AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence.

ABSTRACT:

Motivation

The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences.

Results

To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of

SUBMITTER: Davila A 

PROVIDER: S-EPMC9710585 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature