# AntibodyForge - **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences) - **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT) - **Where:** Online - **Team:** [Hung-Pin Peng](https://cerebralvalley.ai/u/hppeng) - **GitHub:** https://github.com/px172/abdev-suite - **Demo video:** https://www.youtube.com/watch?v=ZViqkndR9Tw - **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery - **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/299 abdev-suite — Project Description WHAT I BUILT AND INVESTIGATED I built abdev-suite, a pluggable in-silico platform for antibody developability assessment. Given a VH/VL sequence — or a stored portfolio of candidates — it models the Fv structure, runs a set of analyzer modules that each score one developability trait, and serves the results through a REST API and a single-page web interface: a sortable and filterable candidates table, a per-candidate detail view that places every score on its reference distribution, a VH/VL sequence map of CDRs and liability sites, and synchronised dual 3D structure views. The pipeline models the Fv with ABodyBuilder2, protonates and repairs it with PDBFixer, and then runs modules of three kinds under one uniform contract: - Structural computation — a histidine scan (phswitch) that predicts each position's pKa with PROPKA3, flags pH-switch-window hits, and classifies each as CDR or framework. - Rule-based scanning — a chemical-liability panel (liabilities) that flags PTM and degradation motifs (N-glycosylation, deamidation, isomerization, oxidation, Asp-Pro clip, free cysteine), split framework-vs-CDR and weighted by solvent exposure. - Data-driven scoring — sequence-similarity modules (solubility, affinity, gh_pssm) calibrated on a curated functional-antibody library. Heavy computation runs on a durable job queue with background workers: ESM-2 latent scoring (plm), explicit-solvent molecular-dynamics stability (gromacs), and constant-pH molecular dynamics (cphmd), with live GROMACS/Amber runners wired to GPU hosts. Results persist per candidate, so a portfolio accumulates structural, MD, and sequence-level evidence over time. The scientific thread underneath the platform is a structural pH-switch design study: engineering histidine-based, pH-dependent switches into an antibody framework by scanning positions for the pKa-versus-burial "sweet spot," triaged quickly from structure and validated against constant-pH MD. WHAT I FOUND The most significant finding concerns the tool itself, which I characterised honestly rather than overstating: - The mechanistic modules are trustworthy. Rule-based liability scanning (grounded in documented degradation chemistry) and the structural histidine-pKa switch scan rest on chemistry and physics rather than fit-to-library, and are the signals worth acting on. - The similarity modules are heuristics, not predictors. affinity and solubility interpolate a reference library by sequence similarity; similarity to a library of other binders does not causally predict a new antibody's absolute affinity or yield against its own antigen, so a single "predicted" number is false precision. The honest form is nearest-neighbour retrieval with distance gating — "your closest known antibodies and their measured values" — not a headline value. - gh_pssm is honest only within its scaffold. Its H3 model is length-anchored, so off-scaffold sequences must read N/A rather than a low score. - The PLM latent score is a real signal presented poorly — an ESM-2 latent novelty/naturalness measure whose raw Mahalanobis value is uninterpretable and unactionable as shown. - A methodological point: framing "kNN versus PLM latent space" as a choice is a category error — the representation (a protein-language-model embedding) and the algorithm (k-nearest-neighbours) are orthogonal. The strong form is retrieval on PLM embeddings with distance gating; dimensionality-reduction plots (UMAP, t-SNE) are non-metric and exploratory only; and nothing here is a genuine predictor until held-out validation shows it generalises beyond its training distribution. Along the way I also resolved real correctness issues. The modeled structures are raw-numbered rather than IMGT-numbered, so an IMGT-range CDR classifier mis-tagged boundary residues; I unified all CDR handling — sequence map, GH-PSSM, 3D viewer, phswitch, and liabilities — onto a single sequence-based definition. I also removed UI elements that implied more than the method supported, such as a solvent-dominated MD "potential" number and a hotspot ranking that failed to recover the constant-pH-MD-validated switches. WHY IT MATTERS In-silico developability screening is crowded with tools that present weakly validated, correlational scores with unwarranted confidence. A tool that is explicit about which signals are mechanistic versus correlational — and that refuses to extrapolate beyond its reference set — is more trustworthy for real triage decisions than one that hides that distinction behind a polished number. The mechanistic core connects to a genuine engineering goal: pH-dependent switches enable antigen release for recycling antibodies and conditionally active binders, and pairing a fast structural triage with constant-pH MD validation is a defensible workflow rather than a single opaque score. Finally, the pluggable architecture — a uniform module contract, reference- anchored display, and a durable job queue — makes the platform a reusable substrate: better-validated modules can be dropped in, and the honest framing (scope warnings, retrieval rather than prediction, N/A when out of scope) lives in the presentation layer itself. As much as it is a screening tool, it is a case study in building computational-biology software honestly — calibrating the interface's confidence to the method's actual epistemic standing. --- Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/299. 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