Targeting the BIR Domains of Inhibitor of Apoptosis (IAP) Proteins in Cancer Treatment
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.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".