Severe deviation in protein fold prediction by advanced AI: a case study
Abstract Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences. In spite of these remarkable advances,...
Saved in:
Main Authors: | Jacinto López-Sagaseta, Alejandro Urdiciain |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-02-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-025-89516-w |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
AlphaFold 2, but not AlphaFold 3, predicts confident but unrealistic β-solenoid structures for repeat proteins
by: Olivia S. Pratt, et al.
Published: (2025-01-01) -
ER Dysfunction and Protein Folding Stress in ALS
by: Soledad Matus, et al.
Published: (2013-01-01) -
Identification of Protein Folding Cores Using Charge Center Model of Protein Structure
by: Ivan Y. Torshin, et al.
Published: (2002-01-01) -
The Mitochondrial Disulfide Relay System: Roles in Oxidative Protein Folding and Beyond
by: Manuel Fischer, et al.
Published: (2013-01-01) -
Folded and Unfolded Conformations of Proteins Involved in Pancreatic Cancer: a Layman's Guide
by: Jerónimo Bravo, et al.
Published: (2010-01-01)