Co-Generative De Novo Functional Protein Design

• URL: http://arxiv.org/abs/2605.00948v1
• Date: Fri, 01 May 2026 10:39:49 GMT
• Title: Co-Generative De Novo Functional Protein Design
• Abstract summary: De novo functional protein design aims to generate sequences that realize specified biochemical functions without relying on evolutionary templates.<n>Existing approaches adopt either direct function-to-sequence mapping or decoupled structure-sequence generation strategies.<n>We propose CodeFP, a Co-generative protein language model for de novo Functional Protein design that simultaneously decodes sequence and structure tokens.
• Score: 12.461287904565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: De novo functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad applications in biotechnology and medicine. Existing approaches adopt either direct function-to-sequence mapping or decoupled structure-sequence generation strategies but often fail to achieve functionality and foldability simultaneously. To address this, we propose CodeFP, a Co-generative protein language model for de novo Functional Protein design that simultaneously decodes sequence and structure tokens, thereby enabling superior simultaneous realization of functionality and foldability. CodeFP utilizes functional local structures to enrich functional semantic encodings, overcoming the suboptimal translation of flat encodings into structure tokens, while introducing auxiliary functional supervision to alleviate training ambiguity stemming from the one-to-many structure-to-token mapping. Extensive experiments show that CodeFP consistently achieves average improvements of 6.1% in functional consistency and 3.2% in foldability over the strongest baseline.

Related papers

• SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign [53.93453139057735]
  We present SimpleDesign, an effective multi-modal protein design model trained directly in the data space.<n>We train SimpleDesign on over 2M sequence-structure pairs achieving strong performance across co-design and unconditional sequence/structure generation benchmarks.
  arXiv  Detail & Related papers  (2026-09-03T05:25:05Z)
• SaDiT: Efficient Protein Backbone Design via Latent Structural Tokenization and Diffusion Transformers [50.18388227899971]
  We present SaDiT, a novel framework that accelerates protein backbone generation by integrating SaProt Tokenization with a Diffusion Transformer (DiT) architecture.<n>Experiments demonstrate that SaDiT outperforms state-of-the-art models, including RFDiffusion and Proteina, in both computational speed and structural viability.
  arXiv  Detail & Related papers  (2026-02-06T13:50:13Z)
• Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation [0.9332987715848714]
  Large language model (LLM) agents operate in parallel, each assigned to a specific residue position.<n>This position-wise, decentralized coordination enables emergent design of diverse, well-defined sequences.<n>Our method achieves efficient, objective-directed designs within a few GPU-hours and operates entirely without fine-tuning or specialized training.
  arXiv  Detail & Related papers  (2025-11-27T10:42:52Z)
• ProteinAE: Protein Diffusion Autoencoders for Structure Encoding [64.77182442408254]
  We introduce ProteinAE, a novel and streamlined protein diffusion autoencoder.<n>ProteinAE directly maps protein backbone coordinates from E(3) into a continuous, compact latent space.<n>We demonstrate that ProteinAE achieves state-of-the-art reconstruction quality, outperforming existing autoencoders.
  arXiv  Detail & Related papers  (2025-10-12T14:30:32Z)
• BioBlobs: Differentiable Graph Partitioning for Protein Representation Learning [3.6641231031729173]
  We introduce BioBlobs, a plug-and-play module that represents proteins by dynamically partitioning structures into flexibly-sized substructures ("blobs")<n>The resulting blobs are quantized into a shared and interpretable codebook, yielding a discrete vocabulary of function-relevant protein substructures used to compute protein embeddings.<n>We show that BioBlobs representations improve the performance of widely used protein encoders such as GVP-GNN across various PRL tasks.
  arXiv  Detail & Related papers  (2025-10-02T03:25:02Z)
• CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models [16.17372298740389]
  CFP-Gen is a novel diffusion language model for Combinatorial Functional Protein GEN.<n>It enables the de novo protein design by integrating multimodal conditions with functional, sequence, and structural constraints.
  arXiv  Detail & Related papers  (2025-05-28T21:05:46Z)
• DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design [21.43301218674909]
  Inverse Protein Folding is a critical subtask in the field of protein design.<n>We present DS-ProGen, a dual-structure deep language model for functional protein design.<n>By incorporating backbone coordinates as well as surface chemical and geometric descriptors into a next-amino-acid prediction paradigm, DS-ProGen is able to generate functionally relevant and structurally stable sequences.
  arXiv  Detail & Related papers  (2025-05-18T18:08:35Z)
• DPLM-2: A Multimodal Diffusion Protein Language Model [75.98083311705182]
  We introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures.
  arXiv  Detail & Related papers  (2024-10-17T17:20:24Z)
• Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation [55.93511121486321]
  We introduce FoldFlow-2, a novel sequence-conditioned flow matching model for protein structure generation.<n>We train FoldFlow-2 at scale on a new dataset that is an order of magnitude larger than PDB datasets of prior works.<n>We empirically observe that FoldFlow-2 outperforms previous state-of-the-art protein structure-based generative models.
  arXiv  Detail & Related papers  (2024-05-30T17:53:50Z)
• Protein Sequence and Structure Co-Design with Equivariant Translation [19.816174223173494]
  Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models. We propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state. Our model consists of a trigonometry-aware encoder that reasons geometrical constraints and interactions from context features. All protein amino acids are updated in one shot in each translation step, which significantly accelerates the inference process.
  arXiv  Detail & Related papers  (2022-10-17T06:00:12Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.