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Record W1553579855 · doi:10.20381/ruor-5507

Rational Design, Synthesis and Evaluation of Novel Second Mitochondrial-Derived Activators of Caspase (Smac) Mimetics That Induce Apoptosis in Human MDA-MB-231 Breast Cancer Cell Line

2012· dissertation· en· W1553579855 on OpenAlexvenueno aff
Tasbir Cheema

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2012
Typedissertation
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
Fundersnot available
KeywordsApoptosisCaspaseCaspase 3Human breastMitochondrionCell cultureCancer researchChemistryLine (geometry)Cell biologyBreast cancerMedicineBiologyCancerBiochemistryProgrammed cell deathInternal medicineMathematicsGenetics

Abstract

fetched live from OpenAlex

Programmed cell death (apoptosis) is the most common mechanism of cell death in eukaryotes. The ability of cancer cells to evade and inhibit apoptosis has become a hallmark feature of cancer. This is accomplished through a family of proteins known as the inhibitor of apoptosis proteins (IAPs). X-Linked inhibitor of apoptosis protein (XIAP) is one of the best characterized IAPs. XIAP suppresses apoptosis by forming complexes with cysteine-aspartic proteases (caspase), through one of its baculovirus IAP repeat (BIR) domains. Its activity is endogenously antagonized by a second mitochondria derived activator of caspase (Smac). The anti-apoptotic behaviour of XIAP and the critical role it plays in the apoptotic program makes the Smac-XIAP interaction an important drug target. To this end, our laboratory is interested in synthesizing biologically related Smac mimetics which can induce apoptosis in a MDA-MB-231 cell line. Efforts have focused on (1) understanding BIR domain binding sites which allow for this interaction, and (2) the design and synthesis of molecules which are much more effective at inducing apoptosis compared to other well known analogues. Through the synthesis and evaluation of various divalent Smac mimetics we have been able to support the hypothesis that the likely binding site on XIAP is the BIR3 domain. As well, through the synthesis of a library of novel compounds, as described in the thesis, we have been able to assess the nature of the linker which joins the two tetrapeptide units. In our effort to understand which domains Smac binds with, various divalent analogues were synthesized containing MeAVPI-linker-IPVMeA (forward-reverse) and MeAVPI-linker-MeAVPI (forward-forward) sequence, which incorporated linkers with varying degrees of flexibility. We hypothesized that the forward-forward divalent mimetics would have decreased activity compared to the peptides synthesized in a forward-reverse fashion. Lastly, information gathered from structure activity relationship (SAR) studies have shown that substituting the lysine (P2) and isoleucine residues (P4) in the AVPI protein can create more potent inducers of apoptosis than its native AVPI sequence. As one of the most potent Smac mimetic that has been previously made known contains an alkyne bridge at P2 and a large hydrophobic moiety at P4, we hypothesized that similar Smac mimetics containing a propargyl glycine residue at P2 and a bulky hydrophobic moiety at P4 will be much more potent in inducing apoptosis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.236
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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Same venueLibrary and Archives Canada (Government of Canada)Same topicComputational Drug Discovery MethodsFrench-language works237,207