Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models

Tomer Cohen, Matan Halfon, Lester Carter, Beth Sharkey, Tushar Jain, Arvind Sivasubramanian*, Dina Schneidman-Duhovny

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD < 4 Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87 Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.

Original languageAmerican English
Title of host publicationSmall Angle Scattering Part B
Subtitle of host publicationMethods for Structural Interpretation
EditorsJohn A. Tainer
PublisherAcademic Press Inc.
Pages237-262
Number of pages26
ISBN (Print)9780323991810
DOIs
StatePublished - Jan 2023

Publication series

NameMethods in Enzymology
Volume678
ISSN (Print)0076-6879
ISSN (Electronic)1557-7988

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Inc.

Keywords

  • Antibody-antigen complexes
  • Deep learning
  • Multi-state modeling
  • SAXS profiles
  • Structure prediction

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