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Detect, correct, retract: How to manage incorrect structural models.

Alexander Wlodawer1, Zbigniew Dauter2, Przemyslaw J Porebski3

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This summary is machine-generated.

Biomolecular crystallography errors, though rare, can lead to flawed scientific records. Addressing cognitive biases and improving validation are crucial for maintaining scientific integrity.

Keywords:
Protein Data Bankelectron densityerror detectionevidence-based scientific discoverystructure validation

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Area of Science:

  • Structural Biology
  • Biomolecular Crystallography
  • Scientific Integrity

Background:

  • Despite advances in biomolecular crystallography, errors in crystal structure models can occur.
  • These errors, or outliers, highlight that structure determination is not infallible and can lead to misinterpretations.

Purpose of the Study:

  • To investigate the causes and consequences of severe errors in biomolecular crystal structure models.
  • To propose mechanisms for correcting, improving, or eliminating flawed models and restoring the scientific record.

Main Methods:

  • Analysis of published literature and data from public repositories like the Protein Data Bank.
  • Case summaries to illustrate errors and guide model correction or improvement.

Main Results:

  • Cognitive biases and flawed epistemology are identified as root causes for structural model errors.
  • Examples demonstrate how errors evade validation and impact scientific claims.

Conclusions:

  • Correction or removal of deficient crystallographic models is necessary when scientific claims are unsustainable.
  • Retraction of publications may be required to maintain the integrity of the scientific record.