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Refinement of Macromolecular Structures by the Maximum-Likelihood Method
Abstract
This paper reviews the mathematical basis of maximum likelihood. The likelihood function for macromolecular structures is extended to include prior phase information and experimental standard uncertainties. The assumption that different parts of a structure might have different errors is considered. A method for estimating sigma(A) using 'free' reflections is described and its effects analysed. The derived equations have been implemented in the program REFMAC. This has been tested on several proteins at different stages of refinement (bacterial alpha-amylase, cytochrome c', cross-linked insulin and oligopeptide binding protein). The results derived using the maximum-likelihood residual are consistently better than those obtained from least-squares refinement.
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Metadata
- Authors
- Murshudov, G. N., Vagin, A. A., & Dodson, E. J.
- Venue
- Acta Crystallographica Section D Biological Crystallography
- Published
- 1997-05-01
- Volume
- 53
- Issue
- 3
- Pages
- 240-255
- ISSN
- 0907-4449 (ISSN-L)
- OpenAlex
- W2038840577
- Cited by
- 14,742
- References
- 0
- FWCI
- 104.57
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
