{"paper_id":"2d057569-4d49-4eef-a2a0-7114ec16853b","body_text":"Abstract\nWe present Protpardelle-1c, a collection of protein structure generative models with robust motif scaffolding and support for multi-chain complex generation under hotspot-conditioning. Enabling sidechain-conditioning to a backbone-only model increased Protpardelle-1c’s MotifBench score from 4.97 to 28.16, outperforming RFdiffusion’s 21.27. The crop-conditional all-atom model achieved 208 unique solutions on the La-Proteina all-atom motif scaffolding benchmark, on par with La-Proteina while having ∼10 times fewer parameters. At 22M parameters, Protpardelle-1c enables rapid sampling, taking 40 minutes to sample all 3000 MotifBench backbones on an NVIDIA A100-80GB, compared to 31 hours for RFdiffusion.\nCompeting Interest Statement\nThe authors have declared no competing interest.\nFootnotes\n↵† Work done as visiting researcher at Stanford University\ntianyulu{at}stanford.edu\nrshuai{at}stanford.edu\npetrkouba3{at}gmail.com\nzhaoyangli{at}stanford.edu\nyilinc5{at}stanford.edu\nakios{at}stanford.edu\nzhkim216{at}stanford.edu\nAuthor affiliation of Richard Shuai changed to \"Biophysics Program\" from \"Department of Biophysics\" Typo in Figure 4: La-Proteina (unindexed) changed to 357 from 354. Typo correction \"is a based model\" to \"is a base model\" Add \"replacement guidance\" to initial-release Protpardelle MotifBench hyperparameters. SHAPES citation updated to published Cell Systems version.","source_license":"CC-BY-4.0","license_restricted":false}