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SPR-2026-94CA·July 27, 2026Published

Dynein casts off: how an enzyme switches off the cell’s membrane motor

AI-generated hypothesis · Pre-publication · To be tested experimentally

Biophysics
Cell Biology
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Table of contents — full brief

  • Hypothesis and mechanism
    Causal chain, key assumptions, residual unknowns
  • State of the art
    Verified references and counter-evidence (DOIs)
  • Falsifiable predictions
    Quantitative bounds, statistical tests, H0
  • Experimental protocol
    Three phases — in silico → minimal → full
  • Impact analysis
    Novelty, residual gaps, available data
  • Panel review
    Five personas + meta-review

Verified references

5 of 11 references

+ 6 more references

Detailed panel scores

Methodologist8.2
Accept

Excellent articulation between the in silico, minimal in vitro, and full-length in vivo phases, with clear GO/NO-GO/PIVOT criteria that permit a rational decision before committing substantial resources (full-length dynein, TIRF). This represents a rare example of a well-conceived sequential experimental design.

Domain expert7.8
Accept

The hypothesis is mechanistically well-defined, proposing a clear and testable causal chain from specific phosphorylation events to a quantifiable change in membrane binding kinetics, grounded in established electrostatic theory (Debye-Hückel, Gouy-Chapman) and single-molecule kinetic frameworks (Kramers' rate theory).

Devil's advocate3.5
Weak reject

The hypothesis proposes a clear and testable electrostatic mechanism, with precise quantitative predictions (a factor of 4–8 on Kd).

Industry reviewer4.2
Weak reject

The upstream market is clear and solvent: biotechnology and pharmaceutical companies developing CDK1/cyclin B inhibitors (e.g., Pfizer, Merck KGaA, Syros Pharmaceuticals) would pay for a validated screening assay measuring the impact of drug candidates on dynein localisation, an essential mitotic cargo. The immediate need is a robust test to avoid off-target effects on vesicular transport.

Funding strategist6.5
Weak accept

A clear and testable mechanistic hypothesis is presented, with progressive validation steps (in silico, minimal, complete), thereby reducing technical risk for the funder.

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