General Multimodal Protein Design Enables DNA-Encoding of Chemistry

• URL: http://arxiv.org/abs/2604.05181v1
• Date: Mon, 06 Apr 2026 21:21:11 GMT
• Title: General Multimodal Protein Design Enables DNA-Encoding of Chemistry
• Abstract summary: Deep generative models can design new proteins that bind, but none have created enzymes without pre-specifying catalytic residues.<n>We introduce DISCO, a model that co-designs protein sequence and 3D structure around arbitrary bio-structures.<n>Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries.
• Score: 87.86845886894861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evolution is an extraordinary engine for enzymatic diversity, yet the chemistry it has explored remains a narrow slice of what DNA can encode. Deep generative models can design new proteins that bind ligands, but none have created enzymes without pre-specifying catalytic residues. We introduce DISCO (DIffusion for Sequence-structure CO-design), a multimodal model that co-designs protein sequence and 3D structure around arbitrary biomolecules, as well as inference-time scaling methods that optimize objectives across both modalities. Conditioned solely on reactive intermediates, DISCO designs diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B-H, and C(sp$^3$)-H insertions, with high activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further confirmed that enzyme activity can be improved through directed evolution. By providing a scalable route to evolvable enzymes, DISCO broadens the potential scope of genetically encodable transformations. Code is available at https://github.com/DISCO-design/DISCO.

Related papers

• Towards A Generative Protein Evolution Machine with DPLM-Evo [73.57855006698269]
  We present DPLM-Evo, an evolutionary discrete diffusion framework that explicitly predicts substitution, insertion, and deletion operations during denoising.<n> DPLM-Evo improves sequence understanding and state-of-the-art mutation effect prediction performance on ProteinGym in the single-sequence setting.
  arXiv  Detail & Related papers  (2026-04-30T19:59:07Z)
• De novo generation of functional terpene synthases using TpsGPT [14.65546198539368]
  Terpene synthases (TPS) are a key family of enzymes responsible for generating the diverse terpene scaffolds that underpin many natural products.<n>We introduce TpsGPT, a generative model for scalable TPS protein design built by fine-tuning the protein language model ProtGPT2 on 79k TPS sequences mined from UniProt.
  arXiv  Detail & Related papers  (2025-12-09T16:29:53Z)
• Regulatory DNA sequence Design with Reinforcement Learning [56.20290878358356]
  We propose a generative approach that leverages reinforcement learning to fine-tune a pre-trained autoregressive model.<n>We evaluate our method on promoter design tasks in two yeast media conditions and enhancer design tasks for three human cell types.
  arXiv  Detail & Related papers  (2025-03-11T02:33:33Z)
• Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification [55.98854157265578]
  Life-Code is a comprehensive framework that spans different biological functions.<n>We propose a unified pipeline to integrate multi-omics data by reverse-transcribing RNA and reverse-translating amino acids into nucleotide-based sequences.<n>Life-Code achieves state-of-the-art results on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation.
  arXiv  Detail & Related papers  (2025-02-11T06:53:59Z)
• Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design [44.258193520999484]
  We present CrossDesign, a domain-adaptive framework that leverages pretrained protein language models (PPLMs)<n>By aligning protein structures with sequences, CrossDesign transfers pretrained knowledge to structure models, overcoming the limitations of limited structural data.<n> Experimental results highlight CrossDesign's superior performance and robustness, especially with out-of-domain enzymes.
  arXiv  Detail & Related papers  (2024-11-26T17:51:33Z)
• Reaction-conditioned De Novo Enzyme Design with GENzyme [64.14088142258498]
  textscGENzyme is a textitde novo enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex.<n>textscGENzyme is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes.
  arXiv  Detail & Related papers  (2024-11-10T00:37:26Z)
• EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics [51.47520281819253]
  Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology. Traditional methods for enzyme function prediction or protein binding pocket design often fall short in capturing the dynamic and complex nature of enzyme-substrate interactions. We introduce EnzymeFlow, a generative model that employs flow matching with hierarchical pre-training and enzyme-reaction co-evolution to generate catalytic pockets.
  arXiv  Detail & Related papers  (2024-10-01T02:04:01Z)
• Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates [16.5169461287914]
  We propose EnzyGen, an approach to learn a unified model to design enzymes across all functional families. Our key idea is to generate an enzyme's amino acid sequence and their 3D coordinates based on functionally important sites and substrates corresponding to a desired catalytic function.
  arXiv  Detail & Related papers  (2024-05-13T21:48:48Z)
• 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)
• ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution [18.726398852721204]
  We propose an efficient, experimental design-oriented closed-loop optimization framework for protein directed evolution. ODBO employs a combination of novel low-dimensional protein encoding strategy and Bayesian optimization enhanced with search space prescreening via outlier detection. We conduct and report four protein directed evolution experiments that substantiate the capability of the proposed framework for finding variants with properties of interest.
  arXiv  Detail & Related papers  (2022-05-19T13:21:31Z)

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.