Here, we present the HUB (heterogeneousunderlying-based) method and associated Python codes thatmodel (HUB-forward code) and interpret (HUB-backward code) the results ofdrop-freezing experiments. HUB-forward predicts fice(T) and Nm(T)from a proposed distribution Pu(T) of IN temperatures, allowing itsusers to test hypotheses regarding the role of subpopulations of nuclei infreezing spectra and providing a guide for a more efficient collection offreezing data. HUB-backward uses a stochastic optimization method to computenm(T) from either Nm(T) or fice(T). The differential spectrumcomputed with HUB-backward is an analytical function that can be used toreveal and characterize the underlying number of IN subpopulations ofcomplex biological samples (e.g., ice-nucleating bacteria, fungi, pollen)and to quantify the dependence of these subpopulations on environmentalvariables. By delivering a way to compute the differential spectrum fromdrop-freezing data, and vice versa, the HUB-forward and HUB-backward codesprovide a hub to connect experiments and interpretative physical quantitiesthat can be analyzed with kinetic models and nucleation theory.