Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas.
J Mol Graph Model
; 118: 108364, 2023 01.
Article
en En
| MEDLINE
| ID: mdl-36356467
ABSTRACT
Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibodyâprotein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for proteinâprotein binding affinity usually fail to predict the binding affinity of an antibodyâprotein antigen complex with a comparable level of accuracy. Here, new models specific for antibodyâantigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibodyâantigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibodyâprotein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibodyâprotein antigen binding affinity prediction are superior to the previously-used general models for predicting the proteinâprotein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibodyâprotein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibodyâprotein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Proteínas
/
Complejo Antígeno-Anticuerpo
Idioma:
En
Año:
2023
Tipo del documento:
Article