Conditional Protein Structure Generation with Protpardelle-1c
Lu, T.; Shuai, R. W.; Kouba, P.; Li, Z.; Chen, Y.; Shirali, A.; Kim, J.; Huang, P.
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We 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-1cs MotifBench score from 4.97 to 28.16, outperforming RFdiffusions 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.
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