A Hit Prioritization Strategy for Compound Library Screening Using LiP-MS and Molecular Dynamics Simulations Applied to KRas G12D Inhibitors
Bibliographic record
Abstract
An important step in screening small-molecule libraries for drug discovery is hit prioritization and validation in order to rule out false positives. This is usually performed using biochemical and biophysical assays, so the development of orthogonal assays that are highly sensitive and can accelerate the hit-to-lead process would be valuable. Limited proteolysis combined with mass spectrometry (LiP-MS) is a technique used to study changes in protein structure upon ligand binding. In LiP-MS, proteins are exposed to low concentrations of proteases under native conditions. The resulting proteolytic patterns are sensitive to the protein structure near the cleavage site, which can change upon ligand binding. We have characterized the interaction of small-molecule inhibitors of the KRas G12D mutant oncoprotein by LiP-MS combined with molecular dynamics (MD). Intact mass spectrometry and top-down analysis were used to detect and identify KRas G12D cleavage products in the presence and absence of inhibitors, thereby locating the cleavage sites within the protein. Cleavage sites that were found to be protected upon compound binding correlated well with the switch II binding site. The degree of cleavage depended on the binding affinity and the presence of specific functional groups in the inhibitor's structure. A comparison of MD simulations for the ligand-free and ligand-bound proteins revealed the atomistic mechanisms by which the cleavage sites, located in flexible and disordered regions, were stabilized upon compound binding. We suggest that LiP-MS combined with MD (LiP-MS-MD) could be valuable in small-molecule screening campaigns and can be added to the current list of methods for high-quality hit selection in early stage drug discovery.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".