De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13914
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0092197
- Title of journal
- PLOS ONE
- Article number
- ARTN e92197
- First page
- -
- Volume
- 9
- Issue
- 3
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
https://acm-prod-cdn.literatumonline.com/3025453.3025915/fcb795d0-bbad-4594-83f8-cb7eff4f2735/p2282-balestrini.mp4?b92b4ad1b4f274c70877518315abb28be831d54738a81f1de54388f7ee07e5e64ad2301e1e02b6a6cd8f1a42b0caca6c9c3e9f10f4a062e9bfc3bcc89bd5e1ad5a72212995051d30f0a0cf1dccb1c4ca3f1286b08afe3b6ce7b3fad4d2f2c18151b419126b
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 71
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper described a new method for building 3-D protein models from distance constraints generated by amino acid covariation. It applies a Sparse Inverse Covariance Estimation algorithm (the Graphical Lasso) to compute a sparse precision matrix, and converts this information into potential functions which were then added to the force-field of our existing fragment-based protein modelling algorithm (FRAGFOLD). The study was also the first to show covariation modelling results for a statistically meaningful benchmark set, including over 150 different protein families, and consequently has enabled many follow-up investigations of different approaches to be properly evaluated.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
