Protein structure prediction based on deep learning: HER2 in complex with a covalent inhibitor
Bibliographic record
Abstract
HER2 protein overexpression is associated with the malignant degree and poor prognosis of breast cancer. HER2 levels are elevated in 20% of breast tumors. Several covalent tyrosine kinase inhibitors have been found to reduce tumor cell survival and proliferation in vitro and inhibit downstream HER2 signaling. In the field of protein structure prediction, AlphaFold2, which achieved excellent results in CASP14, can periodically predict protein structures with atomic precision in the absence of similar protein structures. In this study, AlphaFold2 was used to predict the monomeric structure of the HER2 protein. This predicted structure was compared to the conformation of HER2 in complex with a covalent inhibitor, allowing for an examination of the conformational changes induced by the inhibitor. By combining the conformational changes of HER2 protein with the docking results of Protein-Ligand Interaction Profiler, other potential binding sites were identified, which could further reveal the mechanism of drug discovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".