Targeting the BIR Domains of Inhibitor of Apoptosis (IAP) Proteins in Cancer Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inhibitor of apoptosis (IAP) proteins are characterized by the presence of the conserved baculoviral IAP repeat (BIR) domain that is involved in protein-protein interactions. IAPs were initially thought to be mainly responsible for caspase inhibition, acting as negative regulators of apoptosis, but later works have shown that IAPs also control a plethora of other different cellular pathways. As X-linked IAP (XIAP), and other IAP, levels are often deregulated in cancer cells and have been shown to correlate with patients' prognosis, several approaches have been pursued to inhibit their activity in order to restore apoptosis. Many small molecules have been designed to target the BIR domains, the vast majority being inspired by the N-terminal tetrapeptide of Second Mitochondria-derived Activator of Caspases/Direct IAp Binding with Low pI (Smac/Diablo), which is the natural XIAP antagonist. These compounds are therefore usually referred to as Smac mimetics (SMs). Despite the fact that SMs were intended to specifically target XIAP, it has been shown that they also interact with cellular IAP-1 (cIAP1) and cIAP2, promoting their proteasome-dependent degradation. SMs have been tested in combination with several cytotoxic compounds and are now considered promising immune modulators which can be exploited in cancer therapy, especially in combination with immune checkpoint inhibitors. In this review, we give an overview of the structural hot-spots of BIRs, focusing on their fold and on the peculiar structural patches which characterize the diverse BIRs. These structures are exploited/exploitable for the development of specific and active IAP inhibitors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it