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Record W4248977310 · doi:10.1158/1538-7445.am2019-3461

Abstract 3461: Characterizing the PTEN - p85alpha interaction

2019· article· en· W4248977310 on OpenAlexaff
Jeremy L. Marshall, Paul Mellor, Xuan Ruan, Dielle E. Whitecross, Stanley A. Moore, Deborah J. Anderson

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsSaskatchewan Cancer AgencyUniversity of Saskatchewan
Fundersnot available
KeywordsPTENDocking (animal)PI3K/AKT/mTOR pathwayPhosphataseC2 domainBinding siteComputational biologyPhosphatidylinositolProtein kinase domainChemistryCell biologyKinaseCancer researchBiologyBiochemistryPhosphorylationSignal transductionGeneMutantMedicine

Abstract

fetched live from OpenAlex

Abstract Introduction: The phosphatidylinositol 3-kinase (PI3K) pathway plays a key role in regulating cell growth and cell survival and is frequently deregulated in cancer cells. p85α regulates the p110α lipid kinase, and also stabilizes and stimulates PTEN, the lipid phosphatase that downregulates this pathway. We set out to identify residues in both PTEN and p85α that mediate their interaction to better understand the regions important in mediating binding. Experimental Procedures: We previously showed that the BH domain of p85α is sufficient to mediate binding to PTEN. In this work, a deletion analysis and point mutations were used to mutate each of PTEN and the p85α BH domain to identify residues important for binding, determined using a pull-down analysis. Mutations in the p85α BH domain and in PTEN that reduced their interaction were then used as input data and docking software was used to model possible interaction interfaces for the two the proteins. Further mutagenesis and follow-up binding experiments provided support for our model of the PTEN - p85α BH domain complex. Results: We identified key residues responsible for mediating PTEN - p85α complex formation. Based on these experimental results, a docking model for the PTEN - p85α BH domain complex was developed that is consistent with the known binding interactions for both PTEN and p85α. This model involves extensive side-chain and peptide backbone contacts between both the PASE (R84, Q87, Y88, E91, E99) and C2 (R189, P190, Q219, C250, D252) domains of PTEN and the p85α BH domains with a buried surface area of 1211 Å2 (PTEN - bovine p85α BH) and 1366 Å2 (PTEN - human p85α BH). The p85α BH domain residues that directly contact PTEN in the two docking models were not identical, however both models implicated p85α residues E212, Q221, K225, R228, H234 and W237. The majority of these p85α BH domain residues were confirmed experimentally as important for PTEN binding. We also verified experimentally the importance of PTEN-E91 in mediating interaction with the p85α BH domain. Conclusions: These results shed new light on the mechanism of PTEN binding and regulation by p85α. Citation Format: Jeremy Marshall, Paul Mellor, Xuan Ruan, Dielle Whitecross, Stanley Moore, Deborah Anderson. Characterizing the PTEN - p85alpha interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3461.

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 score0.003
Threshold uncertainty score0.011

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)0.0030.001

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.

Opus teacher head0.071
GPT teacher head0.415
Teacher spread0.343 · 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
Published2019
Admission routes1
Has abstractyes

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