Improved AlphaFold modeling with implicit experimental information.
Nat Methods
; 19(11): 1376-1382, 2022 11.
Article
in English
| MEDLINE | ID: covidwho-2151063
ABSTRACT
Machine-learning prediction algorithms such as AlphaFold and RoseTTAFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including new experimental information such as a density map, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We show that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for interpretation of crystallographic and electron cryo-microscopy maps.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Algorithms
/
Proteins
Type of study:
Prognostic study
Language:
English
Journal:
Nat Methods
Journal subject:
Laboratory Techniques and procedures
Year:
2022
Document Type:
Article
Affiliation country:
S41592-022-01645-6
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