Improved low-resolution crystallographic refinement with Phenix and Rosetta

By Frank DiMaio, Nathaniel Echols, J. Headd, T. C. Terwilliger, Paul D. Adams, David Baker. 2013

187 citations recorded by the source corpus.

Refinement of macromolecular structures against low-resolution crystallographic data is limited by the ability of current methods to converge on a structure with realistic geometry. We developed a low-resolution crystallographic refinement method that combines the Rosetta sampling methodology and energy function with reciprocal-space X-ray refinement in Phenix. On a set of difficult low-resolution cases, the method yielded improved model geometry and lower free R factors than alternate refinement methods.

Subfields: Materials Chemistry, Molecular Biology, Radiation, Computational Biology & Bioinformatics, AI for Physical Sciences

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. David Baker
6. Nigel W. Moriarty
7. T. Croll
8. Frank DiMaio

In Molecular Biology

1. Paul D. Adams
2. David Baker
3. Randy J. Read
4. Pavel V. Afonine
5. T. C. Terwilliger
6. Frank DiMaio
7. T. Croll
8. Nigel W. Moriarty

In Radiation

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

In Computational Biology & Bioinformatics

1. David Baker
2. Sergey Ovchinnikov
3. Frank DiMaio
4. Paul D. Adams
5. Ivan V. Anishchenko
6. Pavel V. Afonine
7. T. Croll
8. Randy J. Read

In AI for Physical Sciences

1. Paul D. Adams
2. Pavel V. Afonine
3. Sergey Ovchinnikov
4. David Baker
5. T. Croll
6. Nigel W. Moriarty
7. Frank DiMaio
8. I. Usón

Citations in the corpus

• De novo protein design by deep network hallucination (2020, methodology): “Sidechains were rebuilt and the model was refined with Rosetta-Phenix 54 , yielding a map with readily interpretable density.”
• Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix (2019, methodology): “(iv) Rosetta refinement (phenix.rosetta_refine) integrates the Rosetta method for conformational sampling (DiMaio et al., 2013) with the X-ray targets, ADP refinement and map generation in phenix.refine.”
• Automatically fixing errors in glycoprotein structures with Rosetta (2018, unknown): “Following refinement of these 12 structures with our Phenix-Rosetta protein structure refinement pipeline, which alternates real-and reciprocal-space refinement, we were able to markedly improve the carbohydrate geometry, as assessed by Privateer.”
• On the application of the expected log-likelihood gain to decision making in molecular replacement (2018, methodology): “…solution in an enriched list by taking each potential solution through to refinement, particularly wide-convergence radius refinement as implemented in REFMAC jelly-body refinement (Murshudov et al., 2011), phenix.mr_rosetta (DiMaio et al., 2013) or phenix.den_refine (Schröder et al., 2010).”