On the application of the expected log-likelihood gain to decision making in molecular replacement

By R. Oeffner, Pavel V. Afonine, C. Millán, M. Sammito, I. Usón, Randy J. Read, A. McCoy. 2018

24 citations recorded by the source corpus.

The expected log-likelihood gain can be used to predict the outcome of molecular replacement and optimize molecular-replacement strategies.

Subfields: Materials Chemistry, Radiation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Paul D. Adams
2. Pavel V. Afonine
3. Randy J. Read
4. T. C. Terwilliger
5. Nigel W. Moriarty
6. T. Croll
7. A. McCoy
8. G. Bunkoczi

In Radiation

1. Pavel V. Afonine
2. Paul D. Adams
3. A. McCoy
4. Randy J. Read

Citations in the corpus

• Assessment of three-dimensional RNA structure prediction in CASP15 (2023, positive): “These values are unambiguously indicative of successful Molecular Replacement: for example, TFZ > 8 corresponds to ‘Definitely’ solved according to Phaser software guidance.(75) The result (Figure 9) has an Rfree of 43%, an acceptable value for a model immediately after MR.”
• Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix (2019, background): “This depends on a combination of the model quality and completeness, the resolution of the data and the number of diffraction observations (Oeffner et al., 2018).”