BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability
1 BoltzProt-1 is a de novo protein binder (incl. nanobody/VHH) design pipeline that explicitly targets two bottlenecks at once: improving prospective binding hit rates on novel targets and ensuring therapeutic-style developability (stability, solubility, low nonspecific binding, etc.).
2 The key technical shift is ranking designs with a dedicated protein–protein interaction predictor (BoltzPPI), rather than relying mainly on structure-prediction confidence proxies. In a budget-matched test on the same candidate pools, this changes selection—not generation—and directly tests whether better ranking alone improves experimental recovery.
3 On 10 low-homology (novel) targets, replacing BoltzGen’s selection with BoltzPPI ranking increases confirmed-binder rate from 3.3% (5/150) to 8.0% (12/150), a 2.4x gain. Screening hits also rise (revised hit rate 4.7% to 9.3%). Confirmed target coverage improves from 2/10 to 3/10 via ranking, and to 4/10 when paired with an improved generative model.
4 The paper also argues for stricter experimental reporting: it separates “screening hits” (including ambiguous sensorgrams) from “confirmed binders” (clean kinetics plus orthogonal confirmation). Confirmation uses a flipped assay orientation to reduce format-specific artifacts and avidity effects, with additional independent testing for some hits.
5 BoltzPPI is built on Boltz-2 representations and adds a PPI prediction head trained jointly with a confidence head. It uses interface-focused signals: token/pair features, predicted coordinates, distance embeddings, and binder/target masks, refined by a 4-block Pairformer stack (16 heads, dropout 0.25).
6 Training uses PDB and patent-derived complexes as positives, plus synthetically generated protein pairs as negatives. A multi-view training scheme drops trunk pairwise representations 50% of the time to encourage geometric reliance and reduce overfitting to internal trunk signals; additional regularization injects Gaussian noise into representations. The interaction head is trained with focal loss and combined with confidence losses.
7 On an external 10-target panel used by Chai-2, BoltzProt-1 reports screening hits on 7/10 targets, compared with 6/10 reported by Chai-2 and 3/10 for BoltzGen in this study’s setup. This suggests improved target coverage across diverse classes (signaling/adaptor proteins, cytokines/hormones, SUMOylation enzymes, calcium-binding regulators).
8 Developability is treated as a first-class outcome. Confirmed binders from the low-homology panel are evaluated across a broad assay suite (Twist Bioscience): thermal stability (Tonset, Tm1, Tm2), aggregation onset (Tagg), monomer purity (aSEC), heterogeneity (DLS PDI), hydrophobicity (HIC), polyspecificity (BVP ELISA), and self-association (AC-SINS).
9 Under combined developability criteria, 58% (7/12) of BoltzProt-1 confirmed binders pass every filter, exceeding BoltzGen confirmed binders (40%, 2/5) and clinical-stage controls measured in parallel (IgG 25%, 3/12; VHH-Fc 21%, 5/24). Attrition is minimal until hydrophobicity (HIC), which is the dominant failure point.
10 Novelty checks indicate recovered designs are not near-duplicates of known antibody/nanobody CDRs: every recovered design has minimum CDR3 edit distance ≥4 to its closest SAbDab match (with larger distances when considering CDR1 2 3). Structural context is provided via binding-site similarity to known PDB interfaces (FoldDiSCO), highlighting that the low-homology benchmark emphasizes limited prior structural precedent.
📜Paper:
biorxiv.org/content/10.64898…
#ComputationalBiology #ProteinDesign #DeNovoDesign #Nanobody #AntibodyEngineering #ProteinProteinInteraction #MachineLearning #DrugDiscovery #Developability #StructuralBiology
