De novo design of binder proteins targeting Helicobacter pylori adhesin BabA

Abstract

Helicobacter pylori has been classified as a Group 1 carcinogen by the International Agency for Research on Cancer of the World Health Organization and is one of the most well-established risk factors for gastric cancer. Long-term colonization by H. pylori depends on adhesin-mediated attachment to the gastric mucosa, among which the blood group antigen-binding adhesin BabA is a key surface factor involved in host recognition, tissue tropism, and persistent infection. In this study, we established a structure-guided computational design pipeline to develop compact protein binders targeting functionally relevant epitopes of BabA. First, using experimentally resolved BabA-antibody and BabA-nanobody complex structures as templates, we extracted structural contact residues on BabA through heavy-atom contact analysis, thereby defining antibody-recognition epitopes supported by complex-structure evidence. In addition, sequence-based, structure-based, and evolutionary conservation analyses were integrated to identify candidate functional epitope residues with high antigenicity, strong conservation, and surface-exposed features. On this basis, constrained de novo backbone generation was performed around the prioritized epitope regions, followed by amino acid sequence design and structural back-validation of the candidate binders. Candidate BabA-binder complexes were further evaluated using molecular docking, molecular dynamics simulations, and residue-level interface perturbation analysis to assess interface stability, epitope occupancy, and potential binding hotspots. This workflow enables systematic screening of BabA-targeting binders that may compete with antibody-recognized functional surfaces. Although these candidates still require experimental validation, this study provides a transferable computational framework for designing compact protein binders against pathogen adhesins by integrating experimentally resolved complex-structure resources with computational epitope prioritization based on sequence, conformation, and evolutionary conservation, and establishes a preliminary library of BabA candidate binders for subsequent validation and optimization.