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模型验证:局部诊断、修正和何时停止。

Model validation: local diagnosis, correction and when to quit.

机构信息

Department of Biochemistry, Duke University, Durham, NC 27710, USA.

出版信息

Acta Crystallogr D Struct Biol. 2018 Feb 1;74(Pt 2):132-142. doi: 10.1107/S2059798317009834.

Abstract

Traditionally, validation was considered to be a final gatekeeping function, but refinement is smoother and results are better if model validation actively guides corrections throughout structure solution. This shifts emphasis from global to local measures: primarily geometry, conformations and sterics. A fit into the wrong local minimum conformation usually produces outliers in multiple measures. Moving to the right local minimum should be prioritized, rather than small shifts across arbitrary borderlines. Steric criteria work best with all explicit H atoms. Backrub' motions should be used for side chains and P-perp' diagnostics to correct ribose puckers. A `water' may actually be an ion, a relic of misfitting or an unmodeled alternate. Beware of wishful thinking in modeling ligands. At high resolution, internally consistent alternate conformations should be modeled and geometry in poor density should not be downweighted. At low resolution, CaBLAM should be used to diagnose protein secondary structure and ERRASER to correct RNA backbone. All atoms should not be forced inside density, beware of sequence misalignment, and very rare conformations such as cis-non-Pro peptides should be avoided. Automation continues to improve, but the crystallographer still must look at each outlier, in the context of density, and correct most of them. For the valid few with unambiguous density and something that is holding them in place, a functional reason should be sought. The expectation is a few outliers, not zero.

摘要

传统上,验证被认为是最后的把关功能,但如果模型验证在结构解决过程中积极指导修正,那么细化会更加顺畅,结果也会更好。这将重点从全局转移到局部措施:主要是几何形状、构象和立体化学。错误的局部最小构象通常会导致多个测量值的异常值。应该优先考虑移动到正确的局部最小值,而不是在任意边界上进行小的移动。立体化学标准在所有显式 H 原子上效果最佳。应该使用“回推”运动来修正侧链,使用“P-perp”诊断来修正核糖的构象。“水”实际上可能是离子、拟合错误的残留物或未建模的替代物。在建模配体时要小心一厢情愿。在高分辨率下,应该对内部一致的替代构象进行建模,并且不应降低密度较差的区域的权重。在低分辨率下,应使用 CaBLAM 来诊断蛋白质二级结构,使用 ERRASER 来修正 RNA 骨架。不应该强制所有原子都在密度范围内,要注意序列不对齐,并且应该避免非常罕见的构象,如顺式非脯氨酸肽。自动化技术不断改进,但晶体学家仍然必须查看每个异常值,并根据密度进行校正。对于那些具有明确密度且有某种因素使其保持原位的有效少数异常值,应该寻找功能原因。预期会有一些异常值,而不是零。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c94/5947777/40e0fc8fc6f6/d-74-00132-fig1.jpg

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