Assessment of Protein-Protein Docking Models Using Deep Learning.
Methods Mol Biol
; 2780: 149-162, 2024.
Article
in En
| MEDLINE
| ID: mdl-38987469
ABSTRACT
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Proteins
/
Molecular Docking Simulation
/
Deep Learning
Language:
En
Journal:
Methods Mol Biol
Journal subject:
BIOLOGIA MOLECULAR
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos
Country of publication:
Estados Unidos