Overcoming structural complexity in Galectin-3BP through an integrative computational antibody design workflow

Galectin-3 binding protein (Gal-3BP) is a clinically relevant oncology target, with overexpression associated with poor prognosis across multiple tumor types. However, its therapeutic exploration has been hindered by extensive glycosylation, conformational heterogeneity, and context-dependent oligomerization, which restrict epitope accessibility. Antibody-based strategies remain promising for targeting such complex proteins, yet their development is costly and experimentally demanding. To address these challenges, we established an integrative in-silico workflow tailored to the specific structural and biophysical features of Gal-3BP combining validated methodologies of structural prediction, molecular dynamics (MD) simulations, and antibody engineering. By mapping Gal-3BP across oligomeric states and characterizing its N-glycan conformational diversity, we identified two glycan-free epitopes within the BACK domain, termed E1 and E2. Scaffold selection using 3D Zernike descriptors-based similarity search identified BDBV-43 as a compatible candidate for E1. For E2, which lacked similarity-based matches, naïve repertoire mining retrieved the unmatured antibody E2-Ab1, broadening the set of viable templates. Engineering approaches included point mutations in BDBV-43 and full CDR swapping in E2-Ab1. Iterative refinement yielded variants with improved interaction profiles and robust stability during heated MD simulations. Furthermore, Gaussian accelerated MD (GaMD) revealed reorganized conformational landscapes together with modest shifts in the underlying free-energy profiles for the engineered antibodies relative to their native scaffolds, in line with the interpretative limits of GaMD reweighting. Collectively, this study positions Gal-3BP as a tractable therapeutic target and presents optimized antibody candidates capable of engaging epitopes minimally affected by glycan shielding, illustrating the potential of integrative computational pipelines for antibody design against structurally complex proteins.