Simpkin, Adam J 
ORCID: 0000-0003-1883-9376, Caballero, Iracema, McNicholas, Stuart, Stevenson, Kyle, Jimenez, Elisabet, Rodriguez, Filomneno Sanchez, Fando, Maria, Uski, Ville, Ballard, Charles, Chojnowski, Grzegorz et al (show 5 more authors)
(2023)
Predicted models and <i>CCP</i>4.
ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 79 (Pt 9).
pp. 806-819.
Abstract
In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | CCP4, predicted models, molecular replacement, macromolecular crystallography, structure determination |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 02 Oct 2023 09:37 |
| Last Modified: | 27 Oct 2023 11:01 |
| DOI: | 10.1107/S2059798323006289 |
| Open Access URL: | https://doi.org/10.1107/S2059798323006289 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3173282 |
Dimensions
Dimensions
