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Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2

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arxiv 2405.15489 v1 pith:646E7O2X submitted 2024-05-24 q-bio.BM cs.LG

Yeqing Lin , Minji Lee , Zhao Zhang , Mohammed AlQuraishi This is my paper
classification q-bio.BMcs.LG
keywords genieproteindesignscaffoldingdesignsmethodsmodelmotif
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Protein diffusion models have emerged as a promising approach for protein design. One such pioneering model is Genie, a method that asymmetrically represents protein structures during the forward and backward processes, using simple Gaussian noising for the former and expressive SE(3)-equivariant attention for the latter. In this work we introduce Genie 2, extending Genie to capture a larger and more diverse protein structure space through architectural innovations and massive data augmentation. Genie 2 adds motif scaffolding capabilities via a novel multi-motif framework that designs co-occurring motifs with unspecified inter-motif positions and orientations. This makes possible complex protein designs that engage multiple interaction partners and perform multiple functions. On both unconditional and conditional generation, Genie 2 achieves state-of-the-art performance, outperforming all known methods on key design metrics including designability, diversity, and novelty. Genie 2 also solves more motif scaffolding problems than other methods and does so with more unique and varied solutions. Taken together, these advances set a new standard for structure-based protein design. Genie 2 inference and training code, as well as model weights, are freely available at: https://github.com/aqlaboratory/genie2.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching

    cs.LG 2025-07 conditional novelty 7.0 of 10

    La-Proteina generates full-atom protein structures and sequences via flow matching over an explicit alpha-carbon backbone plus fixed-size per-residue latents, achieving state-of-the-art co-designability and scaling to...

  2. SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

    cs.LG 2026-07 conditional novelty 6.0 of 10

    SE(3)-MeanFlow trains a protein backbone generator to predict average Lie-group velocities, reaching comparable designability to flow matching at 20–100 steps and leading at 10 steps after rectification.

  3. Exploring the Alignment of Generation and Understanding in Protein Structure Modeling

    cs.CE 2026-07 conditional novelty 6.0 of 10

    Aligning a protein diffusion generator's internal representations to a pretrained structure encoder (ProteinMPNN) raises the MotifBench motif-scaffolding score from 39.2 to 47.1 (~20% relative) over the Protpardelle-1...

  4. Variable-Length Generative Protein Design via Generalized Poisson Flow

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Generalized Poisson Flow learns variable protein length via an inhomogeneous Poisson rate plus within-length flow matching, with KL bounds and gains on structure, sequence, motif, and peptide tasks.

  5. Design-CP: Context Parallelism for Design of Protein Nanoparticles

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Context-parallel inference for RFdiffusion 3 enables end-to-end all-atom design of large symmetric protein nanoparticles on multi-GPU hardware without retraining.

  6. HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens

    cs.CE 2025-12 conditional novelty 6.0 of 10

    HD-Prot shows that a protein language model can jointly generate sequences and structures using continuous structure tokens instead of quantized tokens, reaching competitive performance on four protein design tasks.

  7. Calibrating Generative Models to Distributional Constraints

    stat.ML 2025-10 conditional novelty 6.0 of 10

    CGM-relax and CGM-reward fine-tune generative models to meet distributional constraints by minimizing a miscalibration penalty or a KL divergence to an estimated maximum-entropy tilt.

  8. Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators

    cs.LG 2026-02 conditional novelty 5.0 of 10

    Conditioning coarse-grained implicit transfer operators on ESM protein language embeddings improves out-of-distribution equilibrium sampling, with PLaTITO-Big beating BioEmu on fast-folding benchmarks using roughly 10...

  9. Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design

    cs.LG 2025-07 conditional novelty 5.0 of 10

    Protein-SE(3) is a unified benchmark that retrains six SE(3) protein backbone generative models on the same data and evaluates them with identical metrics for designability, diversity, and novelty.

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