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

Detect, correct, retract: How to manage incorrect structural models.
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DOI:
10.1111/febs.14320
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发表时间:
2018-03
期刊:
The FEBS journal
影响因子:
--
通讯作者:
Rupp B
Rupp B
中科院分区:
其他
文献类型:
--
作者:
Wlodawer A;Dauter Z;Porebski PJ;Minor W;Stanfield R;Jaskolski M;Pozharski E;Weichenberger CX;Rupp B

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生物分子晶体学的巨大技术和计算进步产生了一些不利的副作用。大多数晶体结构模型,由晶体学家或训练有素的结构生物学家制作,构成有用的信息来源,但偶尔极端的离群值提醒我们,结构确定的过程是不安全的。严重错误或严重误解的发生引发了根本性的问题:为什么会出现这种偏差?他们是如何避开复杂的验证程序的,这些程序往往会产生明确而可怕的警告,为什么存款人自己、他们的监督者、裁判和编辑都没有注意到严重的错误?一旦发现,可以做些什么来纠正、改进或消除这些模型?不正确的模型如何影响潜在的主张或生物医学假设,他们打算,但未能支持?这种错误传播的长期影响是什么?最后,可以设想哪些机制来恢复科学记录的有效性,并在必要时撤回因缺乏实验证据而明显无效的出版物?我们认为,认知偏见和有缺陷的认识论可能是问题的根源。通过使用已发表的文献和公共知识库(如蛋白质数据库)中的实例,我们提供了案例摘要,以指导结构模型的校正或改进。当强有力的声明由于晶体学模型缺陷而无法持续时,删除这样的模型甚至撤回受影响的出版物对于恢复科学记录的完整性是必要的。在已发表和/或已保藏的大分子模型中发现了各种错误。对选定的结构进行了校正和重新存储,目的是帮助防止将来出现类似的错误,并提高蛋白质数据库的可靠性。
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. A variety of errors were identified in published and/or deposited models of macromolecules. Selected structures were corrected and re-deposited with the aim of helping to prevent similar errors in the future, and to increase the reliability of the Protein Data Bank.
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