Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties
Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights...
Main Authors: | Davide Sala, Peter W. Hildebrand, Jens Meiler |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-02-01
|
Series: | Frontiers in Molecular Biosciences |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1121962/full |
Similar Items
-
Using AlphaFold Predictions in Viral Research
by: Daria Gutnik, et al.
Published: (2023-04-01) -
Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2
by: Tsz Kin Ng, et al.
Published: (2023-12-01) -
An automated pipeline integrating AlphaFold 2 and MODELLER for protein structure prediction
by: Fabio Hernan Gil Zuluaga, et al.
Published: (2023-01-01) -
Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction
by: Amy O. Stevens, et al.
Published: (2022-07-01) -
SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer
by: Wenxing Hu, et al.
Published: (2024-12-01)