Detect, correct, retract: How to manage incorrect structural models
Abstract
The massive technical and computational progress of biomolecular crystallography has generated some adverse side effects. Most crystal structure models, produced by crystallographers or well-trained structural biologists, constitute useful sources of information, but occasional extreme outliers remind us that the process of structure determination is not fail-safe. The occurrence of severe errors or gross misinterpretations raises fundamental questions: Why do such aberrations emerge in the first place? How did they evade the sophisticated validation procedures which often produce clear and dire warnings, and why were severe errors not noticed by the depositors themselves, their supervisors, referees and editors? Once detected, what can be done to either correct, improve or eliminate such models? How do incorrect models affect the underlying claims or biomedical hypotheses they were intended, but failed, to support? What is the long-range effect of the propagation of such errors? And finally, what mechanisms can be envisioned to restore the validity of the scientific record and, if necessary, retract publications that are clearly invalidated by the lack of experimental evidence? We suggest that cognitive bias and flawed epistemology are likely at the root of the problem. By using examples from the published literature and from public repositories such as the Protein Data Bank, we provide case summaries to guide correction or improvement of structural models. When strong claims are unsustainable because of a deficient crystallographic model, removal of such a model and even retraction of the affected publication are necessary to restore the integrity of the scientific record. Copyright 2017 Federation of European Biochemical SocietiesSponsors
This work was funded by the Austrian Science Fund (FWF) under project P28395-B26, by the Polish National Science Centre (NCN) through grant No. 2013/10/M/NZ1/00251, by the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute, Center for Cancer Research, and by NIH grants U01HG008424, R01GM117080, R01GM117325.Keyword
electron densityerror detection
evidence-based scientific discovery
Protein Data Bank
structure validation
Identifier to cite or link to this item
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035216855&doi=10.1111%2ffebs.14320&partnerID=40&md5=afee4b7d018e3e9daab5485f04a81880; http://hdl.handle.net/10713/9434ae974a485f413a2113503eed53cd6c53
10.1111/febs.14320
Scopus Count
Collections
Related articles
- Correcting the record of structural publications requires joint effort of the community and journal editors.
- Authors: Rupp B, Wlodawer A, Minor W, Helliwell JR, Jaskolski M
- Issue date: 2016 Dec
- You are lost without a map: Navigating the sea of protein structures.
- Authors: Lamb AL, Kappock TJ, Silvaggi NR
- Issue date: 2015 Apr
- Protein crystallography for aspiring crystallographers or how to avoid pitfalls and traps in macromolecular structure determination.
- Authors: Wlodawer A, Minor W, Dauter Z, Jaskolski M
- Issue date: 2013 Nov
- Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures.
- Authors: Wlodawer A, Minor W, Dauter Z, Jaskolski M
- Issue date: 2008 Jan
- Twilight reloaded: the peptide experience.
- Authors: Weichenberger CX, Pozharski E, Rupp B
- Issue date: 2017 Mar 1