The atmospheric ice nucleation behavior of biological macromolecules: how top-down and bottom-up approaches help disentangle the role of proteins
Bibliographic record
Abstract
Certain biological macromolecules can play a unique role in heterogeneous ice nucleation, triggering freezing of atmospheric cloud droplets at high sub-zero temperatures (Pummer et al., 2012). Some ice nucleating proteins (INPs) of procaryotic organisms (e.g. Pseudomonas syringae) have been identified as highly efficient ice nuclei, but the isolation and identification of INPs from eucaryotic cells (e.g. pollen or fungal spores) remains challenging due to the increasing complexity of the samples’ matrices (Burkart et al., 2021, Seifried et al., 2020). To analyze INPs from birch pollen extracts, we applied a top-down workflow, including ice-shell purification, size exclusion chromatography and gel electrophoresis as separation techniques followed by fluorescence spectroscopy, infrared spectroscopy and mass spectrometry for characterization and the Vienna Optical Droplet Crystallization Analyzer (VODCA) for determining the ice nucleation activity (Felgitsch et al., 2018). We found several proteins as possible contributors to the freezing activity of birch pollen at around -16°C. However, the exact sequence of the INP and the molecular mechanism behind the ice nucleation remains elusive. To address this knowledge gap, we are currently focusing on a broader bottom-up approach which illuminates the ice nucleation mechanism of proteins in general. Specific peptides can be synthesized in-vitro and the ice nucleation activity of pure synthetic substances will be analyzed by using the drop Freezing Ice Nuclei Counter (FINC) (Miller et al., 2021). Exchanging or modifying single amino acids will allow to determine the mechanisms behind the nucleation and to draw a picture of sequences that indicate possible INPs in various organisms. Such a method can provide a basis for the investigations of INPs across the borders of genera and species and can help building fundamental understanding behind biologically induced ice nucleation at high sub-zero temperatures in the atmosphere. References Burkart, J., Gratzl, J., Seifried, T. M., Bieber, P., and Grothe, H.: Isolation of subpollen particles (SPPs) of birch: SPPs are potential carriers of ice nucleating macromolecules, Biogeosciences, 18, 5751–5765, https://doi.org/10.5194/bg-18-5751-2021, 2021. Felgitsch, L., Baloh, P., Burkart, J., Mayr, M., Momken, M. E., Seifried, T. M., Winkler, P., Schmale III, D. G., and Grothe, H.: Birch leaves and branches as a source of ice-nucleating macromolecules, Atmos. Chem. Phys., 18, 16063–16079, https://doi.org/10.5194/acp-18-16063-2018, 2018 Miller, A. J., Brennan, K. P., Mignani, C., Wieder, J., David, R. O., and Borduas-Dedekind, N.: Development of the drop Freezing Ice Nuclei Counter (FINC), intercomparison of droplet freezing techniques, and use of soluble lignin as an atmospheric ice nucleation standard, Atmos. Meas. Tech., 14, 3131–3151, https://doi.org/10.5194/amt-14-3131-2021, 2021. Pummer, B. G., Bauer, H., Bernardi, J., Bleicher, S., and Grothe, H.: Suspendable macromolecules are responsible for ice nucleation activity of birch and conifer pollen, Atmos. Chem. Phys., 12, 2541–2550, https://doi.org/10.5194/acp-12-2541-2012, 2012. Seifried, T. M., Bieber, P., Felgitsch, L., Vlasich, J., Reyzek, F., Schmale III, D. G., and Grothe, H.: Surfaces of silver birch (Betula pendula) are sources of biological ice nuclei: in vivo and in situ investigations, Biogeosciences, 17, 5655–5667, https://doi.org/10.5194/bg-17-5655-2020, 2020.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".