Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking.

ABSTRACT:

Motivation

Antibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking.

Results

In an information-driven scenario, we find that HADDOCK can generate accurate models of antibody-antigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using k

SUBMITTER: Giulini M 

PROVIDER: S-EPMC11483107 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature