HDX-MS optimized approach to characterize nanobodies as tools for biochemical and structural studies of class IB phosphoinositide 3-kinases
Bibliographic record
Abstract
Abstract There is considerable interest in developing antibodies as modulators of signaling pathways. One of the most important signaling pathways in higher eukaryotes is the phosphoinositide 3-kinase (PI3K) pathway, which plays fundamental roles in growth, metabolism and immunity. The class IB PI3K, PI3K γ , is a heterodimeric complex composed of a catalytic p110 γ subunit bound to a p101 or p84 regulatory subunit. PI3K γ is a critical component in multiple immune signaling processes and is dependent on activation by Ras and GPCRs to mediate its cellular roles. Here we describe the rapid and efficient characterization of multiple PI3K γ single chain camelid nanobodies using hydrogen deuterium exchange mass spectrometry (HDX-MS) for structural and biochemical studies. This allowed us to identify nanobodies that stimulated lipid kinase activity, blocked Ras activation and specifically inhibited p101-mediated GPCR activation. Overall, this reveals novel insight into PI3K γ regulation and identifies sites that may be exploited for therapeutic development. Highlights – HDX-MS rapidly identifies epitopes of camelid single-chain nanobodies raised against Class IB PI3K complexes, p110 γ /p101 and p110 γ /p84 – A nanobody targeting p101 improves local resolution in EM studies with p110 γ /p101 facilitating structural characterization of the complex – Nanobodies that bind at the interfaces with the lipidated activators Ras and G βγ can prevent activation of p110 γ /p101 and p110 γ /p84
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".