DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations.
IEEE/ACM Trans Comput Biol Bioinform
; 20(3): 2089-2100, 2023.
Article
in English
| MEDLINE | ID: covidwho-2264735
ABSTRACT
Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability:
https//github.com/lennylv/DGCddG.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
ACM Trans Comput Biol Bioinform
Journal subject:
Biology
/
Medical Informatics
Year:
2023
Document Type:
Article
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