Nanodiscs-based proteomics identify Caj1 as an Hsp40 with affinity for phosphatidic acid lipids
Bibliographic record
Abstract
ABSTRACT Many soluble proteins interact with membranes to perform important biological functions, including signal transduction, regulation, transport, trafficking and biogenesis. Despite their importance, these protein-membrane interactions are difficult to characterize due to their often-transient nature as well as phospholipids’ poor solubility in aqueous solution. Here, we employ nanodiscs – small, water-soluble patches of lipid bilayer encircled with amphipathic scaffold proteins – along with quantitative proteomics to identify lipid-binding proteins in S. cerevisiae . Using nanodiscs reconstituted with yeast total lipid extracts or only phosphatidylethanolamine (PE-nanodiscs), we capture several known membrane-interacting proteins, including the Rab GTPases Sec4 and Ypt1, which play key roles in vesicle trafficking. Utilizing PE-nanodiscs enriched with phosphatidic acid (PEPA-nanodiscs), we specifically capture a member of the Hsp40/J-protein family, Caj1, whose function has recently been linked to membrane protein quality control. We show that Caj1 interaction with liposomes containing PA is modulated by pH and PE lipids, and depends on two patches of positively charged residues near the C-terminus of the protein. The protein Caj1 is the first example of an Hsp40/J-domain protein with affinity for membranes and phosphatidic acid lipid specificity. These findings highlight the utility of the nanodisc system to identify and characterize protein-lipid interactions that may not be evident using other methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".