Simultaneous prediction of antibody backbone and side-chain conformations with deep learning.

ABSTRACT: Antibody engineering is becoming increasingly popular in medicine for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side-chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side-chain dihedrals of the antib

SUBMITTER: Akpinaroglu D 

PROVIDER: S-EPMC9200299 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature