Derek Croote

Protenix-v2: A Biomolecular Modeling System for Structure Prediction and Zero-Shot Antibody Design @ai4s_protenix 1. Protenix-v2 achieves massive gains in antibody-antigen structure prediction, with up to 13-point improvements over Protenix-v1 at DockQ >0.23 and comparable gains at the stricter DockQ >0.8 threshold. Most remarkably, its 5-seed performance surpasses previous 1000-seed results, representing a dramatic leap in sampling efficiency. 2. The system demonstrates 100% target-level success rate in zero-shot VHH antibody design across novelty-controlled targets, with BLI-confirmed hit rates ranging from 2% to 48%. The resulting hits show exceptional developability with 100% thermostability pass rate, 98% self-interaction pass rate, and 93% polyreactivity pass rate. 3. On challenging GPCR targets with small and flexible exposed epitopes, Protenix-v2 achieves hit rates of 16%-88% in VHH-Fc format and up to 50% in mAb format, despite testing only 16-30 designs per target. This demonstrates effective sample efficiency on difficult membrane proteins. 4. The model introduces training-free guidance (TFG) variants that significantly improve ligand-related plausibility, reaching 60.46% success rate on recent protein-ligand benchmarks under a revised stricter validity criterion that checks planarity around sp2 centers and non-planarity at sp3 centers. 5. Protenix-v2 successfully designs dual-specific binders against both prototype and Omicron SARS-CoV-2 RBD variants with nanomolar-scale KD, showing potential compensatory mechanisms at the structural level to accommodate sequence differences. 6. The system supports flexible, target-conditioned generation with granular control over CDR loop lengths and integration of predefined frameworks, spanning diverse formats from miniproteins to VHH and full-length antibodies. 💻Code: github.com/bytedance/Prot… 📜Paper: github.com/bytedance/Prot… #ProtenixV2 #AntibodyDesign #StructurePrediction #AlphaFold3 #BiomolecularModeling #DrugDiscovery #GPCR #MachineLearning #ComputationalBiology #ZeroShotDesign