Antibody–Antigen Affinity Prediction with Chain-Aware Protein Language Modeling 1. The paper introduces AbAffinity, a sequence-only affinity predictor that keeps antibody heavy chain, light chain, and antigen as three separate streams (instead of early concatenation), aiming to preserve chain-specific paratope signals and antigen epitope signals during modeling. 2. Core modeling idea: use frozen ESM-2 (650M) residue embeddings, then train only a compact “interaction module” that performs heavy-chain CDR-focused pooling, heavy–light self-attention, adaptive heavy/light fusion gating, and gated cross-attention to model partner-specific antibody–antigen compatibility. 3. A notable biological inductive bias is heavy-chain CDR-focused pooling (CDR-H1/H2/H3) rather than full-chain pooling; this concentrates the representation on likely paratope residues while keeping the antigen pooled globally because the epitope is unknown at prediction time. 4. Architecture details: heavy and light chain are treated as a 2-token “antibody sequence” for self-attention; a learned softmax fusion gate produces an antibody embedding; two stacked gated cross-attention blocks refine an antibody-conditioned antigen embedding; cosine similarity in a shared latent space is mapped to predicted pKd. 5. Main benchmark (SAAINT-DB, 2,575 complexes from 1,271 PDBs; includes 1,913 paired antibodies and 662 nanobodies): AbAffinity reports strong random 10-fold CV performance (Pearson r 0.86, Spearman 0.84, RMSE 0.69) and improved robustness on antigen-cold splits (Pearson r 0.57, Spearman 0.55), indicating partial generalization to unseen antigens. 6. External generalization: across SAbDab, AB-Bind, SKEMPI 2.0, and a held-out docking/affinity benchmark, AbAffinity consistently outperforms the compared sequence baseline MVSF-AB on correlation and (where reported) RMSE, supporting transfer beyond the primary training distribution. 7. Affinity maturation setting: on AbBiBench landscapes, zero-shot transfer is target-dependent (can be weak or even negative), but few-shot fine-tuning with only 10–30% labeled variants substantially improves within-target ranking (e.g., on 1n8z reaching ~0.69 Pearson and ~0.70 Spearman at 30% labels). 8. Mechanistic ablations/interventions support the design choices: (i) preserving heavy vs light chain identity beats collapsing them; (ii) CDR-focused pooling improves over full-chain mean pooling; (iii) closing the learned gate sharply degrades performance, and shuffling antigens across antibodies causes major drops—evidence the model relies on correct partner pairing rather than antibody-only priors. 9. Interpretability: Integrated Gradients attributions (with differentiable pooling) localize importance to antibody CDRs and known antigen epitope residues in structurally validated complexes (e.g., D1.3–lysozyme), and recover more known paratope residues than a fused two-stream baseline, especially improving light-chain attribution sharpness. 💻Code: github.com/harshitsinghsnu/A… 📜Paper: biorxiv.org/content/10.64898… #ComputationalBiology #Bioinformatics #Antibodies #ProteinLanguageModels #DeepLearning #TherapeuticDiscovery #AffinityPrediction #ExplainableAI #ProteinEngineering