MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning
Abstract Assessing changes in protein–protein binding affinity due to mutations helps understanding a wide range of crucial biological processes within cells. Despite significant efforts to create accurate computational models, predicting how mutations affect affinity remains challenging due to the...
Saved in:
| Main Authors: | Pengpai Li, Zhi‐Ping Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-09-01
|
| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202402918 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity
by: Jiashan Li, et al.
Published: (2025-02-01) -
GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity
by: Junwei Luo, et al.
Published: (2025-02-01) -
FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction
by: Xuekai Zhu, et al.
Published: (2023-03-01) -
Design and Binding Affinity of Antisense Peptides for Snake Venom Neutralization
by: Ivan Biruš, et al.
Published: (2025-02-01) -
Binding Affinity of Synthetic Cannabinoids to Human Serum Albumin: Site Characterization and Interaction Insights
by: Rita M. G. Santos, et al.
Published: (2025-04-01)