Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability
Yesol Sapozhnikov, Jagdish Suresh Patel, F. Marty Ytreberg, Craig R. Miller · 2023
Read Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability by Yesol Sapozhnikov, Jagdish Suresh Patel, F. Marty Ytreberg, Craig R. Miller (2023) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-10642056.txt.
Description
BackgroundComputational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability.
Catalog record
| CREATOR | Yesol Sapozhnikov, Jagdish Suresh Patel, F. Marty Ytreberg, Craig R. Miller |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 97 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |